tag:blogger.com,1999:blog-46087551071820238032024-03-13T20:23:15.423-07:00SportsTribution - rambling about sport and dataJohannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.comBlogger32125tag:blogger.com,1999:blog-4608755107182023803.post-20387800044852225282019-12-03T12:42:00.003-08:002019-12-03T12:46:50.261-08:00Man vs Machine<br />
<h2>
<span lang="EN-US">Man versus Machine (Part I)</span></h2>
<h3>
<span lang="EN-US">What the comparison of two annotation
sources can tell us about SportVU and psychology</span></h3>
<div class="MsoNormal">
<span lang="EN-US">Preamble: I wrote this post in summer 2015. It never got published at Nylon Calculus, as we did not want to anger the NBA and SportsVU gods to take our data away. Alas, away they took. So, while there is a discussion about tracking data on Twitter right now, here's the article... (I'm sorry, links won't work and grammar errors will remain. Also, I think there is a figure missing in the beginning. I think it died with my hard drive.) (Also, Part II was about assists iirc. That's also gone.)</span></div>
<div class="MsoNormal">
<span lang="EN-US"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-US">When you start scraping, analyzing and
visualizing nba.com's tracking data, you feel like a kid in a candy store. But
after the first sugar rushes start to go away, you realize that some of the
candies may be dirty and you probably should gobble them a bit more carefully.
Before you now start to mistrust everything that has ever been written on
Nylon Calculus, let me assure you that the number of bad candies we are talking
about is rather small and should not affect any of the previously done results.
We are basically talking about one booger between a thousand bonbons. But
obviously it would be nice to eliminate as much discrepancy between data and
reality as possible.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">The easiest extractable SportVU data is
shot related. And the information that SportVU gives us about each shot is the
shot distance, touch time and dribblings before the shot occurred. In the
following I will first tackle the shot distance and then look at problems with
touch time and dribblings. I will compare the manual annotation of assists with
the information we have given by SportVU in a later article, as assists are a
more philosophical question.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">The data comprises all shots of the 2014-15
regular season (mandatory hat tip to Darryl). It might be that there are some
hiccups that occurred outside of the SportVU data. But I re-checked several
entries manually and always found the bizarre results to already exist in the
nba.com database.</span></div>
<h3>
<span lang="EN-US">Shot distance</span></h3>
<div class="MsoNormal">
<span lang="EN-US">Shot distance exists already for the time
before SportVU, as there have always been hardworking people that manually
annotated every shot taking place on an NBA field. If we compare the shot
distance given by those worker bees with the shot distance we receive from our
new electronical overlord, we get the following picture:<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">At first sight, this might look worse to
you than it actually is. You have to be aware that the color scale is a log
scale, so everything that is between blue and yellow are only a fraction of the
close to 200'000 shots. Almost all shots occur in the slightly skewed diagonal
rectangle in the middle, which is also reflected in the histogram measuring the
difference between manual annotated shot distance and SportVU distance.<o:p></o:p></span></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://1.bp.blogspot.com/-MmP41l2B9QM/XebHgmo8sjI/AAAAAAAAAlo/oWOxWfTRERwo1P15zqWZew62CN3_dm8JwCLcBGAsYHQ/s1600/shot_distribution.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1000" data-original-width="1500" height="265" src="https://1.bp.blogspot.com/-MmP41l2B9QM/XebHgmo8sjI/AAAAAAAAAlo/oWOxWfTRERwo1P15zqWZew62CN3_dm8JwCLcBGAsYHQ/s400/shot_distribution.png" width="400" /></a></div>
<div class="MsoNormal">
<span lang="EN-US">So, as a batch measurement, SportVU seems
to produce useful distances (Phew!). Yet, we can see two clear regions of
artifacts in the 2D histogram, which I underlined red. The reason for the upper
one seems obvious and a bit embarrassing: SportVu simply does not believe that
somebody could shoot from the own side of the field. This explains the negative
slope for shots that are manually declared as more than 50 feet away from the
basket (with the length of an NBA court being 94 feet).</span><!--[if gte vml 1]><o:wrapblock><v:shapetype
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<span lang="EN-US"><o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">The problem we see on the lower right
corner, where manual annotations estimate a distance of less than 5 feet and
SportVu goes up to 30 becomes more obvious when we use an additional
information given by or worker bees. Because they gave every shot an action
type label, we can for example look at shots that are labeled as “Jump Shot” or
shots labeled as “Dunk Shot”. In the following plot, I compare all shots for
which the action type contains the words “Layup” or “Dunk” with all shots
containing “Pullup”, “Fadeaway” or “Stepback”:<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">As you can see, we have a clear problem for
Layups and Dunks. For the manual annotation, none of them where declared to be
further away than 5 feet (Note: Actually, 3 of 10'000 were. One example is a
miss by Young, followed by a missed put back </span><a href="https://www.blogger.com/u/1/null" name="gjdgxs"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">http://stats.nba.com/cvp.html?GameID=0021400385&GameEventID=199#</span><span lang="EN-US"> ; no idea what happens on the other two). In comparison, 30% of <u>all
dunks alone</u> where apparently made from outside the charging area (4 feet
circle), according to SportVU. That is an interesting definition of a dunk.
Looking at some of the biggest offenders of dunk distance, we </span><span lang="EN-US" style="mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">can get an idea what goes wrong: You often either have very
straight drives to the basket (Noel, SportVU distance 23.9 feet: </span><span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400043&GameEventID=388"><span style="color: black; mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">http://stats.nba.com/cvp.html?GameID=0021400043&GameEventID=388</span></a></span><span lang="EN-US" style="color: black; mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";"># ; Olynik, SportVU distance 23.8 feet), or
alley hoops (Jordan, SportVU distance 24.5 </span><span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400391&GameEventID=071"><span style="color: black; mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">http://stats.nba.com/cvp.html?GameID=0021400391&GameEventID=071</span></a></span><span lang="EN-US" style="color: black; mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">#). But some of them just do not make any sense
(like this 25 feet dunk from Gerald Henderson, where the ball is never further
away from the basket than may 8 feet </span><span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400742&GameEventID=049"><span style="color: black; mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">http://stats.nba.com/cvp.html?GameID=0021400742&GameEventID=049</span></a></span><span lang="EN-US" style="color: black; mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";"># ). Under these circumstances, it can of
course lead to the problem that we inflate shooting percentages from shots that
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://1.bp.blogspot.com/-BAhi0MHJYcY/XebIBebB2UI/AAAAAAAAAlw/Ji1IvfEpaD0_5vq92j_0Z_vyuBhQH7GoACLcBGAsYHQ/s1600/shot_distribution2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="620" data-original-width="1410" height="280" src="https://1.bp.blogspot.com/-BAhi0MHJYcY/XebIBebB2UI/AAAAAAAAAlw/Ji1IvfEpaD0_5vq92j_0Z_vyuBhQH7GoACLcBGAsYHQ/s640/shot_distribution2.png" width="640" /></a></div>
<div class="MsoNormal">
<span lang="EN-US" style="color: black; mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";"><br /></span></div>
<div class="MsoNormal">
<span lang="EN-US" style="color: black; mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">In comparison, for shots
for which we can expect motion that is not directed towards the basket seem to
work quite well. An interesting observation is that for all jump shots we have
a good agreement between manual and SportVU distance for those shots that are
from 23 to 25 feet – basically all three pointer. For midrange jump shots on
the other hand, SportVU sees the player a little bit further away from the
basket.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US" style="color: black; mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">There are two scenarios
for this. The more likely one is that manual observations lack the precision
when the 3 point line is not there to guide your estimation. The ugly
alternative would be that SportVU has some kind of ridge regression, pulling
shots that are likely 3 pointers towards the 3 point line. Let's not hope that
that's the case...<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">For a few shots I looked at, where manual
annotation and SportsVU strongly disagreed, the manual annotation was more
often right than not.<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">As examples where the manual one is
correct:<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">SportVU says 38.4 ft distance, manual 23 </span><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">http://stats.nba.com/cvp.html?GameID=0021400714&GameEventID=157#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">SportVU says 37.3 ft distance, manual 24 </span><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">http://stats.nba.com/cvp.html?GameID=0021400946&GameEventID=197#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US" style="mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">SportVU says 42.9 ft distance, manual
19 </span><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">http://stats.nba.com/cvp.html?GameID=0021401154&GameEventID=323#<o:p></o:p></span></div>
<div class="MsoNormal">
<a href="https://www.blogger.com/u/1/null" name="_30j0zll"></a><span lang="EN-US" style="mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">On the other hand I
am pretty sure that this shot by is closer to 25.3 feet than to 31 feet<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">http://stats.nba.com/cvp.html?GameID=0021400108&GameEventID=085#<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">not a 31 footer<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<a href="https://www.blogger.com/u/1/null" name="1fob9te"></a><a href="https://www.blogger.com/u/1/null" name="_3znysh7"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400108&GameEventID=085#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:2564;
Scoring Player:Boris Diaw; ShotType: Pullup Jump shot; Touch time: 0.9; Shot
clock: 2.6; Dribbles: 0; SHOT_DISTANCE: 31.0; SHOT_DIST: 25.3; Distance
defender: 6.6; Game Clock: 2:23"<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">much closer manually<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">manual is right<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<a href="https://www.blogger.com/u/1/null" name="2et92p0"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] http://stats.nba.com/cvp.html?GameID=0021400714&GameEventID=157#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:2045;
Scoring Player:Hedo Turkoglu; ShotType: Jump Shot; Touch time: 0.0; Shot clock:
24.0; Dribbles: 0; SHOT_DISTANCE: 23.0; SHOT_DIST: 38.4; Distance defender:
21.0; Game Clock: 8:07"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400946&GameEventID=197#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:201228;
Scoring Player:CJ Watson; ShotType: Jump Shot; Touch time: 4.4; Shot clock:
24.0; Dribbles: 0; SHOT_DISTANCE: 24.0; SHOT_DIST: 37.3; Distance defender:
7.4; Game Clock: 2:48"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021401154&GameEventID=323#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:202339;
Scoring Player:Eric Bledsoe; ShotType: Pullup Jump shot; Touch time: 5.6; Shot
clock: 18.3; Dribbles: 6; SHOT_DISTANCE: 19.0; SHOT_DIST: 42.9; Distance defender:
20.8; Game Clock: 6:04"<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">closer automatic<o:p></o:p></span></div>
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<a href="https://www.blogger.com/u/1/null" name="tyjcwt"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400058&GameEventID=313#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:101112;
Scoring Player:Channing Frye; ShotType: Jump Shot; Touch time: 0.0; Shot clock:
15.8; Dribbles: 0; SHOT_DISTANCE: 27.0; SHOT_DIST: 19.0; Distance defender:
9.4; Game Clock: 5:36"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400301&GameEventID=391#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:101139;
Scoring Player:CJ Miles; ShotType: Jump Shot; Touch time: 0.0; Shot clock:
24.0; Dribbles: 0; SHOT_DISTANCE: 26.0; SHOT_DIST: 14.8; Distance defender:
11.2; Game Clock: 8:12"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400394&GameEventID=162#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:201583;
Scoring Player:Ryan Anderson; ShotType: Jump Shot; Touch time: 0.0; Shot clock:
24.0; Dribbles: 0; SHOT_DISTANCE: 26.0; SHOT_DIST: 12.1; Distance defender:
4.2; Game Clock: 8:04"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400606&GameEventID=499#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:201163;
Scoring Player:Wilson Chandler; ShotType: Jump Shot; Touch time: 0.0; Shot
clock: 24.0; Dribbles: 0; SHOT_DISTANCE: 27.0; SHOT_DIST: 14.9; Distance
defender: 11.3; Game Clock: 1:42"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400668&GameEventID=289#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:203081;
Scoring Player:Damian Lillard; ShotType: Jump Shot; Touch time: 10.2; Shot
clock: 14.0; Dribbles: 12; SHOT_DISTANCE: 25.0; SHOT_DIST: 19.9; Distance
defender: 6.5; Game Clock: 8:44"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400694&GameEventID=281#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:201155;
Scoring Player:Rodney Stuckey; ShotType: Jump Shot; Touch time: 2.8; Shot
clock: 24.0; Dribbles: 0; SHOT_DISTANCE: 26.0; SHOT_DIST: 18.1; Distance
defender: 6.1; Game Clock: 7:06"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021401137&GameEventID=207#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:203496;
Scoring Player:Robert Covington; ShotType: Jump Bank Shot; Touch time: 5.6;
Shot clock: 17.7; Dribbles: 5; SHOT_DISTANCE: 25.0; SHOT_DIST: 17.1; Distance
defender: 4.1; Game Clock: 6:01"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021401145&GameEventID=381#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:204060;
Scoring Player:Joe Ingles; ShotType: Jump Shot; Touch time: 2.0; Shot clock:
5.8; Dribbles: 1; SHOT_DISTANCE: 25.0; SHOT_DIST: 17.7; Distance defender: 5.9;
Game Clock: 1:16"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021401158&GameEventID=087#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:203897;
Scoring Player:Zach LaVine; ShotType: Jump Shot; Touch time: 2.2; Shot clock:
10.1; Dribbles: 1; SHOT_DISTANCE: 25.0; SHOT_DIST: 17.0; Distance defender:
3.2; Game Clock: 2:27"<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">Distant dunks<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">Yes<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<a href="https://www.blogger.com/u/1/null" name="3dy6vkm"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400043&GameEventID=388#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:203457;
Scoring Player:Nerlens Noel; ShotType: Dunk Shot; Touch time: 3.5; Shot clock:
21.6; Dribbles: 0; SHOT_DISTANCE: 0.0; SHOT_DIST: 23.9; Distance defender: 5.8;
Game Clock: 11:02"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">Tough<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400173&GameEventID=236#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:203100;
Scoring Player:Tony Wroten; ShotType: Dunk Shot; Touch time: 0.0; Shot clock:
14.9; Dribbles: 0; SHOT_DISTANCE: 0.0; SHOT_DIST: 24.9; Distance defender: 0.5;
Game Clock: 2:56"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">Yes<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<a href="https://www.blogger.com/u/1/null" name="1t3h5sf"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">1]
http://stats.nba.com/cvp.html?GameID=0021400371&GameEventID=353#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:203482;
Scoring Player:Kelly Olynyk; ShotType: Driving Dunk Shot; Touch time: 0.9; Shot
clock: 18.7; Dribbles: 0; SHOT_DISTANCE: 0.0; SHOT_DIST: 23.8; Distance
defender: 4.2; Game Clock: 11:28"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">Yes<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] http://stats.nba.com/cvp.html?GameID=0021400391&GameEventID=071#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:201599;
Scoring Player:DeAndre Jordan; ShotType: Alley Oop Dunk Shot; Touch time: 4.0;
Shot clock: 20.6; Dribbles: 0; SHOT_DISTANCE: 0.0; SHOT_DIST: 24.5; Distance
defender: 5.0; Game Clock: 5:50"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">Tough<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400406&GameEventID=143#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:101123;
Scoring Player:Gerald Green; ShotType: Driving Dunk Shot; Touch time: 0.0; Shot
clock: 24.0; Dribbles: 0; SHOT_DISTANCE: 0.0; SHOT_DIST: 24.0; Distance
defender: 3.7; Game Clock: 12:00"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400547&GameEventID=349#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:203084;
Scoring Player:Harrison Barnes; ShotType: Alley Oop Dunk Shot; Touch time: 4.8;
Shot clock: 18.6; Dribbles: 0; SHOT_DISTANCE: 0.0; SHOT_DIST: 22.4; Distance
defender: 4.4; Game Clock: 4:34"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400691&GameEventID=189#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:201148;
Scoring Player:Brandan Wright; ShotType: Dunk Shot; Touch time: 3.4; Shot
clock: 24.0; Dribbles: 1; SHOT_DISTANCE: 0.0; SHOT_DIST: 23.6; Distance
defender: 7.6; Game Clock: 6:22"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400742&GameEventID=049#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:201945;
Scoring Player:Gerald Henderson; ShotType: Alley Oop Dunk Shot; Touch time: 0.0;
Shot clock: 9.3; Dribbles: 0; SHOT_DISTANCE: 0.0; SHOT_DIST: 25.0; Distance
defender: 8.4; Game Clock: 6:42"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021401101&GameEventID=147#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:202687;
Scoring Player:Bismack Biyombo; ShotType: Dunk Shot; Touch time: 0.6; Shot
clock: 9.1; Dribbles: 0; SHOT_DISTANCE: 0.0; SHOT_DIST: 24.6; Distance
defender: 10.1; Game Clock: 7:41"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<br /></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<a href="https://www.blogger.com/u/1/null" name="4d34og8"></a><span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400009&GameEventID=486"><span style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">http://stats.nba.com/cvp.html?GameID=0021400009&GameEventID=486</span></a></span><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">http://stats.nba.com/cvp.html?GameID=0021400189&GameEventID=537#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">walking the dog<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400053&GameEventID=509"><span style="color: navy;">http://stats.nba.com/cvp.html?GameID=0021400053&GameEventID=509</span></a>#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">takes after shot clock reset<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400045&GameEventID=552"><span style="color: navy;">http://stats.nba.com/cvp.html?GameID=0021400045&GameEventID=552</span></a>#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">misses the skip pass<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400234&GameEventID=015"><span style="color: navy;">http://stats.nba.com/cvp.html?GameID=0021400234&GameEventID=015</span></a>#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">shot clock stops<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<a href="https://www.blogger.com/u/1/null" name="2s8eyo1"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">http://stats.nba.com/cvp.html?GameID=0021400730&GameEventID=008#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">momentarily unclear position<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400047&GameEventID=498"><span style="color: navy;">http://stats.nba.com/cvp.html?GameID=0021400047&GameEventID=498</span></a>#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400194&GameEventID=336"><span style="color: navy;">http://stats.nba.com/cvp.html?GameID=0021400194&GameEventID=336</span></a>#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400908&GameEventID=088"><span style="color: navy;">http://stats.nba.com/cvp.html?GameID=0021400908&GameEventID=088</span></a>#<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">shot clock reset<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400601&GameEventID=307"><span style="color: navy;">http://stats.nba.com/cvp.html?GameID=0021400601&GameEventID=307</span></a>#<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US">no idea<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">Alley Hoops<o:p></o:p></span></div>
<div class="MsoNormal">
<span lang="EN-US"><a href="http://stats.nba.com/cvp.html?GameID=0021400111&GameEventID=293"><span style="color: navy;">http://stats.nba.com/cvp.html?GameID=0021400111&GameEventID=293</span></a>#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<a href="https://www.blogger.com/u/1/null" name="17dp8vu"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">"player ID:203500; Scoring
Player:Steven Adams; ShotType: Alley Oop Dunk Shot; Touch time: 14.1; Shot
clock: 24.0; Dribbles: 0; SHOT_DISTANCE: 0.0; SHOT_DIST: 5.6; Distance
defender: 3.9; Game Clock: 8:10"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<a href="https://www.blogger.com/u/1/null" name="3rdcrjn"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">"player ID:202685; Scoring
Player:Jonas Valanciunas; ShotType: Alley Oop Layup shot; Touch time: 9.8; Shot
clock: 24.0; Dribbles: 0; SHOT_DISTANCE: 1.0; SHOT_DIST: 1.5; Distance
defender: 0.8; Game Clock: 4:25"<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">Dribble Alley Hoops<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt;">
<a href="https://www.blogger.com/u/1/null" name="26in1rg"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400456&GameEventID=239#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:203081;
Scoring Player:Damian Lillard; ShotType: Alley Oop Layup shot; Touch time:
11.3; Shot clock: 12.8; Dribbles: 9; SHOT_DISTANCE: 2.0; SHOT_DIST: 4.1;
Distance defender: 3.9; Game Clock: 11:49"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400500&GameEventID=415#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:201163;
Scoring Player:Wilson Chandler; ShotType: Alley Oop Dunk Shot; Touch time: 7.8;
Shot clock: 16.9; Dribbles: 6; SHOT_DISTANCE: 0.0; SHOT_DIST: 3.1; Distance
defender: 1.9; Game Clock: 7:52"<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] http://stats.nba.com/cvp.html?GameID=0021400909&GameEventID=406#<o:p></o:p></span></div>
<div class="MsoNormal" style="border: none; mso-border-shadow: yes; mso-padding-alt: 31.0pt 31.0pt 31.0pt 31.0pt; mso-pagination: widow-orphan;">
<span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1] "player ID:201566;
Scoring Player:Russell Westbrook; ShotType: Alley Oop Layup shot; Touch time:
12.2; Shot clock: 12.0; Dribbles: 10; SHOT_DISTANCE: 1.0; SHOT_DIST: 4.3;
Distance defender: 3.4; Game Clock: 6:59"<o:p></o:p></span></div>
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<br /></div>
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<a href="https://www.blogger.com/u/1/null" name="lnxbz9"></a><span lang="EN-US" style="color: black; font-family: "ubuntu mono"; font-size: 10.5pt;">[1]
http://stats.nba.com/cvp.html?GameID=0021400031&GameEventID=327#<o:p></o:p></span></div>
<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-25879693359757892782015-07-23T12:49:00.001-07:002015-07-23T12:49:19.339-07:00Small list of ggplot2 examplesHello everybody,<br />
<br />
I didn't use ggplot2 that much until 2 weeks ago. Which in my opinion was a big mistake on my behalf. I think my train of thought shifted from<br />
"I just want to plot something, I don't directly get what's going on with this ggplot2 thing. I'll just find a quick solution"<br />
to<br />
"Wow! Once you get the basic idea, the world becomes your oyster!"<br />
Personally, I blame Matlab. I'm just so used to use a different function for calling each plot (<b>do a specific thing with Input data</b>), that I did not directly realize that a plot is basically <b>(Input data) + (do something with it)</b><br />
Once you get a hang for it, it get's really fun, because you often just have to change a geom_point() to a geom_smooth() to get a completely different thing - or simply put them both in a row!<br />
Anyhow, without further ado, here is my <a href="https://drive.google.com/file/d/0B_SbsMzZALXUVHpVT1o2ZjJfWVk/view?usp=sharing" target="_blank">tutorial-like pdf</a>:<br />
<br />
<iframe height="480" src="https://drive.google.com/file/d/0B_SbsMzZALXUVHpVT1o2ZjJfWVk/preview" width="640"></iframe>
Cheers,<br />
HannesJohannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-33422633472948324142015-07-09T03:50:00.000-07:002016-01-20T05:10:22.015-08:00Links I favorited about data scienceI had a few weeks on where I used Twitter mostly on my phone. So I started blindly favoriting tweets that could be usefull. This blog post is mostly for me to curate all these data related links. If it comes in handy for others the better. I try to sort them a bit after topics... hat tips go to all the data scientists that show an immigrant like me some interesting things (too lazy to list them right now...)<br />
<h3>
General Methods and algorithms</h3>
<br />
<ul>
<li><a href="http://dataelixir.com/" target="_blank">Data Elixir</a> - what I am doing with this blog post in big</li>
<li><a href="http://blog.dato.com/topic/machine-learning-primer" target="_blank">Machine learning primer</a> - did just skim over it, but it seems this series is great to communicate very important and central concepts with people new to the field</li>
<li><a href="http://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/" target="_blank">Statistical learning overload</a> - haven't watched the videos yet, but the <a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/" target="_blank">Hastie book</a> (freely available) and all the things coming with it are probably a first step for any data scientist immigrant</li>
<li><a href="http://www.autonlab.org/tutorials/list.html" target="_blank">Statistical Data Mining Tutorials collection</a></li>
<li><a href="http://designimag.com/best-machine-learning-cheat-sheets/" target="_blank">Machine learning cheat sheets</a> - a great combination with the Hastie book. Use some of the quick cheat sheet information first and then get down to the more gory details using the book and videos. Check out <a href="http://scikit-learn.org/stable/tutorial/machine_learning_map/" target="_blank">this sheet</a> for example</li>
<li><a href="http://www.startup.ml/blog/hyperparam" target="_blank">Hyper parameter selection</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/20-data-science-r-python-excel-and-machine-learning-cheat-sheets" target="_blank">A lot of data science cheat sheets</a> (which basically forces you to read more links)</li>
<li><a href="http://moderndata.plot.ly/machine-learning-visualizations-made-in-python-and-r/" target="_blank">Machine Learning Visualizations</a> (made in Python and R, horray)</li>
</ul>
<h3>
R</h3>
<div>
<ul>
<li><a href="https://github.com/gjm112/ISSR" target="_blank">R introduction plus text mining course</a> - @StatsInTheWild is probably my favorite twitter handle</li>
<li><a href="http://www.dataschool.io/dplyr-tutorial-for-faster-data-manipulation-in-r/" target="_blank">dplyr tutorial</a> - I still live in a dplyr less world. Which I guess I should regret every time I write df[df[,'Stat1']>0 & df [,'Stat2']>2,] - people might call that dumb, I call it oldschool</li>
<li><a href="https://github.com/slowkow/ggrepel" target="_blank">ggrepel</a>: I finally can use the ggplot package for messy textplots </li>
</ul>
<h3>
Python</h3>
</div>
<div>
<ul>
<li><a href="http://blog.dominodatalab.com/pandas-categoricals/" target="_blank">Speed up Pandas by using categoricals</a></li>
<li><a href="http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#pipe" target="_blank">pipe data frames through functions</a> (seems neat)</li>
<li><a href="http://stanford.edu/~mwaskom/software/seaborn-dev/examples/horizontal_boxplot.html" target="_blank">Sometimes you might need a horizontal boxplot</a></li>
<li><a href="http://rasbt.github.io/mlxtend/docs/classifier/neuralnet_mlp/" target="_blank">Neural Network Implementation (HowTo)</a> - I have no practical experience with neural networks and haven't gone indepth here yet. But I usually like to steal Sebastian Raschka's code ;) - this <a href="http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/machine_learning/neural_networks/ipynb/activation_functions.ipynb" target="_blank">network activation cheat sheet</a> belongs here as well</li>
<li><a href="http://peterroelants.github.io/posts/neural_network_implementation_part01/" target="_blank">More neural networks</a></li>
<li><a href="http://kyrandale.com/static/talks/reveal.js/index_pydata2015.html#/" target="_blank">Data visualization with Python and JavaScript</a> - presentation (side note: presentations are not the nicest form to digest afterwards. But I guess this one tackles a lot of points) </li>
<li><a href="http://axialcorps.com/2013/08/29/5-simple-rules-for-building-great-python-packages/" target="_blank">How to build Python packages</a></li>
<li><a href="https://github.com/mmmayo13/scikit-learn-classifiers" target="_blank">Scikit-learn-classifiers</a></li>
</ul>
<h3>
Other languages</h3>
</div>
<div>
<ul>
<li><a href="https://github.com/jlevy/the-art-of-command-line" target="_blank">The art of command line</a> - really need to get into this one, as my command line skills suck :D</li>
</ul>
</div>
<br />
<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-25522746443234764872015-03-19T08:47:00.001-07:002015-03-19T08:54:19.552-07:00NBA players are creatures of habit<div class="western" style="line-height: 100%; margin-bottom: 0in;">
Hello everybody,</div>
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
<br /></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in; text-align: justify;">
this
is a follow-up to my last post on <a href="http://nyloncalculus.com/2015/03/06/freelance-friday-distancology-deducing-player-types-from-shot-distribution/" target="_blank">'distancology' </a>– the science of
turning all shot charts into one colorful picture. You can find a half-way clean code in my <a href="https://github.com/SportsTribution/heatMap" target="_blank">github account</a>. If you run MakeHeatMap.R, you should actually be able to reproduce the result.</div>
<div class="western" style="line-height: 100%; margin-bottom: 0in; text-align: justify;">
<br /></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in; text-align: justify;">
One
question that one naturally can ask, when comparing the shot
distribution of players, is how consistent or reliable those shot
distributions are. For example, in my last article I sorted around
200 players into 10 distinguishable groups, using a (vague) cutoff.
But I could as well have used 5 or 20 groups. Now, the question
regarding reliability is: If you compare year to year, how many
players would remain inside the same shot cluster?</div>
<div class="western" style="line-height: 100%; margin-bottom: 0in; text-align: justify;">
Because,
if I would label somebody as a 'corner three guy' due to his shot
distance distribution in one year, but the next year there is a 50%
chance that he's actually a 'typical wing player' guy – that would
be pretty useless.<sup><a class="sdfootnoteanc" href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#sdfootnote1sym" name="sdfootnote1anc">1</a></sup></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in; text-align: justify;">
<br /></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in; text-align: justify;">
Long
story short, what I did is to combine the distance distributions for
two years – and the result is pretty mindblowing<a class="sdfootnoteanc" href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#sdfootnote2sym" name="sdfootnote2anc"><sup>2</sup></a>.
The following plot works very similar to the one that I used in my
previous article. I just changed the column on the left, so that it
indicates the effective field goal percentage instead of the shot
attempts. This way it is easier for people to be in awe about Stephen
Curry. It shows both seasons for every player that had at least 600
attempts (data is from the 3rd of March) during this and the last season – and here it is<a class="sdfootnoteanc" href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#sdfootnote3sym" name="sdfootnote3anc"><sup>3</sup></a>:<br />
<a name='more'></a></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in; text-align: justify;">
<br /></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://2.bp.blogspot.com/-vyAXjkSft8Y/VQruA48KZDI/AAAAAAAAAVg/FVs0vZPVeco/s1600/ShotDistanceFrequency2seasons-600AttInkscape%2B.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-vyAXjkSft8Y/VQruA48KZDI/AAAAAAAAAVg/FVs0vZPVeco/s1600/ShotDistanceFrequency2seasons-600AttInkscape%2B.png" height="640" width="379" /></a></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
I
hope you find it as amazing as I did when I saw it the first time.
Shooting distance distribution is a complicated construct that
combines team strategy with the personal role of a player inside the
team and things like fast break percentage or shooting habits. So, you
would expect that most shot distributions vary at least a little bit.
But no, 47 of 63 players are their own closest neighbors between this
and last year. I have no statistical method to compare it with
correlation values, but I would say that it has a p-value of bonkers. As an
example, look at the top, how DeMarcus Cousins, Enes Kanter and
Derrick Favors all have similar shot distributions between mid-range
and close-up, but Cousins takes his mid-range shots from 20 feet out,
Kanters from 17 and Favors from 14. Or that Jeff Teague and Michael
Carter Williams have their close range shots a little bit further
away from the basket than most players (floater?), but Jeff Teague
shoots a bit more three pointers. Or that Steph Curry and Damian
Lillard are the only two MFers that regularly shoot from 27 feet out.
I don't know if Damian Lillard is lazy or something, but more than
130 times this year (3rd March) he decided 'I don't want to walk this additional
meter'. And 43 times he got away with it.</div>
</div>
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
Having
shown that most players are reliably clusterable (is that a word?), I
will now present the four players (6%) that changed their cluster<a class="sdfootnoteanc" href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#sdfootnote1sym" name="sdfootnote1anc"><sup>4</sup></a>:</div>
</div>
<span style="line-height: 100%;"></span><br />
<ol><span style="line-height: 100%;">
<li><span style="line-height: 100%;">Serge Ibaka: Exchanged a bit of his midrange and a lot of his
close-up game for a three point shot. I do not feel in the position
to comment on this.</span></li>
<li><span style="line-height: 100%;">Thaddeus Young: Replaced half of his three point attempts this
season with long twos. It seems like somebody flip'd his game. (Ba
dum Tss!)</span></li>
<li><span style="line-height: 100%;">Chris Bosh: Even so the two leaves are very close to each other,
they are on two different branches. This years Chris Bosh was not
found close to the basket that often. I also hope that he gets well soon, I miss his photo-bombing</span></li>
<li><span style="line-height: 100%;">Trey Burke: Is a good example why clustering is not a precise
science. If you compare his two distributions they both look very
similar. Something in the clustering process made 2013-14 Trey Burke
fall into the 'We don't go to the basket that often' group, while
2014-15 Trey Burke became 'Stephen Curry without actually making the
shots'.</span></li>
</span></ol>
<span style="line-height: 100%;">
</span>
<br />
<div>
<div id="sdfootnote1">
<div class="sdfootnote-western" style="page-break-before: always;">
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
<br /></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
That's
it for today. Just wanted to repeat one technical thing: clustering
always consists of two parts. First you find a distance metric and
then you find a rule how you cluster things together. I hope I could
convince you that the distance metric part is pretty awesome. I'll
try to work on the linkage part if possible.</div>
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
<br /></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
Cheers,</div>
<br />
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
Hannes</div>
</div>
</div>
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
<br /></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
<br /></div>
<div class="western" style="line-height: 100%; margin-bottom: 0in;">
<br /></div>
<div id="sdfootnote1">
<div class="sdfootnote-western" style="page-break-before: always;">
<a class="sdfootnotesym" href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#sdfootnote1anc" name="sdfootnote1sym">1</a>
It's like saying somebody is a good defender because people shoot a
low 3 point percentage against that person <span style="color: navy;"><span lang="zxx"><u><a href="http://nyloncalculus.com/2015/02/09/defending-the-three-pointer-mean-avoiding-three-pointer/">without
realizing that this is mostly random</a></u></span></span> – wait,
I digress...</div>
</div>
<div id="sdfootnote2">
<div class="sdfootnote-western" style="page-break-before: always;">
<a class="sdfootnotesym" href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#sdfootnote2anc" name="sdfootnote2sym">2</a>
Note to myself: Turn this sentence into a clickbait for Twitter ;)</div>
</div>
<div id="sdfootnote3">
<div class="sdfootnote-western" style="page-break-before: always;">
<a class="sdfootnotesym" href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#sdfootnote3anc" name="sdfootnote3sym">3</a> It
is so much easier to publish heatmaps if you don't have to make them
fit on a DinA4 page.</div>
<div id="sdfootnote1">
<div class="sdfootnote-western" style="page-break-before: always;">
<a class="sdfootnotesym" href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#sdfootnote4anc" name="sdfootnote1sym">4</a> I kept it at 7 clusters, as I have no idea where to cut the big 'we shot three and close range and sometimes in the middle' cluster. Even though the distance metric is precise, the subsequential clustering is not</div>
<div>
<br /></div>
</div>
</div>
</div>
Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com1tag:blogger.com,1999:blog-4608755107182023803.post-50729507511363620282015-03-02T09:44:00.004-08:002015-03-02T11:09:57.183-08:00Before this probable nonsense about 'fouling while leading' gets spreadHi everyone,<br />
<br />
I thought about letting it slip, but then I read this quite from an <a href="http://www.si.com/nba/2015/03/01/mit-sloan-sports-analytics-conference-nba-adam-silver-daryl-morey?page=2&devicetype=default" target="_blank">SI.com article</a>:<br />
<blockquote class="tr_bq">
"28. The most staggering NBA stat from Sloan? Fouling when winning can increase your chances by 11 percent, according to a paper written by Franklin Kenter of Rice University. The paper shows that fouling near the end of games pretty much makes sense in every situation, whether you’re trailing or leading. When behind, it advises fouling one minute out for every six points you are behind. When leading, it suggests fouling one minute out for every three points you lead."</blockquote>
Side note here: 'The paper shows that fouling...' is a typical case of 'reporter overstates what scientist says'. I guess 'The paper says that their model indicates...' would be more realistic.<br />
<br />
But more or less, that's what the paper said. Their reasoning was the following:<br />
<blockquote class="tr_bq">
"The concept of fouling when ahead may be counterintuitive. However, toward the end of the game, the main goal of the trailing team is to increase the total variance in order to widen the window of possibilities that win the game. One main component in this wider variance is the riskier 3-point shot. The trailing team can limit this variance by
fouling. the leading team may give up points, on average, but limit the trailing team to 2 points per possession. This
decreases the total variance and, with a sufficient lead, increases the leading team’s chances of winning."</blockquote>
Now, I could go on a very lengthy statistical rant about this. I tried to figure out where they made the mistake in their model, but the paper was too vague in terms of their methods.<br />
The point is this:<br />
<br />
<a name='more'></a><br /><br />
<ol>
<li>Fouling stops the clock earlier than shooting. Even if you wait 3 or 4 seconds, the possession is still much shorter than what you would need until you get a half way descend three point shot</li>
<li>This easily doubles the number of possessions</li>
<li>Increased number of possessions increases the variance</li>
</ol>
<div>
This last point is crucial. <b>If you have two times more possessions, the variance increases by square root of two</b>. </div>
<div>
And that the number of possessions would only double is a very weak assumption. If you foul to late, things <a href="https://www.youtube.com/watch?v=7xlCbpPN8rs" target="_blank">like this</a> can happen easily.</div>
<div>
<br /></div>
<div>
I don't want to say that the whole paper is nonesense. But regarding the part that contains the punchline (the leading team should foul), either the assumptions of the paper are very generous, or just plain wrong. So, please don't start fouling while ypou are leading in your rec league games.</div>
<div>
<br /></div>
<div>
Cheers,</div>
<div>
Hannes</div>
<div>
<br /></div>
<div>
<br /></div>
<div>
<br /></div>
<div>
<br /></div>
Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-42283684777057347002015-02-27T04:38:00.000-08:002015-03-06T08:27:31.496-08:00Let them handle their business – a case against over-helping<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
Note: I was first thinking about making it a longer post, but then <a href="http://nyloncalculus.com/2015/02/16/play-types-varying-importance/" target="_blank">re-read this post by Nick Restifo</a> and realized that he made already most of my points (btw: @itsastat is a great Twitter handle). So I'll keep it in short form.</div>
<div style="text-align: justify;">
Note2: After I finished writing, I realized that it became a longer post :D</div>
</div>
<br />
<div style="text-align: justify;">
Hi everybody,</div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
for the last week, I
was playing around with data scraping and visualization. In my
opinion, the use of clustering and heatmaps is a great but underused
way to get a first impression and overview of data. Especially, as
there are no numbers needed to understand the results (yes, I'm
looking at you Chuckster!).</div>
</div>
<div style="text-align: justify;">
<br /></div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
Anyhow, I was
looking at the <a href="http://stats.nba.com/playtype/#!/" target="_blank">NBA.com playtype data</a> to see if there
is any rhyme or reason in what makes teams good. The Synergy playtypes are very interesting in those regard that the word playtype is used very loosely. The following is in my opinion very important: Those playtypes that they use can be separated into three and a half main subcategories:</div>
</div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
<br /></div>
</div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
1: Active Playtypes: Isolation, PnR Ball Handler, Post Up, (Misc)</div>
</div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
One player receives the ball in a more or less neutral position and tries to get into an advantageous position. It is really hard to prevent the initiation of these plays.</div>
</div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
1.5: Semi-active playtypes: Hand-Off, Off-Screen</div>
</div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
The defense can sometimes prevent the initiation of the plays (which can be more costly than allowing the play itself). Note that they produce on average slightly more points per possession than active playtypes, but they also happen less often (combined 10% of plays). This my be due to the fact that they are more complex (more actions/players involved)</div>
</div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
2: Passive playtypes: Spot-Up, PnR Roll man, cuts, putbacks</div>
</div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
These are plays that can be avoided. Note that 'can be avoided' does not necessarily mean that the defense is at fault if they happen. Take <a href="https://www.youtube.com/watch?v=Usbm6pIWhkM" target="_blank">this cut by DeAndre Jordan</a> (I guess it's a cut) one of the league leaders in cuts. You can either let Blake dunk directly or hope that he somehow botches the pass to DeAndre</div>
</div>
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
3: Transition. You could argue that transition is more a result of offense than of defense. But that's a different story</div>
</div>
<div style="line-height: 100%; margin-bottom: 0in;">
</div>
<a name='more'></a><br />
<br />
<div style="line-height: 100%; margin-bottom: 0in;">
<div style="text-align: justify;">
This separation is important to understand the basketball side of the analysis. You can hardly avoid for a team to initiate the Pick and Roll, but you can decide if you want to risk keeping the ball in the ball handlers hands (and giving him whatever he gets) or if you want to force the ball out of his hands and risk a spot up shot, or the Roll Man or another cutter to beat you.</div>
</div>
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I will now show you a plot that will blow your mind. Are you ready Chuck?! No worries, I will explain them directly afterwards (click to enlarge).</div>
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<a href="http://4.bp.blogspot.com/-ssUFJ5M_OdI/VPBImhK76eI/AAAAAAAAAUE/YPib5vHMs1U/s1600/Synergy_OffPPP%2B.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-ssUFJ5M_OdI/VPBImhK76eI/AAAAAAAAAUE/YPib5vHMs1U/s1600/Synergy_OffPPP%2B.png" height="320" width="400" /></a></div>
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<a href="http://2.bp.blogspot.com/-BuDK36EwmcY/VPBImi0qLmI/AAAAAAAAAUA/sDFISXY_8rY/s1600/Synergy_DefFreq%2B.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-BuDK36EwmcY/VPBImi0qLmI/AAAAAAAAAUA/sDFISXY_8rY/s1600/Synergy_DefFreq%2B.png" height="320" width="400" /></a></div>
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What you can see here by playtype is differences in 1. <b>Points per possession for team offenses</b> by plays and 2. <b>play frequency for team defenses</b>. I normalized the plots by subtracting the PPP or respective frequency means for each play. The first column shows you the offensive respectively defensive ratings of the different teams.</div>
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What you can see on the first plot:</div>
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For Off Rating & Points per possession, <b><span style="color: #38761d;">green means good</span></b>. So, here you find almost all the good teams in the lower half. As you can see, these teams are all above average in points per possession for PnR Roller. BUT you can furthermore see that those teams that are not as good in offensive net rating (Toronto, Orlando, Boston and (up to now) San Antonio) do not produce as good with their ball handlers. Furthermore, these teams are mostly good at HandOff and SpotUp shooting (as noted by Nick). Off course this is a chicken egg problem, as good SpotUp shooter allow for easier rim attack and Handjobs are basically more fancy pick and rolls (just wanted to check if you are still awake).</div>
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And off course, better PnR handler allow for easier cutting and rolling, so there is most likely a trickle down effect. (Side note: I'm looking at some other stats that indicate that high usage ball handler always have/need a midrange game. So don't worry, the midrange game will never die.)</div>
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What you can see on the second plot:</div>
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For Def Rating, <b><span style="color: red;">red means good</span></b>. For Frequency, <b><span style="color: #38761d;">green means more</span></b>. What we can see is a big cluster of mostly good defenses on the upper half. Of course, this separation is not perfect, as 5 of the 13 teams are below average. But three of them (Sacramento, Minnesota and the Lakers) allow a lot of transition baskets, which does not really help. Side note: the same for Houston, Toronto and Phily, only they allow 0.1 points less per transition. I guess that helps...)</div>
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Anyhow, in my opinion, the plot shows that teams that focus <b>less on the ball handler and more on avoiding the SpotUp shooter have in general a better defensive net rating</b>.
<span style="line-height: 100%;"> The correlation between those two options is pretty obvious from the plot (given that Handler frequency and SpotUp frequency colors are always the opposite). The R</span><span style="line-height: 16px;">^2</span> for PnR frequency and Spot up frequency is 0.654 (for Chuck: That means they are super highly correlated. What, you are asking what correlated means? Never mind...)<br />
So, to conclude: Teams need a good PnR game and optimally ball handler to be good on offense. And teams are in general better on defense if they take away the Spot up shooter I rest my case...</div>
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ALMOST: Of course, what I described here is not Houston Rockets science and teams know all this (interestingly, the Rockets seem to disagree with my points). On the one hand a defense if of course bound by its personnel. On the other hand, it is interesting to note that increased frequency of PnR Handler plays strongly correlates with increased points per possession for these plays. At the same time, decreased frequency of Spot up shots, does not lead to that much of a decrease in PPP efficiency (Figures below). So, from a game theory perspective, you surely reach a breaking point, where transform a pick and roll into a layup line is not helpful (duh).</div>
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<span style="line-height: 100%;">Here are the additional figures (let me know if you want more):</span></div>
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<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-oPWVLaNpNK0/VPBg9_FOlII/AAAAAAAAAUc/xp8YMpVUAjk/s1600/_FreqVSpppTeam_Def_PnR_Handler_percent.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://3.bp.blogspot.com/-oPWVLaNpNK0/VPBg9_FOlII/AAAAAAAAAUc/xp8YMpVUAjk/s1600/_FreqVSpppTeam_Def_PnR_Handler_percent.png" height="280" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Frequency vs PPP PnR Handler: There seem to be two groups (over and under the red line). You should probably be worried if you are above this line.</td></tr>
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<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-PC7cFKEVvnM/VPBg9xUun3I/AAAAAAAAAUg/8SLBxdkZQ5g/s1600/_FreqVSpppTeam_Def_SpotUp_percent.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://1.bp.blogspot.com/-PC7cFKEVvnM/VPBg9xUun3I/AAAAAAAAAUg/8SLBxdkZQ5g/s1600/_FreqVSpppTeam_Def_SpotUp_percent.png" height="280" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: 12.8000001907349px;">Frequency vs PPP Spot Up shooter. No correlation. Interestingly 'stabilizes' for most teams with a frequency above 20% (which fits the <a href="http://nyloncalculus.com/2015/02/09/defending-the-three-pointer-mean-avoiding-three-pointer/" target="_blank">Seth Partnow article</a> that once you shoot the 3 it becomes a coin flip). Also: Milwauuke and their tentacles...</span></td></tr>
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<tr><td style="text-align: center;"><a href="http://4.bp.blogspot.com/-ANC99oT6GQ4/VPBg90NbmlI/AAAAAAAAAUY/RP1yHAwfzMI/s1600/_FreqVSpppTeam_Def_Transition_percent.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://4.bp.blogspot.com/-ANC99oT6GQ4/VPBg90NbmlI/AAAAAAAAAUY/RP1yHAwfzMI/s1600/_FreqVSpppTeam_Def_Transition_percent.png" height="280" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-size: 12.8000001907349px;">Frequency vs PPP: If somebody knows the difference between LAL/SAC/MIN and HOU/PHI/TOR transition D: I'm listening.</span></td></tr>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-Zo9Tw-zvVic/VPBhKqmVoOI/AAAAAAAAAUw/5-S0CS22KUg/s1600/Synergy_DefPPP%2B.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://1.bp.blogspot.com/-Zo9Tw-zvVic/VPBhKqmVoOI/AAAAAAAAAUw/5-S0CS22KUg/s1600/Synergy_DefPPP%2B.png" height="256" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Defensive PPP for different play types. If you find anything of interest let me know. Golden State does not defend the roller very well, but is killer in everything else.</td></tr>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-HK5vuPkFQFE/VPBhK6lVwLI/AAAAAAAAAU0/AC5Dhl6iG7s/s1600/Synergy_OffFreq%2B.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://3.bp.blogspot.com/-HK5vuPkFQFE/VPBhK6lVwLI/AAAAAAAAAU0/AC5Dhl6iG7s/s1600/Synergy_OffFreq%2B.png" height="256" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Offensive Frequency for different play types. Also no rhyme nor reason (in my opinion). I guess it's partly your style/preference, partly what the defense dictates.</td></tr>
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<span style="line-height: 100%;">In any case, I hope you like this way of presentation. It's mostly <a href="http://sebastianraschka.com/Articles/heatmaps_in_r.html" target="_blank">R heatmap.2</a> with some polishing. I'll use it more often now that I got more into data scraping :)</span></div>
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<span style="line-height: 100%;">Have a good weekend (especially you SSAC15 people!),</span></div>
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<span style="line-height: 100%;">Hannes</span></div>
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Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-20370135149538036842015-02-19T07:56:00.000-08:002015-02-19T07:56:08.545-08:00Data dump:Isolation is a meritocracy, miscellaneous is something you should avoidHi everybody,<br />
<br />
this is mostly a data dump after I looked at the new nba.com Synergy Sports data (next stop tracking data!). Feel free to use the plots.<br />
For players I always show those that have enough attempts<br />
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One personal note: The Synergy data can be easily wrongly interpreted. For example, a cut is mostly not a play itself. If you look at the players that attempt a lot of cuts, you find mainly center that most probably are beneficiaries from other stuff that is going on (cue to Blake Griffin throwing a lob to DeAndre Jordan).<br />
Even though cuts have a high value for Points per possession, cutting all the time is not the solution. (This is a very personal note, as my last rec league team had a disastrous knack of cutting into whatever real playtype was going on at that moment...)<br />
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Cheers,<br />
Hannes<br />
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<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-70973469413570205772015-01-30T03:10:00.000-08:002015-02-18T04:30:09.461-08:00Revisiting Stats stabilization<h1>
Revisiting Stats stabilization</h1>
<div class="western">
a warning before you start reading this. You can find a more polished version at Nylon Calculus (memo to myself: add link here as soon as you got it). They also published another <a href="http://nyloncalculus.com/2014/12/19/freelance-friday-getting-xs-os-blueprint-use-tracking-data/" target="_blank">piece of mine</a> and have a lot of other great stuff. But if I would draw a Venn Diagram of people that read my blog and people that read Nylon Calculus, I am pretty sure that you know all this already...<br />
This version has a bit more (probably boring) details on why I find previously used methods impractical. It also has a bit more shiny plots, which in the end where not helpful for understanding. So, if you are here for the shiny plots scroll down to the end. There is also an R script so that you can produce shiny plots yourself. You can find a github for the R function that I wrote <a href="https://github.com/SportsTribution/StatsStabilization.git" target="_blank">here</a>.<br />
Side note: One reason for this blog entry is that I'm starting to move from Matlab to R. If you find technical flaws in it let me know. :)<br />
<br />
Hello everybody,<br />
over
the last years there seems to be one main way to estimate the
stabilization of a stat, (
<span style="color: navy;"><span lang="zxx"><u><a href="http://nyloncalculus.com/2014/08/29/long-take-three-point-shooting-stabilize/">http://nyloncalculus.com/2014/08/29/long-take-three-point-shooting-stabilize/</a></u></span></span>
,
<span style="color: navy;"><span lang="zxx"><u><a href="http://www.fangraphs.com/blogs/stabilizing-statistics-interpreting-early-season-results/">http://www.fangraphs.com/blogs/stabilizing-statistics-interpreting-early-season-results/</a></u></span></span>
, <span style="color: navy;"><span lang="zxx"><u><a href="http://www.baseballprospectus.com/article.php?articleid=17659">http://www.baseballprospectus.com/article.php?articleid=17659</a></u></span></span>
) based on the work of Prof. Dr. Pizza Cutter. While the work itself
is technically sound, it has in my opinion several drawbacks. In
short, the method is in my opinion unnecessarily complicated, can be
easily misleading and is as a result impractical to use. In the
following, I will explain these three points of critique, while
introducing a simpler and more practical method that works perfectly well for a certain kind of commonly measured data.<br />
<a name='more'></a></div>
<h3>
1. The method is unnecessarily complicated</h3>
<div class="western">
This problem actually has been fixed sideways. The
originating study “took each player’s plate appearances and
numbered them sequentially, from his first to his last. Then split
them up into even-numbered and odd-numbered appearances”. This is
unnecessarily complicated in the regard that you would need the
actual sequential order of outcomes. The idea of the pairwise split
means that you want to compare to randomized sub samples against each
other. So, you could as well just say that a player that made 30% of
100 threes made the first 30 and missed the other 70, then randomly
split those numbers into two subgroups and get the same result.
</div>
<div class="western">
In later studies, the whole process was made more
technically sound and 'simplified', using the Kuder and Richardson
Formula 21 (KR 21). I am not sure if simplified is the correct term,
because literature like this is not what I would call simple (even gorier vortexes ahead!
<span style="color: navy;"><span lang="zxx"><u><a href="http://www.real-statistics.com/reliability/kuder-richardson-formula-20/">http://www.real-statistics.com/reliability/kuder-richardson-formula-20/</a></u></span></span>
<span style="color: navy;"><span lang="zxx"><u><a href="http://www.education.uiowa.edu/docs/default-source/casma-technotes/technote02.pdf?sfvrsn=2">http://www.education.uiowa.edu/docs/default-source/casma-technotes/technote02.pdf?sfvrsn=2</a></u></span></span>
). There is one additional complication with this method. The idea of
KR 21 is to estimate the internal consistence of a given data set.
Studies on stats stabilization turn this question around and increase
the data set to see at how many observations per test object KR 21
reaches a value that is deemed '50% signal 50% noise'. This can of
course easily lower your number of test objects. For three pointers
for example, this number was found to stabilize at 750 attempts. That
will most likely decrease the number of bad three point shooters, as
those won't be allowed to freely fire away and therefore make the
group of controlled players more homogeneous. This leads to the
second problem of the methods</div>
<h3>
2. The method can be easily misleading</h3>
<div class="western">
Reading a few of the articles that use this
method, I have the impression that the authors themselves are not
100% sure what the stabilization point of the KR-21/ pizza cutter
implies. The 'number of attempts' value is easily misread as 'please
wait approximately X number of attempts before you say anything about
a specific player'. A more correct understanding of the value is 'ON
AVERAGE you need X attempts of a test object to reliably asses if a
player is above or below average'.</div>
<div class="western">
Let us use free throws and three pointer as
exemplary comparisons.Player A scored on 160 of 200 FT attempts and
on 80 of 200 three point attempts. We assume league average for FT%
as 75% and for 3P% as 35. The binomial distributions (which is the
underlying assumption for KR-21) predicts that an average shooter
would make 160 or more free throws less than 5.8% of the time and 80
ore more threes less than 8.1% of the time. Both numbers are very
close to the typical value for significance of 5% (aka 'the value
that Statisticians bend there data for to reach it'). Yet, I am 100%
certain, that the free throw stabilization number will be somewhere
around 100 (or even less), while the aforementioned three pointers
are only assumed to stabilize after around only 750 attempts.</div>
<div class="western">
The reason is simple: Center take a lot of free
throws and are usually not very good at it. But three pointers are
mostly taken by players that are quite good at it (insert Josh Smith
joke here). So, while free throw percentages are all over the place,
there are a lot of players that shoot three pointers with around 35%
accuracy (or not at all). And while we don't need 750 shots to be
certain that Kyle Korver is a good three point shooter, we will have
a lot of players for which 2000 attempts won't be enough to have any
certainty. This leads us directly to the third problem.</div>
<h3>
3. The method is impractical in reality</h3>
<div class="western">
This is mostly for reasons already explained
during the last paragraph. The reason is simple. The method wants to
answer the question 'Can we say something with certainty about Player
A', but instead answers the question 'On average we need 750 attempts
to say something about a player'. So, if your goal is to say
something about the question 'How similar are players?', then the
method is not bad. But then it would probably be easier to just use
the KR-21 value for one season and use it to check the internal
consistency (its intended use). Instead, it is much more effective to
use the general assumptions about a binomial distribution directly
and assess every player individually.</div>
<h3>
4. Solution: Giving each player its own
certainty value</h3>
<div class="western">
So, let's shift our view a little bit. Our
assumption is 'there is a yes/no situation with an average percentage
of <span style="font-family: Liberation Serif, Times New Roman, serif;">μ</span>%
yes', a typical binomial distribution. Our question is 'How likely is
it that Player A, who has X yes on Y tries is the result of this
distribution'. This is a typical statistical question that we can
easily estimate. If the percentage X/Y of yes is below the average <span style="font-family: Liberation Serif, Times New Roman, serif;">μ</span>,
we sum up all the probabilities of the binomial distribution that a
player has X or less yes. If the percentage is above <span style="font-family: Liberation Serif, Times New Roman, serif;">μ,
we sum up all the probabilities of X or more. This gives you a
certainty value, which you have to multiply by two (long story short:
Because you have a two tailed test
</span><span style="color: navy;"><span lang="zxx"><u><a href="http://en.wikipedia.org/wiki/One-_and_two-tailed_tests"><span style="font-family: Liberation Serif, Times New Roman, serif;">http://en.wikipedia.org/wiki/One-_and_two-tailed_tests</span></a></u></span></span><span style="font-family: Liberation Serif, Times New Roman, serif;">
).</span></div>
<div class="western">
<span style="font-family: Liberation Serif, Times New Roman, serif;">This
is easily computated, you only absolute values of attempts and mades
for each player and an estimate for the average percentage μ. For
the latter, there are several simple possibilities. You can either
take sum of all mades divided by sum of all attempts, or you take the
median individual player percentage. The first might overvalue
players that shoot a lot and the second might overvalue players with
only few attempts, so in both cases it might be necessary to use a
minimal threshold. On the plus side, you can easily include player
with at least 50 attempts into your calculation. They might be up to
10% of their real probability, but this should be distributed quite
evenly in both directions. Furthermore, a player with less attempts
has a much higher confidence interval – which I will show you in
the following</span></div>
<h3>
<span style="font-family: 'Liberation Serif', 'Times New Roman', serif;">5.
Result: comparing stabilization of free throw and three pointer
numbers</span></h3>
<div class="western">
<span style="font-family: Liberation Serif, Times New Roman, serif;">In
the following, I will simply show how this attempt looks like for
free throws and three pointer, also highlight the limit to the whole
thing. Let us start by looking at all attempts and made percentages
for both shot types during the regular season 2013/14:</span></div>
<div class="western">
<img align="left" border="0" height="562" name="Image1" 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" 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" width="642" />oth
values for correlation and linear fit include only players with more
than 50 attempts (dotted vertical line). The dotted curves indicate
the 2.5 and 97.5 percent probability for the assumed binomial
distributions. For free throws 43.3% of players are outside of this
95% confidence interval. For three pointers it are merely 10.9%. This
underlines my assumption that you would need less than 100 attempts
to stabilize free throw attempts. 10.9% is not that much, given that
the theory of binomial distribution would assume that 5% of outliers
would happen just by random chance. Two obvious factors reduce the
number of outliers for three pointers. The first is that bad shooters
are usually quickly asked to stop shooting. The second is that good
shooters are usually asked or required to take more complicated shots
(as shown here
<span style="color: navy;"><span lang="zxx"><u><a href="http://nyloncalculus.com/2015/01/19/quick-firing-kyle-korver-co/">http://nyloncalculus.com/2015/01/19/quick-firing-kyle-korver-co/</a></u></span></span>
), something that is not the case for free throws. If we now want to
see the pValue (the probability that an outcome could be due to
random occurrence), for our three point shooter, we can use a
slightly different plot</div>
<div class="western">
T<img align="left" border="0" height="562" name="Image3" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAK8CAIAAACYwUyzAAC0D0lEQVR4nOzdC1hM+f8H8NNdF9ewsUgUIUsulaTclfumVLtSLtva5BaSO1ks209Y615al8Uuubay7uSSFeUvt0oRuaaSSqH+nzNnf/Mb0zSSaeacer8ezzxnzpzzPd+ZMZ33zOec79EsLi5mAAAAAEBxNFXdAQAAAIDKBgELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELAAAAQMEQsAAAAAAUDAELoMJlZWXVq1dv6dKl06ZN4+bcu3dv4sSJFy9e1NHRGThw4E8//VSnTp2yr86JjY3t1KkTTfz8889SD0kpbXNGRkZPnz4VL9akSZPBgwcvXry4Ro0a8p/RnTt3/P39L1y48OWXXzo7O8+dO1dLS4vmL1y4cMGCBZJL5uTkGBgYSM2hzkRFRenp6dG69Lw0NTXFD6mrq+vr64sXzsvLo6ffsGFD+f1RiKtXr7q7uycnJ4eEhPj4+Ojq6mpoaLx7905qsRs3brRt27Zly5a3b99WQq8kvXnzprReqYRkf/jWNwCVQ8ACqHChoaGSex2KETY2Ns+fP+/evfvNmzc3bdr04sWLiIiIMq4uFhgYWJatf3RzTZs2pUxDUSYlJWXNmjXZ2dlbt26V02BmZqa9vf2zZ8969ux5//79RYsWvXr1auXKlfRQYmIiZThzc3PxwhSYpFYfNGjQmTNnOnTokJubGxwcnJGRERYWRi18++23hw8fpgVcXV1/++032lvT9Pjx483MzGbNmlWWZ/qZ6ClQ/+3s7Nq3b6+mptamTRuKC0rYbtmVvVeUvCl/U6Smt14JHQOAkhCwACpKcXHxnTt39u/fP3/+fMn5R44cobjzzTff7NixgwIK5ZuDBw++efOmWrVqZVmdc1yEYsrVq1fld+Ojm9uyZQtlL5r4/fffKeXs3r2bEo/4V6WSaDFKV97e3rQitdOqVasNGzZQzKpevXpSUlKXLl1OnTpV2rpxcXGUriwtLS9fvpyfn0+doTD3s8jff/9NeaugoGDOnDmUDwICAuipUVNr166V/wQV5eXLl3Q7ZcoUio+M6Jcq5Wy37Ci88rBXACATAhZARXn//j2Fj5LzdXV1hwwZMmzYMJpu1KiRtra2mkgZVydFRUWBgYGDBw82MTH5aMAq4+ZI79696bawsJCiRv369cPDw0eNGmVtbX3p0iXJxZKTk+nWzs6Obimlde7c+c8//zx//ryjo2NiYuLAgQPldIYWo1vKhRoaGgYGBqampjExMRcvXszOztbT06PgRQGLOpyZmUn5curUqUuWLOF+ypIpIyNj+vTpFDSzsrKoG5TSqGWan5ubS6mUcmR6enqLFi0mTZo0cuRIespPnjxp0KCBoaHhpk2b5s6dS3GTkuXmzZu/+OIL7icfWpdeqNmzZ1PIkyx4UaD84YcfTp482aRJkwkTJkj2ITU1dfLkydHR0dwLSBmRXmQ5G5LTbZlNSW5LsgxXludCYXfnzp3u7u7yO3no0CF6UvQ/iiYo79JrPnPmTFqSOklL+vv7/+c//6He0lYo3lHypt6K+wwApUHAAqgo6urqx44do4lffvmFdvbi+YNEaOLRo0cbNmygQOPr66ujo1PG1cmePXvi4uK2b9++fv36j3ajjJsj3C9PlHvq1asnp0Fur3/mzJkxY8bk5+f/888/dJf21pSKKJnduXPHzMyMEknXrl1Xr15NEUpy3efPn9MtpTfu7pdffkm3L168mDFjxt9//92rVy+627p1a0oD9JTz8vIoHJTWDUqZTk5OtPW2bdsaGxtT+unbt29CQgI1ToHyxIkT1E8HBwea7+3tTVGGYha3Ik17eXlRJ6n9w4cPU5aijBIUFDRr1qz4+HhazMXFRXJDlHT79Olz/fp1apDelO+//1780OvXr21tbelJUU/ohd29e/fly5f/7//+T86GSuu2vr6+zKYkj0grqbTnMn78eApVlDJtbGzkd5JCm5ubW1paGk3TBAWsI0eOcAGL++/n6upKj1Kap3W7detG7zj12dnZOSUlRU7HAAABC6Ci0M6Y+01o//79MhegCHL//n3ag/r7+5d99bdv386ePfu7776TPNSpLErb3NixYylU0Y4zMTGR7lKA4H7fopzRvXv3klGMdsNz587dtm1bcnIyhTZqkxHtp7nVr1y54ujoqKurS/vpfv363bhxQ/InKAoEdFurVi3uLpe0srOzmzZtevPmTdrxa2hodO7cubi4ePr06WFhYfQi0CrUZ+4geknHjx+nmEIB7urVq5qamp6enjt37qSQQWmD0hVFN8oQtCFKA5TbFixYIP7liQLTuXPn2rVrR2GC0klMTAzN7N+//9q1aylg0VNu3749PR3xhqhNSletWrWizenp6dErHxoayj1EPXz8+PHo0aOXLVtGd4cPH04hNTIykisyytxQad3Ozc2V2RRNyHlPS3suhoaGFLDojaAXlmKunE7SdimW0ZOi/wP0vteuXfvChQuUlem/Gb0ajRs3tra2Pnv2LC1M/3/ofedOuaA3PSMjQ+r0BQCQhIAFoDKbNm2iXd3y5cutrKzu3Lkzc+ZMSi3cQxEREbSblLnW5s2baX8pdbJeOTZXt25dbj5X8iMNGjSg8PTjjz9ydw1ESrZD2YXC05QpUyg8URyxsLCgvbWRkRHNp6zQpEkTW1tb2vHTVihDUNaRLBpy5ydyv2Mx/z3siXbqjOgAo27dunHzV65cSaGBolKPHj1Onz5drVq1xYsXS+XChIQEuqVVuMPFtonQxJo1a+h28ODBXIzr2bMndYyC4MOHD7W1tbnNUeM0QUmOEZ2oKP91u3btGiOKm9yPSRSJxAGL+x0oTES8/O3bt7nsInNDpXWbIo7MpuT3rSzPRX4n6bUNCAgQn4vg7OxMz+7YsWNcbdTV1ZVSl4ODA21ox44d9GJSOuQewtmCAPIhYAEoW1JSEu3sKT30EaGYcujQIQoib9++LSgo4JYpKioqbfUzZ87k5uZSoBHPmT59elZWljgYlXFzlKW4BSh1cQe5lx3tm7mjfAi1SbfGxsaUY8QVPQ0NDe4AfNq05IpcqqO4w9198uQJ3XJHJolR6lq2bFl0dPS0adMo3ISEhFy6dGnq1KnUScnjfijDMRI/hokVFxdLzeHSA7c8I3Fio8wD0UriVhQvLHleZGFhId1OmjRJMkRSxJSzodK6Lb+p0pTluchvmVKj5DOi/xUUsChAc3e538/Onz9PGYvCsbe39/jx48eNG/fixQv5HQMABCwAZTt48CDFBWdn5z179lCiunnzJiM6FD1c5KOrW1pavn79mptOSEhITU01Nzdv2bLlp27uoxuirdB+VEdHp0GDBpLzjx49unTpUjs7O4p0ycnJZ8+ebdy4cbt27Wjmf/7zn9GjRy9fvjwvL+/cuXO0MPWNIgV3iA8txv1GRcGL9vrUPqU9bW3tjh07SrYfFBQ0YsSI5s2bx8XFUaiaPHlySkrK7t27KWxJBizuDADqDDVFjXh5ee3du3fdunVfffUVzacQSf2pWbPm6dOnaes0QRHw2bNnH33WJXE/EdGrN2PGDHrdtm/fLtUHeiJcMXfDhg2JiYnffPONnIHESut2aU2Vo8Ni3I9Mn9TJHj161KtXjwKWpqYmhTArKyuauW/fPnoTfXx8fvrpJ3oxMzIyPqdXAFUEAhaAsrm4uCxYsCAiIqJp06avXr3KysoyMTGhHVsZV58hwk1T+Fi1atWYMWM8PT0VvjmKFDLPImzdujUlpDNnztAC6enpFBTGjx+vrq7u6Og4f/78n3/+OTIy8uXLl0+ePKEQZm9vT9O0RUZUGWzfvj3NoUxGiTA3N5ceos6Li5Xk7t27f/zxB5cCHRwc1q9f7+7uTjmS9ve2traS3aDNUfSJj4+nNg0NDaOjo7/44gsnJyeapmd36tSptm3bUtg6efIkIxoBteSIXGU0ZMiQZs2aUZcoqVDjXMWQM3bsWEqToaGh2dnZ+fn59MRpSXoR6KmV1lpp3aaHZDZVvj5ztV1636nNT+okvc7Dhg3jTp6gXM79MMYl7F9//ZXeiCtXrnC/Ecr5kRUAGAQsAOVr0qTJ8ePHAwMDY2NjdXV1hw8fvnTp0urVqwtlc40bNz569Oj06dPj4uLMzMxcXV25ceQtLS1p582dzE+ZydfXd/HixVKxhnbYhw8fnjRpErVAnaGASPt+yQUCAgJmzZrFVdCon69fv963b1/9+vV37twpNWiFhobG33//TSGAItSjR4/69esXHBzMZbUDBw7Mmzfv0KFDFLPMzc25YRrK/Xy1tLQoTdLToVxIff7pp5/EQ7zS5s6fPz9lyhR6hWkxirncaysnYMnptsymytdnekcoqlIypk106NDhkzrp5ubGBSx6Z7k548aNo1h58ODBW7duzZw5c/fu3RcuXKBnIeccTwBAwAKocGtEJOdYWVlxv6yUb3WxlSIfbaG0zXGHQJXGW0TmQ126dOEGVZLCHeYlNZPSg+RxUbRrlzzaWorkKZO0pPyyKQUv8WkBkmjFEBGp+UZGRpI9oewleZcbR55TrVo1yYcaNWokOVKG+BdERlSAi4qK+qQNldZtmU1JkuyV/E0MHDgwPT39kzop1r17d6n5+vr6koXRiRMniqfFS0q9YgCAgAUAAACgYAhYAJXB/v37v/76a5kP/fnnn1IjZwIAQEVDwAKoDIYOHYoCDQAAfyBgAQAAACgYAhYAAACAgiFgAQAAACgYAhYAAACAgiFgAQAAACgYAhYAAACAgiFgAQAAACgYAhYAAACAgiFgAQAAACgYAhYAAACAgiFgAQAAACgYAhYAAACAgiFgAQAAACgYAhYAAACAgiFgAQAAACgYAhYAAACAgiFgAQAAACgYAhYAAACAgiFgAQAAACiYwAJWRkZGREREcXGxqjsCAAAAwqajo/PNN99oaWlVROMCC1gnTpw4ffq0g4ODqjsCAAAAwrZp0yY7O7vmzZtXROMCC1ika9euPj4+qu4FAAAACNvly5crrnHhBSwAAAAAnkPAAgAAAFAwBCwAAAAABUPAAgAAAFAwBCwAAAAABUPAAgAAAFAwBCwAAAAABUPAAgAAAFAwBCwAAAAABUPAAgAAAFAwBCwAAAAABUPAAgAAAFAwBCwAAAAABUPAAgAAAFAwBCwAAAAABUPAAgAAAFAwBCwAAAAABUPAAgCozPLymL17GRMTxs5O1V0BqEoQsHjtxo0bFhYWqu4FAAjY9OnM2rXsxPnzjK2tqnsDUGUgYPHXmzdv1q9fv2bNGlV3BABUac+ePampqdOmTXvx4sWgQYO2bt1qZmYmfpT+RJiamjo6Opa24uvX09TViouK1ZKSHtnafqnEjgNUaQhY/FWtWjWkKwDg0DcuDw+P4OBgyXRVFosXF5w8uc3b237kyBYV1DcAKAkBi7/evXu3a9euESNGqLojAKBixcXFY0S6du1Kd11cXNavX1+nTp3evXt//fXXNCczM5PiV05OTv/+/WfPni254owZo8ePbxsYyKar9PR0b2/v9+/fUztBQUH0FS4+Pr6oqCglJeXgwYP6+voODg4RERH0Z0fclHiZ0NBQVT19ACFCwOIvCliXLl1CwAKAtWvXNm3aNDs7m7tL0ScqKqpFixY2NjZqamrcApSc3NzcXF1dExMTxb9y0XxjY+PmzZtzd0NCQsaPHz9kyBBq4f79+zTH0NBw6dKlP//888mTJ2mx9u3bb9iwQbIp8TIqeNoAQoaAxV8oEQIAx8nJacWKFd26dXN2dq5Xrx7Fo6lTpyYkJLi4uFy8eJEWoCQUHx9PqSsvLy8jI0McsGjFZcuW2dvbT5gwgVZMSUnx9/en+Z06daJpmujatStFNIpclL2aNGkyfPjwzZs3SzYlXkZ1zx5AkBCw+AslQgDgNGvWjL5xzZ8/f8aMGWFhYUZGRhkiS5Ys4QKWiYmJra1tnz59wsPDJU89phWrV6++aNEibkW6e/nyZYpTV65cGT169I0bN7S0tGixli1b3rt37/nz54GBgSdOnJBsipbklgGAT4KAxV8oEQKApIEDB27evDk6OtrOzo4y0KtXr8Q/LPn6+np7e4eEhHTp0sXAwKC0FSdPnjxq1KjVq1dTC02bNpVcpkOHDgUFBerq6vKbAoAyQsDiL5QIAYARHdIunt6/fz83kZ+fP2zYMJrw8/Pj5kRGRpZlxaNHj4pnitclP/30EzdRr149yaYklwGAskPA4i+UCAFApo0bN6akpNjY2Ki6IwBQKgQs/kKJEABk8hFRdS8AQB4ELP5CiRAAAECgELD4CyVCAAAAgULA4i+UCAEAAAQKAYu/UCIEAAAQKAQs/kKJEAAAQKAQsPgLJUIAAACBQsDiL5QIAQAABAoBi79QIgQAABAoBCz+QokQAABAoBCw+AslQgAAAIFCwOIvlAgBAAAECgGLv1AiBAAAECgELP5CiRAAAECgELD4CyVCAAAAgULA4i+UCAEAAAQKAYu/UCIEAAAQKAQs/kKJEAAAQKAQsPgLJUIAAACBQsDiL5QIAQAABAoBi79QIgQAABAoBCz+QokQAABAoBCw+AslQgAAAIFCwOIvlAgBAAAECgGLv1AiBAAAECgELP5CiRAAAECgELD4CyVCAAAAgULA4i+UCAEAAAQKAYu/UCIEAAAQKAQs/kKJEAAAQKAQsPgLJUIAAACBQsDiL5QIAQAABAoBi79QIgQAABAoBCz+QokQAABAoJQasIqLi3NycgwMDNTV1ZW5XYFCiRAAAECglBGw8vLyVq5cGR4efv/+/cLCQg0NDRMTkxEjRgQGBuro6CihAwKFEiEAAIBAKSNg+fr6Pnr0aNOmTRYWFjVq1Hj16tWtW7eWL19O80NDQ5XQAYFCiRAAAECglBGw9u/ff/PmzYYNG3J3DQ0N7ezs2rVrZ2JigoAlB0qEAAAAAqWMgEVB6siRI2PGjJGcGRUV1aRJEyVsXbhQIgQAABAoZQSsTZs2ubi4BAcHt23btnr16q9fv05ISMjMzDx48KASti5cKBECAAAIlDICVqdOnZKSks6cOZOamvrixYvatWv7+Pg4ODhoasrb+tu3bymKSc2kORQ7KrKzPIISIQAAgEApaZgGylK9evX6pFWOHj26detWqZnXr183MTGZOHGi4rrGXygRAgAACBR/BxodKCI1c8qUKY8fP1ZJf5QPJUIAAACBUkbAun37dmkPmZubK6EDAoUSIQAAgEApI2D5+/sfOXJET0+vdu3aUg89fPhQCR0QKJQIAQAABEoZAeuvv/767rvvdHR08HvMJ0GJEAAAQKCUdAyWu7v7tWvXlLOtSgMlQgAAAIFSUsDqJaKcbVUaKBECAAAIFH/PIgSUCAEAAAQKAYu/UCIEAAAQKAQs/kKJEAAAQKAQsPgLJUIAAACBQsDiL5QIAQAABAoBi79QIgQAABAoBCz+QokQAABAoBCw+AslQgAAAIFCwOIvlAgBAAAECgGLv1AiBAAAECgELP5CiRAAAECgELD4CyVCAAAAgULA4i+UCAEAAAQKAYu/UCIEAAAQKAQs/kKJEAAAQKAQsPgLJUIAUIIrV66Eh4eX4/fyP/74Y+XKlS9fvrS1td20aZOGhobko9Sgqampo6MjdzcwMNDb29vc3FwxnQbgPQQs/kKJEAB4KzU1lULVyZMntbW1/f39d+3a9e2336q6UwA8goDFXygRAoDSpKene3t7v3//vmvXrkFBQefOnVu2bFlhYeGkSZNsbW09PDxycnL69+8/e/ZsbvmIiIhx48bR90CaXrRoUW5u7q1bj7p3v/v2raGT07EdO6ZyiyUmJo4ePVpfXz8vL4/az8zMlGyKvkPGx8cXFRVRC5JbX79+fUJCwuPHj2vUqBEWFqayFwXgMyBg8RdKhACgNCEhIePHjx8yZAhFn/v370dGRvr5+XXs2JGS1tq1ayn9uLm5ubq6UmAyMzOj5dPS0ih4cetWF1m/PvXZsx7q6szffz8UN7tkyZLFixfb2dlRU3RXqimaY2houHTp0oCAAMmt03wDA4M9e/Y4OztnZGTQMip4RQA+DwIWf6FECAAVav/+/UOHDi0qKqLplJQUf39/mujUqRNNU7oKCgqiPDRp0iRKQvHx8VFRUXl5eRR3uIDVqFEjylg2NjY0fe/evWPHjllZ9dPSyn/3TkdTM5ph+nObSE1NtbKyUldXp1tG9IOWZFM0p2vXrmpqalJbpwlra2u6rVOnztu3b1Xy4gB8JgQs/kKJEAAq1KJFi+zt7ZOTk2vVqqWnp3f58uUhQ4ZcuXJl9OjRu3fvXrhwYfXq1QcOHNijRw9bW9s+ffqEh4dbWFhw61IyGzt27IABA2hFymFt2rSJilpx+vQYY+O2gwZFMcwSbjETE5NLly7RVmJiYri7kk3RtrS0tGh+s2bNJLd++/ZtTU3snkDY8D+Yv1AiBIAKNX36dAcHB4o4e/fu1dHRGTVq1OrVq+3s7Jo2bdq6detevXpR7vHy8qKM5e3tHRIS0qVLFwMDA27d5s2b+/j4dOvWjaYtLS09PT1prcWLZ+nr6xsbG1M+4xabNWsWtaCrq9u4cWO66+vrW7IpMnnyZMmtK/uFAKgACFj8hRIhAFQodxHx3aNHj4qnnUTEdyMjI0uu7iEivttDpORi58+fl7wr2ZSfnx830bBhQ8mtjxs3jpvYvHlz2Z4KAO8gYPEXSoQAAAAChYDFXygRAgAACBQCFn+hRAgAACBQCFj8hRIhAACAQCFg8RdKhAAAAAKFgMVfKBECAAAIFAIWf6FECAAAIFAIWPyFEiEAAIBAIWDxF0qEAAAAAoWAxV8oEQIAAAgUAhZ/oUQIAAAgUAhY/IUSIQAAgEAhYPEXSoQAAAAChYDFXygRAgAACBQCFn+hRAgAACBQCFj8hRIhAACAQCFg8RdKhAAAAAKFgMVfKBECAAAIFAIWf6FECAAAIFAIWPyFEiEAAIBAIWDxF0qEAAAAAoWAxV8oEQIAAAgUAhZ/oUQIAAAgUAhY/IUSIQAAgEAhYPEXSoQAAAAChYDFXygRAgAACBQCFn+hRAgAAMrx9u3bUaNGpaSk5OXlLVy4cPDgwUlJSVlZWZ06dZKz1vr165s2bero6PhJ2/rjjz9Wrlz58uVLW1vbTZs2aWhofF7feQoBi79QIgQAAOU4dOiQkZHR9u3bc3NzraysuICVmpoqP2CVA7VJoerkyZPa2tr+/v60m/v2228VuwmeQMDiL5QIAQBAOQwNDa9evZqcnNy8efMLFy4UFxf/9ttvFIa6d+8+efLknJyc/v37z549e9q0aZTAaDFjY+ONGzfSilu3bqWJOnXqbN68OTMz08PDQ7zw+vXrExISHj9+XKNGjbCwMG5DERER48aNq1atGk0vWrSIWgsODqYYRxui/R2tEh4eHh8fX1RUZGlpyU1kZ2fTfNpE7969T5w4oaampspXqswQsPgLJUIAAFAOe3t7X5GXL1/OmDHDxcXFy8uLAtbevXu9vb3d3NxcXV0TExNpSXNz83Xr1tGjMTExdJcCGeWkoUOHZmVlrV27VmphAwODPXv2ODs7Z2RkUIajOWlpaba2ttxGq4uU7AwtuXTp0l9//ZWb2LJlS1RUVIsWLWxsbISSrhgELD5DiRAAAJQjLi6uZ8+elKuePHkyYMAAylvcfMpJ8fHxlG/y8vIoJNEcKysruqWQlJSURBPW1tZ0W7duXdpnSS5MDT548MDY2PjFixe1atV6+/Yt12CjRo0oY1FUoul79+4dO3ZM3AdqgZvo2rUrF6S4if79+0+dOjUhIYG6p7QX5PMhYPEXSoQAAKAclIco6wQEBNSvX19bW5vLOsXFxSYmJpSl+vTpEx4ebmFhsWfPnpiYmC5duly8eNHHx+f69euamv8LEpILOzk5PX/+vHbt2hS2srKyrl271qRJk5o1a/bq1WvKlCmU4fT09NauXdumTRtq4eXLl0VFRZcvX+ba0dLSkpwwMjLKEFmyZInSX5jyQ8DiL5QIAQBAOdzd3f38/Ozt7SkMff/99w0bNszMzJw3b15oaGhgYGBISAiFKgMDA1qSotWBAweaNWtGcyhgSTbi6+vr7e0tXlhdXV1fX59yVZ06dSwtLSldZWdn6+rqUgLr2LGjhoZG+/btf/zxx/T09JEjR/75559mZmaldY9y26tXrwRUH2QQsPgMJUIAAFAOyj2UpSTntGnThvtJKTIyUnL+woULzc3Nuelx48ZxE5s3b+YmJBcu+ShtxcjIaNasWdOmTaPAlJOTc+fOnbdv39KmKX7VqlWrWrVqlPO4hcUTJD8/f9iwYQp6rkqCgMVfKBECAEClpK2tXVeEERUiX79+nZ2dnZycnJeXp6enR2GrevXqNWrU4H6y2rhxY0pKCnfYloAgYPEXSoQAAMArwcHBCm+TUhR3OmGjRo3obkFBAYWtZ8+e3bt3j+5SzHJ2dh49ejQtlpTEnDzJDBrENGig8F4oHgIWf6FECAAAVY2Ojk59EZouLCyksJWVlZWWllZYqNHbvkVuoU7I93duFZurupsfh4DFXygRAgBAVaatrV1PhKZzcorevn2rxhTnM7qq7leZIGDxF0qEAAAAnOrV1Y+d0YmMZL75xljVfSkTBCz+QokQAABArFs39p9QIGDxF0qEAAAAAoWAxV8oEQIAAAgUAhZ/oUQIAAAgUAhY/IUSIQAAgEAhYPEXSoQAAAAChYDFXygRAgAACBQCFn+hRAgAACBQCFj8hRIhAACAQCFg8RdKhAAAAAKFgMVfKBECAAAIFAIWf6FECAAAIFAIWPyFEiEAAIBAIWDxF0qEAAAAAoWAxV8oEYKU8PDwoKCg5ORkNTU1utu6dWt/f//c3FwLC4tTp065uLi0b9++HOveunXL1NQ0KSmJbh0dHZX3fAAAKi8ELP5CiRBKKigooDxE8SglJeXVq1c0Z9KkSXRLAUv+irm52gUFhTLXpZkV33EAgKoFAYu/UCKEkgYMGHD48GEKSZGRkf3796c5K1eutLCw4B7dtGnTgwcP/P39PTw8cnJyaIHZs2fT/HnzmMWLPWrVcoiI+KO0dTmZmZmS665ZsyY+Pr6oqCg0NFTpzxUAQMAQsPgLJUIoydramuIRTURHR1MGKiwsFD905MiRuLi4Xbt2LVmyxNvb283NzdXVNTEx0czMbN8+priYefnyy1On0ubMkbGu2Nq1ayXXpTmGhoZLly5V2hMEAKgcELD4CyVCKElDQ6NmzZoPHz7U0tLS1taWDEmXLl169uzZ+/fvKRjFx8dHRUXl5eVlZGRQwJo1i5k8OadFi6eNG2fLXFdMal2a07VrV+6wLQAAKDsELP5CiRBkcnJyCggI6NOnj9T8BQsWHDlyZMuWLSYmJra2trRAeHg4VwH08GAKCiJoQk+vn8x1xaTWvXLlCqWxCn06AACVEgIWf6FECDL17dvX09NzxYoVp0+flpyvpqY2efLkHj167Nq1y8/PLyQkpEuXLgYGBmVZV8zX19fb21vmugAAUHYIWPyFEiFIoejDTRQUFNCtu7u7+KHevXtzEzExMXTLHaf1qetyJNeloKaQngMAVDUIWPyFEiEAAIBAIWDxF0qEAAAAAoWAxV8oEQIAAAgUAhZ/oUQIAAAgUAhY/IUSIQAAgEAhYPEXSoQAAAAChYDFXygRAgAACBQCFn+hRAgAACBQCFj8hRIhAACAQCFg8RdKhAAAAAKFgMVfKBECAAAIFAIWf6FECAAAIFAIWPyFEiEAAIBAIWDxF0qEAAAAAoWAxV8oEQIAAAgUAhZ/oUQIAAAgUAhY/IUSIQCAQO3Zsyc1NXXatGkvXrwYNGjQ1q1bjx49ampq6ujoKH9FT0/PlJSU+Pj4du3aaWhoeHh4NG3a9KNrAQ8hYPEXSoQAAIL25s0bSkjBwcFmZmYUsMqyyrZt2+jWxsYmOjqaJtavX1+xXYQKg4DFXygRAgAIV3Fx8RiRrl27imempaWNHDlSW1u7devWISEhLi4uFKHq1KnTu3fvEydOqKmplWxn69atGzdupGU2b96cnp7u7e39/v17ajMoKIjWPXfu3JMnT5o3b56Zmfno0aOdO3f+8ssvubm5ycnJxsbGtCIFtWXLlhUWFk6aNMnS0lJq9YSEhMePH9eoUSMsLEyJr02VgIDFXygRAgAI19q1a5s2bZqdnS0589mzZ8HBwR06dOjSpQvd7d+/f1RUVIsWLWxsbGSmK0LhadGiRUOHDr1589XIkcnt26/YuNFi4MD+9+/fp0epqSlTppiYmNy8eXPv3r3Hjx+nmebm5uvWrfPy8oqJiYmMjPTz8+vYsSNFMYp048ePHzJkCG2XW93AwGDPnj3Ozs4ZGRmGhoYV/qJUJSoIWM+fP9fS0qpVq5byNy0sKBECAAiXk5PTihUrunXrRvGlXr163Mzq1asvX768Zs2aWVlZjChgTZ06NSEhwcXFpbR2rK2t6bZu3boLF+pcvWobG6vx9ddMp06dUlJSaH7Lli3V1dXbtm2rr69fp06dhw8f0kwrKyu6tbW1TUpKonQVFBREaW/SpEm0ir+/Pz0kXp1rnFZ8+/ZtRb8gVY0yApajo+Nvv/32xRdfPHr0aPjw4ZcuXdLQ0LC3t9+2bVuDBg2U0AGBQokQAEC4mjVrVq1atfnz58+YMUNcgFuzZs348ePbtWt36tQpumtkZJQhsmTJktLa0dT8d09taFhUXKyhpkZ5iLly5cro0aNv374tc5WYmJguXbpcvHjRx8dn9+7dCxcupGA3cOBACl6XL18eMmSIeHVx46Bwynhljx49mp+fTxOU0+k/3N9//62lpTVz5kyK1Xv37lVCBwQKJUIAAKGjWLN582buiHXy9ddfz5kzR19f39jYmKKPm5ubra3tq1evSqsPSpo3L7dLl/x162bNmZNoZ2fXtGnT0pakaHXgwAHa4VLMys7O7tWrF017eXn169dv1KhRq1evlr86KIRSo+s///zz119/0X8smqaAZWpqKmfhuLi4kudcUOiuOrVFlAgBAARKsuS3f/9+uqVMw93t0aOH5JL5+fnDhg2TWv3SpUvcxLhx47gJSmkMO4gD/fvfeYXiRw8fPsyIwhzdTps2beHChebm5txDTiLiVSR3rFKNg2IpKWClp6c3atSoTZs2qampLVu2pDkJCQlffPGFnFUMDAwocUvNrFmzpoaGRgV2lE9QIgQAqNw2btyYkpJiY2Oj6o6A4ikjYNnb27u7uz979kxXV/fBgwf9+vU7e/bs0KFDly1bJmctUxGpmRcuXHj8+HFFdpZHUCIEAKjcfEQU22ZwcLBiG4TyUUbAOnPmDN0WFhZSunr+/DlNU9I6dOiQ+PdSkAklQgAAAIFS3jFY2tra4h+lOnfurLTtChdKhKplYWHh6uo6f/78kg8lJSVlZWVduXLlcy5hER4eHhQUlJyczB3c2rp1a39//7Fjx35WpwEAgB9wfiZ/oUSoQomJiSYmJkePHi0tYKWmpn7+VgoKCm7dukXRKiUl5dWrV5/fIAAA8AQCFn+hRKhCBw4c8PT0DAsLe/jwYaNGjaQuKPHbb79RwHJ0dDx79qycS1hIrsJdEENPr46T04DTp//ifrUaMGDA4cOHKWBFRkb279+fEZ0OItkIJez4+PiioiJLS0tuYtGiRZILdOvW7cyZMwEBAZqamosXL7a3tz9//ryqXzwAAEDA4jGUCFUoKipq3759T58+PXjwoK+vL/PhBSW8vLy4X7DoPeIuYZGVlSX/GhQ0c82a2BUrehcW7j97Vs3Bgd2KtbU1RSuaiI6OpgUKCwtLNmJoaLh06dJff/2Vm6AsJbkABa+bN29SLMvPz79x4waK7wAAPIGAxV8oEaoK5arr16+7urrm5eXp6elxAUvmBSXEl7CgpCX/GhSUh/r1O5+To66urn30KMMFLA0NjZo1az58+FBLS0tbW5sCVslGunbtyv3cxU1ILdCjR48zZ87Q6rTu2bNne/bsqeTXCgAAZELA4i+UCFXl0KFDc+fOnTBhAiO6kGpOTg4jcbUKTnFxMcUdyZnNmjWTcw0KIyOjmjX/qlXL3Ny8zciR/2vHyckpICCgT58+Mhu5ceMGhSfuIW5CaoGvvvqKMtbIkSPz8/PDwsJOnz5dMS8JAAB8GgQs/kKJUFX2798v/u2wd+/eUVFRUgs0btx43rx5/fv3l7zWxOTJk+Vfg6J3b2Mrqy1SQ9T07dvX09NzxYoVXDb6aCMlF6AYZ21tXVhYSLmwZs2a5X7WAACgQAhY/FX5SoTR0dFubm4mJiYvXrxwcnIKCQmRWmDatGljx44VX+GBnr6pqanMcRDkNLVq1SoLC4tevXpJLi9zphTxMtxFJzjLly+XXIa7oISRkdHly5elZjIfuwaF1AUxvL29uYmCggK6dXd3L9mIn5+f1ETDhg2lLiEVGxvLTcTExMh5dgAAoEwIWPxVKUuEHh4ewcHBRUVFVlZWaWlpjRs3VnhTkyZNKrmwzJnlWKbcquAFMRQ70NeePXsof3/55ZfPnj0LDAwcM2aM1AJcHL9z585HkzS3ZFJSUhmXBwAoBwQs/qrEJcL8/Pw3b95oampSMBo5cqS2tjbtfblfoRYtWkR7UNqPbtmyhVtYauQCqaays/9tSjyiQdu2bWmX2bJly1GjRmlpadGc0NBQ2j3TTNqKnJEUVq5cScv07t27Ip5yRVwQg/8UO9CXn58fZay8vDxLS8uSAYvzqSm5QlM1AFRlCFj8VflKhGT37t1XrlzJzs6m9NOgQYPY2Njg4OAOHTp06dKFW8Da2nrixIleXl7R0dHcHKmRC4yNjbn5v/9+at26+wUFdb79dgk1xfx3RINVq1YxotLe/PnzbW1tW7RoId66/JEUlPk6VBElB/qSitTiWEx5d+zYsRTC6BsFvUFyI3V2/fr1aSIzM9PDwyMnJ4danj17Nvcol5KTkpIko7PMJSWXr6BUDQBVGQIWf1XKEqGbm5vkUd7Vq1enJFSzZs2srCxujpWVFd1SMOICEJEamEAcsDp2nHL4sLGmJpOXN5ibIx7RgBENtk7La2hoWFpaijcnfySFCnzaVVXJgb6ePXsmFam5WDx16tTp06d379595syZJSN1TAwzfXqfp09vREQMTky8OGvWLFpx7dq1lMPof5Srq2tiYqLUpiWj8/r16+UsCQBQERCw+KsSlwjF1qxZQ7vSdu3anTp1ipsTExNjY2Nz/vx5Pz8/7kByqYEJxOs2aZJI++gHDxjxPPGIBoT2yrQ8Ra64uDjxTPkjKYDClRzoq2Sk5mLx3bt3O3furK6uvmzZMhcXF6lIvW4dk5ZW4/37rmPHHhwxorBv377ffPMNRaX4+PioqKi8vLySP0BKRmf5SwIAVATsXfirUpYIpXz99ddz5szR19enneju3btpTkJCQs+ePVu2bEm7Wy5glTZyga5u7oULpbYcEBBAEYr2r3Xr1tXR0eFmfnQQBFA4qYG+SkZqLhbT2xEbG+vg4ODu7m5iYiIVqQcOZHbuLNLVLbCz06PlKaVlZmbSYra2ttRyeHi4hYUFLSy5XcnoLH9JAICKgIDFX5WvRGgnIjmnh4j4rpubm+Sj4rEJpAYmkNmUeGFKUXQbERERFhZGe1Z7e/vatWtzMxm5IymIlwEFkhroq2Sk5kyfPn3MmDGBgYGUgAcNGiSVg11cmPz8Q3PmTBs92ujVq1ddunShCO7r6+vt7R0SEkJ3DQwM5PSh7EsCACgKAhZ/VYUSYcUxMzOjfaqhoaGrqyvqgCpR2kBfkpHawsKC3iBHR0fKW8ePHxfPLxmpPT2H0j/JOfXq1eMO8BK3QyGbmsrIyAgKCqJ8RtmaElVcXBzNFC8pxiVymaOsAQB8Pux4+KsqlAgrTtu2bc+ePavqXoA8iYmJJiYmlKXmz59fvhYyM5mrV5kGDZIk27l27drQoUNfvHhBt9ra2qWtW1jIlP4gAMDnQsDir8pXIgSQdODAAU9Pz7CwsIcPHzZq1Eg8ZIOlpSU3sWjRIjnjlr1+zbRsyTx/TrcaQUH/a2fbtm23bt2KjIzMycnp2bOnqalpyeHWduwwHeJUUJ05+1WPYDs765KDQQAAfCYELP5CiRAqt6ioqH379j19+vTgwYO+vr7Mf4ds+PXXX7mJgIAAOeOW5ecbvnjBqKszaWnaTk5O4nYotN24ccPCwoJuuepwybEh/v5d562aVkZxn40b+0yc+MH4agAACoGAxV8oEUIlRnno+vXrrq6ueXl5enp6XMASj2TGTcgft6xRI2bTJubgwTdnz/7g6loo2Y4UqbEhaF27oSmH9xk4DalmYqIrNb4aAIBCIGDxF0qEUIkdOnRo7ty5EyZMoOkOHTrk5OQwEiOZcRMfHbdszBimuHh77959pNqRIjk2xJs3b548eTJyZGbz5kspxmloSI+vBgCgEAhY/IUSIVRi+/fvF/9A27t376ioqJLLlGXcspLt1K5dW2oZ8dgQjRo1CgkJ+eKLL3R0dDAoGgBUKAQsJaFvyeHh4Z9U8pNfIixHgwD8cfjwYfH08uXLJR8SD2nWsGHDo0ePRkdHu7m5nThx4sWLF05OTgMHDuTGLRO3k5SURB+HTp06ce2sWrWqbdu2vXr1El9hkNLV4sWLzc3NExISWrVqpaenR58dX1/f5ORkriLZunVrf3//3NxcCwuLW7dumZqaUpt0i0EcAKDcELD4S6pEGBfHPH7M9OnDYFAnqGo8PDyCg4OLioqsrKzS0tIaN24s+SiFodTUVApY3N1JkyaVbKGwsFCcrrg5BQUFlKUoWqWkpLx69Uq8Is2s2CcDAFUD9tVKJXW6+Pr168+dO/fkyZPmzZtnZmY+evRo586dNWrUoN1JTk5O3759nz9/bmxsvGzZsmfPzK5cCSkuZmbPZn788X8NpqenyzmPndrkmurfv//s2bNpW9QU7WloRzJgwADVvQwA5ZGfn//mzRtNTU2pz9Fvv/1GAYs+PvRRohDWtm1bCwsLylJjx46l5ERfUei7SnJy8suXL/fv379o0SKuNfoIHD58mFqIjIykDwjNWblyJa0ouUV8ZACg3BCwlKrk6eI0PWXKFBMTk5s3b+7du/f48eO0k6DM5Obm5urqunTp0tDQUD8/vydPbEeNYs9If/r0gwZp7yLnPHbKW+KmEhMTaUdCTXXs2JF2G8p/7gDltnv37itXrmRnZ48aNapBgwaxsbGSnyMvLy8KWJSi3r59Sx+ZVatW0cz//Oc/06dP7969+8yZMykh3blz59atW7t27RK3aW1tzQ3vHh0dTR8fWqbkdvGRAYByQ8CqcPSleejQofTFmilxujhp2bKluro6fefW19evU6fOw4cPKQnFx8dHRUXl5ubu2LGD/r4HBQU9frzex+dnAwOzadM+aFD+eezipvLy8ihvcU2tXbtWZg0FgLfoSwIlKvHdkp8jjniUB3L37t3OnTvTh2vZsmVz5sy5du0afb15//69+DxEDQ0NaoE+cVpaWtra2jIDFj4yAFBuCFgVbtGiRfb29snJybVq1ZI8Xby05U1MTGxtbfv06bNp0yb61k7f3RcuXEh7lIEDB3KXy5VsUE9PT8557OKmwsPDLSwsNmzYIG6qV69eSnjuABWh5OeouLiYkRjlgTRt2jQ2NtbBwcHd3d3U1HTBggVHjhzZsmXLd999J17GyckpICCAPiClbUjy04ePDAB8EgSsCjd9+nT6K09/+vfu3ZuamsqdLm5sbEx/u2Uu7+vr6+3tHRIS0qVLF4pEtFegv+zNmjXz8vIq2aCOjo6cU80lmzIwMGjdurVUUwBCJB52gfsc0ZeHefPm9e3bl4KUeBn6mIwZMyYwMJA+IGlpaWpqapMnT+7Ro4eHhwd9FrhlaBVPT88VK1ZwX11KwkcGAMoNAavCuYtw0yYmJvQnvuQy3Cnr9C2Zu8sdGvLu3bvt27ePGDGCvmeX1iA5evSoeHrcuHHchPg8dq4pjpPIZz8hAKWyE5Gc00NEcs7ly5fF0xSkuInjx49LNRUTE8NN0BcPbqKgoIARfabEy4gHd+DgIwMA5YOAxV8YaBQAAECgELD4S85Ao9woo2ZmZhYWFoo6NOT06dMU5rg645dffllaBVMsMDDQ29vb3NxccmZSUlJWVlatWrXoVjwuEQAAQFWDgMVfH70WocLPbKJt/fTTT5/TAjfkI6U0yYEfAQAAqhoELP76aImQGxeRMk3J0UqPHDkiZwhTbtzRNWvWxMfHFxUVhYaGvnnDLFzY5v/+r023bgw3nqL40eDgYMm1EhMTR48era+vn5eX5+3tLTWWKTfko66ubn5+fvfu3SdPnix+SHkvHAAokaenZ0pKCv25aNeunYaGxpkzZxTSLP1xc3V1nT9/vuTMVatWKfBne4AKhYDFX/KvRSip5GilMmdKDmFKOYmWMTQ0XLp0KU2cP8+cPl2PQt2IEdfatZvi4+MjfnTJkiWSa9HdxYsX29nZdezYkZZZu3at5KPckI/cL1i0UcmHzMzMKvDFgqqBK2QbGxsXFBR888033CBwn+nAgQM//vgjfZ+pX79+WFjYl19++fltVinbtm2jWxsbm+jo6PK18OIFc/06Y2//v+uA0V8M+tt19OhRqYCFAclAQBCw+EtmiVBylFGxkqOVypwpNe4oIzEwY9u2TIMG+U+fas+fbzl58mlG9AsW96jUWpScrKysqGW6ZUR/B6XaFJN6CAELFIIrZNOnw9nZ2cHBgQv65Xbv3r2VK1fS14+aNWsePHiQ9t979uxRVFerptJ+/O7Zsye9wvTniN61HTt2jBkzhrvG18SJQU2aFObna5uY/HPvXmeuEUq9np6elHfpD1ejRo3EbXLXQUpLS4uMjHzx4kXt2rVXr14ted0k1T53AEkIWPwls0QoOcropzYoNe7olStXxAMz1q/PbN8ec+jQmcmT//d9kXtUai26S72iPnBnvEs9Sl9huSEf6Vbqoc95KQCkaGpqfv/997SXrV+/vtT1PSUvxyl118XFhebQ943evXufOHGCvj9ERERMnDiR0hW1OXjwYG70h9IiglRBXKpxVb8kPCLzx29HR8eTJ082b968ffv2lIrE1/iKi0vPz2+oplZcp05ncQv0xWzfvn1Pnz6lTObr6ytuk7sO0qhRo6hlanbBggUlrz8GwBMIWPwls0QoOcoofcn7pAalxh2VelRdndHRyf/oWrNmzfLy8tLV1W3cuHHJR2nmvHnz6O/gli1bQkNDAwMDS9scwGcyMjKiHXDJ/avk5Til7tLunPbcLVq00NMb36GDmqMjk5V1jzugZ8WKFbQvp/AUGxvLlBIR/vjjD6kiu2TjtIrqXgx+kfnjN8Up+mvQpEmT4cOHr1y5UnyNL03Nu66uSU+emAYHN+RWp7f1+vXr9CLTinp6elzAkrwOEvnll18oDdM3t7t378q8bhKAyiFg8ZfMEqHkKKPcgIricRGlRiuVOVNy3FE/Pz/JJbuLlHy0Xr16kmuZmpqeP39eckXJR9u0acMN+cjdSj6kKNwQFR89Ok3mKBIyfer4FMATT548oYxV8rqE1tbWly4xJ/b9bL/v0Tc/Vpe8OicFrKlTpyYkJFy9uvD5cyYujpkypU1ycrKlpaW/SLdu3bhGZEaEkgVxycZV9DLwkcwfvymM3rt37/nz5/TZpCwreY0ve/vDpqZ5Vlb/BqxDhw7NnTt3woQJjOhYUkq9zIfXQaJ34dy5c5R3GVnXTQLgCQQs/sJAo0ogGsdbAeNTgJLRp4O7tmbJ/aumpuaOHUyOWtObxRrx8a/btXspXosCWYbIoEHaGzcyrVszo0f38/EZSSGAItrx48evX7/OLVlafVzyblxcnOSlP0FKyZ/MKS0VFBSoq6vTl0M51/jav3+/+BsUfYGkRCu1wOzZs1+/fu3q6tqwYUOp6ya5ublV/DMDKBP8deCvsp9FWAWlp6fT327uINmgoCDxQTP05VhyFIm0tDQ5B+h89x2zZQv90f+qZ8//XW5IahVxy507dy458gXtKmQuHBoaqsIXp3Lbvn07vRHcWYSWlpZZWVklr+9pZZW2eZ1e3fevW7d+wDAflKcpIb169So4mPnxR6ZuXUZNzXTatGmUmSgBWFtbU5uSC0tFBPlF9iqOvg1yE6X9+E3EX2MoGEle40vq13Tud3fO8uXLJR+S+tmeI/P6YwAqh4DFXx8daLQqo52c+CDZ+/fvM/89aIbSleQoEvIP0PnjD0M1Neaff+qkp/916dJFetTHx6dly5ZSq3Atb9iwoeTIF+3bt5e5sApekaqhe/fu3EmyYiWvS3jnzh1d3eKcN4bq6vTPmJspvjpnfn7+sGHDGHb3/+/yziKSLZQWEaTulrz0JwCAWFUPWKtXr7a3t+fOauHbhI2NDX0pfPnyJU/6o/KJ1NTUBw8e1KlTh964Y8eO9evXjyaKiopomibohYqPj6dl/vnnnxo1alhZWW3bto1uDx06dO/evadPn9Iyp06doleVJh4/fhwXFxcQ0GvFitUdO7bo0MGJvlJz25ozZ05CQkKzZs1SUlJoGXHLNHH37t3r16+3bduW+4FKW1t73759MhfmyStW1Sa6deump6dHX0soeTdp0qTkMu7u7llZWUuWLFF5VzGBCUx8zoTKQsOnqOoBq127dnXr1uXnBO3y16xZc+bMGZ70R+UTY8aMWbBgAWWsWrVqtWrViiboIdpfckNOmJmZ0TImJiaampoUwmJiYqZOnXrgwIEJEybQMhMnTqSFGzVqVLt2bUb0OxMtM3s2Y2fX7tmzZ7Gx/9vWrVu3vv/++759+1Iy4+ZwLTOiI3LE/aFE9ebNGzkL8+EV489EmzZtgoKCuJP+qlevTn8fFb6Jr776Kjs7m2JuWloaN4di9JEjRyS39cMPPzRv3lxNTY0nLwsmMIGJ8k0IQlUPWA4ODrydePfu3fbt28UlQpX3R+UT06dPnzVrFjdEhY6OzqhRo3bs2NGvXz8bGxsKWG3btqX8xI0iQXGncePGnTt3pjncATqmpqa7d+8Wpx8jI6MGDRpwLZ8+fVpyW35+fsHBwWFhYZQJuPMluZYZ0bU7uAlaOCcn5+HDh3IW5sMrxp8Jij7NmjWLiIjIzc21srKi964sa9nbO8TFMXXqfHwTxcXF9J7SG/369eumTZtyb0HLli0pBKv8uWOiyk5kZDDDhtF/fgfuYFqV96cyTQhCVQ9YfIazCKVIDlFBZB4kKzWKhLm5ucwDYCUPmpEan6LkMT0c8QE3UiNf4ADbsjA0NLx69WpycnLz5s0vXLhAeYjiMoUtmmNsbLxx48asrKySY3geOuR8/34PIyPm9On0yZPHvHr1ij4O9H+g5CU1MzMzQ0JCKFVzxVxJUmctdOvW7cyZMwEBAZqamosXL7a3t6c3VLLBc+fOLVu2rLCwcNKkSQO4C3MClMvOnUxEBHv9HwpYOGGpCkLA4i+cRQiVBuUYX5GXL1/OmDHDxcWFEcXfdevWeXl5xcTEnDhxouQYnjVq9FBTe//kicaSJdspkFEOnjlzptTlL7krB6xYsYIb+ZaEhoZyl+N88uTJ5MmTpU50sLS0vHnzZnp6en5+/o0bNzp37izVYGRkJOX1jh07UtJS2esFlULnzoy+PjsWzH+HV4OqBQGLv3AWIVQacXFxPXv2pFxFoWfAgAGUtxh2MAX2cpa2trZJSUkyx/Ds25fx9Lzt6Wn8f/93tnPnH9TV1ZctW0ZhSHLJnJwcGxubJk2aiLc1ZsyYOXPm0ER4ePibN2+kRiLt0aPHmTNntLS0CgsLz549S72KiIiQbJDSVVBQEKUuXFcYPpO1NZOezrx5w16LDKogBCz+QokQKg0KWMeOHQsICKhfv762tjb936aZMTExXbp0uXjxoo+Pj8wxPHv0YBwdQyZN+vHHH5vGxsY6ODi4u7u3atWqa9eukpfU/PLLL+VsWmokUmqEMtbIkSPz8/PDwsJOnz597do1yU1z45dSLBs4cCB3FR2AcqtRg/0HVRMCFn+hRAiVBgUjPz8/e3v7rKys77//vmFD9qIoFK0OHDjQrFkzillmZmZyxvCcPn36mDFjAgMDR40a5ezs/EmjfZYc6Zuim7W1dWFh4aFDh2rWrCk1fGjr1q0pV1GvvLy8KuS1ELJx45i//mJvZ81SdVcAeA8Bi79QIoRKQ1dXt+To9gsXLhRfLPKjY3hyh1Vx5FxSc6AIN02xiZuQOhGBu5wzI/oJreSmnUQ+4blVGc+fMxs2sIdsBwcjYAF8HAIWf6FECAD8UbcuM3gwExXF4Kc9gLJAwOIvlAihEgsODlZIO8uWMX/+yYwfz4wapZD2oFRqasyBA0xhIaOtrequAAgBAhZ/oUQIqiKInWhcHHPuHBMYyBat/P0RsD4XRdWff2asrNijrOTg/38MAJ5AwOIvlAhBJZYuZYKCmC5dmCNHGB0dVfemFCkp7CBD798ztWoxWVkMRnv9fPPmscGa3vRjx5g+fVTdGwDhQ8DiL5QIQSXCw9kd7alTTFIS06aNqntTirdv2XSlpsZYWjKbNzPNmqm6Q8LXoAFz/z6jrs5YWKi6K1AxMjKYZ8+YVq1U3Y8qAwGLv1AiBJWYNIn9MaN7d6ZlS1V3pXQtWjD79zMxMcx33zFNm6q6N5XCvXvM7t1Mv37sxR+h8rl5k+nYkR31NDiYmTpV1b2pGhCw+AslQlAJX1/2H/8NHsz+44msLCY2lr0iinAPUVJXZzw8VN0JqDAJCWy6onf5n39U3ZUqAwGLv1AiBBCE3Fz2175nz9hDl/7+W9W9AZCFvo1MmMBWgUXXkQJlQMDiL5QIAQQhO5sdhFNdnUlOVnVXgGeKipi1a5mnT5kffmBE1y9QGR0dZvVqVXagCkLA4i+UCAEEgXacoaHs+Xfjx6u6K/CJ3r9nbzU0Kqr9AwfY3400NZnHj5n/XpUAqgoELP5CiRCAJz46MNioURiIS3guXWKcndmfHikGdexYIZswNGRv3737dwKqFAQs/kKJEIAP5s5lR+Ds3Zs5eJDdGUOlQbnq6VOmuJgdW7WCApa9PXPxIvPyJdO3b4W0D3yGgMVfKBEC8EFYGFtIioxkHj5kmjRRdW9AcdzdmX372Pqgi0sFbsXGpgIbBz5DwOIvlAgB+GDiRGb5cqZ/f6ZRI1V3BRSqXTvm9m1VdwIqLwQsRYqNZV68YEsJn3/IZHExc/bsu/j4XRMnVvJfsIqKmOPHmdq12SufAPDQjBnsvyrl1i12vFl9fWbdOsbISNW9UamCAvb0hVateD3uLvATApbCnDrF9OzJTixZwsyc+bmtBQQwwcHvNDQuOTmNMDP7/N7xV3Dwv3uvY8fYbArAE7//zpw4wYwcyTg4qLorSke56uRJtjBqb89MmaLq3qjUDz8wW7YwWlrMnTuMiYmqewOCgoClMGlp7K2mJjuS2+e7e5eaqvbu3Zp795jKHbDS09kX7d079gAXAJ549owZMYL9KZoyVmqqqnujdN27M+vXsydO2tqquiuq9vgx+wfq7Vv2cHgELPgkCFgK4+HBPHnCjjeokMs8LV/O1Kjx7s2bXX36VPIS4Zw57Ah4tWox336r6q4A/JeBAVOvHhuzjI1V3RVVcHZmHj1iA1bNmqruiqqtWcOsXMl89RWOVYdPhoClMFpabF1PUVq2ZDZtejdt2iV19UoesOrWZZYtU3UnAD6kp8eWhK5cYS8vWDVRvgTSvDnzyy+q7gQIk7yAlZ+fT7e6urrK6owgxcczjRtXyPXn+XwW4fv37OVBKuJZA/BErVqqPyiwqIgZN449HGr2bAxkCiAw0gErNzd3586dBw4cuHDhAk0z7Dc5va5duw4ZMsTDw0NfX18VneSv+fOZoCBKQuw33TZtFNw4bwcaTU9nOnRgi6E//cRMn67q3gBUXnfuMJs2saObLljAeHlhmFMAIfkgYK1bt27NmjXDhg3z9/f/9ddfG4lGfXn06FFycvKpU6esra39/PzG0fcp+K/bt9njH9+8YScqImDxc6DR69fZ4z3piR87hoAFUIFMTBhLS+baNebBA2bAAHa8AAAQig8CVp06da5du6b94TW3Got079599uzZ+/btU273+G7ePDZdGRszAwcqvnHelggdHJjRo9lM+fmjUQCAHNWqMUePMl98wf52RTELAATkg4Dl5uYmZ1EKXvIXqILatGGvZlVBeFsi1NVlQkNV3QlFi41lLzlnYsKOy4XDDoE/6tVjVq9m/86MH6/qrgDAp/ggYFlYWJS23I0bNyq+M/AB3pYIK6Wff2Yrnu/esWeNuburujcAEvz82H8AICwfBKzt27erqh9QEm9LhJWStTWzezdbkWnbVtVdAQAA4fsgYLVv317mQtu2bSvtIag4vC0RVkpTpjAuLuywijVqqLorUCkUF7NjuBgbs9fZBIAqSMY4WLdv3w4JCcnOzubu5ufnx8TEeHp6KrdjgBKhsjVurOoeQCUycyY7gq6eHntweosWqu4NACidjIA1cuTINm3aNG7c+ObNmx4eHr/88kto5TukmU/om25WloyvuSgRAggXN4ZLXh57XVEELIAqSEbAun79+pEjR2jvPnTo0BEjRvTp08fFxWXAgAHK71xV8Po106ULc+sWO5DgnDkfPIQSIajQ06fMunWMqSmD/4Dls3Ahe8EDegH79VN1VwBAFWQELCMjo5s3b3br1i1TpEaNGnRX+T0TouRkJiODsbL6hFXoa+6NG+wgN/v2yQhYx49fGjRoBK63qkwPH7KXnc7LY8LCqvQB71OmsEf9FxUxDRsyPXuqujcC1K4dc+iQqjsBAKojI2DNmjWrd+/eiYmJTk5ONGFgYNCpUyfl90xwzp9nz/AvLmaWL/+E8c3bt2c8PZlLl5gZM6Qf2rix2m+/rdm1i/nnnyq9p1cyShVnz7LFndBQZuVK2cv8+Sc7KBElD24QyEqJYj2lKzU1Rma+X7qU2buX8fVlh5wFAICSZAQsHx8filb16tWbP39+q1atXr58OXLkSOX3THCSk9l0pa7OTpQd7ci3bpX9UEzMOw2NXQUFI+LiELCUx86O0ddng4WcC/3+/juTmclejfHMGWb48PJvKyaGbcHdnWnSpPyNVJD//IctXpuaMh07Sj/08iV9DWP/q0+bhoAlz4MHbKGwfn32oqXVqqm6NwCgXB8ErEmTJrm5udnY2DT+7/lU33zzjSp6JUhubuyhVLTTnT1bMQ1On86eRdiv3whnZ8U0WFDA6Oj8O/3wIXvUl6Ehe7Fq8UxgRANi0ZtYVMTIubL5t9+yP1iamTHdu5d/Q0+esD950oZ272bHkZeJogx9BB8/Zg+HsrUt/7bKQU+PKe2LVa1a7NW+r15levVSapfEDhxgs4uXF9/H1KAPV3g4+xa3bs3gPOzKqrCQ+fDycgD/+iBgvX79evDgwbq6uq6ursOHD7e2tlajL/JQNhRTli5VZIPt21dLTlbYWYS0N9qxg3Fy+ve4kMWLmd9+Ywcupz/99BBI+uilclxc2H8l0SscHc0Oul2WK39rarI/Ar19y06U5vBhtgpJC6xdq+yAJQd1+59/mNRUplkzFWz9xAlm6FC2D3fvMr/8ooIOlF3Llmy6oq7iLMLKJy+PSU9nJk5kTp5kf8r98UdVdwj454M/7aGhoRs2bIiOjo6IiKCARelquEinTp2QtJRP/lmEubnMhQtM167sLw1l8ccfbNmLdtivXzMGBuyukdIVzWneXJF9rspof0/vFYWh+Hj2rfmounXZ+iD98/AodRk7O6ZBA7YcybezeCk0qCRdMezn4t8Jyi48N306Y2/PXkxQVa8VVJCHD9kvUa9esdP0Wdi6FQELZJD+7qypqdldZNWqVbGxsZS0nJ2daWZKSopK+leVyR9otFMn9gzEr75id+dlsXAhs349uy+ndMWI/vR3784OvmVqqrgeV230wlarxrx58wmHvVtbs//koB3zgwfsd2Xl18Jou0ZGfKx99OvHnnKbliaMX17lv78gUHfusOmKvk0ZGrI/Qk+apOoOAS+VWpx49uzZP//8c/HixYyMDCcnJ2X2CThyBhotLGQo8dLHOzGRLUtReKK/46Gh8naHAQHsP0mdOyu0u6UoLmZ+/ZXtp78/e9mQSqxhQ+bmTfbIJMV+XOhdVn66mjmT+ekn9hnFxbE/wPDN0KGq7gFUbQ4O7HfUhARm7lzGxkbVvQG+kg5Yz58/j4iI+OOPP6Kjo7t37+7l5bV///6aGIhJFeSUCClI7d7NbN/OHjm7YAFz7x6bYCZM+LQhuJTj5Em2Y5QSnjxh+1y5mZiw/yqBs2cZDQ32EJO7d/kYsABUi/6gLV+u6k4A730QsPr06XP69GkrKysPD4/ff//9i8o6wo9AyC8RDhnC/iP/93/M9evsAQGtWyu1e2VUpw57jMK7d5V2vKhKaeFC9qt5hw74dg7wCZ4/Z+vXHTvKGNwEqqAPAlavXr02bdrUtGlTFXUGPlDGaxHOns2O91irFsPP8xAsLdn8d++eyi4YQtmOEh79U4m0NGbjRnY42WHDlLG54mL2xDp6tf39mUaNyn+iX+/e8oYBAwCZxo5lTySiP8U3b+LUUfgwYAUGBnITxcXFSUlJjx49atCggZmZmbqq9k5VW9mvRVjyQtGKkpHBHsX5mdq0KdOwBRXh9Gl2fLJq1djBDszNVdCBceOYqCj2fLerV9msWdH+/ps93lZTk72SYEIC++ump2epI9mCJIrC9FHLz2ePZcS4vlA+hYXsLX3eCwpU3RXgARkHud++fdvd3f3hw4fGxsYPHjxo2LDh7t27zVWyd6ra5JcIlWDiRGbNGvaSaufPl3UwCL6JiGDH6nz3jjlyRDUBq3p19q+thoa8YUsV6Isv/i3I0nYpXVHSOn7801qYNYt90/v2ZS8H9NHfRLOymGPH/h1LgiceP2Y2bGDHgP32209bkbtEEr16W7YwK1ZUTOegstu0iT3rqHNnZHRgyQhYo0aNcnR0XLRokZaW1tu3b+fOnTtmzJjztI8F5SpjibDi0L6TkkFcHHvGoqp+gvpMtJf96y823HDHq5XmyhX2XIFWrdhT5+gpKxD9we3Xj/1rq5x6Qfv27LAd9H45OrLnAB4+zNYKPwl1OD+fvc7gkycfj029e7Nj0FOqS0tjtLTK3WtFmjCB2b+fef+e+fLLTxtnv1cv9njBwkI2XAKUT6NGMq4qC1WWjIB169atQ4cOaYn+XtLt1KlT161bp/SOwSeUCCvI7Nnswc60l6LkIVDW1kxS0scXmzuX/aUnMpINQ4o99qh6dfrGosgGP8rCgv1HKDLSv0/l48P+gkV5tCwnJTx+zP7k8/IlO1IXT041NjRk01Vp16iWw9KSPWvy3Tsl/dYIAJWejIA1cODAvXv3fv/999zdPXv29FPV8clVm8pLhLRl1W38fwoL2dpNp07sgfwVhBqPimJHCjUzq6hNCMXixey/Mtq7ly2oDR5cIelq2TL234AB7DWdyn4U6IoV7OUN6H0sxxFvOjq4LicAKIyMgKWrq+vn57d27VoTE5OUlJTr16/379/f3d2de3TXrl3K7WHV9dESYVERc+0aW37i4XDbCkS72OPHmfr12cpXuQ8Fe/uWLf9lZjJTp7LFIymLFrHHwjdqVIEZrlKysanAcRxCQpjsbHawt6VL2bemjPT1/71GdWIi4+3NnlZJ+Qy5GQCUT0bAchBRfldAykdLhP36scnD3Jw9X6wSn+hJe0p6ds+fs9emKHfA2rePmTePPeibMtaWLTIW4Mpq5ZOUxJbJeDjKq6CNHcv8/DP7n7xhw/KsTsns0qV/JxYuVGzXAAA+7oOAdfTo0T59+pS2Ry8qKjp27JhSegWsj5YIY2PZxMBdFasS//Ty22/M2rVM//7spfHKrUkT9ricd+8+4beQsrh7lz2o3M2N/aWE0sC0aYpsnBFd8uz1awUPWvjsGXtslrY2Gzt4cuCUTD/+yGZi+b/O7t3Lnorh48OOiSqlZ09m9Wr2fcGAXiqXk8OOk2Jjo+BPHwDPfRCwYmNjZ8yYMXLkyJ49ezZr1qyG6BJor169unfv3smTJ7du3erm5objsZTmoyXCzZuZdevYESw/KV3RWjNmsEeo7NvHHoLNfw4OzOf/okp/3K9dYx49UuQ5Ylu3stcbVldn9+KUdJOTpRd4+ZI9Ma3cKDpwvd2wgc0QirJyJdtgURHTuDFbMOUz+ekqPZ0ZPpydiI5mbtyQfpT+z9ACDHvMQ8V0Dsps0CDmzBl2uL60NJxDAFXIBwFr1qxZPj4+4eHhEyZMuHz5MncJwuzsbCsrqyFDhhw/frxu3boq6mdV9NESobMz++9TUSx49Yo5cYK5fJk9Nb3qoBikWLdvs2M6UFLp3p1p3pwdQUqSry+zfj3TpQu7a9Es9aLq8iQmsrfcj5QK1LIl22duQtBoV03fELKzSz3hkQ/Rij5lY8eyQ93u2lXOUfUrAe5sU3qncnIQsKAKkf7DTxFqmsj79+8zMjIY9rRnQw3FDg3EJ8uWsUfkzJ0re1hCCiI+PuyAQKtWseNtKlkFnUXo5cUes0U7106dFNswr138f/buAy5q8/8DeBjixIGoOAEXVqEu3HvjqIpiHf0rOGpRaR0V66qtUDdWa6vVqhRXnbgH1oFbS3HVojjBgXsgFpX9/34Jv/M4juOAGwl83i9evHK5XC6XyyWf5HnyPGf55jLKWPRd+/joZp5eXtwXTYECwsKFQsZTj+3buVDyzBluVD1jtXpteHhwpzd0TNJtySNtALQxW1hItPNK7dEJ4JUrwl9/Ga0XJm1s2CBcu8Zl0zt2SP16of5s3MgdRtHXlJtSfgDZyfTMmkJV2bJlDbkohnfzpiB2DjR4cFrAWrpU+O47PiGmvXaxYrxP3LyZz71+/JFrAhmYchEhHaR79uQTQX//3NYpGT6c/3IgLo4vySQlcfcvKpXNlyzhDNq3r3R7mH/wIK0gLypKZ/OsUEH4449Mn/3+e66VRd+aIl09e8axRvtqT7SS/fxysmDv3vF7VamS6QT16uVktjlDp2mXLwutW+fwMp5mlSvzn5R168bXjIsXFzp1MvaiGE+DBrzrkLKEBMHbm89nZs40RJdWkE+o2edl7Oy5WLFiZcqU6d69++jRo4vItM8UdQoVUh1D6YqOB/RHiWHqVL4vjPaMsbF8eBg5UggO5lq3lMYMQ7mI8OBBLmugQ9Tvv38IWGfOcOzr1Yt34gZAu8gJE/iqTHKy6jUVChOvX3OemDRJzbUcKejTh68zUczSeT30zFAMpT+FnTu5whB9g0eP6rFpg7AwvqhGmwx9HdOmcfMTuUSZnn4COS7WiY7mJhJevRLc3LjvnXyoSxe+q8DMLC/f55sHHDjAp4j0HdE3RefVADqhJmB9//33/v7+3t7elStXvn//vp+f36BBgyh1/fzzzzdv3lyxYoXhl1JP6NyXMsGqVVxsJKpTJ60/svbt+eFHH3E92ZgYPkKMGMHjZ8/mkib6NbZtq/feY5SLCCnh0YHq+fO0Wr2ivn153x0QwImwWDH9LgyhA61Yd4fOyLt3T9e8Ox0+aTW2a5erOt16RfvN7HYao1u0XaWk8LWl06ezHbDOnuWrrfR1aw46x47xVyCiJHf8eA4XVcHfn88r6JSKwn3OenJ88oR/OzqvRiYvEulECDSoXZtr7NHPM7sVJ+ic7dw5jtGyuGEIDExNwPruu+/ouF4+tR+yevXqNWzYsF27dtevX2/dunWNPNdg38SJ6S5p0DHp1CluEklxXx4d0uivVCm+bnz5suDqyhXD793jnxOFG21a+MzxrWTKRYR2dnyISkxMt7OuUIHrh9Gy5ayh0fPnhYED+bXbt2vVU567O5ecUsC+do17U1m69MNTv/3GzXhKNl0pxMby12GUQqXRo/nyUqFC2W4c/+LFtNpjZ85kUc5C+3ohNVrRz7RECb4cm0vBwXzB8s0b/t7VBqxHj4Q9e/hspHr1dOPj4riohTZOOnWh7YR+VuPG5XZhAPSHNmD6+dD2nK3TZvppODnxZVo6KGS3V3XID9RXi4iKiir/v45eafgNbUe8Mb0x3HIZT8uW6R4mJHCjBjdv8hUaSjmUIdau5fGUdZKSsp7bZ59xNZ0ePfg4lF0qdxHSoU7lVPjIEb6Tv1WrtJA0ahTXHtu7V9i9m0/F6KHmy1qbNnGdg+Rkfq1YF03Z0aN8OO/enXOVyNSUh/39+WDfrJnq9NJPV5GRvDekjDV7tprPq2+0Bz94MCcv/O8/XuHiTViaffopN3lK09M5g05qE48fz0cd2qjovEKtzp25fQT66h8+TNfJzJYt3Py6mRkv+e+/c7gEFfv3C7t28X0MGX9KYBS0GWd3J0a/tZgYPqURz20AVKgJWDNnzuzWrZuHh4etre3du3cDAgJ8fX2PHz8+cODAcbk7D01JSaGUVqxYMVP51Ef480/usoOWl/INBRchtbSe0hIljyxvAqejS2Ag//z27ePecLNbe03zXYQjRnAFfHqyXz9+uHEjX5t5+pSr3VAWpDeljDVjhqb501Fz/XoObWqrcA0Zwudz27YJXbsKirsd6GDw5AmHTmne//Drr/zZaZ1QMlC4cIHXfK1afOGNdohi2ZnhA1aOUYCmr4kivnKNLrUoZ+egd2cNGjTgi1gaiMV/tG3Hx6cLWLS26SdDZyA5K1jM8+hctXdv3j/QngHHZhVxccLJk0KjRpJuBVdUvjzXLKRjxOefG3tRQJLUBCyKVg0bNty0adPff/9dvnz5AwcO0MPw8PDAwMBmOTrbevv27eLFiymoUVyLj483MzOzt7en3DB58uSCku9blY4QdHimQ4iibN7Jif+0QZmM4g4d9emQn4N7AzQ0NErHM39/vkKwciWX1tEAnQqfO8cNJYtNWicmZv2OzZtzu390IFQbdx0c+IY72oOo7OZKlUoboJj18KFga5vNT6U3dDgfO5b/X7rEl0zELWvFirRcsn8/X8afMIFLWnNfdmZgatsQMYrbt3mraNkybZuhk40NG/hkQ6UCiliwTr+azFqo0t7ff3NqbNOG75/IMygKFy/OVSdzv350Ys4c3pPQPkTzKZlh0Pne0aO854mIkEHf2zlrjBDyCfVFhE5OTrVr13727Jm1tbV56t3VtXJxKjp69OioqKiVK1c6OjoWL148Jibm2rVr8+fPp/GrV6/O8WxzadUqNacd9FkpFbVowZeC6MyyYkW+ckDH7BEjVHc9mzZxqzavX3O4oenpMEMno3TuRVlKeaeQs8Ytt28XpkwRGjZM7NJlk7u7mitYtIMePpwLYpo04STUpQvHLEpL4jtS6Hn3jhsIyJKGO+d79eJLPq6u6vdx0dHCxx/zO06cyDcKGAWt/EGDuD7c0qV8EwB9Ee3bczEcDSuW+dYtjgLJyVwY2rUr30ioguICHb/pHJQO4ZRZNTclQFvFrl18SenxY24Ey86Oy39T+zvIAmVfynlly/KFxtKlVZ+dNUug38HQoR9utlB503r1+PLkzJlcWq3B8+e8Qmh6ipWNG/NlvD17+OZzMWLu3s0ZpW5dLuPWfNyiCejEoF07LiNWDt+0PdCXTtmdguzixTyGzjTmzlUzh5AQvkhDK/PIET5s50aPHvyJKB9Xrcr3UkjZ1Km8W6ANI8vbKWj9X7nC1T2l0HYD7d/EnZu4gRk904SH85ZDPzH6gUvzSjmAltQcTyhXeXl5bd++vWDBgnFxcX369Pnll1/KlCmT4/fYuXPn1atXK/yvy9bSpUu3bNmybt269vb2GgJWYGDg8gx1em/cuKGrivZqC1zo4CGk3o1FMYuSCh0kxIRE+006w1NG53yPHqU9S+fx06bxlSo6GtGBkA5pInqW9ly0//rhBy6T0v4iFu2pKRDcuJEYH39ObcAitDz0R8d4igjLlvE+vVo1Hk9LrpNWG2iZX73iOU+fLvyvPt4HlDvFq1+0rrRBq4Lm8/ffnEppQxDLWOloffEiN9eUs5YdgoL4uEv7YgqXFKrEMbdvp2svm/Kf2Hh0Zod5OvukNEDWrRO+/JILJjSgzPrXX/w9UpK7fp0rrVMy0+aoT9sGTXz5MqccClLx8Vzp++5dvpbWoAH/p29NPLxlvF+B4iNtaYTSoeaAReHv0CFeIfS7qVSJbzunYR+ftE3dz49XDi32qFF8D6wGFPgiI3nrooVUboz04UP+gajtFEhF796cdGmxc1+3XRF5jX7g14zSAO0TaGnp29TmflX6TYmF+0ZHZyZ9+3J9A9q8pbCS6ZdIGzAFa6QrkDs1AWvkyJElS5Z8+PAhhSoKWxMnTvT09KS4k+P3oCB14MCB4elbtwwKCqqioSVEboOAqYwcP378I/Fok2uVK/NRJDMUCOiYR0e7uDh+2KKF6gS9en3o/szGhg/qNL0gpKv5TmPoSEN7Lko82qcrmlVEBA9Ur15o9eq0IkKKDmK9DZV67jTm55/5ckXO7ox79owjCB3v589XnYOrKx9iW7VSv5tr2JDjCKUNLSv90HF99mzeldOJu5B6rYWO4kOG8J6U9ul0zpqh8bWsURiirEaJlrLa4cN8scTdXbXST7lyWdx5l5CQFqZr1sy6wlBsLGdKegkFLIoyNPMmTbRaVBcXrqtRunTaLRSUSily0cGY8goFETqw7djBX6Xau0HpWVp1NGWWDcw2b86LFBPD2xt9a82a8ZUzRfnFJ5/ww+rVOdeqRdvY9u38pdCSUBqj5KfStUu7dhy76cuaMiWLJSlZkq/V6QStK19fvtJDyy9lxYvzF0TboRwLjOgEkk6lJHKfCv24xFZyAOROTcA6cuTI3bt3S6XWtaGM9eOPP1bNXR9aK1eudHNz8/Pzc3JysrS0/O+//8LCwl69erVbrDRuJGKIKVSIdyt0qLt/n3/VFCk6d047QR87lg9sFJgo0WW8aubj86EmtVhIRMc22r1SWImO/tDKQw72XIqmxhcuTNyzh+8ipHArXiahw9u0aekmXryYL5LleM+4ejWXB1HusbVVLetZsYLPyDObM+WMJUuy8UaVKvEpO63Jx485UVFcoD86VaU1TxH2zp2cBCzaKu/e5dDz/j3PPzmZL4xl7PRXsy1b+I8SaocOaRFZAwpJa9bwcZQmHjaMb9LUshOpgQP5jJw2NjEfU5Kj1/73X1rFPoo1GtryoCxLn5FWXZbV3T76iDdjWhtijagzZ9LN1tuby8RpW1WpckdfhKcnV1y7fDntllKKv998o2Z56IUqm58B0E9PvG9X4mjjocydy+69jUimiw0gZWoClo2Nzfnz5zv+73z54sWL5TMWEWWHs7PzrVu3jh8/HhkZ+fz5c4puI0eObNOmjbk++s7IJjoaCakVt8Umtrt25WNeiRJc+2rBgrTiiczKJBXVvUWUG+hIQC95/TpdXR9xz0VvFBTEBS5Ztjj19dd80aVRI4p6iRMn8l2EdCQWUg9vb9+qmT43e0b6sGJ3xXQU1+2cr13ji3aKTEB5go7fFIPocE5JlD4RxVDKDRQ7mjfPosRKA1rb9GWlpPDKp7kpcq32qldX7aRZAwpGFDpfveJ31OYWJ5qMgouDA68K5WrgVapwEnrwgFtcE2le1RYW2t5MQAFO+RqnymzVrp9//uGcLdY+pC2BvqMsl0f6KOjQ77doUY6Vhiz2kvt6AwAdUhNx5syZ4+bm1rt3bzs7O4pEO3fu9Pf3z+3bmJt3oLN+qfLx4UtBd+8KN27w3/r1H+7bCgzkiy7u7nwcOn2aKxGXKcOlfq6uqq1Mia02UIBQ29y2pydf/KAj5ezZXMIidsjg4qJmSpqA/lKl3UX4f//H6eHNG649o1uUoulTJyTk5AISuXqV1wmtCpVKVL/+mtb0EWVWRdvitN4OHuSUSR+H4tf8+fzxte/LJSqK7yRo0EBNSRnlhr//5irDvXvn5FNoLy6OA9YPP/C1orNns249f+hQ/tIpUIaHq4YbephlHIyI4E9EYZSCe8ZCal2hiGlvz8XlYt0sLXu6pO15/37+r308NaR587j/UPox2tjwHSoAAIanJmD17du3fv36u3fvfvz4cb169WbMmJHLIkLp+/JLPpLRYUb04gXvoC0tuVqSmxtfOrpyhW8xS0riWwUvXeJrVBSVKPSIl7ieP+fyNRretYsvMqltlfH+fZ4gPp5LISnQ0N/PP6sPWAqKhkYpjYl5JTk5XfmjTii6Is4uWkuNGvHnDQjgmKVMvA+IDm+UwBQBi9DxW7xY5eiY7Rq+dCyncEZfwcWLfCucCko8yl336IOHBydvSs8pKVwQefNm1p3Cnj/PSfrJEy7gy8G3RpsTxXra/DZu1GPAKl6c77V8+TIbtxrQBr9iBX/FCxZINGCVLZt2w4pE2kHQBi3wn39ytUJDdsUNAPqjvpCOElUu2xSVHQpSYsV2Oo4+fsxHDtrfff112rNiH3xCah32mBgeoKj07Fna7XU//cQXwGgaepXiJSqWL+fJKBlQtKI8R2+UZWNaKg2N0nG6aVO+k4vmQ+Fmxw7hq6+4SrKx0AehlUCH/3fvVJ+aMIEzKB2wFa3AZ+b2bb6IVaUKt1CgKNuiNRwZqVoXW6y5RW9nrILlLVv4W6Nlq1aN6/5r0xYafVOU1Nu0yWH469qV50A5PsPNHjpGazVbN3KWLs03eRw4kPX3ayzjx3NMoTSsv361tUS7hWPH+PJ2lm0ITp/OWwttYydOqPYnAQBylO5g5aioEpLBv9mtPCw3YmtJFhZ8LWr16rTT3+bN+e/RIz6QUPA6dYqLDhct4t4/unX70HiBrW3azYMaCtpq1BAUjYYeP85Ha9qZ0krduzfTl6g0NBoayrGDlnPrVi56o5xBYc6IAcvGhq/nBQeraQGBVkhAgFYzoVy1eTOv+Tp1hP79eQwlyNq1OZ9RwqdVrbBiBTf3RUfNjJ2FiRf29F39ZeZMTsm0kP8rwM1aLu+HcnDgAEqfTgKVFdOhELBzJ8frnHWCaQD0M5FIlYRly/iESki9Hqm5abrnz9Ou+z59qssF+OMPjpt0ard9uyG6hAcAhXR77vXr1xtrOYyOUgvtidq04cKFiRO5YoqlZboiPDFsxcTwHez0FxHBoUq8iWz4cL7cQqfLWnYUevFi2sDly5omU+mLsG1bLse8fp0Xj/LEhQtpB286ANNwtWqqle4NoHNn/ssNyhDi1cFRo3jtUcKnSEHpig7hYiMRipveS5dW3x8FTVy/Pjc3On26jjuKUeHt/aGFM7VSUjjyUgqfNo0v8GRrzqtW8W2JGdtBzaydfe29e8eL1KyZ7g+ukk1XmoWEcMXKfv0+9M2gV3TCIJ68Zdkljq8v3zlRoUL2Np4s/forR7dDh7ieonJ5PQDoW7qAVS8fF/5XqvShL44CBT7UEKIU1agR16D65RcOAa9ecdlQw4Z8haZWLY5KhQpxGmjcWNPM//qLL8ZQehNrqa9bx+360D5Xczv2KkWEFOB27Eh7qksXvpolVo3z8OAZUhykaKJlc7D0cShHSqFRwRkzeB3u2cMr9uRJDlgtWvDKp7wbGysMHpx1q0K3bnHYFSvA6TVgZemff9K6raQPpf0xMjGRX0Wb0OLFXINe56mFQur585y/b97Muh0KI/r3X96kacNWaelNt2ht0ykBbVr+/lzmboA+UcVQXrgwB2jNypdXk7Bzb8gQ/ol99JH6O4UBQH8kVvZgVBSDBgzgHDNuHN959PIlj/zmm7QLTpRjxDYdwsLSesgJD+faWtrcf+fpycePzZu52KJmTW6dUqzIpZlKEWFwMBdWUuCgSEcHBsWNB2KN8jdvODZpE7B8fDiIlCvHgSAX7fPrBh3yv/2Wl7xkybQsRR+NlpACliBo1QtN3bp8CDl71vj9N4uXHx4+zF7hFH13w4dzobO7ezbSFYVR2jQopmu+RS4lhXMVvcXdu3wpKwcdYhrGhQt8GkOnHOPH891/+kNbF/3A6fdLiccwcbNUqWyUKevD559nb9MCAF1BwPqgefO04iraJd2+nVYBSHHeWawYl2ddvcotSvfvz/XZXVy0bd2AQtWlS5whslWVWLmI8OjRtMP2119zAaUyOiAtWcILL3bznKUjRzgdUjS8ckUSLSbTifX58+nG1KrF1dT27EnXM8yrV+rLQCk9rFmj3yXUBiWYjz7iCE6JJ7sRYcUKvqU0W4dAymS0iW7ZwtdjNHSIQBmCYv3q1cKnn2qbroxSs+rJE/7pUfqhAb2it6AsfuAA16GU8vU83UK6AjAKBKwPlDtm/ucfvktOSK3KTfHl9GnVBtnpiKUQE8NXlWgvFhzMxRxDh354KiqKq7HPmiV88QXX01KpiE1HZZqgWTP1+3rlIsLYWB6jtq3Rli2zd8/Rt99yGZZYGCdZrVundS8oomWeM4craZ05o76ZMZ14/pxzc4kSXMstu8ck+t4pXVHau3MnJ2+d3bejyH7zJl+AzLJqP50GaG4NRJmnJ5ed0flDnz58RZCyL4VXvZbZiWgJFy/mot7MbsLVIcqj9GMEANA3BKwPfvuNCwfNzLjIydub60tR7qG0VLly2n1Aam3YwFe56KhMByex2cxdu/geKxGlBDrilivHQUqlW5VLl7iardiV/cyZauasXETYowfXg370KK1BLAU6ym7dyhe3tOwUT0htXFTLxiSlY8cOjr+Uem/fzrQrPbXOneN2TSmT0TeiaOcsM5ThKEwnJnJJX2adQ2emVSu+3nblCn+bBkAr5MQJvmapw6rrycmcrug/bdI3bojdjfP2ZoAmA+iHNnas3t8FAMCQELA+GDEiXY2Wffu0etWxY3xMevaML1+JKPSIKBNER/NVjdhYPmyrBCw6gFG6MjVN6/owI+UiQjoCpe8sO02/flwn7PvvuRqTjNpUzK6pU4XvvuO0quV9mgqUFe7e5ZVPAUvRd2RmFK1Tli6d7SWk71GlM0e9KlBA980Q0EcQu2lyd+cThtBQvk5Wu7aO3wWM7skTvuieWQ9gAKArCFi55enJVYhKleILYJSFXr4UFB0LUSo6fJj7e+7dW80tez17cssCFIy+/Vb9nFXuIlRLbH5TrPauV//8w+mzcGGOLGJ3dXpF2XTcOC5dnTSJC3QGDeK/HKA1v2ULrx9tism8vblQrHjxLO4JzcPmzOE/0dixvFXnn4pK+cRff/HV1oQETtIoKgXQKwSsdF694jZyHB3TGoAOD+c6T5orj9Mh+cKFtOGzZz+MP3qUO74YMoTbxpw2jeOUStv45uZZ9MSnchehWtu3C7t3cwMQ+r4fkFLjxYsc5iivTJig3/cSUuuuLVnCq8jHJ1eHgQ4deFZatiZF08iu8FQtiqcUFk+c4F4v1XbcpA30W5wnibVLaVO/dMnYiwKQ1yFgpfPll9zvGx2fzp/nUr8uXXjkypWZ3gx/5QrXmS1RQnX88+d8ySQpiUumXr/mh8HBXC++QoVsLIxKQ6NqVayo+x6gRT//zPWOe/Tg/yYm3G0LZawCBQzUVqGNDdfBP3Mm63awspTLZtAPHeJk+dlnOe+0UQWF1Jkz+crljBmC5k4+xe4BVEqWtREWxi0q0UH0229zHrAgTxo4kFscffmS7+QAAL1CwEonPp7TFf01bJjWbjgdpa5fTzdNdDTfVz9kSFpPc8WLC9euqSYnCwsulnrzhptmqFWLq73b22evjQZBuyLCjN694zxE755Lvr68F16yhOtu06ejrPnoEYcVwzRPSm906hQvgHGvo9y7x0GZtgH6BlU6tM4xmtUPP/AHpM1MQwMTdBTs3ZsHdu/OdhORdnYc3e7ckUp3MSAdxYpxZQYAMAAErHQoT+zcyZfQ6eB34waXhcXGqt46XrEilxtOm8YFc3TojYnhBKYSsCh1hYRwGQ0dIylXXbzIxY7a34pPx93U+kCFAgOzKCJUcewYtyFOS3XgQG67ue3XjzvZaNXqQ915/bWPkBmjl1JRDDIz4+1Bh70BVqnCX1BiIreurgHlqseP0wayG7DoIErb5IMH2rbTBgAAOoeAlY6NDVdU//13Pqz6+qq/Qf3dO/6flMT1rwsV4uSk3GKTQq1a/CfK7gFy2TLhxQshKChx7txNPj7ZuIL155+cCJOTuWArlwFr6VJeAzmLOGfOcC83lpZ8tcbWNleLYVyUm4OD+ePkrIq9Wo0a8X2jDx9y+2ca9O3L1QEp6Lu5aTvnuDiuov7ff1z6Q1sy0hUAgBEhYKny9/9wG6BaP/zAl3bo+NelS1olLZ1zd+eudRwdE588OScIWQSsiAi+KEKJ8No1vnxFgYCGVQIBHXTPnuW8WLhwNhYjxxeQNmzgwrXERA5YX32Vw5lIRIsWum+RlaJPlumnXj3uNiBbtmzh2l3m5uluZYUcoGh76ZIwciS3gQcAkDMIWFlISeGgoNyY9dSp/KdXo0dztXoLi0KCkEURoYcHlyfWrMkDtFQFC3LdHSendNMkJwv163OnyB9/LFy+rMfFVqD0uW0bhzk9BdAci43lPlL++YdT8pgxxl4aXRP7zKHNdd06DtNZ9i4MatFvxM2NcyplrD17jL00kEFSEpcz/Pkn3yaC9mlByhCwVK1fL3h5ceX0kBCuX9W2Lde48vdPq3GsoO8u22jm2txFeOIEX6+6cYMvUJmaciHRtWuqAYsW9e5dfjYigvOiAVo2at8+G40jGBJFK1pjdOwMCMiDAatNG+7qx9ubh3ftQsDKIToxoN8U5VTDVzoEbURGcuOC9CtevBgBCyQNAUvVtGncsAL9+fpyC1gUFOiXvG1buoD13Xd8/yBlr717s6j+/PAhVzxv2VJTj7yZ0eYuwvnzeUlateKjKaWZSpW4lFBFoUK8P9q4ka9y6TxdrVzJK2fyZNXmG3RYK1yH6tbl9iZCQ7l52Fy6epXrORm9Gr6KESO4n+w7d2RfMmtENWtyD0tXrvCFWJAgW1u+NE77VeVeXwEkSJKHQaNq04ZLWCiI1KjBB2PKLhS2FG1d/vcf36Ll7893lh08yK0Zae7hjl6eWV+EWdKmoVE3Ny4TXLCAO6e7fJnvXlSrTx8dNCiV0ZMnXE+FBAfzdTLJun2bL91Vry4UKSLs36+DGc6dy214UnI9f15ancmULIlSLR1wdua/zNBmTxuSpaUBFwiU0MlbUJDeyxAAcg8BS9Xatdwn3cKFfPGZzmVPnEgbn5jINV4fP+a788aPF2bN4haSNN8ip+iLMCaGA5nmgHXqFF8PGziQr7L87x2zLiIUUlvCpDlHRvKd+Y0aafsxdYLel5KoYYodc2zbNm5ygmzezG296sSFC3y98P17butfOWCJ33ipUrp5F5AgOvsaNowrO548yVUbwViQrkD6ELDUSEriVESJ6sGDDyOvXk1rlygkhCs8adNdjHJfhJpb/vzvP24Tkt6Xdt9RUWkjtWxo1MeHSzObNFHd3YeF8cUzSoHKNfQzQ1lhyBAuFvHzE7p3z3p6kbU199UzcSLfM+jhwRWbJOjatbSqYNm9KU+DGTO4uhvF6x49Pox884bvN6R3mTIli06QQL7ojCs5me+WCA1FwAIATRCw1Jg2jc9QLS05cyg4OnIZ0+3b2euujnbB2uyFKQMVK8Z315cs+WGkShEh7dNjYoTy5VVfS8d45cO86PJlrkBGRwIKggsXZr0Ap08LW7dyrKSJtQ9Y4ru7ufGVG4qGK1dqFeYM7IsvuCSXvlAvL53NkzaGXbtUR9K2QQnVzIyfQsDKq8aN4/MWK6u0y6IAAJlBwFLD2por2agwNRVu3vzwMDKSq41TeHJx0cE7is0rHD6cFpXev+cSw48+SgwOTisiDA/na1SUsX77Tau7w5484XRFy6y4HqZZvXrcsPi9e9wpdbZQJqMMR9GKlkqC6YqULWugvkGcnPgyHn1xU6aon4AOzBTZk5KEVauEOnUMsUigc/TFHTli7IUAADlAwFLj4UNusNHSkv9ndqu2uzs38E0h5saNLPo80VLVqmkVxsnw4cIffwhFiiT265dWREjxKyaG08zRo1oFrE6duKvE27dV+/nJTOnS/EHoLZQvoWlp/nz+AzMz7gNAgw0beJsRB2bPNsxCgZHRLuLYMW76QXPD/QCQ9yBgqUFxYdUq3jNScho1Sv00BQumNUCa3XsDtXH/Pmept28LeXmlFRF+8okwYAB3t6xlYDIxyfaN+qamOUlXoD1KvT/+yNtV5845eXlYGOezPn04DYNcLFsmfPklD+zale3LwwAgawhYajg48FFQHFAxdizfZujpyVWOdu7kOwr10ePbzz/zfrlRo8Tw8E3OznwFi6LPxo26fyMwJNpaKCLTplWkSLZf++wZ3yL67h3fC3n4sB4WDvRDPFlSuWMGAPIDBCw1Ro0SGjfmIsKaNdONp8PbkiW8u/zxR26mQdE4ls7VrSusWCG8f584cWLWdxGCjGi+mVSDuDhu+MfUlFtlA6P75x++lEhfx4YNWbSN8s03fJG7cGE0rA+Q7yBgqdewoZqRtJf87DPuUnfoUEN0AqNNQ6MSgUb/9K1SJW4m7fhxHKclgb6LO3d4J0B7A80By8oK9e0A8ikErOxZv56bcTdMmNCyoVH9efmSm7lq2pRbJdBgxAguNh00SKLtYOUZLi45uWU1KUm4eJGviUrzHk+Z+uQT3g9QwEJjDQCQGQQsrfTsyYUCc+ZwS+tZpquVK7kZqrFjubOd3NCyoVH96d+fq/sUKsR9RZctq36axMS0XCXZdrDyufbtuW1MJyfeJqXc4L680Pq8dcvYCwEA0oaAlal79/iAVLky33sv9u82ahQHLM3OnePWFsRGsw4ezNUCGL2I8M0brnAWH8+VzzIj/Xaw8rOUFG7gg76jsDDh7dtM2xyRHdoyX73KSQfqAAAGg4Cl3s6dQt++PLBjh/Dxx2kjbWyyfmGZMpwzEhK40kwuGb2IcMMGYfVq7q9ac5eLaAdLsugMwd+f2xwZMCDvpKurV7mK5Pv33OuANj1WAQAYBQKWepcu8dm/kHq7UM+eXLxy9KgwenTWL6xWjftLuXZN6No1t8tg9CJC+iwGrp975Ijw9Cnfn1WwoEHf1yhoAxszRvjzT2HqVD1WXadoRX95Cf2+KF2Zmwt//WXsRQEAyBwClnqenlzHwsQkrXX1jz/+cB0rSw4OahrQygGjFxEa2IEDQrduPDBlSr648erOHeHXXzko/PAD7g3MBjrhoWB6/74wfbqxFwUAIHMIWOrZ2PANg1k6doybaba15Z5tihfX8TLkuIjw7FmuNdW2rSHaktCh2Fj+T8v89q2xF8UgKlfm5tZCQwVXV2MviqwULizkp/MOAJArBKxcoR19eLjw779cn13nN2znrIhw5860A/bSpVqVaUpHnz7cK/PTpzJb7ByzsOBCrpcvuakkED1/zoXyrVvzhT0AAFnDbixXPvlE2LdPKFeOG4vSuZwVEd6/z//p+BQVpftF0itTU+Hzz429EAaHdKVA6ap6dW6qns5Vtmwx9tIAAOQOAlYOhYdzRzru7kLv3kKxYrnq8vncOeH0aS5qVGlhK2dFhCNGCNHRXEQ4cWLOFwnA8J4943RFOfv2bWMvCgBAriFg5YSDg3DjBrfASYeEEiVyNauLF4VmzXjgl1+EiIh0T+WsiLBwYeHbb3O1SABG8dFH/Cs4eRKNLwBAXoCAlRPiGfb798KFC1xfJDcUoer5c9Wn8ttdhABjxvAfAEAegICVE0OGCJs2CfXrCy1bqp8gOVnw8OAeYSdNEiZP1jSrPn24UO/sWWHWLNWnjN7QKBjdy5fcGVFm/RQBAIBkIWDlhL8//2nw4AH3zWdqKvz8cxYBS0jtu1AttUWEt25xQ4suLlwUqFfx8Qbq0zqvmjuXG7jv3p17ws5ZJ4AnT3JPgvRa2pb699f18gEAgD4hYOlFxYp8ZD10SBg6NOczUS4ivHs3rS85R0chLo4Pt5s26WZR1fr8c2HNGuHTT7VqDAzUWrSIq2zTCqSkRdtDDly4wJevzM35HggELAAAeUHA0gszMy4fzOVFIEUR4eHDQqdOPGb6dE5XpqbCkye6WlI1kpM5XdH/P/7gq2v6vlSWV40YIfj58bXG8uVzOIfPPhOOH+eOjVEtCQBAdhCw9CiXRWyKIsLwcH5obi7ExHDRZGio8NVXOllA9SjATZzIvbgMGYJ0lXOzZgnffZerbcDaWti+XXcLBAAABoSAJTk3b3KKatNGcHFJKyKkoHPtGpcWTZrEhU25KXbU0uzZue0N8OlTbsZC590HyQsqsQEA5FsIWJIzcCA3jjV/PiWtxDNnuIiQYsrSpcZerOzYskX4v/8TChQQjh4VmjQx9tIAAAAYHAKW5JQowfWfChcWLCxy0tCoFJw8KSQlCQkJQkiItgHr3j3OZO/fC7//LtSpo+flAwAA0DMELMnZsoVr3jRvLlSqJNeGRr28uCshS0u+GqelTZs4lpmbc8Dy89PnwgEAAOgfApbklC6d1uexfBsadXDgJiqypW1b7vY4IUHo3Dnd+Bs3OG527y44OelwAUFOaBuYNUuoWpXvos1Np58AAIaEgCVdOeuLUKYaNxYePuSK/EWLfhiZksKV/Z89E+bM4f+oM54/TZ0q7NrF20ajRkK3bsZeGgAA7SBgSZfavgiPHTtGkcvW1jYuLm7QoEETstkv7q1bt6Kjo52dnTVPFhAQ4OPjc/v2bZPUNshr165NbzRixIiMszp9+rSjo2OHDh2Un1qxYoWdnV2XLl2ytWwFC/KfMnrzAgW4OpepaQ4bQ4c8wMEhrcFVW1tjLwoAgNYQsHTm/XuunF6kiM5mmFkRIY2ZO3cuPdunT582bdo0bNhQ+3lSKoqMjMwyYBEKcNeuXaNoFRERERMTk9msxo4dm/GpL774QvtF0uzIESEwkIsIKWlB/vTDD3zhqkoVoXJlYy8KAIDWELB0IzSUKw/Refb27ULHjrqZp+YiQnNzc8ox+/btK1++vIeHR1JSUosWLXx8fJYvXx4WFvbo0aPixYv7+/vfv39/yJAhFhYWFJUWLVq0Zs0aMWB5e3srRp48eXLevHnx8fGUlrpTlklFA3v37qUJ6C26pRbMPHz4UPmNxFndvXu3Q4cOFPIGDhz45s0bmnLatGm0DHZ2dv/++y+9Udu2bekj0Jj169drWDC1y0Bq1Mi6M0fI20xMhBYtjL0QAADZhIClG0eOCK9ecUlWUJDOApbaIkJlNjY2T548oXQyZsyYXr16UbihuEPjixUrtm3btj59+rx48eLp06d+fn4NGjRo1qwZPeXu7k6pyMTERHkkRSgvLy8KSZRyFDNv0qQJjaeBU6dO0Zwp+qi8kTir9+/f0zTLli2j7NW/f/9+/frdvHkzswXWsGBqlwEAAECmELB0Y+BAigjcUaCHh87mmeVdhI8fP6aMdfnyZbEmlrOzc0REhJCajei/lZVVQkKCpaXl/PnzS5QoER0drXihykhKNj4+PhSSlMv7zMzMaIIrV65QtLKwsKD/NHOVN1KgUEWLERQU9PbtWwpPGT+IOKBhwdQuA+R5ly9z5Sq0fAYAeQ8Clm5UqSKcOKHjeWouIqRnV6xYMXPmzDdv3oSEhPTq1Ss0NHTYsGHh4eHm5h++1l9++WXMmDF169YNDg4Wx6SkpKiM3Lx5M82HEk+PHj2Uq6u3b99+0qRJ/fr1Ex9WrVpV5Y1oVuJT9vb2zZs379SpU0BAgKOj46VLl4TUQsyXL18mJyfTq8TJNCxYZssAedi6ddwNFNm9W/jkE2MvDQCATiFgSVdmRYTr168/efKkeBdh/fr1y5UrN3To0CVLlrRs2dLOzk5lYldX1+nTpxctWtTW1pZCDKWfGTNmjB8/Xnlk7dq1KdNQfnJ3d1e8kIIRxaajR4/+/vvvx44dozHjxo1TfqPY2FiaFYUwmufo0aM9PDwWLVrUrFmzYsWKiXPo2bPnkCFDtm7dWqNGjYyfQmXB1C4D5G3h4dyuVXKycP06AhYA5DUIWNKltoiwbdu2Dx48UB5ToUKFgwcPKh56enqKA6tWrRJS62m1a9dOeXrxetLA9I2sd+3aVfkhpZwrV65UrlyZYhw9HDBggDhe8UYpKcLWrXWsrEK6dxdateIxYoUtxZILqVe8Tp06pf2CqSwD5HnjxgnR0VxEKLasCwCQlyBgScLq1cKff3IPM2JYERmxodHIyMhSqTKb4MIFYeZMrtT/+rVw9my6p6KiorZu3UrRUO9LCTJXpozMejEHANAeApbx3b/PZ/AmJlzhNzz8w/gs7yIUhYaGBgQE6LDXwkOHDtEMN2zYoDxSbHnBxcVFfGhry136vHgh1K+f7rWRkcKPP1bs3/94+fK6WhwAAAD5ye8B682bN4ULFzY3NzfiQKlS5qVLv3n+vHC1aumeKlCgwLZt23r16qX55fQpEhISEhMTdbI8ycnJt2+/fvmy4YsXiaVLf3jq/fv3SUlJijVmbW3+779voqIKN2yY7uXjxxfeu9c8MfFNrVqF27c35lrFAAYwgAEM5NUBY2cHrchjKfXHy8tr4MCBLi4uxh3o2NGrbt2BEya4fP75h6f69u177ty5Q4cOaX65tbX18ePHN27c6O/vf+PGjcaNG+/YsaNz5860FZqYmNy/f79cuXKUjSpUqDBgwIBVq1ZduHChW7duAQEBn3766cuXL0uUKEHPent7163r4uExplu3Jr6+n7948XnDhkE1ay65cuVK+/btN2zYsHnz5kKFCq1evZrG/Pzzz5GRkT/99BO9dY0aNejlTk5Ox44dozlXrjw3MXGIIIwZPfrJp582uXv3bvHixWk979mzx8HB4eDBgxJZ4RjAAAYwgAH5Dhg7O2glvwesNWvWSGFg40b1T/Xs2TPLRQ0NDe3YsWPt2rVVGhSNjY0dP368vb19UFBQYGBgQkLC9evXPTw8Dhw4IDYHWrdu3bZt24pte9aq5VK1wvv3wtpy5eKiowuYmv7+5EncTz8lKTcrGhUV5evr27t376ZNm1LAooE5c+b06dNn5cqVCxYs8EptJjQ4+PSmTQHt23t4ebWnF/7666/0dvRyml5sX1QiKxwDGMAABjAg3wFZyO8BS8qybGh0586dlHKSk5OFDG2HCtxFroOpqamTk1PRokWtrKwePHig0hyol1LbnvfvC++FQiZC8pN1x2fNtV+79lmZMgcaNx4tZGi/1NraWrxJULnVUOVZmZqu79v354wvFKfU8zoDAACQBAQs6cryLkJfX9/WrVvfvn27ZMmSGRsUzUilOVCxnVKxbc/g4A5z5ggLpmyccarHvXv7XV3D4uPjNbRfKqRvNVS5mdDGjRtrfiEAAECehyOfdGV5F6G3t3ebNm0KFCgQGBgYGRmp3G6n2ulVmgNVbtvTxIT7VLazM/P0bCnOsGDBghraL1WhPKsuXbpo/0IAAIA8CQHLyMLDuRlrFxehYEHVp7IsIhyQShy2t7dXabdTtHfvXvrfo0cP8aFyc6BdU2U2Q0GpWVEhQzOhKg9VZqXhhQAAAPkBApYxUbRycqIgJYwaJSxbpvqsERsaBQAAgNxAwDKmZ884XZmaCuk7v0mjZUOjAAAAIDUIWMbUsiVfuLp6VRg/Xs2zWRYRAgAAgDQhYGnrzBnh9Glh0CChYkVdznbUqEyfQhEhAACATCFgaSUqSmjbVkhKEnbs4KRlGCgiBAAAkCkELK2YmPD/5GTBkC06oYgQAABAphCwtFKhgnDsmHDqlPDZZ4Z7UxQRAgAAyBQClraaN+c/Q0IRIQAAgEwhYEkXiggBAABkCgHLOF6+FEaOFF69EhYv5rZG1UIRIQAAgEwhYBnH5s1CYCA3Mbp0qbB8ufppUEQIAAAgUwhYxtGsmVC0qBAXJ7Rpk+k0KCIEAACQKQQsbaWkCOPGCXv38v8vv8zt3OrVEx49Et6+FcqVy3QaFBHK2pYtWxYvXvzy5cvmzZuvXLnSzMwsZ/PZtm3bxIkTK1as+PTp08mTJw8fPly3ywkAAPqAgKWtx4+FJUsEOkr6+uogYBFLS/7L6NAhYcECoV07YcoUFBHKVWRkJIWqo0ePWlhYTJgwYdOmTZ/looUPLy8vylhv376tX78+AhYAgCwgYGmrXDmhdWvh5Emhb9+0MXv3Ct7egqOjsG6dUKiQzt7oq6+EGzc4ZvXunXj+PIoIZWn79u2enp6FUjcLX1/f2NjYhw8fenh4JCUltWjRwsfHh6Lz5cuXk5OT/fz8Bg4c+ObNm27duk2bNs3NzW358uVWVlYdO3Y8cuSIidjEbarXr1+XLVuWBjJ7batWrY4fPz5p0iRzc/NZs2a1bt167969yhMoXrh69WqjrRoAgPwBAUtbpqbC8eN895+VVdqYhQuFW7eE8HDB01Po0EFnb+TszPOsUEGwtkYRoVzdv3+/+f+aTbNM5e3tPWbMmF69elHWuXv3Lo0vXbr0nDlzZs+eTcGrf//+/fr1u3nzJj0bFBRUs2bNpk2bUrp68kSYMaPtrVuv166d8OjRuqlTp4rzVPva+vXrX716lZLcu3fv/v3330aNGi1btkx5AsULjbVaAADyDwSs7FGkK/LJJ9z9c7VqXKFKh9auFcaPFz76SChcWE5FhIMHD46IiLh8+XLdunXNzMyOUxpVMnHixBEjRtSqVUt8+NNPPzk6OnbIKpbu3bv30qVL06dPp+GAgID37997UpjVSMs561ulSpUoY1FIouE7d+4cOnSIVs6ECRMEDtDONEwDLVq0oAhFuYdWGoWqt2/fvnjxggLW119/HRYW5ubmRtMEBQnXrlmbmlo5Of0YGjq3c+fOgwYNyuy17dq1o9VeoECB+Pj4EydOtG/ffvv27coTKF5ozFUDAJA/IGDlHB0uhw0Tihfni1s6RIe/Bg14QF53Ea5bt47+U6Q4depUlhOPHTtWT4uhvzlnS+/evSlQdu/evUiRIsuWLatTp07VqlVDQkJ69eoVGho6bNiwf//9l5IQTWlvb9+8efNOnTpRgqRoWKxYsRepZs+eTc+2bi2ULk3pyLRPn0I0vaWl5atXr2i82tfWrFmTMtaQIUPevXvn7+9/7NixixcvKk9Aby2+UOH6dYFyV7NmAkIXAIBuIWDlSsmSepy5rO8iVKlyJKRWRXr69GnFihV///138TqTg4MDpQELC4vatWsvWrTo5MmT8+bNv3Wrq4ND159/tq9SRc1s79+/r/wSRaWi+vXriwNOTk4054IFC86bNy8+Pp7yFqUcQ394QahWrdrIkSNbtWpFw7RsgwcPps8+dOjQJUuWtGzZ0s7OTjHl6NGjaUXRZ2nWrBmlK4E7ZWoeExMjXmeytxd+/XX/xImTFy2ymTkzhqahlXb48OHMXmtubt6kSRP64Hv27ClRokTGCZSFhgqNG6WkCCbTp/OtGwAAoEMIWNIl64ZG6aCuUuWIDvxfffWVu7u74hIXZQ4/P78GDRrQ4Z8e7tu3r2nT7/fta3jjRkrhwsKmTTzN6tWrxTzx+PHjcePGqbxE+F+loqVLl4oDFN3EWXl5eTVs2JBCmzE+PRuYSvGwQoUKBw8eVDykxRMHypQpQ0ur/MJ37971VdxJIQj9+rnRn/IEGl57/vx5ceCvv/7KOIHihaIHDzhdmZsk3bmTwyYkAAAgMwhY0iWvIkIVGascNW7cWEi9PCPmLSG19vf8+fNLlCgRHR0tpB7+x4793cSkQUqKSaVKafMZPny4ch0slZcISpWKlGsX0ax8fHyWLVsmkRJD7f3222+0usTKW/pWvXr4d99VeP26ROoXBQAAuoSAJV2yLiJUqXIkpF5Todxw+vRpSj/Pnz8XUpsbGDNmTN26dYODgwXuPmjzL7+M+Oabd8OH+2Z2p5vKS4T/1UZSHhBnNXPmTEpjPXr0MHqF92wZmSqzZ2llUtCsUaNGLivyb9u2bdy4cTY2Nq9fv548eXLlyqpta926dYsiLG1+1atXd3FxyfEbAQDkWwhY0iXrIkI6fqtUOQoLC2vfvr2Dg0OjRo3OnDlDY1xdXadPn160aFFbW1uKRLVr16bQQMlswoS+6atif6DykszeXTErd3d3vXw8o8r9Zblnz55RcJ87d25mjZdSwIqMjMzluwAA5GcIWNIlxyLCc+fOiQMqVY78/PyUJ6P4JQ60a9dOeXzXrl2VH/ZIJQ57eHiofYlIUbtIMWeVWeUlixcvdnR0pAx08uTJx48fV6tW7dWrV1FRURs3bjxw4EDGkV9//bVy46UPHjyIj4+3trYWlBovpS/I2dm5bdu2tL3RxGvWrKGARdNXr16d5oPWSgEAsgsBS7pkXUQIBtCgQYPx48fb29tfvXo1MDBQvBsg40jlxktfvHgRHR1N8Xfy5Ml79uyhaRSNlypzd3engEVbIA2jtVIAgBxAwJIuWRcRgm7t3Lmzd+/eycnJyiMdHBxMTU2dnJyKFi1qZWX14MEDtSPphYrGS83MzGrXrn39+vVRo0ZNnDgxPj5e0XipSAxVylSaMxXQWikAgBYQsKRLjkWEoCe+vr6tW7e+fft2yey3vWZjY6NovFQlGCkaLzU3N3/58iUFuJCQEPGplJQUcUClOdOMrZUCAEBGCFjShSJCUPD29m7Tpg0lm8DAwF27dmX35cqNl4p++eWXnTt30kix8VILC4shQ4Zs3bq1Ro0a9GzlypVnzJjRuXPn6tWra26tFAAA1ELAki4UEYLCgFTisFiRv2PHjuLDvXv3Cqk3BChPrzJSpfFSt1TK01etWlW5j6M6deooLmUJqQ23KoZVWisFAAC1ELCkC0WEoBOGbLwUAABECFjShSJC0AnNjZcCAIA+IGBJF4oIAQAAZAoBS7pQRCgjYic22Q3EkydP9vDwqFWrFg0nJCQMHTo0IiLi7du3M2fO7Nmzp8rENPPq1avfunWL/l+/fj2XveUAAIBeIWBJF4oI85U9e/bY2NisX78+Nja2cePGGQOWMtl1Yg0AkN8gYEkXighl5/79+0OGDLGwsKhdu/aiRYsUvcr4+fkp9zZz8+bNYcOGFS1a9O3bt4ougEqXLn3hwoXbt29Xq1btzJkzKSkpCxcuVO6+RvmNFL3lhIWFPXr0qHjx4v7+/vTuQ4cOLVCggNiPDc0fXdwAABiLEQLWs2fP6BiQg/YS8xsUEcrO06dPKUs1aNCgWbNm4hixV5nZs2cr9zZDD2fNmtWyZcuGDRsqXtu6devRqV6+fOnqOn/16nbv3rn7+d3R/I7FihXbtm1bnz59Xrx4MX/+/O+++6558+Y1a9YU0MUNAIBRGSJgubi4rFmzply5clFRUZ9++um5c+fMzMzocLJu3bry5csbYAFkCkWEsqDciY2lpSWlnBIlSkRHR4vPir3KqPQ2ExkZ2bhxY1NTU/qvmM+lS5fat2/v5ub2+PHjjz8+8eKFkJxc5syZ14MGqem+RqFJkyb038rKKiEh4datW87OzvTjql+/voAubgAAjMoQAevgwYPv3r2jga+//rpq1ap//vlngQIFpkyZ4uXlFRgYaIAFkCkUEcqCcic29H2NGTOmbt26wcHB4rNirzIqvc3QQ4rO9Kq//vpLMR8KWIcOHZo0aVLZsmXLlDmRkuKWlPRfrVoRyclVldv8VGFu/uEnbGtrGxoaSimKZpXxTdHFDQCAIRm0iPDvv//ev39/0aJFaZgCVvXq1Q357rKDIkJZUO7EJjIycvr06bSFU9bZvHmzYhqV3mamTp3q7u5euHDhypUrK6YZMGAAnXJQ6oqOjh49+osvvjCNjHzu4THz9Gl/sfuaLFE4GzZsmJWVlbW1dcGCBdHFDQCAERkoYD18+LBSpUp16tShI5CDgwONCQsLK1eunIaXxMbGPnnyRGXk69evxbIY+XrzRihUSNDmUgKKCGVBuRMbe3v7du3aZZymTJkyyr3N0KnF6dOnVaahvKVS/bx69XTd1yj3UePi4iIo9ZazatUq+r99+3Z/f39aBkpppUqVMjc3Rxc3AADGYoiARbt7OgI9ffqUDiH37t3r0qXLiRMnevfuPW/ePA2vOn78eMZObUNDQzXHMonbulVwdxcsLQU6vGZ5/U5XRYSDBw+OiIi4fPly3bp1zczMaMVm+ZLp06e7ubnVq1cvswmOHTtGyc/Ozk58qJwDwFhq1Kjh4eFRunTpfv36KRcdAgCA4RliLywe0ePj4yldPXv2TEg9Wd+zZ0/Lli01vKpbKpWR48ePf/Tokf4WVd/27RMSEoSnT2mdZB2wdFVEuG7dOvrftGlT3cYgWrC5c+fqcIaQS05OTnTqYuylAAAAZrjTXAsLi+qp/P39hw0bZrD3lZSRI4XLlwVra6FXr6wn1lMRoZub2/Lly62srDp27Ojq6vrPP/9kbKhJnHLlypWUiSdMmKB46uuvp6WkUD5WM9v27dvv3r27aNGibdq0Wbt27dChQxXNQdHbKTfXdPLkyXnz5lHgHjt2bPfu3XX76QAAAKTACOUIdMDOtwGreXPh4kVtJ9bTXYSUk4KCgmrWrNm0aVMTExO1DTXRZAcOHLh06dKmTZsUT7VvP33+/GRBMN2xQzA1Ff744w/KfzSljY0NTebi4nL06NFq1arVq1fvxYsXKs1BKTfXtG/fPi8vr4YNG1LS0vmnAwAAkAJU1JAuPd1FmHoh6uuwsDA3N7ezZ8+qbaiJJqPw9PTp06SkJMVTUVEd37wxpaeOHBE6dRIGDRqkXETYq1evRYsWValS5dNPP83YHJRyc02Urnx8fJYtWybN/l4CAgJo8W7fvi02GVW7dm06JYiNjVXp+2/58uV2dnZiZfMsid0IKibesmXL4sWLX7582bx585UrV5qZmal91a1bt2gFOjs7a555jx49aG3Tcnp7e9Os6Eu5evXqN99807FjR1rma9euoQdDAADDM0LA8vX1NfybypGeightbGxepJo9ezYFLLUNNe3du/f7778/cODA77//rnhqwYLA7duTBMFs8GDh8WPV2To4ONy5c+fZs2eTJ08eN26cSnNQynWuN2/ePHPmTAphlAykebCPi4ujXEKRJSIiIiYmRtBp33+RkZEUqo4ePWphYUHRjTL0Z599pjJNQoKwc6dw/frr0qVDswxYzZo1O3/+PC0t5SqxSVJ6SCPFZaYPophSmokWACBPMkLA+vLLLw3/pnKkv4ZGKTBRblBu1Dtjm0n0LOWkdu3aUQLw8vISnzp7tq84PQWs9evXK2rNb9iwwdbWtkGDBhRNTE1NXV1d1TYHJaIoQLmqatWq7u7u+vh0ude9e3eKmLSc+/btE++0EPv+++ijj0aMGEGrjlIvrZ+1a9f+9ttvVlZWq1atUumF8NWrVxnrtIm2b9/u6elJX66QerIRGxubsQfDtWvL//03reqGNWtubdcunL4IDV0K0veye/duWudFihSh9U8zvHDhQs+ePcVlVn5rcQxFYeW3W758+cmTJx8/flytWjVa7KioqI0bN967dw/15AAAcgNFhNKl2yJCsb6U6N27d337clRStI2k0lDTDz/8IA6ITY0rPyVq27btgwcPVEYqSgzbpcq4DGJzTV1T5eKj6F2TJk3Ej0wJkmIN5Qxx/MKFC729vemzT5kyxd7enhIJJaTevXtHR0er9EKYsR9ABYpTFHDFYctU58+fV6myZm5uZW5OG4DQtKlLYGCg5i4FGzVqNHPmzLNnz9LLaVHPnDlDCYwWjP6r/XQZO0yk4fHjx9Mnunr1Kr3d4cOH6Y1QTw4AIDcQsKRLT0WEv/32W0RERNOmTXU727zEzMysRIkSlCALFChgYWGhCFg3btygNGNqajpv3rzly5eLFcusra3pm1KpdpaxTptCpUqVKGOJ6//OnTuHDh2iMKpSZW3cuPeRkcLdu1fr1LkREqK+S8Fff/31yJEjnp6eHTt2TEpKCg4OdnV1pSXZvXs3/dfQdHvGGnIODg70oZycnIoWLWplZUUfXOL15AAApA8BS7r0VEQ4MpXOZ5vHdO3addKkSZ06dVIeaWdnd/78+TZt2gwYMKBt27bKFctUeiHM2A+gYsrevXuPGDGie/fuRYoUoQRTp06djD0YFi9uMmmSEBR0LyIiKbMuBUelEqenbBQYGEiRKDk5eciQISqLrSLj22Uk/XpyAAASh4AlXeiL0Ig6d+48ePDgH3/88dixY4qR3t7ew4cPnzx58tChQ1NSUpSnV6l2pqEfwGrVqlHAbdWqFQ3Xr1+f3oVym9oqa5UrV54xY8bq1avpHTV3KUgJ7NatW2Lgo5koyv7U0lxDTiT9enIAABKHgCVd6IvQKCgYiQNxcXFCaleDys8ePnxYZXqxYlnGamca+gEcmErxMLMqa3Xq1AkJCdE8K9HgVCpLOG7cOEGpv0Lhfz0Yiu+YcSZ79+4VUht9EB9KvJ4cAIDEIWBJl/7uIgQAAAC9QsCSLhQRAgAAyBQClnShiBAAAECmELCkC0WEAAAAMoWAJV0oIgQAAJApBCzpQhEhAACATCFg6dGVK8K+fUKfPkLNmjl5OYoIAQAAZAoBS1/i4oRWrYQ3bwTKSPfvC0odK2sLRYQAAAAyhYClL5So6C85WTAzy0m6ElBECAAAIFsIWPpiYSGcPCns3Sv07ZvDOaCIEAAAQKYQsPTI0ZH/cgxFhAAAADKFgCVdKCIEAACQKQQs6UIRIQAAgEwhYEkXiggBAABkCgFLulBECAAAIFMIWNKFIkIAAACZQsCSLhQRAgAAyBQClnShiBAAAECmELCkC0WEAAAAMoWAJV0oIgQAAJApBCzpQhEhAACATCFgSReKCMGIQkNDAwICNGyBy5cvt7Ozc3FxUR6ZkJAwdOjQiIiIt2/fzpw5s2fPnmpf+9NPPzk6Otra2kZHR9MbZZwPAIDcIWBJF4oIQXb27NljY2Ozfv362NjYxo0bZxawxo4dS/+DgoIiIyMNunwAAIaCgCVdKCIEo3v48KGHh0dSUlKLFi18fHxOnjw5b968+Ph4MSGtXbv2t99+s7KyWrVqFT08c0aYOLHLq1dVXV3vtmhhe+bMmZSUlBUrVtCrHj9+XK1atVevXkVFRW3cuHHHjh2Ojo5r1qyhgOXi4nLixAnl+QAA5AEIWNKFIkIwukWLFo0ZM6ZXr17dunW7e/fuvn37vLy8GjZsSJmJnqXM5Ovr27t37+jo6JIlS65bJ9y/XzQxsd6IEb8WK+b/zTffuLm50WQNGjQYP368vb391atXAwMDDx8+LM7c3d1dvIJF5xLK8zHexwUA0BkELOlCESEYxc6dOynrJCcn03BERMSECRNowNnZmYYpXfn4+Cxbtmzs2LFPnz5t0qQJPWVtbU3bKg107y6sX59kZWUSGDjKysq1e/furVu3pvEODg6mpqZOTk5Fixa1srJ68OCByjuqzAcAIA9AwJIuFBGCUfj6+lIwun37dsmSJYsUKRISEtKrV6/Q0NBhw4Zt3rx55syZlpaWPXr0GDBggLl5uh1Ijx7CokXrX7x4Wru2d3JyWQsLiywDU0pKiomJicp8AADyAOzXpAtFhGAU3t7ebdq0KVCgQGBgYMGCBYcOHbpkyZKWLVva2dnVrl27Q4cOVatWdXd3j4uLy/jazz771MvLi/JZdHT0F198UaFCBQ1vVLly5RkzZnTr1o3mrK8PAwBgJAhY0oUiQjCKAakUDw8ePKgY7ppKZXrlmumFCxdevXq18rOenp7iwN69ewW+ytVD+dmQkBC18wEAkDsELOlCESEAAIBMIWBJF4oIAQAAZAoBSwdSUoTjxwVzc6FFC8HERGezRREhAACATCFg6cCqVcLIkTywZYvQr5/OZosiQgAAAJlCwNKB+/cFMzMhOVnI0L5PrqCIEAAAQKYQsHRgwgROV+bmwhdf6HK2KCIEAACQKQQsHShZUvjhB93PFkWEAAAAMoWAJV0oIgQAAJApBCzpQhEhAACATCFgSReKCAEAAGQKAUu6UEQIAAAgUwhY0oUiQgAAAJlCwJIuFBECAADIFAKWdKGIEAAAQKYQsKQLRYQAAAAyhYAlXSgiBAAAkCkELOlCESEAAIBMIWBJF4oIAQAAZAoBS7pQRAgAACBTCFjShSJCAAAAmULAki4UEQIAAMgUApZ0oYgQAABAphCwpAtFhAAAADKFgCVdKCIEAACQKQQs6UIRIQAAgEwhYEkXiggBAABkCgFLulBECAAAIFMIWNKFIkIAAACZQsCSLhQRAgAAyBQClnShiBAAAECmELCkC0WEAAAAMoWAJV0oIgQAAJApBCzpQhEhAACATCFgSReKCAEAAGQKAUu6UEQIAAAgUwhY0oUiQgAAAJlCwJIuFBECAADIFAKWdKGIEAAAQKYQsKQLRYQAAAAyhYAlXSgiBAAAkCkELOlCESEAAIBMIWBJF4oIAQAAZAoBS7pQRAgAACBTCFjShSJCAAAAmULAki4UEQIAAMgUApZ0oYgQAABAphCwpAtFhAAAADKFgCVdKCIEAACQKQQs6UIRIQAAgEwhYEkXiggBAABkCgFLulBECAAAIFMIWNKFIkIAAACZQsCSLhQRAgAAyBQClnShiBAAAECmELCkC0WEAAAAMoWAJV0oIgQAAJApBCzpQhEhAACATCFgSReKCAEAAGQKAUu6UEQIAAAgUwhY0oUiQgAAAJlCwJIuFBECAADIFAKWdKGIEAAAQKYQsKQLRYQAAAAyhYAlXSgiBAAAkCkELOlCESEAAIBMGTRgpaSkvHnzplixYqampoZ8X5lCESEAAIBMGSJgvX37dvHixQEBAXfv3o2PjzczM7O3t6fcMHny5IIFCxpgAWQKRYQAAAAyZYiANXr06KioqJUrVzo6OhYvXjwmJubatWvz58+n8atXrzbAAsgUiggBAABkyhABa+fOnVevXq1QoYL4sHTp0i1btqxbt669vT0ClgYoIgQAAJApQwQsClIHDhwYPny48sigoKAqVaoY4N3lC0WEAAAAMmWIgLVy5Uo3Nzc/Pz8nJydLS8v//vsvLCzs1atXu3fvNsC7yxeKCAEAAGTKEAHL2dn51q1bx48fj4yMfP78ealSpUaOHNmmTRtzczQSoQmKCAEAAGTKQBGHslSHDh0M8155BooIAQAAZEq615BCQkKCg4NVRoaGhhYvXtwoy2N4KCIEAACQKUMErPDw8MyeqlWrVmZPlStXrmHDhiojnz17RrFDZ0smbSgiBAAAkClDBKwJEyYcOHCgSJEipUqVUnnqwYMHmb3KNpXKyJcvXz5//lz3iyhJKCIEAACQKUMErP3793/++ecFCxZEgVe2oIgQAABApgxUB2vAgAEXL140zHvlGSgiBAAAkCkDBawOqQzzXnkGiggBAABkygh3Efr7+w8bNszw7ys7KCIEAACQKSMErAkTJiBgaQNFhAAAADIl3XawAEWEAAAAMmWEgOXr62v4N5UjFBEa3eDBgyMiIi5fvly3bl0zM7Pjx49n6+U9evSYP39+7dq1vb296eVz5869evXqN998s2fPHrXT37p1Kzo62tnZWRfLDgAAxmSEgPXll18a/k3lCEWERrdu3Tr637Rp01OnTuXg5c2aNTt//jwFLMpV9G3SGHpIIzObngJWZGQkAhYAQB6AIkLpQhGh1Dx8+NDDwyMpKalFixY+Pj7Lly8PCwt79OhR8eLF/f393dzcaIyVlVXHjh2PHDliYmJCWWr37t2urq5FihSJi4uLjY29cOFCz549f/nll8uXLycnJ/v6+irPcM2aNRSw2rZtO27cuDdv3nTr1m3atGkq72LsdQAAAFpBwJIuFBFKzaJFi8aMGdOrVy+KPnfv3qUxxYoV27ZtW58+fV68eEEjg4KCatas2bRpU0pX9GyjRo1mzpx59uxZGpOQkHDmzBnKVRSqKDCVLl16zpw5kyZNUp6hu7s7BazAwEBKXf379+/Xr9/NmzdV3oVeaOS1AAAAWkDAki4UEUpNRETEhAkTaMDZ2ZmGaaBJkyb038rKivIThaSvv/6awpObm5s4vaWlZVJS0p49ezp06FC2bNndu3fTd0ppiZ5q0aIFhbCMMyQUqiiHUVZ7+/YtJSqVdzHCxwYAgOxDwJIuFBFKTdWqVUNCQnr16hUaGjps2LDw8HBz8w+/IBsbmxepZs+erRhZvXp1ylULFy5MSUkZMmRIp06dxPEFChRQO0OazN7evnnz5jRlQECAo6PjpUuXlN8FAABkATtu6UIRodSMGzdu6NChS5YsadmypZ2dXcYJKBjFxMSI5YPk9evXNWrUePDggRinbG1tVWq4q8wwNjZ2xowZq1evnjx58qJFi2hi8XIXAADIDgKWdKGIUCLOnTsnDlSoUOHgwYOK8Z6enuLAqlWrxIF379717duXBqZOFZbNi+n6adzatZOmTZsmPnv48GFxwMvLS+0M69SpExISQgP79u3T8C4AACB9CFjShSJCefntt98iIiKaNm2akiIsnBOfaGK5eZOlv79J6tUrAADIXxCwpAtFhPIyMpU4PGK0xcplCf3/r0DhwsZdKAAAMA4ELADdW7pUWLSogIWFsZcDAACMBAELQC+QrgAA8jMELAAAAAAdQ8ACAAAA0DEELAAAAAAdQ8ACAAAA0DEELAAAAAAdQ8ACAAAA0DEELAAAAAAdQ8ACAAAA0DEELAAAAAAdQ8ACAAAA0DEELAAAAAAdQ8ACAAAA0DEELAAAAAAdQ8ACAAAA0DEELAAAAAAdQ8ACAAAA0DEELAAAAAAdk1nAKlmy5PTp0/39/XU1w3///dfMzExXc8uHEhISChQoYOylkDGswFzCCswlrMBcwgrMJVNTUwcHB2O9e0xMjI+Pj55mLrOA1blz5xs3buhqbklJSV26dDl8+LCuZpjfJCcnd+rU6ciRI8ZeELlKSUnp0KHD0aNHjb0gMtauXbvg4GBjL4WMYQXmElZgLuXhFSizgAUAAAAgfQhYAAAAADqGgAUAAACgYwhYAAAAADqGgAUAAACgY/k6YJmYmJib5+s1kHtYgbmEFZhLuEM+l7ACcwkrMJfy8ArM1zt3U1PTXbt2GXspZIxW4O7du429FDJGEX/Pnj3GXgp5wwrMJazAXMIKzKU8vALzdcAiBQsWNPYiyBtWYC5hBeYSVmAuYQXmElZgLuXhFZjfAxYAAACAziFgAQAAAOgYAhYAAACAjiFgAQAAAOgYAhYAAACAjiFgAQAAAOgYAhYAAACAjuXfgBUaGjpy5MiIiIjevXsvW7ascOHCxl4iqevWrduPP/5Yq1Yt8aHaFYi1qtbhw4cnTpx48+bN+vXrr1ixok6dOgJWoNZSUlK+//77lStXRkdHN2zYkFZg7dq1BazA7AsPD3d2dv7vv//Eh1iB2mvTps2JEyfE4S5dugQFBQlYgdnx6NGj4cOHnzp1in68a9ascXBwEPLBCsynASsxMdHNzW369Oldu3Z1d3dftGjR1KlTjb1Q0nXkyJFNmzYdOHCAApY4Ru0KxFpVi/Ysrq6u/v7+nTt39vPz+/TTT8PCwrACtXf06NGAgADaCK2trSdPnkxRdf/+/ViB2ZWUlDRs2LD379+LD7ECs4WO96dPn65QoQINi4d8rEDt0TlSjx49Bg0aRLvB+fPne3p6BgcH54cVmE8D1vHjx+lHMmLECBqm75Lysqy/RX27ePFiwYIFixQpohijdgVirapFJ76Ojo79+vWj4W+//XbWrFmvXr26cOECVqCWqlatum3btlq1asXExNjY2FBQELAFZt/PP/9cqVKlkJAQ8SFWoPbi4+OfPn3apEkTMzMzxUisQO2Fhoa+fft2woQJJiYmc+bMuX79upA/VmA+DViRkZFOTk7iMA3cvXuXIjZ998ZdKsmaOHEi/d+5c6dijNoViLWqVrdu3dq1aycOnzt3zs7OrmTJkliB2rNPtXnz5gEDBlhbW//9998CtsBsun379vLly/fv3799+3ZxDFag9u7du0dHfVdX16tXrzZu3HjBggUVK1bECtReWFiYg4PDF198cezYsY8//njRokVC/tgC82nAev78uaWlpThcvHhxOkF58+YNDRh3qWRE7QrEWlXLMhXtJnbv3j1mzJhff/2V9hdYgdnVv3//Tz755Lvvvhs6dGhwcDBWoPaSk5M///zzhQsXKq8KrEDtPXv2zNbWdtSoURT0Z82aRZviqVOnsAK1RyuQ9n5Lly6lc/WffvqJVuCZM2fywwrMpwGrVKlSipqeMTEx5ubmxYoVM+4iyYvaFYi1mpkXL17QEY7Og3fu3Ons7CxgBWbHjRs3ChUqVKVKlSJFikyePLlChQpxcXFYgdpbtWoVrbTu3bvT0UsxEitQe82aNbt06ZI4TCdIJUqUoDWJFag9+uW2bt2aEioN+/n5UYTKJyswnwasqlWrLlmyRBy+du2anZ2dqampcRdJXtSuQKxVtSgNdO7c2cXFZevWrYo6HFiB2qNU+vDhw8WLF9Pw27dvaZ3QbhcrUHtHjx7dv3+/tbV1cnJyUlISDezduxcrUHt///33u3fvKCLQsIWFBf2KsQVmi62trWKYVoiJiUk+WYH5NGC1bdv21atX27Zto8PeggUL/u///s/YSyQzalcg1qpalA/oqPb555/fv39fHFO5cmWsQO21b9++e/fugwYNop3v9OnTe/bsSUc4rEDtLV26lPIBDbx8+bJ+/fqXLl0qU6YMrUOsQC3ROVKvXr127dpVp06dOXPmUNIqWbIktkDtderUacSIERs2bKDVMmvWrBYtWuSTFZhPAxbFZ/q10Ff+xRdf0P568uTJxl4imVG7ArFW1bpw4cLly5ft7e0VY549e2ZtbY0VqCVnZ+e5c+cOHTo0KiqqS5cuFBcEbIHZUbp0aXGgUKFCJiYmlSpVEh9iBWqJAgFtgbRanjx50qFDh3Xr1gnYArOjYMGCu3fvHjVqlKenZ6tWrdavXy/kjxWYTwOWkLrXVhSrgzYePHig/FDtCsRazWheqozjsQK1NzSVykiswOyiWJ+YmKh4iBWoJUqlX6RSGY8VqL3GjRufP39eZWSeX4H5N2ABAAAA6AkCFgAAAICOIWABAAAA6BgCFgAAAICOIWABAAAA6BgCFgAAAICOIWABAAAA6BgCFgAAAICOIWABAAAA6BgCFgAAAICOIWABAAAA6BgCFgAAAICOIWABAAAA6BgCFgAAAICOIWABAAAA6BgCFgAAAICOIWABAAAA6BgCFgAAAICOIWABAAAA6BgCFgDkxOPHj8uXL79o0aJx48aJY/bu3evn53fs2LEczG39+vVBQUH0X5eLKAje3t6rVq26fv162bJlNUxmbm7+/v1781TigK4WQOczBAC5wM8eAHLIxMTEx8fHzc2tUqVKxl4W9ShdXbt2TXO6AgDQBwQsAMghCwuLESNGfPXVV9u3b1cef+7cuXHjxtF/5eHw8PDhw4c3a9YsICCgVq1aCxYsoBeKIxcvXkxTvnv3bsCAAQcOHPjoo48oGDk6OtLIkydP0mQ3btxo1arV6tWrK1as+O+//3p5eXXs2HHLli3//POP4k1pGaZMmfL48WN6avny5WXKlHF1dX39+nXjxo3Pnz9PD8XJVq5cOXv2bJqsbt26a9ascXBw6Ny5c1JSUrVq1SgmigNXr169cOGCyvtqXv7169cfP348Li5u9+7dderU8ff3V54zLefkyZM3bdpkZmY2duzYb7/91nBfEgD8fzt3ExLlFgdgXHNUwo+xMtAyr5MFYhFiVJsEzWxRKJZmEFMLi8DKFqkLrdSFG0UFv6CBIaEEDQnR1GAU3AipG8dyEagNpaFOhIMYKCreP/NyXwZnbs3V91LG81vImeOZc47j5uHt4xchsABsXWlpaXx8/OvXr9PT03+6WDLr3r17JSUlUh6ZmZkjIyOzs7OSLI8ePfJxFpLJZGpoaKiurs7JyXn//r3D4ZBIam5uPnv2rESJ0WgcGBiQlVar1WAwtLa2qjvbbLbc3Nz29vaTJ08WFBTk5+dLzXR0dISFhUktBQcHK8ump6clzvr6+uTORUVFtbW1cqLFYtHpdFNTU8ofEcpAsszjuT++v4RXfX19TU2N3P/atWujo6PqznIT2UFmFhYWZP3169eluv6PXweA3weBBWDrpF0aGxslO1JSUn66ODIyUtpCBufPn5d4+usfEjQymZiYeOfOHRlUVFSYzeaJiYnh4eHk5GQl3aRa9u3bt76+7uN81vX06dPAwEB1587OTimetLQ0GVdVVR08eFBW+vn5bbrA/v37Zdvo6Ojv37+Hh4dLb3m8Z3d3t8dzf3x/iba8vDz1/pOTk0ePHlX3XF1dtdvtp06dmpmZCQ0N9f4TBrBDEVgAtiUjI6O5ubm8vFyixP27Gxsb6lh9kqTT6SIiItSxMjAYDMrA398/JiZmbm5OAshischYmQ8ICJBGkcGhQ4dc68rH+Tfu1WVSUbLy69ev6hEqOUvS582bN3q9XnYICQnx+BP927n/6f5fvnxRA+vKlSuLi4uSj/Pz8xKjBQUFHs8F8CchsABsV11d3YkTJ/bs2aPOrK2tKYOZmRkvN7HZbMpgdXX106dPUlGRkZFpaWmvXr2SyfX19dHRUcmab9++uf+jPJkfGxtTxpJWKysr4eHh7ke0t7f39PT09fXt3bu3paWlu7vb4008nqs8pvLm/vKzf/78WTZRv/Xx48dz587dunVL5q9evRoWFnb//v2ffiAAdjQCC8B2RUdHl5aWPn78+PTp0/JSr9dL7litVomkpqYmLzeR9WazOTMzs6qqymAwHD58OCgoqLi4uLe3V7atrKx8+/bt4OCgx/emp6eXlZUZjcbExMTCwsLLly97/J8RJM6Cg4N3795tt9sbGhpcH3EtLS1J9yiDS5cueXmuq3fv3plMpqysrJqamgMHDqiPr2TDrq4uabvOzk7JNYk/uYCXnwmAnYvAAqCBBw8ePH/+XBnHxcXl5eUlJSVJJ0l4VVdXe7PD7du3e3p6Hj58mJCQ0NbW5uvrKwHU0tIiMzab7cyZM+r+7mJjY589e3b37t3Z2dnU1FQJHY/Lbty4Ia0TFRUlN3zy5Imc+OLFC5mUKpJGlPeqAy/PdXXx4sX+/v6ioqJjx47J/Xft2iWTyoYTExOSaHLJwMDAnJycmzdverMhgB2NwAKwFVI/y8vL6kudTme1WpWxtFG9k/IyOzvbx1ldHz58UGYqKirUN05OTsrXI0eOGI1G91MuXLgwPj7uOnP8+HF1H1dZTpsmHQ6H60u9Xm+xWNSXc3NzyuDly5ebBu7n/vj+Q0NDISEh0lWbLqBu2NHR4X5nAH8wAgsAAEBjBBYAAIDGCCwA2C6j06++BYDfCIEFAACgMQILAABAYwQWAACAxggsAAAAjRFYAAAAGiOwAAAANEZgAQAAaIzAAgAA0BiBBQAAoDECCwAAQGMEFgAAgMYILAAAAI0RWAAAABojsAAAADRGYAEAAGjsb8xCPyaZiIMNAAAAAElFTkSuQmCC" width="642" />he
horizontal dotted lines mark where there is an only 5% probability
that the result is a random occurrence. So, if you want to read this
plot for a specific player, you could say for example 'disregarding
effects like differences in defensive pressure, there is an
exp(-6.82)=0.13% probability that Josh Smith was in reality an
average 3 point shooter during the 13/14 season' or 'disregarding
effects like differences in defensive pressure, there is an
exp(-12.9)=0.00051% probability that Kyle Korver was in reality an
average 3 point shooter during the 13/14 season'. Which in both cases
doesn't need 750 attempts.<br />
<h3>
6.Epilog: Going full circle</h3>
</div>
<ol>
<ol start="6">
</ol>
</ol>
<div class="western">
Now, I hope I could convince you that this
approach is more elegant and telling then the previous way of looking
at stats stabilization. (I especially hope that Seth uses it for some
of his studies, especially for uncontested team three point defense
(Houston etc.) and shot contesting.). One elegant addition is that
you can visually show things that are similar to KR-21. The idea is
to compare the percentage distribution of your players with a
simulated distribution that assumes all players have the same average
probability of yes, but different amounts of tries. The reason I
simulate this distribution instead of solving it analytically is
because <strike>I am lazy</strike> different amounts of attempts lead
to different shapes. If all players would have 50 attempts, we would
get a much broader distribution than if all players had 500 attempts.
As the distribution is mixed, I simply simulate the random draw 100
times for every player and add the whole thing up to a histogram.
Here the results for free throws and three pointers</div>
<div class="western">
<img align="left" border="0" height="562" name="Image4" 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" 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" width="642" />Unsurprisingly,
the shapes between real data and simulation is much closer for three
pointers than for free throws. I have no underlying technical formula
that describes this similarity – but I guess it is a start :)</div>
<div class="western">
Cheers,</div>
<br />
<div class="western">
Hannes<br />
<br />
P.S.: I have one more side note. in the case that your only information is made or miss, a binomial distribution works perfectly well. Other, more complicated examples that allow for more outcomes then yes or no could be implemented using bootstrapping (I guess...). Bootstrapping is not necessary for binomial distributions, because if you bootstrap a binomial distribution (which is more or less what Prof. Dr. Pizza Cutter did), you would simply get an estimation of the same binomial distribution (I have no proof for this, but it sounds reasonable to me...).</div>
Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-41762992668853461792015-01-08T02:21:00.001-08:002015-01-08T02:21:08.544-08:00Scatter Plot DataHi everyone,<br />
<br />
I decided to release the code that I am using for my scatter plots. Mostly because I am not using it often enough to keep it for myself, so I hope that it is useful for somebody else. Use it at your own risk. Feel free to remove my name or change anything. And so on (we all know these proclamations...).<br />
<br />
In my opinion, the output is pretty cool, especially if you combine it with Inkscape or Illustrator (which works well if you save the plots as pdf)<br />
As an example, take the plot I have here: http://sportstribution.blogspot.de/2014/03/three-quick-points-on-kyle-korver-and.html<br />You just have to use the same axes settings and different filter or data points and you get two plots that you can easily merge (and color differently).<br />
<br />
Especially for those of you that have the data readily at hand (I'm looking at you, Nylon guys!) it could be worth to give it a try.<br />
This code was my first try in learning Python and it is therefore far from perfect. It is only 400 lines and not so many packages, so it might also be interesting for those of you that are as well interested in learning Python.<br />
<br />
The code should work with Python2.7 and the right packages. If someone knows how to update this, I am glad to listen.<br />
<br />
Oh, I almost forgot to add the github: <a class="twitter-timeline-link" href="https://github.com/SportsTribution/SportsTribution-Scatter.git" role="presentation" style="background: rgb(255, 255, 255); color: #0084b4; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px; text-decoration: none !important;">https://github.com/SportsTribution/SportsTribution-Scatter.git</a><span style="background-color: white; color: #292f33; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;"> </span><br />
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Cheers,<br />
HannesJohannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-48245104510859240942014-12-09T14:06:00.002-08:002014-12-09T14:25:11.505-08:00Catch & Shoot vs Pull Up and the risk of binning<div class="separator" style="clear: both; text-align: left;">
Hi everyone,</div>
a short note as I observed several articles making use of data binning. I am not saying that binning is automatically wrong, but it has at least one important potential flaw.<br />
<br />
Let's use the catch & shoot vs pull up shooting percentage as an example. It is shown that catch and shoots are generally more open than pull ups. Furthermore, let us say that we define an uncontested shot as any shot where no defender is in a distance of 6 feet (that is binning). <br />
Now, a study might find that uncontested catch and shoot attempts have a higher FG% than uncontested pull ups. But, if we now compare hypothetical distributions for both shot types<br />
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we can clearly see that the mean weight is not the same for catch & shoot and pull ups. </div>
Both for contested and uncontested shots, hypothetical pull up shots are clearly more contested.<br />
<br />
That's it for right now. Don't say I didn't warn you.<br />
Hannes<br /><br />
Update: I only mentioned the problem, but didn't offer any solution. I never had to work with a problem like this myself, but <complete id="goog_896834167">@neuteufel mentioned <a href="http://en.wikipedia.org/wiki/Propensity_score_matching" target="_blank">Propensity score</a>. I guess there are probably a few more standard procedures, but the most important is that you are aware that open is not necessarily open (that sounds weird...)</complete>Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-39227147487720992062014-12-02T08:04:00.000-08:002014-12-02T08:08:25.520-08:00Looking for soulmates (even though I don't believe in them...)Hi everyone,<br />
<br />
I was using hierarchical clustering quite a bit over the last month.<br />
And two days ago I found <a href="http://rstudio-pubs-static.s3.amazonaws.com/11288_111663babc4f44359a35b1f5f1a22b89.html" target="_blank">this nice R-script</a>, so I combined the data with my clustering.<br />
Short info before it gets self-explanatory:<br />
- Used all players that had at least 10 games and 12 minutes per game<br />
- as the list is too long to show all the names, I will use subclusters where you can find the names<br />
- tried to use minute normalized data (per 36)<br />
- the effective field goal column is not used for clustering. I did not include any FG percentages in the end (for no real reason...)<br />
- If a value is green, that means that there was a division by zero (mostly because the player never drove)<br />
- If you have other interesting data, feel free to send it to me and I'll send you the figures back. Or if you have Matlab I can give you the code as well (probably should clean it beforehand...)<br />
<br />
Without further ado (click on the figures to enlarge):<br />
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Hope you enjoyed it. Cheers,</div>
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Hannes</div>
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<br /></div>
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P.S.: The Alexey Shved/ Stephen Curry cluster disappeared after I took out some values. Clustering can be a bit tricky... but the amount of Shved Pull Up 3s is still amazing</div>
<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-90268510973938781922014-10-27T16:36:00.002-07:002014-10-28T13:20:51.189-07:00The 42 of the NBA lotteryHello everybody,<br />
<br />
I think I just found the perfect (albeit crazy) solution how we can fix several things that are wrong about the NBA: Let us support the mediocre teams instead of the incompetent!<br />
<br />
Before I explain my idea, first the status quo:<br />
<br />
<ul>
<li>The goal of every team is to win a title. Therefore, you need to get good players, small markets are the most likely to get good players at the draft. Therefore it is good to be bad.</li>
<li>The problem with one good player is, that he is usually not good enough to give you a title. Unless his name is LeBron or Tim Duncan, your typical number one pick does not necessarily lead to being a title contender. Therefore, you need to be bad for several years. Which is not easy if you just drafted a good player</li>
<li>Let's face it: A general number one pick cannot make you a title contender. It makes you at best an eighth seed, aka 'the place where you do not want to be'</li>
<li>For an eighth seed on the other hand, a number one pick could be exactly what the team needs to make it a title contender</li>
<li>Over the last years, I have heard several times that a team is dumb for trying to be a number 8 seed. Examples are the Bucks of the last years, or the Hawks every year. Or the Pacers this year. 'This is a lost year, the team should just give up!' and the likes</li>
<li>Last year I heard several times 'these scrappy Suns should deserve the number one pick for trying so hard!'</li>
<li>I think that every smart basketball organization can become mediocre in 3 years. Even if the team would play in the depth of hell or any other place in North Dakota (hell actually might give you a huge home court advantage)</li>
<li>Furthermore, the lottery system leads to 30% of teams waving the white flag after less than half of the season. Hell, 30% of teams probably even wave the white flag before the season</li>
<li>This does not happen in European leagues, du to relegation. The last teams are even the one that fight the hardest to win. I know, this is not possible in the US system that in general likes to have neither poor nor rich people - sorry, I'll stop my bad jokes now...<a name='more'></a></li>
</ul>
<div>
I guess you get the by now. What I will propose might sound crazy, but I think it makes a lot of sense. It might need some tinkering, but my idea is to change the number of 'ping pong balls' in a way, that the number one seed of each conference gets one ball, the 2 seed to and so on, up until the number 9 seed, who would get 9. The number 10 seed gets 8, 11 gets 7 until the last place team of each conference gets 3 balls.<br />
As before, the lottery decides picks 1 to 3 and afterwards the worst team that did not get pick 1 to 3 gets pick 4 and so on. The curve would be quite flat. It would give the highest 2 seeds and lowest seed of each conference a less than 2 percent chance to get the number one pick. 10 teams would have very similar excitement with around 5% probability of getting the next big thing. It is better if the curve is relatively flat, because like that times always want to be better. The momentary lottery situation is like trying to optimize something and then putting strict thresholds into the system that penalize you if you try too hard (that was as good of a nerd metaphor as they get).<br />
As I said, there could be some tinkering, especially with the high places. But for a few reasons I would not make a cutoff for the very high seeds. First, cutoffs are always a bit stupid, as they severely penalize anybody that barely crossed it. See making fun of the Atlanta Hawks under the momentary conditions. Second, with the new CBA, its almost impossible to make a superduper team out of a super team for an extended period of time. And third (in all caps) HOW CRAZY WOULD IT BE IF THE SPURS WOULD GET A NUMBER ONE PICK!?</div>
<div>
But back to serious, just imagine that the Suns had Jabari Parker or the Timberwolves had Andrew Wiggins - AND Kevin Love. Wouldn't that be way more fascinating than trying to play the game 'is it a 76ers player or is it a janitor?'<br />
<br />
I guess you get the idea, but let me know what you think. (Preferably on Twitter @SportsTribution as I usually forget to check for comments...)</div>
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<br /></div>
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Cheers,</div>
<div>
Hannes</div>
Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-86946703206487320392014-10-22T14:35:00.000-07:002014-10-22T14:35:34.154-07:00why you do not want to be stuck in the middleHi everybody,<br />
just a quickie. Hope it speaks for itself.<br />
Seth Partnow (I'm never sure if that's a spelling mistake), collected some data https://t.co/3hrprry8vR<br />It's pretty awesome, because it means that I can make interesting plots without an hour-long copy and paste job. Just ask if you feel like looking at a certain aspect of the data.<br />
Back to Morey ball: Morey ball tells us that it makes sense to shot only from the rim or from behind the three point line if possible (sounds intuitive? Ask Byron Scott).<br />
Short side note: I'll try to post something longer about this topic, as it is not as simple as it sounds to get open shots at the rim or from three point land (duh)<br />
Interestingly, there is even a positive correlation for the Morey Ball shots (more shots from 3 or rim means higher effective field goal percentage from there) and a negative correlation for midrange shots (more shots from midrange means lower effective field goal percentage from there). Both with a small but existent correlation of around 0.4 (or -0.4).<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-alkXnCQoJ7g/VEghU07rNuI/AAAAAAAAAL0/Wt39ZPcsHK8/s1600/MoreyVsNonrim.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://3.bp.blogspot.com/-alkXnCQoJ7g/VEghU07rNuI/AAAAAAAAAL0/Wt39ZPcsHK8/s1600/MoreyVsNonrim.png" height="320" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Click on me</td></tr>
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Cheers,</div>
<div class="separator" style="clear: both; text-align: left;">
Hannes</div>
<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-65262339787885781612014-10-08T11:38:00.000-07:002014-10-09T01:38:52.669-07:00Good cops - bad cops in the NBAHi everybody,<br />
<br />
just a quicky as I stumbled over plus minus data. There are a lot of adjusted Plus Minus (PM) stats, but the idea is always similar: A players PM is obviously related to a teams point differential. So I took all players that had during the season a PM of more than 100 or less than -100 and plotted their PM per minute against the Team +/- per game. I leave the analysis to you.<br />
Enjoy,<br />
Hannes<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://4.bp.blogspot.com/-7iCFjEUlQc0/VDWD1yaINSI/AAAAAAAAALY/UoLWQ1GvVMQ/s1600/All%2Bcops.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://4.bp.blogspot.com/-7iCFjEUlQc0/VDWD1yaINSI/AAAAAAAAALY/UoLWQ1GvVMQ/s1600/All%2Bcops.png" height="252" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">All Cops</td></tr>
</tbody></table>
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<tr><td style="text-align: center;"><a href="http://4.bp.blogspot.com/-YoaaFqXAB4w/VDWD17PEkcI/AAAAAAAAALc/L7NzUJcIRl4/s1600/Good%2BCops.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://4.bp.blogspot.com/-YoaaFqXAB4w/VDWD17PEkcI/AAAAAAAAALc/L7NzUJcIRl4/s1600/Good%2BCops.png" height="252" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Good Cops</td></tr>
</tbody></table>
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-jKi9cEIGgmA/VDWD1xGAN3I/AAAAAAAAALU/HbUSrusnRBc/s1600/Bad%2BCops.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="http://3.bp.blogspot.com/-jKi9cEIGgmA/VDWD1xGAN3I/AAAAAAAAALU/HbUSrusnRBc/s1600/Bad%2BCops.png" height="252" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Bad Cops</td></tr>
</tbody></table>
<br />
<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-72828299688173193842014-10-02T12:27:00.002-07:002014-10-02T12:35:08.215-07:00Why shooting only one free throw would not increase the average value - but slightly change the gameHi,<br />
<br />
just a short one regarding an idea that was first mentioned here:<br />
http://espn.go.com/blog/truehoop/post/_/id/70581/hoopidea-is-one-trip-to-the-free-throw-line-enough<br />
The idea is to change the free throw concept to speed things up. Instead of two free throws that are worth two points, we could as well shoot one free throw worth one point. Or three points, if it's for a three-point foul and so on...<br />
The always amazing Nylon Calculus crew spun it a little further<br />
http://nyloncalculus.com/2014/10/02/shooting-one-free-throw-offense/<br />
calculating that the expected points per free throw attempt would not change (so basically the mean), but the variance would increase (due to less attempts).<br />
My first reaction was to say 'but what about offensive rebounds!? You cannot rebound the first shot!'<br />
This is true, but it doesn't change the math (too bad, I love to be a nitpicker...)<br />
<br />
FT% - Free throw percentage of a player<br />
OR% - Offensive rebound percentage<br />
POR - Points we expect after an offensive rebound<br />
Expected points two free throw situation (resulting points multiplied with their probability):<br />
1 *(1-FT%)*FT% +<br />
1 *FT%*(1-FT%)*(1-OR%) +<br />
2 *FT%*FT% +<br />
(1+POR) *FT%*(1-FT%)*OR% +<br />
POR *(1-FT%)*(1-FT%)*OR%<br />
0 *(1-FT%)*(1-FT%)*(1-OR%)<br />
=2*FT%+OR%*POR-FT%*OR%*POR<br />
<br />
Expected points one free throw situation (resulting points multiplied with their probability):<br />
2 *FT% +<br />
POR *(1-FT%)*OR% +<br />
0 *(1-FT%)*(1-OR%)<br />
=2*FT%+OR%*POR-FT%*OR%*POR<br />
<br />
So, even this doesn't change.<br />
The aspect of the game that it would really influence is the 'it is late in the game and we lead by three points' situation.<br />
Because the opponent would not be able to make the first shot and intentionally miss the second shot - as there would be no second shot...Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-8359669214332915072014-07-14T07:40:00.000-07:002014-07-14T07:40:00.409-07:00(Part of) How the world cup was wonHello World,<br />
I haven't written anything in a while (there is this thing called thesis looming...). But today for some reason I do not feel like working (I feel more like drinking Krombacher or something like that).
After seeing everybody on the German team running his ass off yesterday (either that or getting punched in the face), I wanted to show a plot related to this. It shows all field players that played in at least 5 games (so you needed to at least reach the quarter finals). The x-axis is minutes per game and the y-axis kilometer per 90 minutes (as always, click on the image to enlarge).<br />
<a href="http://4.bp.blogspot.com/-XBnF9zZCBFg/U8PiHiB6iCI/AAAAAAAAAKw/elDZXiuX5JA/s1600/Distance_More.png" imageanchor="1"><img border="0" src="http://4.bp.blogspot.com/-XBnF9zZCBFg/U8PiHiB6iCI/AAAAAAAAAKw/elDZXiuX5JA/s320/Distance_More.png" height="272" width="320" /></a><br />
As you can see, there is a clear negative correlation between minutes per game and average speed. But if you focus on those players that played more than 80 minutes per game, you can see that all of Germany's offensive players and full-backs are running a lot. And Boateng and Hummels are also running quite much for centre-backs. (Note: In German full-back and centre-back is simply outside and inside defender - makes way more sense in my opinion...)<br />
Now, I do not want to imply that this is the best way to play. After all, the Finals were super close and Argentina could have won as well. And if your strategy is to defend deep and then try to strike quickly, your offense doesn't need to run that much (so, if you want to bash Messi or brazilian one syllable strikers do it somewhere else please).<br />
BUT, I wanted to show that a big part of the German game style is to run as much as possible as a team.<br />
So, thanks to Schweinsteiger, Müller, Lahm and everybody else for playing great football the last 10 years (Klose since 2002!). I appreciate that you are running your asses off.<br />
<br />
Cheers,<br />
HannesJohannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-45534507113426187132014-05-10T03:29:00.002-07:002014-05-10T03:36:43.451-07:00Bridging the gap, Part III - Throwing the kitchen sinkHi everyone,<br />
<br />
<div style="text-align: justify;">
for now, part III is going to be the final chapter of my Plus Minus / box score / tracking data comparison. Basically like 'Return of the Jedi', only with less Ewoks.</div>
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After looking at correlations between <a href="http://sportstribution.blogspot.ch/2014/05/plus-minus-box-scores-sportsvu-bit-of.html" target="_blank">offensive</a> and <a href="http://sportstribution.blogspot.ch/2014/05/bridging-gap-part-ii-defense.html" target="_blank">defensive</a> Plus Minus and pairs of other data, I decided to look at general plus minus and everything at the same time. Using 17 different statistics, I am going to look at all possible combinations, to find the best ones for predicting the data. All possible combinations are in this case 2<sup>17</sup>-1 = 131071 = a shit load of possibilities. I also decided to add a new type of players to my previous groups of Point Guards, Wings & Bigs, which I called 'Very Bigs'. It's basically only those bigs that are not attempting any three pointer. The idea is to get a more homogeneous group.<br />
<a name='more'></a><br />
For testing all possible combinations, I have to consider the following: If I would use straight-forward multiple linear regression with 17 predictors and 52 Point Guards, I would get a super R<sup style="text-align: justify;">2</sup><span style="text-align: justify;"> value (around 0.8), but basically I would simply overfit the whole system. </span><br />
<span style="text-align: justify;">So, there is one important analytical trick I have to add to get results of any value: cross-validation. In my case, cross-validation simply means that I split my Point Guards into 5 groups and then use 4 of these groups to 'train' my regression model and the fifth group to test the predictive value. And then I rotate these sets so that each group is once tested against the four others. After I am finished with one round of these, I will split my Point Guards into 5 different groups and repeat the whole thing. With this, I make sure that my results are not biased by the selected groups. It is good that computer became so fast during the last years, because mine now had to do 131,071 (# of combinations) * 5 (# of cross validation groups) * 4 (# of player types) * 2 (# of general repeats) = 5,242,840 regression calculations. Which nowadays takes probably around an hour.</span><br />
<span style="text-align: justify;">What you will see in the following, is that the combination of statistics that have the best prediction are usually combinations of around 8 or 9 statistics. This is in part simply due to the fact that there are the most different possibilities for combinations of 8 or 9. If I use only one predictor, I have 17 possible combinations. The same for 16 predictors (I can leave one of the 17 out). But my number of possible permutations that use 8 predictors is - I don't know right now, but it's definitely way higher than 17.</span>
<br />
<span style="text-align: justify;">So, optimally, I would like to pick combinations of around 5 predictors with good correlation values. But take a look at the results for yourself. The figures show for each number of predictors and each predictor the maximum R<sup>2</sup> value:</span><br />
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<a href="http://1.bp.blogspot.com/-l-gTaBDz-JE/U23-FyJ-KMI/AAAAAAAAAKQ/0n4OpuAUlsY/s1600/BigPicture.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-l-gTaBDz-JE/U23-FyJ-KMI/AAAAAAAAAKQ/0n4OpuAUlsY/s1600/BigPicture.png" height="320" width="197" /></a></div>
<span style="text-align: justify;"><br /></span>
<span style="text-align: justify;">What we see is basically the following: It is possible to see in box score numbers what comes with being a good point guard. Things like scoring effectively and distributing the ball and stuff. In addition it seems to be indicative if you can grab contested rebounds and are good at contesting opponents at the rim. For wing players, things are similar, yet slightly less indicative. Probably due to the less homogeneous form of that group, which merges defensive specialists like Tony Allen with scoring machines like Kevin Durant (just like the first round of the Play Offs). But I have to admit, at least with my data set it gets pretty messy for Bigs.</span><br />
<span style="text-align: justify;">I guess the general message is: As soon as you start to handle your data carefully, your predictions start to loose their boldness. Data analysis is a noisy thing. So don't trust anybody with a small data set and a bold announcement. Which also doubles as a good advice when you meet a stranger in a bar ;)</span><br />
<span style="text-align: justify;"><br />I wish all of you a nice weekend,<br />Hannes</span></div>
Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-6940202060143703732014-05-05T10:41:00.001-07:002014-05-05T14:37:45.088-07:00Bridging the gap, Part II - DefenseHi everybody,<br />
<br />
<div style="text-align: justify;">
last time I bored you to death, I tried to find those stats that correlate well with winning a game on offense.</div>
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Today, I will take a look at defense and check what in general correlates best with winning a game (because to win a game, you have to usually play both defense and offense).</div>
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As all the groundwork got laid out in my <a href="http://sportstribution.blogspot.ch/2014/05/plus-minus-box-scores-sportsvu-bit-of.html" target="_blank">last post</a>, I will directly start with the goodies.</div>
<br />
<b>DEFENSE</b><br />
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<a href="http://3.bp.blogspot.com/-nE5EDazPn5c/U2fA8MLqngI/AAAAAAAAAJg/VC_gfvZCNdw/s1600/2dDefReggR2_Point+Guards.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-nE5EDazPn5c/U2fA8MLqngI/AAAAAAAAAJg/VC_gfvZCNdw/s1600/2dDefReggR2_Point+Guards.png" height="240" width="320" /></a></div>
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<a href="http://3.bp.blogspot.com/-SKkeQZETDPE/U2fA8IVRgrI/AAAAAAAAAJc/H_bWmSbUgTA/s1600/2dDefReggR2_Wings.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-SKkeQZETDPE/U2fA8IVRgrI/AAAAAAAAAJc/H_bWmSbUgTA/s1600/2dDefReggR2_Wings.png" height="240" width="320" /></a><a href="http://3.bp.blogspot.com/-OHLza3JQTAo/U2fA8HUtIAI/AAAAAAAAAJY/owASNe8Wh-s/s1600/2dDefReggR2_Bigs.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-OHLza3JQTAo/U2fA8HUtIAI/AAAAAAAAAJY/owASNe8Wh-s/s1600/2dDefReggR2_Bigs.png" height="240" width="320" /></a></div>
<br />
<div style="text-align: justify;">
Opponent FG Percentage at the rim above all: SportsVU data and nba.com/stats gives you the information how well an opponent shoots at the rim while you are in the vicinity. And especially for Bigs that seems to be the one stat that is somewhat informative (R<sup>2 </sup>slightly above 0.1). Generally, it seems that we can get the most information for Wings, were other than Opponent FG%, Steals Rebounds and contested rebound percentage gives us an information. And the combination of steals and Opponent FG% alone gives already an R<sup>2</sup> above 0.3. There are others that came to <a href="http://georgetownsportsanalysis.wordpress.com/2014/04/09/steals-regularized-adjusted-plus-minus-and-ignoring-position/" target="_blank">similar conclusions</a> about Wings. For Point Guards, other than the steals Opponent combo (which could be something out of Street Fighter II), we have also the funky combination of Opponent FGA at the rim and Time of ball possession that are 'informative'. But in my opinion, this shows you the problem with multiple linear regression and only a few data points: You start to find random combinations that explain your data. It is similar with assists and Opponent FG% for center. Andrea Bargnani and Amar'e Stoudemire are very bad in Defensive splits (very surprising, I know). But their opponent FG% is only slightly below average. </div>
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<a href="http://4.bp.blogspot.com/-w5TsNHx67v0/U2fKZ2hxRmI/AAAAAAAAAJ4/h4EXh1ctyyY/s1600/OppFGP_DefSplits.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-w5TsNHx67v0/U2fKZ2hxRmI/AAAAAAAAAJ4/h4EXh1ctyyY/s1600/OppFGP_DefSplits.png" height="248" width="320" /></a></div>
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As their assist percentage is also very low, it is the perfect combination to explain their data. More on how to fix this in one of the next 'chapters'.</div>
<div style="text-align: justify;">
One last thing that is in my opinion a great example of 'real correlation, careful with causality'. The second highest correlation for Bigs is Turnovers. Which sounds absurd at first, but if you take a closer look:</div>
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<a href="http://3.bp.blogspot.com/-UpKu9lzRERE/U2fLFJ_M4mI/AAAAAAAAAKE/xT9I18ykb8o/s1600/DefVsTov.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-UpKu9lzRERE/U2fLFJ_M4mI/AAAAAAAAAKE/xT9I18ykb8o/s1600/DefVsTov.png" height="230" width="320" /></a></div>
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What do Omer Asik, Andrew Bogut and Kevin Garnett have in common: Exactly, stingy defense and bone breaking picks - which sometimes get whistled as turnover. So yes, players with a lot of turnover are good for your defense! (I should have used that as my title to generate more clicks...)</div>
<br />
<div style="text-align: justify;">
That's it for today. Have a nice 'whatever time it is right now at your place',</div>
Hannes<br />
<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-80045137298079935242014-05-03T08:46:00.001-07:002014-05-05T09:38:14.991-07:00Plus Minus, Box Scores & SportsVU - a bit of bridging the gap (Part I)Hello everybody!<br />
<div style="text-align: justify;">
I recently hit 1000 viewers which means - actually nothing (memo to myself: try to be first responder to as many Grantland posts as possible). Some weeks ago, after ESPN published Real Plus Minus (RPM), I dabbled a bit into comparing <a href="http://sportstribution.blogspot.ch/2014/04/defensive-real-plus-minus-and-its.html">it to</a> <a href="http://sportstribution.blogspot.ch/2014/04/are-you-teamper-or-are-you-teamrpm-or.html" target="_blank">'normal'</a> box score stats - and I have to admit it was partly rubbish (but at least it looks cool!).</div>
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(Note: If you get bored during the next paragraphs, simply scroll down to the new fancy figures...)</div>
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The biggest critique points are in my opinion that RPM is a stat that already includes box score stats and that I used a model that only compared one stat with RPM at a time. The first problem is directly obvious: If I compare assists with a stat that indirectly concludes 'assists are super!', then I don't know nothing. The second problem is a little bit more tricky. Imagine that assists correlate with turnovers (which is actually true, so you do not have to try very hard). This influences the analysis, as turnovers are generally seen as negative and assists as positive (for some strange reasons), but both could correlate as positive.</div>
<div style="text-align: justify;">
So, I started to use multiple linear regression, which sounds more dangerous than it is. Linear regression is basically: you have beans lying on the floor. Put a stick on the floor so that the beans are on average as close to the stick as possible. In the case of two factors, your beans are <a href="http://www.sjsu.edu/faculty/gerstman/EpiInfo/cont-mult1.jpg" target="_blank">floating in space</a> and you have to put on your space suit and adjust a board in a way that the beans are as close to it as possible<a href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#1" name="top1"><sup>1</sup></a>. I decided to not go further than two dimensions for the moment. That's probably another post.</div>
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<br />
<a name='more'></a><br /></div>
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To give you an idea about the data analysis:</div>
<div style="text-align: justify;">
Player selection:</div>
<div style="text-align: justify;">
- To avoid noisy data, I focus on players that played at least 42 games and 20 minutes per game.</div>
<div style="text-align: justify;">
- It does not make sense to compare the assist numbers of Chris Paul with the assist numbers of Dwight Howard. So players are sorted into Point Guards, Wings and Bigs which are separated using Time of possession (PGs dribble the ball a lot) & FG attempts against Player at the rim (bigs are standing close to the rim a lot), both normalized for 36 minutes. See the figure below to convince you that it works pretty neat. It also means that some players are considered as playing two positions - but we can live with that.</div>
<div style="text-align: justify;">
- These criteria give me 52 Point Guards, 87 Wings and 85 Bigs</div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://3.bp.blogspot.com/-G06gaQK0c6U/UyHAAW0-I-I/AAAAAAAAAA8/ALbQuxL7dpA/s1600/filter_starter_AI.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-G06gaQK0c6U/UyHAAW0-I-I/AAAAAAAAAA8/ALbQuxL7dpA/s1600/filter_starter_AI.png" height="256" width="320" /></a></div>
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<div style="text-align: justify;">
Data selection:</div>
<div style="text-align: justify;">
- <a href="http://www.gotbuckets.com/statistics/rapm/2014-rapm/" target="_blank">RAPM</a> (from gotbuckets.com and @talkingpractice): regularized Adjusted Plus Minus does not use any box score stats. It's split up into offensive and defensive Plus Minus, so that we can neatly separate offensive and defensive stats</div>
<div style="text-align: justify;">
- <a href="http://www.basketball-reference.com/leagues/NBA_2014_advanced.html" target="_blank">Pace Adjusted stats</a> from basketball-reference.com: Quick explanation. Given all other players are equal and and your RAPM is minus 10. If you play 100 possessions then your team is 10 points behind (and you probably shouldn't play basketball). So, the stat does not depend on the pace that your team plays. Thus, I mostly tried to use stats that work this way. In the following, if there is a stat like AST, it means Assist %, which means 'an estimate of the percentage of teammate field goals a player assisted while he was on the floor'. I will explain some of the stats on the fly. In any case, you should check both gotbuckets and basketball-reference, they give you so much information that you never needed. Speaking of so much information</div>
<div style="text-align: justify;">
- <a href="http://stats.nba.com/playerTracking.html" target="_blank">SportsVU stats</a> from nba.com: You can get lost in these stats. For now, I focused on two things. First, the 'contesting shots at the rim' stats, which is a new dimension in box score stats. Second, the contested rebound stats, because people like to throw them around. They basically give you an information, which percentage of your rebound attempts are contested and if you get those contested rebounds.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
Okay, enough with the setup. Let's look at the data! What you will see in the following, is how well different stats correlate with plus minus. If I were a commercial page, I would probably use 'explain' instead of 'correlate' and find other ways to make my data way more predictive sounding than it is. Please keep in mind, that what I measure does not necessarily imply causation. Anyhow, we start with</div>
<div style="text-align: justify;">
<b><br />The Offense</b></div>
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<a href="http://2.bp.blogspot.com/-wi9RbmoYteA/U2T8Tw6iz2I/AAAAAAAAAI4/QNfr5Gy8kW4/s1600/2dOffReggR2_Point+Guards.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-wi9RbmoYteA/U2T8Tw6iz2I/AAAAAAAAAI4/QNfr5Gy8kW4/s1600/2dOffReggR2_Point+Guards.png" height="240" width="320" /></a></div>
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<a href="http://3.bp.blogspot.com/-xIFiQc_pPfs/U2T8UGm9y-I/AAAAAAAAAI8/hcTvcsIzoQ4/s1600/2dOffReggR2_Wings.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-xIFiQc_pPfs/U2T8UGm9y-I/AAAAAAAAAI8/hcTvcsIzoQ4/s1600/2dOffReggR2_Wings.png" height="240" width="320" /></a></div>
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<a href="http://1.bp.blogspot.com/-mMAUrlnKpFw/U2T8T2REDKI/AAAAAAAAAJA/F2ivVRPsm7Y/s1600/2dOffReggR2_Bigs.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-mMAUrlnKpFw/U2T8T2REDKI/AAAAAAAAAJA/F2ivVRPsm7Y/s1600/2dOffReggR2_Bigs.png" height="240" width="320" /></a></div>
<br />
What we see here for both Point Guards and Wings, is that having a good shooting percentage (TS or eFG), while being able to distribute the ball, is what mostly seem to define a good player. There are some additional factors (Steal Percentage for guards or Possible REBP for Wings - which means the percentage of rebounds a player gets compared to the number of rebounds he could get), but mostly you simply should play like LeBron James or Kevin Durant (surprise!). R<sup>2</sup> values of 0.5 or higher are pretty solid in terms of correlation.<br />
The picture is by far not as clear for bigs, where the maximum R<sup>2</sup> values are around 0.2. One reason for this might be that bigs contain two main player types. On the one hand you have traditional center like Dwight Howard, on the other hand you have <a href="http://www.youtube.com/watch?v=HBxfBpwPz3M" target="_blank">Dirk Diggler</a> and even Paul Pierce nowadays often plays as some kind of second big man. You see this at the red square that combines USG (Usage percentage of a player) and 3PAr (number of 3 Pointers a player attempts). GotBuckets recently had <a href="http://www.gotbuckets.com/2014/05/01/part-2-big-men-and-three-point-shooting/" target="_blank">similar conclusions</a> paired with deeper analysis. It is interesting to note that it's only a few of the bigs that even attempt three pointer - but most of them to a very positive effect. It is also interesting to see that for Centers TOV (turnover percentage) has the highest R<sup>2 </sup>value. Please keep in mind that offensive fouls (like brick wall blocks) also count as turnovers. So Kendrick Perkins favorite occupation on offense is tracked. TOV & AST, combined with TS and USG would be my WOC (weapon of choice) if I had to evaluate big men.<br />
<br />
But you know what, enough TS and other BS for today, the sun is still shining and there are some great games on tonight. I'll tackle defense and general PM the next days.<br />
<br />
I wish everybody a nice weekend,<br />
Hannes<br />
<hr width="80%" />
<span class="Apple-style-span" style="font-size: x-small;">
<a href="https://www.blogger.com/null" name="1"><b>1 </b></a>I hope this helped. Otherwise it was probably the most boring sequel to Gravity that you can imagine.<sup><a href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#top1">↩</a></sup>
</span><br />
<span class="Apple-style-span" style="font-size: x-small;"><br /></span>
<span class="Apple-style-span" style="font-size: x-small;">Data from basketball-reference.com nba.com/stats & gotbuckets.com </span><span style="font-size: x-small;"><a href="https://dl.dropboxusercontent.com/u/5782415/RAPMnCo.txt">https://dl.dropboxusercontent.com/u/5782415/RAPMnCo.txt</a></span>Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com2tag:blogger.com,1999:blog-4608755107182023803.post-73783878195681028742014-04-18T07:21:00.002-07:002014-04-19T12:28:38.457-07:00New brooms try to avoid sweeps - trades and play-off implicationsThe regular season is over folks! Those 82 games rushed by like Goran Dragic on a fast break. And as the Play-Offs will start tomorrow, I will try to use those 82 games to shine a spotlight onto those players that changed their team during the season. Especially with regard to Play-Off implications.<br />
Looking only at players that played at least 12 games for both of their teams and at least 12 minutes per game, I had 23 players that fulfilled this criteria. Of those 23 players, the trade of 22 somehow were at some point involved with Play-Off teams - and number 23 is Spencer Hawes. I stripped the following graphics off most of those players that are now playing for lottery teams (e.g. Jan Vesely). Off players whose season is ended by now, I only kept Rudy Gay, Spencer Hawes and Luol Deng - I thought their numbers may be interesting for the general fan. The numbers that players put up for their Play-Off bound team are in red. So, I hope you will enjoy the following figures and thoughts:<br />
<b><br /></b>
<b>1. General playing and starting time of players</b><br />
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<a href="http://4.bp.blogspot.com/-JCXzlxDVhcg/U1Eie-3j9BI/AAAAAAAAAH4/Ppb8kqYggq8/s1600/Starter_minutes.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-JCXzlxDVhcg/U1Eie-3j9BI/AAAAAAAAAH4/Ppb8kqYggq8/s1600/Starter_minutes.png" height="250" width="350" /></a></div>
<ul>
<li><a name='more'></a>Courtney Lee has by far the most increased role of all traded players</li>
<li>Otherwise only Gary Neal and Caron Butler have increased minutes for their new teams</li>
<li>Several players (namely Turner, Davis, Blake and Crawford) went from starter on a bad team to bench player on a Top 10 team - the later two now being the backup Guards for Golden State</li>
<li>A general trend: Teams that are already good picking up commodities that they hope a) do not mess up their chemistry b) could help them at the right time</li>
<li>Another general trend: Several players are veteran point guards (Vasquez, Miller, Ridnour & Blake) that are now backing up All-Star level point guards (I know, it's a bit of a stretch for Kemba Walker, but he's having a good season...)</li>
<li>Toronto got THREE important rotation players out of a trade that looked like a salary dump (Masai Ujiri should become the next Mayor of Toronto)</li>
<li>Two of the players we thought might have an impact for a contender are Danny Granger and Glen 'Big Baby' Davis. The first had a hamstring injury (expected to be back on Sunday) and the second had 'a bit of a run-in with coach Doc Rivers Saturday, and it ended with his banishment to the locker room...' (source CBS sports). At least Big Baby <a href="http://www.youtube.com/watch?v=kB0N-wR4rEA" target="_blank">didn't throw any keyboards</a> this time.</li>
</ul>
<b>2. General changes in performance (Usage percentage estimates the number of offensive plays a player is involved in. And PER - well, read <a href="http://en.wikipedia.org/wiki/Player_efficiency_rating" target="_blank">the wiki page</a>)</b><br />
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<li> biggest increases in PER: Marcus Thornton (shooting percentage is way up), Patrick Patterson (shooting percentage is way up) and Gary Neal (shooting perc... well, you get the idea). If you include Caron Butler, it seems that leaving Sacramento and Milwaukee automatically increases your shooting percentage - but as you can see with Rudy Gay, it sometimes works the other way around as well</li>
<li>Speaking of Rudy Gay: If you merge Vasquez, Patterson and Salmons together (Pattalmsquez), you Toronto has more points per 36 minutes on a higher shooting percentage - I know, it's not very scientific, but it should be fun to say Pattalmsquez...</li>
<li>I guess Indiana was hoping that a decreased usage would increase Evan Turners efficiency - did not work out so far</li>
<li>Taking it further: looking at usage and PER, Turner and Danny Granger basically seem to have switched bodies</li>
</ul>
<b>3. To summarize: Which traded players should have the most influence on the Play-Offs</b><br />
It is quite possible that Memphis would not have made the Play-Offs without Courtney Lee. So, if they somehow upset Oklahoma, I would give this 'award' to him. And Toronto wouldn't be a three seat without Pattalmsquez (especially not without the Patt and the squez part). Otherwise the clear favorite is Caron 'Tough Juice' Butler, who plays big minutes for a contender.<br />
Dark horses are Danny Granger (if healthy) and Glen Davis (if playing), because they play for another contender. And Marcus Thornton could somehow win a game for Brooklyn. <br />
As always, let me know if I missed something.<br />
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Have a nice long week-end and enjoy the games. Play-offs baby!<br />
Hannes<br />
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<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-9670625075996539572014-04-11T23:31:00.000-07:002014-04-16T02:43:08.710-07:00Quick one before steals get overratedUPDATE: I just realized that parts of my criticism are already addressed here: http://fivethirtyeight.com/datalab/steals-are-predictive-but-are-they-that-important/ . In my opinion the metric that Benjamin Morris uses there is the way more important one (predictive value) - but the result would have destroyed the superlative that his main article produces. 'Steals are super predictive' is more interesting than 'Steals are a bit predictive, around as predictive as Rebounds or assists'. But back to my article... <br />
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I read the <a href="http://fivethirtyeight.com/features/the-hidden-value-of-the-nba-steal/" target="_blank">'The hidden value of the NBA steal'</a> article yesterday and read the same day something about '...Can you really retire as the all-time leader in a statistical category (which is now gaining more favor among stat-heads)...', where they spoke about steals. I am not 100 percent sure what precisely Benjamin Morris did in his analysis, but let me quickly summarize some things that can be fishy about it.<br />
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<b>1. Just showing some absolute values</b><br />
Morris states that in terms of predicting a players value, a steal is worth 9.1 points (and a block for example 1.7 points). He never mentions things like correlation and significance. An absolute value can be very high, but if your data is all over the place, the predictive value is not really given. Morris says 'A player that averages a steal more is worth 9.1 points'. But the second part of the sentence easily could be 'This prediction can be off by plus minus 20 points'.<br />
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<b>2. Ignoring that most of those values are correlated to each other</b><br />
You play more minutes, you produce more everything. For example, assists, steals and turnovers are highly correlated.<br />
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<b>3. (the combination of 1 and 2). Ignoring the number of occurrences</b><br />
So, to elaborate: He just shows some absolute values that basically state something along the line:<br />
'If Player A has 2 steals per game and Player B 1 steal, Player A's team will score 9 points more'. But, as I mentioned before in <b>2</b>, all those stats are correlated and in <b>1</b>, he doesn't show the real prediction value of a steal, but the absolute number. When I take the averages of Rebounds, Assists, Blocks, Turnovers and Steals that players produce and multiply them with his numbers, I get<br />
Rebounds: 1.7*3.5=5.95<br />
Assists: 1.8*2.2=3.96<br />
Turnovers: 5.4*1.2=6.48<br />
Blocks: 0.4*6.1=2.44<br />
Steals: 0.6*9.1=5.46<br />
As you can see, the steal is somewhere in the middle of the pack.<br />
<br />
So to summarize: I cannot reproduce precisely every step that Morris did in his analysis. But I am pretty sure that there are some values that are far from significant. Players that play more, produce more stats. The lines between correlation and causality become very blurred. Best case scenario for the analysis: Players that produce a lot of steals are in general more valuable. Worst case scenario: The analysis is complete bubkes.<br />
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I hope the article is halfway coherent. (It's written at 8 in the morning before I leave for the weekend). As always, let me know what you think, my dear imaginary reader.<br />
Have a nice weekend, <br />
Hannes<br />
<br />
P.S.: I hope that nobody believes that a steal is REALLY worth 9.1 points...<br />
<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0tag:blogger.com,1999:blog-4608755107182023803.post-31469040837665576062014-04-10T15:10:00.002-07:002014-05-04T02:34:36.847-07:00The non-ultimative Plus Minus comparisonHi,<br />
for all two of you that haven't enough of it yet:<br />
There is a <a href="http://www.hickory-high.com/is-espns-real-plus-minus-for-real/" target="_blank">great article</a> on different types of adjusted Plus Minus over at Hickory High (great name by the way :) ). This allows me to keep my number of words to a minimum (I can hear your relieve...).<br />
Instead I'll just quickly show some comparing plots between <a href="http://espn.go.com/nba/story/_/id/10740818/introducing-real-plus-minus" target="_blank">ESPN's Real Plus Minus</a> (RPM on the x-axis) and GotBuckets @talkingpractice (more great names) <a href="http://www.gotbuckets.com/statistics/rapm/2014-rapm/" target="_blank">regularized adjusted Plus Minus</a> (RAPM on the y-axis).<br />
My takes on it: Even though they are similar (as expected, because they are based on the same measurement), we can still see that they have some spread. This tells us that you should never start arguing that player A is better than player B if there PM differs by less than three points (more or less). Also, RPM generally gives a higher impact to single players. Feel free to get really angry at one or both of the two stats because they got something completely wrong ('How is Marc Gasol so bad in offensive Plus Minus!? This doesn't make any sense!'). I'll now show in the following order: <br />
1. correlation between RPM and RAPM for all 425 players I had available.<br />
2. RPM vs RAPM for players that played more than 41 games and 28 minutes per game<br />
3. the same for the offense<br />
4. the same for the defense<br />
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(as always, click on the figures for larger versions. I guess that here it's really necessary) <br />
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<a href="http://3.bp.blogspot.com/--LFjA-7Cak0/U0cUnRi6Q1I/AAAAAAAAAHE/17nnLVsI5Jg/s1600/ORPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/--LFjA-7Cak0/U0cUnRi6Q1I/AAAAAAAAAHE/17nnLVsI5Jg/s1600/ORPM.png" height="400" width="382" /></a><a href="http://1.bp.blogspot.com/-AmiXG8Dwlrc/U0cUnAOijPI/AAAAAAAAAHA/WsyAWAu9BTc/s1600/DRPM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-AmiXG8Dwlrc/U0cUnAOijPI/AAAAAAAAAHA/WsyAWAu9BTc/s1600/DRPM.png" height="400" width="400" /></a></div>
Sorry for not having a better layout. I promise I'm going to work on it. (By the way, if somebody wants to have the app that I wrote to produce these figures - you're welcome!). Let me know if you can find any interesting trends, because here it's midnight and I'll go to bed.<br />
Have a nice night (or day, depending on where you are),<br />
Hannes<br /><br />Data file (data from espn.com & gotbuckets.com): <a href="https://dl.dropboxusercontent.com/u/5782415/RPM_RAPM.txt">https://dl.dropboxusercontent.com/u/5782415/RPM_RAPM.txt</a>Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com3tag:blogger.com,1999:blog-4608755107182023803.post-70816023997561047122014-04-10T07:33:00.000-07:002014-04-16T02:43:58.951-07:00Defensive Real Plus Minus and it's relation to 'normal' Player StatsHello everybody,<br />
<br />
(Note: for those of you that don't like words, it gets more interesting at the bottom of the article)<br />
I made a few more comparisons between real Plus Minus and more common stats. More common stats are in this regard stats that you can measure for individual players on a night to night basis (either advance by tracking or the typical things, like points, assists and steals).<br />
My question today is: <b>How do common stats in general influence the outcome of a game on the defensive end?</b> (Offensive end should be following soon). For example, if you look at one single event like a block, you would say that it certainly gives you a positive result (see <a href="https://www.youtube.com/watch?v=MEtP6DJj8V0" target="_blank">Plumlee, Mason</a>). But often, going for the block (especially as a help defender) opens rebound opportunities, so that blocks are not necessarily a good thing. So, I tried to search for correlations between those single event stats and the outcome oriented Defensive Real Plus Minus (DRPM). I looked at Centers and Point Guards separately, to get a good spectrum of the possible influences. All players I used played at least 41 games and 15 minutes per game. The interesting value is the <a href="http://en.wikipedia.org/wiki/Correlation_and_dependence" target="_blank">correlation coefficient</a>. As rule of thumb, you can discard any correlation with an absolute value below 0.2, while everything with an absolute value above 0.4 makes it very likely that this stat is generally influencing the outcome of a game on the defensive end. Please be aware that correlation does not imply causality. As an example: Turnover Percentage for center has a positive correlation of 0.24 with DRPM - but we would all agree that it's probably not a good idea to loose the ball more often (even though that would explain why Scotty Brooks still plays Kendrick Perkins so much).<br />
If you are interested in other stats let me know.<br />
Phew, those were a lot of words for the Generation ADHD ;) <br />
<a name='more'></a>. The following data comes from either <a href="http://nba.com/stats">nba.com/stats</a> <a href="http://basketball-reference.com/">basketball-reference.com</a> or <a href="http://espn.com/">espn.com</a> . Please click on the images to see them in acceptable size. I'm a failure at blog design today. Without further McAdoo (my geek joke of the day):<br />
<b>Steal percentage</b> (<span class="glossary_desc">an estimate of the percentage of opponent possessions that end with a steal by the player while he was on the floor): <br />Steals are one of the stats that are strongly correlated for Point Guards and not so much for Centers. One part of it is probably that Point Guards produce way more steals than center (so there is more general weight on the stat). Another thing is that you can survive easier to be out of position if your opponent is still far away from the basket. So in the big picture, steal gambling of Point Guards probably doesn't have that much of a negative effect.</span><br />
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<a href="http://1.bp.blogspot.com/-26VtJojTVs4/U0aadtzeTyI/AAAAAAAAAFQ/f7QXIPnlIeA/s1600/C_STL_13.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="http://1.bp.blogspot.com/-26VtJojTVs4/U0aadtzeTyI/AAAAAAAAAFQ/f7QXIPnlIeA/s1600/C_STL_13.png" height="133" width="200" /> </a><a href="http://4.bp.blogspot.com/-rKWQYpqA6D0/U0aahJmTO_I/AAAAAAAAAGU/0L1F4FyN4SQ/s1600/PG_STL_4.png" imageanchor="1" style="float: left; margin-bottom: 1em; margin-right: 0em;"><img border="0" src="http://4.bp.blogspot.com/-rKWQYpqA6D0/U0aahJmTO_I/AAAAAAAAAGU/0L1F4FyN4SQ/s1600/PG_STL_4.png" height="133" width="200" /></a>
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<b>Block percentage</b> (<span class="glossary_desc">an estimate of the percentage of opponent two-point field goal attempts blocked by the player while he was on the floor): This is of course a more important stat for center (correlation of 0.22) than for PG (0.16). But 0.22 is not an impressive number, even though Centers have way more blocks than PG (maybe four times more on average). The more important thing for centers is...</span><br />
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<a href="http://1.bp.blogspot.com/-NwtZ3qR5lB8/U0aacnggUiI/AAAAAAAAAFA/IOvMgjrXHMs/s1600/C_BLK_21.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="http://1.bp.blogspot.com/-NwtZ3qR5lB8/U0aacnggUiI/AAAAAAAAAFA/IOvMgjrXHMs/s1600/C_BLK_21.png" height="133" width="200" /></a><span class="glossary_desc"></span><br />
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<span class="glossary_desc"><b>Opponent field goal percentage at the rim</b> (</span><span class="glossary_desc">Rim protection is defined as the defender being within five feet of the
basket and within five feet of the offensive player attempting the shot): Here we have a small correlation for PGs (-0.25) and a big one for center (-0.38). Of course center are around 5 times more often involved at rim field goal attempts than Point Guards, so it is way more important for them. Speaking of Field Goal attempts (attention: now it's getting interesting!):</span><br />
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<span class="glossary_desc"><a href="http://1.bp.blogspot.com/-I8jU_V7yKsk/U0aacm1sFCI/AAAAAAAAAFI/sUC219UuM3U/s1600/C_OppFGAatRim_06.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-I8jU_V7yKsk/U0aacm1sFCI/AAAAAAAAAFI/sUC219UuM3U/s1600/C_OppFGAatRim_06.png" height="150" width="200" /></a></span></div>
<span class="glossary_desc"><a href="http://4.bp.blogspot.com/-9caq25hYlPs/U0aagTZSiBI/AAAAAAAAAGI/khIr7gGagdw/s1600/PG_OppFGP_-25.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="http://4.bp.blogspot.com/-9caq25hYlPs/U0aagTZSiBI/AAAAAAAAAGI/khIr7gGagdw/s1600/PG_OppFGP_-25.png" height="150" width="200" /></a> </span><br />
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<span class="glossary_desc"><b>Opponent field goal attempts at the rim per 36 minutes</b>: This stat shows two interesting information. First, the number of field goal attempts at the rim for center does not correlate with DRPM. So, both schemes were you have a stationary Rim protector (like Roy Hibbert) or where your big man helps actively during the pick and roll can work similarly well (like Joakim Noah). The important thing is: If your big man is there to contest the shot, he better has an impact on it. The other interesting thing is the positive correlation between FG attempts at the rim against point guards and DRPM (0.28). Before looking at RPM, I thought 'well, people probably like to drive agains Jeremy Lin'. But the more you think about it - Jeremy Lin is most probably still close to the opponent while he is driving, while Brandon Jennings and D.J. Augustin might already have given up on the whole thing.</span><br />
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<span class="glossary_desc">As a last stat, let us look at the <b>defensive rebound percentage</b> (</span><span class="glossary_desc">an estimate of the percentage of available defensive rebounds a player grabbed while he was on the floor):</span><br />
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<a href="http://1.bp.blogspot.com/-4oWeGmw6_nk/U0aaeuVMXrI/AAAAAAAAAFw/USn-8D1z-n0/s1600/PG_DRB_45.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-4oWeGmw6_nk/U0aaeuVMXrI/AAAAAAAAAFw/USn-8D1z-n0/s1600/PG_DRB_45.png" height="150" width="200" /></a></div>
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<span class="glossary_desc">The result is quite remarkable. While defensive rebound percentage has almost no correlation for center (0.21), it is highly correlated for Point Guards. From my own low level experience of organized basketball<a href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#1" name="top1"><sup>1</sup></a>, it's probably the following explanation: If all big men get blocked out correctly, it's the small guy sweeping in that decides the battle. Or the small guy that gets the long rebound for which you cannot even box out correctly. Let me know if you have a better explanation.</span><br />
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<span class="glossary_desc">I wish everybody a nice day,</span><br />
<span class="glossary_desc">Hannes</span>
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<a href="https://www.blogger.com/null" name="1"><b>1 </b></a>gladly, we do not keep track of turnovers and field goal percentage<a href="https://www.blogger.com/blogger.g?blogID=4608755107182023803#top1"><sup>↩</sup></a><br />
</span>Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com2tag:blogger.com,1999:blog-4608755107182023803.post-63591241743643088862014-04-08T14:12:00.000-07:002014-04-16T02:44:23.045-07:00Are you #TeamPER or are you #TeamRPM ? (or do you hate acronyms?)Hi everybody,<br />
just a quick one, since<a href="http://espn.go.com/nba/statistics/rpm" target="_blank"> ESPN published a new kind of adjusted Plus-Minus</a> yesterday (called real Plus Minus, RPM). I don't want to go too far into the merits of either stat. I am pretty sure that RPM has problems like every adjusted PM before and PER is basically just 'boiling down more common stats to one number', but it were the two things I just had at hand. If somebody gives me a table that has both RPM and another adjusted plus minus, I'll gladly repeat the following, which is doing a simple linear regression. I know the word sounds dangerous, but linear regression is basically complicated for ' draw a line through your data and check how far stuff is away from this line'. Using 293 players that played at least 48 games, the correlation between RPM and PER is 0.52, which can be described as 'existing, but not very high'. <br />
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<a href="http://3.bp.blogspot.com/-MgjmXtVhkAE/U0Ri9TGdJ8I/AAAAAAAAAEc/CvodW3BbddE/s1600/RPM_PER.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-MgjmXtVhkAE/U0Ri9TGdJ8I/AAAAAAAAAEc/CvodW3BbddE/s1600/RPM_PER.png" height="327" style="cursor: move;" width="400" /></a></div>
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<a name='more'></a>As you can see, the top of the game are still LeBron and Kevin, but there are some clear outliers. <br />
Now, drawing the line through the data and then showing if the PER is above or below this line, we get the following picture:<br />
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<a href="http://1.bp.blogspot.com/-9sBeqbKJ6Mg/U0RknX4A35I/AAAAAAAAAEo/pJbKUHT00Oo/s1600/RPM_estPER.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-9sBeqbKJ6Mg/U0RknX4A35I/AAAAAAAAAEo/pJbKUHT00Oo/s1600/RPM_estPER.png" height="335" width="400" /></a></div>
To summarize it, unsurprisingly PER is good at showing us those players that are producing measurable stats, while RPM finds players that might produce more by intangibles. And Kent Bazemore is not good in both stats - because they don't adjust for <a href="https://www.youtube.com/watch?v=e52OHLEsBa0" target="_blank">super-bench-celebrating</a>!<br />
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Have a nice evening,<br />
HannesJohannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com2tag:blogger.com,1999:blog-4608755107182023803.post-29995908090215193212014-04-07T09:56:00.000-07:002014-04-16T02:48:54.419-07:00The arbitrary award Part II... aaaaaaaaaaaand we are back! If you for some reason missed out on part one, it's <a href="http://sportstribution.blogspot.ch/2014/04/the-arbitrary-award-statistical-look-at.html" target="_blank">here</a> (that's also the part where I have the better jokes).<br />
To summarize, yesterday brought us several tiers of iMIP (imho MIP) candidates based on Player Efficiency Rating (PER) data. The tiers are:<br />
'Very good third year growth curve' (Alec Burks, Brandon Knight, Marcus Morris), 'Extremely good third year growth curve' (Markieff Morris), 'Pops principles' (Patty Mills, Marco Belinelli), 'Starters becoming All-Stars' (DeMar DeRozan, Goran Dragic, Isiah Thomas, Paul George), 'All-Stars becoming Superstars' (Anthony Davis, DeMarcus Cousins - and yes, I am aware that Cousins isn't an All-Star on paper...) and 'the reason why PER is not perfect' (Brandan Wright, who shows that your PER goes through the roof when you live one foot away from the basket).<br />
Somehow I forgot to mention 'the guy that came from the D-League' James Johnson, who has one of the <a href="https://www.youtube.com/watch?v=CNyNf4Fd86w" target="_blank">best highlights</a> of the year.<br />
I will now underline some of the stats that are related to PER to show you how these players improved.<br />
You can click on the images to get larger ones. I sometimes move the names a tiny bit for readability.<br />
For each pair of images, you will find in the left panel the 2013 statistics and how they changed in 2014. in the right panel, you will see how this data in general correlates to PER. It will start with those stats with a high correlation (or in case of turnovers a high negative correlation) with PER and then we will move our way down. Sounds complicated? You'll see it's not.<br />
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<a href="http://1.bp.blogspot.com/-lKHF9O3QH1Y/U0LJ8O2o-rI/AAAAAAAAAC8/JetnoWRbuhc/s1600/summaryTS.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-lKHF9O3QH1Y/U0LJ8O2o-rI/AAAAAAAAAC8/JetnoWRbuhc/s1600/summaryTS.png" height="234" width="580" /></a></div>
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<a name='more'></a>True Shooting Percentage (TSP): For those of you that don't know: A 3-point shot gives you more points than a 2-point shot. And making a free-throw is also better than missing it. TSG boils this all together. As you can see, all our iMIP candidates kept their TSG at least at the same level and some of them increased it even drastically. Brandan Wright for example now leads the league with a TSG of 70.3%. Taking only shots that are directly at the basket probably helps (just ask Tyson Chandler). Another thing you will probably notice is that the left planel has a negative correlation. The reason carries the mythical 4 words 'Regression to the mean'. Players that shot by chance better or worse than expected last year are now regressing back to their average. The next stat is...<br />
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...Usage Rate (USG): how much is a player involved into offensive play. You can see that almost all iMIP candidates have basically an increased usage rate. Interestingly, USG and TSP are completely uncorrelated. But most of the title candidates have players with both high USG and high TSP. If you have two players with high USG and low TSP - you are basically the Detroit Pistons. But to summarize, if you want to be considered as an iMIP, you should either increase your usage rate without losing your scoring ability or increase your scoring ability without becoming useless. Sounds easy, but just ask 2009 Trevor Ariza.<br />
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Assist Percentage: An estimate of the percentage of field goals a player assisted while he was on the floor. Decrease for Goran Dragic (who already had a high assist percentage), but most other guards increased their percentage. On the right panel, you can see that the correlation between Assist percentage and PER is already lower.<br />
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Turnover percentage: Careful, now lower is better in the left panel. Once again, you can see especially guards that learned to take care of the ball. Also, you can see the reason why Kendall Marshall is probably not an NBA Starter (at least not yet). And at a last stat...<br />
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<a href="http://2.bp.blogspot.com/-o8Yz64QOI5Y/U0LJ7lxB6bI/AAAAAAAAAC4/P-kqNMu5uS4/s1600/summaryTRB.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-o8Yz64QOI5Y/U0LJ7lxB6bI/AAAAAAAAAC4/P-kqNMu5uS4/s1600/summaryTRB.png" height="234" width="580" /></a></div>
... Total Rebound Percentage. Not the typical stat to improve if you want to be considered as iMIP. But it's interesting to see that both Spurs increased their percentage in 2014.<br />
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I have to admit that I can still not decide whom I would pick as iMIP (that's what happens when you stare to long at your screen...). But I hope I could give a little overview of what statistical changes happened to players between this and last year.<br />
Feel free to let me know if you agree or disagree (I'll probably start to pay some people to fill the comment section)<br />
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Have a nice day everybody! I'm looking forward to the Playoffs,<br />
Hannes<br />
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<br />Johannes Beckerhttp://www.blogger.com/profile/04712549329074105025noreply@blogger.com0