New brooms try to avoid sweeps - trades and play-off implications

The 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.
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:

1. General playing and starting time of players

Quick one before steals get overrated

UPDATE: 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...

I read the 'The hidden value of the NBA steal' 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.

The non-ultimative Plus Minus comparison

Hi,
for all two of you that haven't enough of it yet:
There is a great article 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...).
Instead I'll just quickly show some comparing plots between ESPN's Real Plus Minus (RPM on the x-axis) and GotBuckets @talkingpractice (more great names) regularized adjusted Plus Minus (RAPM on the y-axis).
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:
1. correlation between RPM and RAPM for all 425 players I had available.
2. RPM vs RAPM for players that played more than 41 games and 28 minutes per game
3. the same for the offense
4. the same for the defense

(as always, click on the figures for larger versions. I guess that here it's really necessary)

Defensive Real Plus Minus and it's relation to 'normal' Player Stats

Hello everybody,

(Note: for those of you that don't like words, it gets more interesting at the bottom of the article)
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).
My question today is: How do common stats in general influence the outcome of a game on the defensive end? (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 Plumlee, Mason). 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 correlation coefficient. 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).
If you are interested in other stats let me know.
Phew, those were a lot of words for the Generation ADHD ;)

Are you #TeamPER or are you #TeamRPM ? (or do you hate acronyms?)

Hi everybody,
just a quick one, since ESPN published a new kind of adjusted Plus-Minus 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'.

The arbitrary award Part II

... aaaaaaaaaaaand we are back! If you for some reason missed out on part one, it's here (that's also the part where I have the better jokes).
To summarize, yesterday brought us several tiers of iMIP (imho MIP) candidates based on Player Efficiency Rating (PER) data. The tiers are:
'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).
Somehow I forgot to mention 'the guy that came from the D-League' James Johnson, who has one of the best highlights of the year.
I will now underline some of the stats that are related to PER to show you how these players improved.
You can click on the images to get larger ones. I sometimes move the names a tiny bit for readability.
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.

The arbitrary award - a statistical look at the MIP candidates (Part I)

Of all the NBA season awards, voting the most improved player seems to be the worst defined one. The reason therefore is that improvement can happen on so many levels (for those of you that are interested in a good read about the different ways to look at it, check out this article by Rob Mahoney). It is also complicated to keep your eye on the complete picture, as you have to look at two or more years at the same time.
To give at least a data-based picture of potential candidates, I will use the data from Basketball-Reference. The focus will be on minutes independent data, as in my opinion improvement has to be more than just an increase in minutes played. Today, I will present the candidates and in part II (hopefully tomorrow) I will take a closer look at what they improved. Feel free to let me know where you agree or disagree (my dear hypothetical reader)

Filtering for candidates
To be a potential iMIP (imho MIP) candidate, players have to fulfill the following minutes/games criteria:
- Play at least 8 minutes per game in at least 41 games in 2012-13 (henceforth called 2013)
- Play at least 18 minutes per game in at least 41 games in 2013-14 (henceforth called 2014)
This means (more or less) that the player was last year at least a borderline rotation guy and is this season at least a 7th man (second player coming from the bench) and not only playing in garbage time.
Furthermore, I use Player efficiency rating (PER) to summarize the qualities of a player. There are certainly disadvantages to PER, but its advantage is that it is pace and playing time adjusted. The thresholds for a player to be available are:
- increased his PER in 2014 at least by 3
- has a PER in 2014 that is at least 15
I am aware that 3 is an arbitrary threshold, but I picked it on the impression that enough players surpassed it. A PER above 15 on the other hand means simply that your performance in 2014 is 'measured' as above average. It's a feel-good story if you improved from being a bench warmer to rotation guy, but it's not necessarily iMIP material. This gives us the following picture:

click image to enlarge