Jon Nichols


Last Thursday, I published player pair data for every NCAA Division I team at my own site, Basketball-Statistics.com. This was inspired by the NBA player pair data that has been available at 82games.com for the last few years. As I mentioned in my last article:

82games has compiled statistics showing how teams have performed with two specific players on the floor together. These “player pairs” are a complementary data view to our 5-man unit stats that measure unit performance. By focusing on two players at a time we can better understand which guys bring out the best in each other.

Unfortunately, that data is not currently available at 82games for NBA players. If it becomes available, I’ll be happy to point you in that direction. Until then, I have calculated the player pair data for the current NBA season, and it can be viewed here:

http://basketball-statistics.com/nbaplayerpairs.php

Enjoy!


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Creating turnovers on the defensive end certainly is a good thing.  After all, it is one of the Four Factors.  Still, the ability of a defense to generate steals in particular is not always assumed to be beneficial.  Perhaps it is better to play more safe and solid D.  I’ve decided to look at the numbers and see what conclusions we can draw.

Using play-by-play data, I calculated the Steal Rate (the percentage of opponents’ possessions that ended in a steal by the team in question) for each lineup that appeared in at least 400 possessions last season.  I then compared that lineup’s Steal Rate to its Defensive Rating (points allowed per 100 possessions) and plotted the results in the chart below.  If steals are important, a higher steal rate should lead to a lower Defensive Rating, and therefore a negative slope:

stealratevsdrtg

So far, it appears as though steals are important.  Despite a low r-squared, these results certainly are meaningful and are very much statistically significant.  We can’t say that the number of steals entirely explains how well a defense will do (as evidenced by the low r-squared), but we can say that there is a correlation between high steal rates and low Defensive Ratings.

But we should pause for a second.  This graph can be very misleading.  Perhaps there are some confounding variables (hidden factors) that make the results appear to be this way when they really shouldn’t be.  In other words, maybe good defensive teams just have more athletic players in general.  This may cause them to get more steals, but it doesn’t mean steals are the reason they’re better.  If a bad team were to go for more steals, they’d still be a bad team and have a poor Defensive Rating.

However, there is another approach that we can take.  For each lineup, I’ve calculated the projected Defensive Rating based on the individual Defensive Ratings of each player in the lineup.  I then calculated the difference between the lineup’s projected Defensive Rating and actual Defensive Rating.   This difference was regressed against the lineup’s Steal Rate.

What is the point of this?  This method attempts to zoom in on just steals.  By taking a lineup’s projected Defensive Rating into account, we’re trying to adjust for other confounding variables.  This way, if there is a negative correlation between the difference and steals, it is further evidence that steals are important.   A negative slope in the graph below indicates that steals are important:

stealratevsdiff

Again we see more evidence suggesting that going for steals is generally beneficial.  The r-squared is low but the results are statistically significant.

Of course, these graphs don’t specify what types of steals are good.  Risky attempts may very well hurt the defense.

In conclusion, based on the evidence I’ve presented today, I would suggest that lineups (and, theoretically, players) that record more steals are often better on defense.  To some, this may be obvious, but to others it may not be.  We can never know for sure how important steals really are, but the stats think they matter.


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Some of you may recall an article I wrote back in November, when I re-calculated a few common advanced stats using play-by-play data.  That was for last season, and today I will provide the numbers for this season.

The benefit of using the play-by-play data is simple: instead of using estimates for different stats, we can know the real things.  For example, instead of estimating the percentage of Spurs’ rebounds that DeJuan Blair grabs, we can calculate the actual number.  The following stats will be presented: Rebound Rate, Offensive Rebound Rate, Defensive Rebound Rate, Assist Rate, Block Rate, Steal Rate, and Usage Rate.

The numbers are embedded in the table below.  Click the team filter at the top to sort by team.  Also, if you click the arrow in the very top left, you can download the spreadsheet as an Excel document or view it full-screen.

As a side note, for those interested in college basketball, check out exclusive NCAA plus-minus numbers at my home site, Basketball-Statistics.com. Those will be updated in the next day or two.


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