Developing a Measure for Evaluating Coaches, Part I: Impact on Effort

 
by Jon Nichols

*Update: The ratings have been adjusted to account for team height when looking at rebound data. Many thanks to Justin Kubatko. To see the new numbers, go to:

http://spreadsheets.google.com/ccc?key=rTvGfXUp3rTDPsp20SQ3pig*

Is there a method for rating head coaches in the NBA?  Perhaps just as important, should we even bother?  Some have said that coaches are all essentially equal, while others refute those claims.   Today will be the start of my first attempt at evaluating head coaches using statistics.

The rating system will assume coaches can have an impact on a team’s success in three ways: improving offense, improving defense, and increasing the effort level of players (As a side note: there are obviously many more than three ways a coach can impact a team, but these three appear to be the broadest and easiest to quantify).  In actuality, increased effort would be evident in offensive and defensive performance, but I have decided to separate it into its own category.  I have done this because out of the three categories, effort level may be the one that is the easiest to attribute to coaching, as opposed to the talent of the players.

Effort and hustle are extremely important for an NBA team, and perhaps they’ve become underrated.  While talent is necessary to be successful, it is quite easy to notice the difference in effort levels between dominant and terrible teams.  Part of this comes from the innate personalities of various players.  Some just make their living by working harder than their opponent.  But coaches can also have an impact, and today I will reveal which coaches appear to do the best job.

To come up with a rating, I will use three statistics.  The first is the sum of a team’s offensive and defensive rebounding percentages.  Rebounding is the result of effort as much as anything.  The second is the amount of offensive fouls a team draws.  Not every player will stand in the paint and take a charge.  The third stat is fouls committed during loose balls.  While committing fouls is never a good thing, it makes sense that teams that hustle more and go for more loose balls will inevitably commit more fouls in the process.  I have examined the data and the teams that are generally considered to play with the most effort do in fact commit more loose ball fouls on average.  Offensive fouls drawn and loose ball fouls are then adjusted for pace.

Once I have the three statistics, I calculate a team’s z-score in each category.  I combine the three z-scores and multiply by 10 to come up with a team’s effort rating.  As a result, an effort rating of 0 is average and scores generally range from -50 to 50. 

Rebounding percentages and coaching information were obtained from http://basketball-reference.com.  Offensive fouls drawn and loose ball fouls were obtained from http://82games.com.  Because 82games’ data only goes back to 2002-03, that is as far back as my data goes.  For those that qualify, I’ve also included each coach’s rank among those with at least 3 seasons of data (31 qualified).  For teams that had multiple coaches in one season, I went with the coach that was in charge the most games.  To see the final numbers, go to:

http://spreadsheets.google.com/ccc?key=rTvGfXUp3rTDPsp20SQ3pig

There are a couple of things to keep in mind when looking at the data.  Like any simple study, it has some limitations.  Teams with good height are more likely to be good rebounding teams, regardless of how much effort they give.  Also, the data size of just seven seasons is relatively small.  That means that coaches who lasted just one or two seasons could have effort ratings that are greatly overrated or underrated.  It also means that coaches (such as Pat Riley) that got stuck on a few bad teams during the sample period will not look too great.  And finally, don’t forget that in the end, it’s the players who actually produce on the court. 

The next steps are to estimate coaches’ impacts on offense and defense.  That will be coming in the future.

 

 

 

 

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