# What the Box Score Data Says About Shane Battier

On Wednesday, Henry Abbott of TrueHoop posted a link to an article by Jason Friedman of the Houston Press.  Freidman’s very lengthy article was about the statistical focus of Daryl Morey (general manager of the Houston Rockets).  In essence, Morey is described as part of a new generation of decision-makers in basketball, a generation that relies more heavily on statistical analysis.

“I think often what you’ll find when you’re getting negative comments (on statistics), they’re basing it on what they’re used to being available, which is a regular box score. And there’s no question that anything in the box score is highly misleading. So if you’re basing your opinions on that – the box scores that they hand out at the games – you’re going to have an appropriate negative opinion of what you can understand using analytics. I would even have a negative opinion of [statistical analysis] if that’s all I’d ever seen.”

To illustrate the failings of the box score statistics, the article discusses the impact of Shane Battier.  Here is Abbott’s summary of this part of the story.

What we’re getting from this is that Battier is doing “the little things.” Or “playing smart.” Or, essentially, doing the things that win basketball games — whatever they are — but don’t show up in the traditional box score.

The Shane Battier Story Via the Standard Box Score View

Okay, a bit of background on Shane Battier.  With Battier the Grizzlies averaged 48 wins from 2003-04 to 2005-06.  Without Battier last year, the Grizzlies were the worst team in the NBA.  This suggests that Battier might have had some value.

But when we look at the box score data, it’s hard to find this value.  At least, that’s what we are being told.

Let’s start with scoring, the box score statistic that is most frequently cited when discussing an NBA player. The average small forward will score 19.9 points per 48 minutes.  The best Battier has ever done in his career is 17.4, and that was in his rookie season.  For his career he only averages 15.2 points per 48 minutes.  So Battier is a below average scorer.  And for many, that makes him a below average player.

Of course there is more to the box score than just scoring.  Let’s turn to a summary measure like NBA Efficiency. The average small forward will post a per 48 minutes NBA Efficiency mark of 20.3.  For his career Battier’s per 48 minute mark is 18.8.  So NBA Efficiency says Battier is below average also.

Okay, NBA Efficiency is too simple.  Let’s look at John Hollinger’s Player Efficiency Rating, a more complicated measure of player performance. The average player has a PER mark of 15.0.  For his career, though, Battier only posted a PER of 14.2. Again he is below average.

If this were all you looked at with respect to the box score, I guess you would have to conclude the box score data is pretty worthless.  Clearly we need to go beyond the box score data to figure out a player’s value.

A Different Approach

Well, maybe not.  Let’s take a different approach.  What we could do is regress team wins on offensive and defensive efficiency.  Such a regression tells us that 94% of wins are explained by a team’s efficiency marks. To put that in perspective, only about 90% of team wins in baseball are explained by runs scored and runs allowed.  In other words, the link between the current stats and current wins is a bit stronger in basketball than it is in baseball.

In a moment I will return to the comparison between basketball and baseball.  But for now, I want to note that from our analysis of the link between the efficiency metrics and wins we can derive the value – in wins – of each of the box score statistics.  And those values are used to construct the two measures cited in The Wages of Wins – Wins Produced and Win Score.

What do we learn when we look at Battier’s Win Score? For the answer we turn to Table One.

Table One: Shane Battier’s Career

Per 48 minutes the average small forward posts a Win Score of 7.3.  Except for Battier’s rookie season he has bested this average his entire career.  Let me repeat this point.  Win Score, a measure based entirely on box score statistics, tells us that Battier is above average.

If we delve into the numbers we can see why.  First of all, although Battier doesn’t shoot much, he is an efficient scorer.  And the aforementioned regression is quite clear on this point.  Shooting efficiency matters in the NBA.  Or to put it another way, inefficient shooting definitely hurts a team’s chances to win (the valuation of shooting efficiency by NBA Efficiency and PER is a point I have made before in more detail).

Battier’s value, though, goes beyond shooting efficiency.  When we look at steals and turnovers we see another area where Battier helps.  For a typical small forward, if we subtract turnovers from steals we get -1.1.  In other words, typically a player will commit more turnovers than he will get steals.  Battier, though, is not a typical player.  Steals minus turnovers for Battier in his career is 0.2.  This is a 1.3 swing in possessions for Battier in his career.  It’s important to note that Battier is not just a below average producer of points (in terms of totals, not efficiency) and a below average rebounder.  But his ability to hit shots efficiently, generate steals, and avoid turnovers — all stats found in the box score — tell us that he is an above average player.

As we can see in Table One, the story we tell about Battier from the box score depends on how we view the data.  When we rely on scoring — or scoring dominated metrics like NBA Efficiency and PER — we see a below average player.  But when we consider Battier in terms of efficiency, we see a player that is above average and a key player in the success the Grizzlies had from 2003-04 to 2005-06.

Back to Baseball

In closing this post I want to make two more comments about baseball data.  As noted, the link between current stats and current wins is a bit stronger in basketball.  It’s also the case that the box score statistics in basketball have a stronger predictive power than box score data from baseball.  The year-to-year correlation in Win Score per minute in basketball is 0.82.  In baseball the year-to-year correlation in a metric like OPS is only 0.57 (a similar story is told for linear weights).

Of course the box score data in basketball doesn’t capture all a player does on defense.  But the same charge can be made against baseball data.  On-base-percentage, slugging percentage, OPS, and linear weights don’t tell us about a baseball player’s defense.  But these various baseball metrics still tell us a great deal about a baseball player’s value.

Summarizing the Story

So here’s the story I am telling today.  Box score data in basketball is at least as good – and I think it’s better – than data in baseball.  The problem is that box score data in basketball is not well understood. Too often the only stat people look at is scoring.  And scoring, by itself, doesn’t explain much of wins.

Metrics like NBA Efficiency consider more statistics, but this measure is dominated by a player’s scoring.  Inefficient scorers can increase their NBA Efficiency value by simply taking more shots.  A similar story can be told about PER.  When we look at the box score statistics via the measures, again we can be misled.

Faced with this problem we are told to ignore the box score statistics. But a simpler solution is to simply heed the lesson we have learned about wins and efficiency.  It’s well understood that wins are determined by offensive and defensive efficiency.  If we simply take this relationship and apply it to the analysis of individual players we can see that players like Battier are truly valuable.  And we can see this in the very box score statistics reported in every newspaper.

- DJ

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics