Chris Broussard – of ESPN the Magazine and ESPN.com – offered a profile of Danny Granger this past week with the following provocative title: What’s The Difference Between Danny Granger and Kobe Bryant?: Their stats suggest not much. Then again, some will tell you stats lie. Particularly when your team isn’t all that.
Despite the title, Broussard’s story barely mentions Kobe. What it does do is tell us how the stats suggest Granger is a really good NBA player.
Unfortunately – as often happens when sports writers start discussing stats – the actual statistical arguments are quite weak. The primary stat Broussard is scoring totals. Yes, he does mention factors like shooting efficiency and blocked shots. But it’s clear when you read the article that Broussard’s primary focus – as the following paragraph from the article indicates — is Granger’s scoring.
Being able to reconcile the space between a player’s stats and his team’s record is a big part of what separates championship-level talent evaluators from the recycled masses. All sorts of x factors come into play: Who is the guy scoring against? How does he get his points? Is he effective in crunch time? And the consensus on Granger, not just in front offices, but on sidelines and in locker rooms as well, is that he’s legit.
When we look at this paragraph it becomes clear that for Broussard, the proper word is not “stats” but “stat”. In other words, he is primarily interested in scoring. And scoring is captured for Broussard by one stat, the number of points credited to a player. When we move past this one stat, though, a very different story is told about Granger and Kobe.
Granger vs. Kobe
A couple of weeks ago I offered an entire column detailing how all the statistics – not just scoring totals – indicate Granger is not very different from an average NBA player. What was missing from the earlier post was a comparison between Granger and Kobe (it was missing since I had no idea someone would make such a comparison).
That is offered in Table One below.
If we focus just on scoring we see some similarities between the two players. Both take more than 30 shots from the field per 48 minutes and both are efficient.
When we move past scoring, though, we see clear differences. Relative to an average small forward, Granger is below average with respect to everything except blocked shots and assists. In contrast, Kobe is above average – relative to an average shooting guard – with respect to almost everything. Consequently when we look at the big picture – via Win Score – was see that Kobe is much more productive.
Win Score is easy to calculate but not so easy to interpret. In other words, we can see Kobe has a higher Win Score, but what do the differences mean in terms of wins? To answer that question we turn to Wins Produced.
Here is what Kobe produced in 2007-08 and 2008-09 (as of Saturday night):
28.7 Wins Produced, 0.237 WP48 [Wins Produced per 48 minutes].
Granger plays both small forward and power forward. If he played strictly at small forward, though, this is what he would have produced the past two seasons:
13.0 Wins Produced, 0.126 WP48 [Wins Produced per 48 minutes].
If we consider the time Granger spent at power forward as well, these numbers decline to 9.7 Wins Produced and a 0.094 WP48.
When we look at these numbers we see that Kobe is more than twice as productive as Granger. In sum, Broussard’s title is more than a bit misleading. If we focus strictly on scoring – which is primarily the approach Broussard took – then Granger and Kobe are similar. But that conclusion could be thought of as “lying with statistics.” When we consider all the stats, the differences between these two players are substantial.
Often the story in sports is the stats. After all, every contest in basketball is decided by just two stats (points scored and surrendered). And these stats are also determined by a collection of other numbers. So the numbers in basketball matter.
Not to pick on Broussard (since this argument applies to most sportswriters), but one gets the sense in reading Broussard that he has never spent much time learning how to analyze statistics. Consequently his “analysis” of the “stat” has problems.
Before I get to the problem, let me emphasize again that what I am saying about Broussard applies to sports writers in general. Sports writers often tell me that they have no training in formal statistical analysis. Consequently they have trouble knowing what story the stats are actually telling.
And I would add, the lack of formal training also hampers the ability of sports writers to evaluate statistical models. Frequently the results of a model are simply matched to what the sports writer previously believed. If there is a match (as we see with something like PERs), then the model is “good.” If there isn’t a match (as often happens with Wins Produced, Adjusted Plus-Minus, or Dean Oliver’s work) then the sports writer calls for more research.
This approach to research, though, is incorrect. Research is not done to confirm what we already believe. Research is done to teach us something new. Of course that doesn’t mean that all “new research” is “good.” In fact, much of what I see on-line with respect to sports would never be considered “good” by a peer-reviewed academic journal.
But we don’t reach the conclusion of “good” or “bad” by checking the results of the research against our prior beliefs. No, “good” or “bad” is determined by looking at how the research was done and interpreted.
Applying that standard to the work of Broussard we can locate the problems with his analysis. Again, he essentially confined his story to one statistic. In other words, his informal model – if I can be formal for a moment – was mis-specified. Points scored are not the only factor to consider in evaluating player performance. And when you ignore the other factors – or weight these other factors incorrectly – your analysis will be flawed. In sum, despite Broussard’s analysis, Kobe is really much more productive than Danny Granger.
Now if we compared Kobe to Lebron, or Chris Paul, or Dwight Howard, or… okay, that story can be told again another day.
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Our research on the NBA was summarized HERE.
Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:
Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.