It’s taking longer than I anticipated to compile and analyze the context-dependency of various player stats by the method I outlined in my last post, so in the meantime I would like to shift gears and introduce a method that uses team stats to try to understand whether the offensive or defensive team controls various aspects of the game.
There’s an old saying in baseball that “good pitching always beats good hitting.” I want to examine what a claim like this is trying to get at, look at a method that attempts to objectively analyze whether it’s true, and then apply that method to many areas of basketball and see what we can learn.
At that the end of my recent post on evaluating player ratings I said that the next step would be to take a step back from comprehensive ratings and look at how the component stats they are built from change in different contexts. That is what I will begin to look at in this post.
The methodology I’m going to use is pretty complicated, so instead of just presenting the results I’m going to use this post to explain in a step-by-step manner the techniques I plan on using. I’m also going to try to point out what I see as potential problems, but in many ways I’m learning as I’m going so I may miss some things. I’d welcome any critiques or suggestions from anyone who knows what they’re doing (or anyone who pretends to know what they’re doing, like me).
SI.com has an article up on new basketball stats which includes some interesting quotes from Gregg Popovich on how he views stats. This part of the article caught my eye:
The good news is that, just like media options, stats are changing. A sterling example: At NBA.com, box scores for the 2007-08 season now include plus/minus ratings for each player and a category labeled “BA” for blocks against. Even better news is that deflections and contested shots are being studied this season to see how trackable and reliable they would be, as two more stats worth adding.
It’s great that the league is making advancements in their statistical tracking. A lot of teams have been keeping track of stats like deflections for years, but that stays behind closed doors. When the league gets involved then the public can have access to the data and run with it, as has happened with MLB’s Enhanced Gameday and PITCHf/x data. And any additional defensive statistics would definitely be useful considering the current lack of stats on that side of the ball.
There has been a lot of debate recently about comprehensive player ratings such as John Hollinger’s PER, Dave Berri’s Wins Produced, and Dan Rosenbaum’s Adjusted Plus/Minus. Is one of these rating systems better than the others? What methods can be used to make such an assessment? One approach is to analyze and critique the theory behind each measure - does the way it was constructed make basketball (and statistical) sense? An alternative approach is to analyze them empirically - what happens when we actually start applying the ratings to players? Dean Oliver, the author of Basketball on Paper, has suggested two such empirical methods by which to evaluate player ratings: