Comment by davidfischer | Hacker News Reader

Comment by davidfischer | Hacker News Reader

Goldsberry was great at the MIT Sloan Sports Analytics conference[1]. The charts really highlight a player's strengths and weaknesses. A quick look at Ray Allen's chart (in Goldsberry's conference paper) shows how much better he is when he goes to his right. Kobe really needs to cut down on those 12-15' baseline jumpers that he seems to favor.

When I look at the graphics from the NYT article, the player whose chart really sticks out is James Harden. His efficiency this year is amazing and a lot of it has to do with shot selection. Most of his shots are 3-pointers or within 8' which are considered the two highest value shots on the court. Some team with a Moneyball style general manager (somewhat common in the NBA from my understanding) will pay him handsomely yet probably still not what his contribution merits.

[1] http://www.sloansportsconference.com/?p=6132

Edit: trivial edit for small error

I enjoy looking for the same sort of shot pattern team-by-team, rather than just player-by-player. For example, during the parts of the year the Nuggets were playing well, the vast majority of their shots were either from within 5 feet of the rim or from outside the arc (like, they'd have 10 or fewer shots not from those areas, out of 80+ total shots). When they were playing poorly, some games they'd take 30 midrange jumpers.

I wonder how much the quality of the shots he takes are a function of his teammates and their offensive sets. Would be really interesting if shot quality could be completely decoupled from the environment.

Yeah, its not the simple - you can't just take a look at the data and tell the players, "take these shots, not these shots." Generally, you get a sense of where you're most effective and where you're not, but either the offense doesn't set you up that way all the time, the defense is doing too good of a job, and the shot clock is running out.

Bottom line, IMO its really helpful to look at these (I love data visualization) but you have to be careful not to fall into the trap of oversimplifying.

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