Mar 18 2009

NCAA Teams Do Better When Down at Halftime?

Tweet I’ll be the first to admit that I’ve got a long way to go with my education on building and interpreting statistical models, so I find this post by Andrew Gelman very useful. The post is in response to this article by Jonah Berger and Devin Pope (see discussion here). Here is a quote [...]

Mar 11 2009

Referee Efficiency Ratings

Tweet Last Saturday I attended the MIT Sloan Sports Analytics Conference. During the Basketball Analytics panel, this quote from Mark Cuban got me thinking: There’s not 10 players on the court, there’s 13. And three of them determine about 80 percent of what happens out there. Along with this excerpt, he mentioned something along the [...]

Mar 4 2009

Measuring the Relationship Between Players and their Lineup’s Shot Distribution

Tweet In my last post I looked at how we might rate a player’s impact on their lineup’s FG% in the low paint. With this came the obvious question of: “What about shot distribution?” With this question in mind, I’ve finally put forth efforts into trying to make sense out of how players fit together. [...]

Mar 2 2009

Rating a Player’s Impact on Shooting Percentages in the Low Paint

Tweet Studying the relationship between shooting and defensive efficiency has made me wonder what, if anything, we can learn by rating a player’s impact on shooting percentages from various locations on the court. The Model Borrowing from the idea of adjusted plus/minus, I ran a logistic regression for data from the ’07-’08 regular season for the [...]

Feb 17 2009

Basketball on Paper’s Skill Curves

Tweet Recent discussion about Basketball on Paper’s skill curves inspired me to use Dean’s formulas to reproduce these curves. The formulas are a bit daunting at first glance, but thankfully they’re really not that bad once you’ve got the data to work with. For the curious reader, most of the formulas came from Appendix 1 [...]

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