A Bayesian marked spatial point processes model for basketball shot chart
This paper from the December 2020 issue of JQAS is wonderful: A Bayesian marked spatial point processes model for basketball shot chart.
Simply put, the build a model looking at where players are taking shots and then given a location, how often are they making shots from those locations.
I’m particularly interested in this point from the paper:
The preferred models for all four players, which are intensity independent model for Curry and intensity dependent model for other three players, can reduce the MSE by 2.7, 1.3, 2.0, and 7.0%.
I think the correct way to interpret this is that three of the players analyzed have different chances of making a shot based on where they shot is taken. But for Curry, the probability he makes a shot is INDEPENDENT of where he is taking a shot. Basically he’s just good everywhere. (If this is NOT the correct interpretation, let me know!)
I’d love to see the analysis expanded to all players in the league and see who else would end up with an intensity independent model.