I don't know anything about basketball (except that the players are shorter than they say they are, and I don't really even know that); nonetheless, in the recent-but-still-grand tradition of blogging . . .
This doesn't stop me from writing about the topic (see previous thoughts on plus-minus statistics, competitive balance, racial bias, and regression models). Anyway, this attracted the notice of Eli Witus, who presumably does know something about the sport. Eli writes,
You might be interested in a recent blog post as it addresses what I think is a flaw in the methodology of Dave Berri's Wins Produced system, which you have discussed before.I don't have any formal statistical training, so I am learning as I go. Here's another post that you might be interested in. I am very interested in multilevel modeling--I think it could be very useful in basketball since the game is much more interactive than baseball, and player statistics are heavily dependent on the context of the player's teammates and coach. I think multilevel modeling could help answer questions about how a player's statistics are likely to change if he changes teams.
Cool stuff. I agree about the multilevel modeling. And here's a recent post with some pretty graphs.
P.S. To reduce my credibility even further, let me admit that my 12-year-old nephew can regularly beat me at Horse.
Check out these cool graphics!
http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html
Or, perhaps check out THESE cool graphics:
http://arbitrarian.wordpress.com/2008/02/22/nba-similarity-networks/
I still need to try that scaled-inverse Wishart prior you recommended, but I haven't taken the time to work out the complete conditionals for my Gibb's sampler.
The 0 covariance prior I used before led to some inflated estimates since the probability of players on the same team playing together isn't the same as those who play on other teams.