In my , I took as the Bayesian prior the overall distribution of NCAA teams. Now, we know more than that–we can create a pretty good projection of how good a team will be based on how good the team has been the previous few years. So let’s do it! I compiled the Pomeroy Ratings … [Read more…]
I posted my NCAA Bayesian Ratings and methodology. Today I thought I’d update the numbers quickly and add a new twist. What is the objective in basketball? To win the game! When doing a predictive rating system (like this Bayesian method) or even trying to tell how good teams are over this season (KenPom’s ratings), … [Read more…]
Carmelo Anthony was FINALLY traded yesterday, in a mega 3-team deal. How did the teams make out? There are several good trade analyses around, but none of them are really focusing on the financial aspect. Kevin Pelton’s article is a good primer on the trade as a starting point, and Joe Treutlein at Hoopdata has … [Read more…]
A few days ago, I put up a massive post on . Nathan Walker (the Basketball Distribution) commented that I should apply the system to NCAA basketball, and so I have. Thanks to Ken Pomeroy’s incredible NCAA basketball database, the data was quite easy to obtain. Since he already compiles a fully adjusted efficiency rating … [Read more…]
It is fairly easy to construct a retrospective efficiency rating. Take the efficiencies for each game, correct for location and rest, and then solve using an OLS regression for each team’s true efficiency rating. Nice and neat. However, how should a predictive rating work? The best approach would be to adjust for what players are … [Read more…]
It’s been a while since I last put together the fully adjusted NBA efficiency rankings. Here are the latest ratings (prev is 1-13-2011, the last time I updated): If you will recall, I last time was puzzling over what exponent to use when minimizing |residuals|^n. I tried out a number of different exponents in out-of-sample … [Read more…]
Well, everyone else has an All-Star post up already: The actual All-Stars Mike G at APBR, with his eWins selections (also PER shown) (his thread inspired me to write this up) EvanZ, with his ezPM on the West and East All-Stars Kevin Pelton, with his WARP All-Stars John Hollinger’s look at ESPN Zach Lowe’s take … [Read more…]
The ASPM spreadsheet (download here) has a sheet for estimating a game’s results, based on team rest, location, and the rotation of players expected to play. For a quick example, I’ll look at the Magic vs. Heat game this evening. Here’s the sheet: I estimated the minutes each player would play based upon the rotations … [Read more…]
Yesterday’s Thunder-Heat game was a fun game to watch and a great game to investigate. Oklahoma City started out with a significant advantage in this game: OKC was at home (worth 3.24 pts/100 poss) and was playing on 1 day of rest (+1.94), while Miami was playing their 3rd game in 4 days, and on … [Read more…]
Last week, I unveiled a Google Motion Chart that included a large number of advanced stats comparing point guards. This week, we’ll start at the other end: centers. I actually am including players classified as either C or PF/C by BasketballValue, where I got the position information.
Most people feel that the position of center is changing, morphing into something different than it once was. The presence of numerous “centers” that hang around on the perimeter shooting 3’s is an indicator of this phenomenon. Still, there is a defined way a center plays–and to define it, let’s turn to the lovely tool known as K-Means Clustering.