There is a ! I’m putting together an NBA Playoffs preview; this is the first of the data for that: Here is a table of playoff bound teams and their season-long adjusted efficiency ratings. Ratings are adjusted for location, pace (obviously, since these are per 100 possessions), opponent (recursively) and rest day situation. Previous is … [Read more…]
In continuing my series of With-or-Without-You (WOWY) analyses, I will next look at my hometown Oklahoma City Thunder, one of the hottest teams going into the playoffs. In the first two posts on WOWY, I looked at Oklahoma City in January(before the trades), and yesterday I looked at the Bulls and their strength going into the playoffs. In the Bulls post, I also revised and expanded this method with regression to the mean and Bayesian weighting for best future prediction.
VCU and Butler have continued their run. Before the tournament, I had them highly because they elevated their games in the regular season when playing good opponents. They have both continued that and increased that trend. One confounding issue now with adding the “Raising their Game” (“Ag25” in the table below) bonus is that for … [Read more…]
Well, the first weekend of the NCAA Tournament is in the books. I don’t have much time to post, so I’ll make it quick: Predictions did fairly well. In the 8 closest 1st-round games by my pregame predictions (averaging a 52.72% favorite) my stats went only 25%–otherwise, everything matched up really well. The other 3 … [Read more…]
If you are choosing NCAA tournament picks in a LARGE group (like ESPN), then, if possible, you need to account for what the masses have chosen in making your own selections. Fortunately, ESPN publicly shows what everyone has picked–and that lets us account for them. As the number of people approaches infinity, the formula for … [Read more…]
Okay, ready for the tournament? I’ve put together some adjustments based on the work I . The theory behind the adjustments may be found on STATS @ MTSU and Dr. Winner @ Florida. Basically, I’m adjusting for teams that raise their game to a higher level against good foes (or vise-versa). For instance, Long Island … [Read more…]
When we get to the NCAA tournament, it seems that inevitably, some teams will raise their game, matching up with the “better” teams, suddenly emerging as a top team. Some teams play well when their opponent is better, and let the foot off the gas when playing East Popcorn St. Those teams tend to be … [Read more…]
The Bracket was revealed yesterday. Quick thoughts and long ramblings below: I have two rankings systems: my Bayesian predictive power ratings, which tell how good the teams are, and my DSMRPI ratings, which tell how much they have accomplished. I would put teams in and seed them based on DSMRPI, which looks purely at win-loss … [Read more…]
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…]