My post for round 2 of TeamRankings blogging competition is up on their blog. I give a full explanation of my NCAA basketball simulation method and flesh out my predictions for tonight’s games.
Edit: The complete post can now be found below. I moved it here in case TeamRankings changes their links at a future date.
Breaking Down Match Ups: Sweet Sixteen Game Simulations
In round 1 of the Stat Geek Idol competition, I described a procedure to simulate NCAA basketball games based on the few team statistics that really matter: shooting percentages, shot selection, turnovers per play, and offensive rebound percentage. These are basically Dean Oliver’s four factors, though I go a little more in depth. For this round, I’ll break down the simulation procedure and apply it to the Sweet Sixteen match ups. But first, how have my simulations performed so far? For comparison, I list the number of teams correctly predicted to reach the second and third rounds by a few different methods (I give a full summary on my blog):
- Take the higher seed: 22/32, 11/16
- Take the higher RPI: 21/32, 9/16
- Take the higher Pomeroy ranking: 22/32, 10/16
- Take the higher Sagarin ranking: 23/32, 10/16
- Take the team that wins majority of my simulations: 23/32, 9/16
If I forgive first round mistakes and recalculate second round match ups Continue reading
Posted in Basketball, College Sports, Prediction
Tagged adjust for Fab Melo, basketball, basketball game simulations, basketball odds, basketball simulations, Baylor, Cincinnati, college basketball, elite eight, Fab Melo, final four, Florida, florida upset marquette, Indiana, Kansas, Kentucky, March Madness, March Madness simulations, Michigan, Michigan State, NCAA game odds, NCAA Men's Division I Basketball Championship, NCAA tournament, NCAA tournament blogging competition, NCAA tournament simulations, North Carolina, North Carolina State, Ohio, Ohio State, predicted tournament odds, predictions, Stat Geek Idol, sweet sixteen, Syracuse, Teamrankings, Teamrankings.com, tournament game odds, tournament odds, tournament predictions, upset predictions round 3, Wisconsin, Xavier
My first round submission in the Stat Geek Idol competition is up at the TeamRankings blog today. Check it out, along with the other contest entries. Second round submission coming soon . . .
If you’d like to read more about my tournament predictions, check out my recent post on round 1.
Note: This post was submitted for Teamrankings.com’s Stat Geek Idol competition, with a few modifications/corrections made here (including 200 simulations per game instead of 50, which generates more consistent results). Thanks to Teamrankings for the data!
A few years ago, I ran my office NCAA pool. Right at the deadline, a Swiss economist that I worked for came over, bracket and sheepish grin in tow. He knew nothing about basketball, but someone had explained the seeding system to him. He optimized based on the only inputs he had: he filled out the bracket purely by seed (choosing randomly between the one seeds in the final four). He finished second, of course, which was almost as bad as the year my wife won my pool by choosing teams from her favorite places.
Maybe this Swiss fellow saw through the charades. How predictive is seeding after all? Since 2007, the higher seed has won about 72% of all tournament games (picking the winner randomly when seeds are the same). The favorite by the gambling spread has won 73% of the time, so seeding does quite well. This year, I set out to match this performance using team statistics from the regular season (all stats below are for the 2007 through 2011 tournaments). I’ll have some upset picks at the end (which are the only way to differentiate your bracket from the average in a big pool), but you have to do the leg work with me first.
The question to ask is: what really matters in a basketball game? The team that scores more points wins. That seems like a fair (and obvious) starting point. In fact, the team with the higher average point differential during the regular season won 68% Continue reading
Posted in Basketball, College Sports, Prediction
Tagged basketball game simulations, Big East Conference, Bonaventure, bracket busters ncaa tournament, Bracket predictions 2012, Bracket predictions from simulations, bracket regressions, Butler, cinderella teams march madness, efficiency statistics basketball, efficiency stats NCAA tournament, Florida, Florida State University, Horizon League, Mary, Mountain West Conference, National Collegiate Athletic Association, NCAA Men's Division I Basketball Championship, NCAA tournament predictions, NCAA tournament simulations, regression march madness, regression NCAA tournament, Saint Bonaventure, Saint Louis, simulated basketball games, St. Bonaventure, St. Louis, Stat Geek Idol, Team Rankings, Teamrankings, Teamrankings.com, Tournament, University of Connecticut, upset picks march madness, West Virginia