Tag Archives: Stat Geek Idol

Round 2 post for TeamRankings competition

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

NCAA tournament simulations post at Teamrankings.com

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.

NCAAs round 1 results

For the NCAA tournament this year, I simulated each game based on my predicted efficiency stats for each team (shooting percentage, shot selection, turnover rate, and offensive rebound rate). I submitted my work for Teamrankings.com’s college basketball blogging competition and I’m thrilled to announce that I moved on to the next round! I’m pretty excited for round 2 (deadline Tuesday at midnight). If you have a topic suggestion, let me know — bonus points for something semi-related to the game simulations I’ve been running.

Now how about those simulations? Here’s a performance overview against a few other rankings (seeding, RPI, Ken Pomeroy, and Jeff Sagarin):

Simply choosing the higher seed got 22 games right. RPI did slightly worse at 21, and I matched Sagarin at the top with 23 correct (71.9%). The problem? As evidenced by the potential wins columns, I lost my champion. Missouri battled Norfolk State the whole game and came out behind. Every method lost Missouri, Duke, and Michigan as round 2 predicted winners, and Missouri, Duke, or both as round 3 winners. I’ll need some help to catch up at the end, though. No other system had Missouri or Duke advance to the Final Four.

Specifically, where did I go right? Of my “upset locks” (over 60% probability), VCU and NC State came through, and Alabama nearly beat Creighton. It’s worth reminding at this point  Continue reading

Adventures in picking the NCAA tournament, part 2

Thanks to Teamrankings for the data for this work. I hope that my models someday forecast as well as yours!

Yesterday, I tried some approaches to predict the NCAA tournament. My favorite of these is where I use team efficiency stats (shot selection, shooting percentage, turnovers per possession, and offensive rebounding percentage) to simulate whole games possession by possession. This approach predicts win-loss correctly in about 70% of tournament games over the past five years, so I decided to predict the whole tournament with this method for this year.

Below are the win probabilities that I generated for each region (the percentages on each line give the probability of winning the previous game).

The South:

Kentucky is the odds on favorite to win it all, and I predict that they will get out of their region. Their stiffest challenge could be Indiana, Continue reading

Adventures in picking the NCAA tournament

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