Tag Archives: Teamrankings

Third/fourth round results and final four simulations

Unfortunately, I didn’t move on to the final four of the TeamRankings blogging competition. It was fun while it lasted though, and thanks again to TeamRankings for putting it on and providing great data (which I will continue to use for NCAA tourney simulations).

It was a great third round and a so-so fourth round for my simulations. Here’s the update on the initial brackets that I’ve been tracking:

I set up the first four brackets by always choosing the “better” team according to the ranking listed on the left. The last row uses my simulations to pick the winner. I stumbled a little in round 2, but recovered strongly in the elite eight (6 out of my initial 8 predictions made it, with only Missouri and Michigan State coming up short). Pomeroy and Sagarin’s rankings proved the best at predicting the final four — both missed only Louisville (they each had Michigan State). I missed Louisville and Ohio State (I had Syracuse, by a nose).

If I forgive bracket mistakes and re-pick each game based on who actually played, here are the success rates Continue reading

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

Simulated stats for the sweet sixteen

Over the past few posts, I’ve been focusing on the NCAA tournament, simulating games based on predicted efficiency statistics. For the Sweet Sixteen predictions below, I ran 8,000 simulations for each game. I list my predicted winner (including 7 Florida over 3 Marquette), and the predicted efficiency statistics. The stats are based on Dean Oliver’s four factors:

  • Factor 1: 3 pt shooting %, 2 pt shooting %, foul shooting %
  • Factor 2: % of potential offensive rebs secured (including balls out of bounds)
  • Factor 3: % of offensive plays ending in a turnover
  • Factor 4:  3 pt attempts as a % of non-turnover plays, 2 pt attempts as a % of non-TO plays, free throw trips as a % of non-TO plays

Factor 4 is the most confusing. It’s similar to Oliver’s FTA/FGA factor, but has more value for simulations, since it tells me how often teams get a three point attempt, a two point attempt, or a trip to the line (on plays without a turnover).

1 Kentucky, 4 Indiana Favorite: Kentucky (wins 55.3% of simulations):

  • 2 pt %: 50, 46
  • 3 pt %: 35, 39
  • FT %: 72, 76
  • OReb %: 34, 30
  • TO %: 14, 14
  • 2 att %: 62, 64
  • 3 att %: 23, 22
  • FT att %: 15, 14

3 Baylor, 10 Xavier Favorite: Baylor (76.9%): Continue reading

Simulation results through NCAA tournament round 2

On Saturday, I posted the round 1 performance of my NCAA tournament simulations, using data from Teamrankings.com. I did pretty well: 23/32 games correct, similar to some other prediction methods that I tested. Before round 3 kicks off, I wanted to go through my results through round 2. For comparison, in the first four rows of the table below, I took the better team in each game as indicated by the ranking listed on the left (i.e., the higher seed, the team with the better RPI, etc.). For the “Causal Sports Fan” row, I took the team that won the majority of my game simulations.

My picks to make the sweet sixteen did a little bit worse than my first round picks — only 9 out of 16 are still in. However, some sweet sixteen picks dropped out in the first round for each method (I lost 2 Missouri, 2 Duke, 4 Michigan, and 11 Texas). If I forgive first round mistakes and examine the actual round two match ups, all methods did quite well in the second round: 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