Tag Archives: Wisconsin

Michigan: Destined for an early exit?

Michigan is my favorite college basketball team, and for the first time in awhile, they are threatening to make a deep tournament run. However, they just lost three of four during a tough stretch against Indiana (L, away), Ohio State (W, home), Wisconsin (W, away), and Michigan State (W, away). I’m not writing them off — they only lost the away games — but some bad signs appeared in these games. Here’s the Game Stack for all four combined:

Mich 4 games 2-2013

Michigan looks good on turnovers, but that comes at a cost — they get crushed on free throws and two point percentage. Having watched the games, I can connect the dots for you: the Wolverines don’t drive to the hoop much against good teams. They have some great shooters who can get reasonably open (Trey Burke, Tim Hardaway, Jr.) who are happy to “settle” for jumpers.

This keeps the ball out of danger in the lane (low turnovers), but it means that Michigan never gets to the line and shoots a lower percentage on twos as well. Michigan also rebounds a lower percentage of their own misses than the opponent, which could be related — a lot of “second chances” are just put-backs after a shot close to the hoop.

So, is Michigan sunk? We’ll see. I have some faith that Mitch McGary can improve and find some high percentage twos down low, but right now, Michigan is probably not efficient enough offensively and not good enough on the boards to compete with the best teams in the country. I would worry less about four games if the problem was just poor shooting in a small sample, but the problem seems to be about playing style against good defenses. I don’t think that’s going to change.

If you’re interested, here are the Game Stacks for all four games. The trends I discussed are pretty consistent across the games:

Michigan at Indiana 2-2-2013 Ohio State at Michigan 2-5-2013

Michigan at Wisconsin 2-9-2013 Michigan at Michigan State 2-12-2013

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

Rose Bowl finish: doh!

I thoroughly enjoyed the Rose Bowl. Oregon ran a bunch of great scheme plays and showed incredible speed, both individually and in play calling. I’m waiting for the team that only has two or three plays and goes even faster. Defenses will be so lost and tired. Wisconsin showed off their excellent offensive line and had some great calls of their own plus strong quarterback play from Russell Wilson. It was Big Ten football at its finest.

However, the end of the game raised my ire. Continue reading