Tag Archives: NCAA Men’s Division I Basketball Championship

Adrian the Canadian: What’s wrong with the BCS and its successor?

I’ve made my thoughts about the BCS abundantly clear, so on the eve of the college football kickoff, I’ll let Adrian the Canadian give you a more well-reasoned critique of both the old/current system and the new one:

In case you missed it while focusing on the Olympics, Euro 2012, or MLB’s new double wild card chase,  college football’s bigwigs announced that, finally, there will be a playoff in D-I, sorry, FBS college football (starting in 2014). For those interested in the details, here’s Andy Staples. In short, it’s a four team playoff with the four teams selected by the ubiquitous “selection committee.” Now, despite what I’m about to argue, I think this is an improvement over the previous system which was corrupt, illegitimate, and, ultimately, kind of dull.  Still this system fails to identify and address the real issue with college football’s championship. The problem is not that college football does a bad job of identifying and rewarding the “best” team — it arguably does that with more frequency and reliability than any other sport in America — it’s that college football does little crown a legitimate champion. Indeed, what we want from our sports in not a system that determines the best team but one that gives us a legitimate result at the end of the season. The new college football model fails to do that.

As I’m sure Tyler will tell you, the best way to figure out a league’s “best” team is to have a sufficiently connected round-robin style tournament with a large number of rounds. In plain English, have everybody play everybody else lots. Such a system minimizes luck, randomness, and fluctuations in performance, leaving us with a relatively clear idea of who the “best” team is. Most domestic European soccer leagues follow this model, as did baseball prior to the advent of playoffs. This model doesn’t work for American football for an obvious reason: the sport is too physically taxing to play enough games. And yet, college football is pretty adept at determining who the “best” team is in any given year. Compare to the NFL: surely Alabama has a better claim to being the “best” college football team than the New York Giants do to being the best “pro-football” team. Tyler can do the analysis, but I’m willing to bet that the BCS champion correlates much more highly than the Super Bowl champion to statistical measures of team quality. However, no one complains about the Super Bowl champion or demands that the NFL change its playoff system.

The reason for this is simple; we sports fans don’t want a playoff system that determines the “best” team. What we want is a system that crowns a legitimate champion. Let’s look Continue reading

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The final tally

The tournament is over, and Kentucky are the champs. Who predicted that? Well, lots of people. Among the rankings I tracked, however (including my game simulations), only the tournament committee got it right by making them the overall number one seed. Here’s how the initial brackets fared for each ranking:

My simulations (final row) got off to a strong start through the first four rounds, as did the other quantitative approaches (Pomeroy and Sagarin rankings). However, seeding finished strong. By percent correct, the quant methods were slightly better (65 to 66% correct), but choosing based on seeding would have attained Continue reading

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

Optimal drafting

There’s a new paper out this month by Casey Ichniowski and Anne Preston concerning the NCAA tournament and the NBA draft (thanks to my PhD buddies Chris and Felipe for passing it along). Their argument is that unexpectedly strong tournament performance (especially team performance) causes players to be selected earlier in the NBA draft. This isn’t a bad thing though — in fact, they suggest that these strong tournament players tend to outperform their draft position in the NBA.

I believe their results saying that tournament performance affects draft position (this has also been shown by Chaz Thomas in an undergraduate thesis, and by David Berri, Stacey Brook, and Aju Fenn), and I mostly believe their results that strong tourney performers should be drafted even earlier, though their set up is a little odd for this second issue.

The clearest way to show that teams make mistakes in the draft Continue reading