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

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

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