The field has been set and even with the field expanded to 68 teams, there were still some teams complaining about how they got stiffed and shut out from the NCAA Tournament. Georgia Tech, alas, didn't have the push it needed in the ACC Tournament and now we are out looking for a new basketball coach.
Regardless of your team making it in the tournament you will probably be submitting some sort of NCAA bracket into your office pool. With Tech's poor performances this year, I completely lost any knowledge of the basketball season and I'm still throwing in my portion. How does one fill out the perfect bracket? What if you wanted to go polar opposite of the person picking the "cutest color combination" and used a mathematical model to make your picks? Last year around this time we went to the experts, literally. No, not Jay Bilas or Digger Phelps, and definitely not Dick Vitale.
We went to a Georgia Tech Industrial and Systems Engineering Professor, Dr. Joel Sokol. Dr. Sokol has developed a highly intensive probability rankings system that evaluates a team's performance over the entire course of the season and predicts the NCAA Tournament through this method. Though not 100%, the LRMC system did predict the 2009 Final Four perfectly. You can read the full, 3-part series linked below. Snippets of the articles are included below the links.
LRMC was designed to use only basic scoreboard data that's available to everyone -- it looks at the winner, loser, location, and margin of victory of each game between Division 1 teams. [At the time, that's all that was available. These days, you can find all sorts of other neat numbers about each team at Ken Pomeroy's site.]
Among the factors that LRMC considers, the teams that tend to do well in the NCAA tournament generally have blown out a lot of opponents (showing that they're not just better, but much better), won almost all their home games (given a home-court advantage, a very good team should be even less likely to lose), and never been blown out (or maybe been blown out once under some special circumstances, like Duke was last week when Georgetown had one of the best shooting days ever).....But the biggest talking-head point that the numbers don't bear out is the effect of close games on win/loss record. We all probably hear over and over how some team "just knows how to win tight games" (or how another "hasn't learned how to win tight games yet"), but the numbers show that reality is different: close games are mostly determined by luck. So a team that has lost a lot of close games (especially close road games) will probably perform better in the NCAA tournament than its win/loss record would suggest, and a team with a lot of close wins (especially at home) will probably perform worse.
RPI's biggest problem is that it ignores the difference between close games and blowouts, and that difference really tells a lot about what a game means.
We actually met with the NCAA folks to talk about this sort of thing, and they feel that incorporating margin of victory in any way (even a vague one) would encourage running-up the score, and also could raise the stakes for potential point-shavers/gamblers.
We understand their concerns (and with 340+ teams of unpaid players to police, it's much more serious than, say, at the professional level where there are only 10% as many players, and they're paid enough by their teams that the payoff from point shaving is less attractive). So, as a compromise, we now provide the NCAA with a version of LRMC that doesn't take margin of victory into account. It's certainly not as accurate as our regular LRMC, but it's still more accurate than RPI, so it's now one of many pieces of input that the NCAA gives their selection committee.
FTRS: Does your model predict potential "Cinderellas" or is there still some margin of unquantifiable luck involved in winning the tournament?
Both, actually. Our model often does show when a worse-seeded team is actually better than the better-seeded teams it plays in the first round or two (for example, Arizona last year). From a seeding point of view that's an "upset", but we don't consider it to be a real upset since we think the better team won each game.
Our model can also often show which teams are more likely to pull off an upset -- they might not be ranked as high as the team they're facing, but they're much closer than the seeds would suggest.
However, there's always a significant amount of luck in sporting events, so there's no way we can predict for sure who's going to win each game. If we could, we'd be pretty rich by now!
As an upper bound, consider that over the last five years the Las Vegas favorite has won 76-77% of the NCAA tournament games. So that means even to the pros in Las Vegas, about one out of every four NCAA tournament games is a true upset where the worse team wins (so, 15-16 upsets per year in the tournament).
With such a high upset rate, making perfectly accurate predictions is essentially impossible. Even our 2008 success included a good bit of luck -- there's no way we can expect LRMC to do that well. We got lots of "thanks for your rankings; I used them in my pool and won" email that year, and we told each one not to expect LRMC to do that well every year. :)
So, now that you're caught up on what, exactly, the LRMC is, here are some links for you to properly, and mathematically,pick the perfect NCAA Tournament bracket.
LRMC Summary (more in-depth) written by Dr. Sokol
LRMC 2011 NCAA Tournament Predictions (Link here)