No, I’m not talking about the credit markets– I’ll take those up in my next post, I promise. But first I need to discuss something really important, namely, the men’s college basketball tournament.
A popular American tradition is betting on the outcome of the 64-team NCAA basketball tournament (I know, I’m not counting the “play-in” game). Picking the outright winner is not a hopeless task. After all, if you simply picked a team at random, you’d have a 1 in 64 chance of being right. It’s not hard to do much better than that, since some teams are much more likely to win than others. If you trust the probability assignments of Team Rankings (which I’m supposing may be as good as any), the tournament favorite (Kansas, according to them) has a 1 in 5 chance of winning all 6 of its games.
level | probability |
---|---|
winner | 1 in 5 |
championship | 1 in 13 |
final 4 | 1 in 36 |
elite 8 | 1 in 435 |
sweet 16 | 1 in 9,556 |
1st round win | 1 in 61,218 |
Picking both the winner and the team that will play against them in the championship round, however, is substantially harder. Team Rankings thinks there’s almost a 1 in 3 chance Kansas will make it at least that far, and a 1 in 4 chance that Memphis (their most likely opponent) faces them. But the two events are essentially independent, so that the odds of both happening are about 1 in 13. Trying to pick all the teams to make the Final Four, you maybe have a 1 in 36 chance of getting it right. And good luck picking all the winners of each of the first 32 games– you have less than a 1 in 60,000 chance of getting that right.
Those Team Rankings probabilities are reproduced at the end of this entry. Now, if you have opinions in these matters, you will have some different probabilities from these. But whatever probabilities you may think are correct, if you multiply them out you’ll probably get something similar to the numbers in the table at the left. Even if you knew the exact true probabilities of who should beat whom, you can be essentially certain that some of your predictions will be wrong.
But that doesn’t stop us from playing. Following the inspiration of Tim Haab of Environmental Economics, I’ve set up an Econbrowser NCAA Bracket Challenge, where you can compete for the honor and/or glory of being the smartest and/or luckiest Econbrowser sports fan. Just go to the Econbrowser group at ESPN, do some minor registering to create a free ESPN account if you haven’t used that site before, and fill in your bracket. Or create more than one bracket, if you care to “diversify”. I’ve entered the Team Rankings choices as a benchmark to beat, and another doubtless even less likely outcome just for fun.
So feel free to play along, but note you must enter before Thursday.
Team | R64 | R32 | R16 | R8 | Final 4 | Champ | WIN |
1 North Carolina |
100% | 96.96% | 74.68% | 54.84% | 39.12% | 22.45% | 13.06% |
16 Mt St Marys |
73.52% | 2.77% | 0.47% | 0.07% | 0.01% | 0% | 0% |
16 Coppin St |
26.48% | 0.26% | 0.01% | 0% | 0% | 0% | 0% |
8 Indiana |
100% | 62% | 16.95% | 8.25% | 3.77% | 1.26% | 0.42% |
9 Arkansas |
100% | 38% | 7.89% | 3% | 1.06% | 0.27% | 0.07% |
5 Notre Dame |
100% | 73.85% | 36.96% | 12.53% | 5.89% | 2.04% | 0.7% |
12 George Mason |
100% | 26.15% | 7.57% | 1.27% | 0.32% | 0.05% | 0.01% |
4 Washington St |
100% | 80.45% | 49.88% | 19.24% | 10.23% | 4.04% | 1.59% |
13 Winthrop |
100% | 19.55% | 5.6% | 0.79% | 0.18% | 0.03% | 0% |
Team | R64 | R32 | R16 | R8 | Final 4 | Champ | WIN |
3 Louisville |
100% | 81.8% | 55.81% | 30.5% | 13.19% | 5.36% | 2.17% |
14 Boise St |
100% | 18.2% | 5.42% | 1.17% | 0.18% | 0.02% | 0% |
6 Oklahoma |
100% | 54.5% | 21.9% | 8.34% | 2.44% | 0.67% | 0.18% |
11 St Josephs |
100% | 45.5% | 16.87% | 5.87% | 1.56% | 0.38% | 0.09% |
7 Butler |
100% | 65.2% | 29.64% | 15.32% | 5.65% | 1.96% | 0.67% |
10 South Alabama |
100% | 34.8% | 11.73% | 4.42% | 1.13% | 0.27% | 0.06% |
2 Tennessee |
100% | 87.2% | 56.03% | 33.89% | 15.22% | 6.44% | 2.71% |
15 American |
100% | 12.8% | 2.59% | 0.49% | 0.05% | 0.01% | 0% |
Team | R64 | R32 | R16 | R8 | Final 4 | Champ | WIN |
1 Kansas |
100% | 94.2% | 78.5% | 63.4% | 45.8% | 31.04% | 19.78% |
16 Portland St |
100% | 5.8% | 1.61% | 0.41% | 0.07% | 0.01% | 0% |
8 UNLV |
100% | 47% | 9% | 3.98% | 1.25% | 0.36% | 0.09% |
9 Kent |
100% | 53% | 10.89% | 5.1% | 1.72% | 0.52% | 0.14% |
5 Clemson |
100% | 68.33% | 47.53% | 15.94% | 7.3% | 3.04% | 1.12% |
12 Villanova |
100% | 31.67% | 16.57% | 3.58% | 1.05% | 0.28% | 0.06% |
4 Vanderbilt |
100% | 66.8% | 27.02% | 6.37% | 2.03% | 0.58% | 0.15% |
13 Siena |
100% | 33.2% | 8.88% | 1.23% | 0.25% | 0.05% | 0.01% |
Team | R64 | R32 | R16 | R8 | Final 4 | Champ | WIN |
3 Wisconsin |
100% | 81.35% | 53.82% | 33.28% | 15.62% | 8.3% | 3.98% |
14 CS Fullerton |
100% | 18.65% | 5.37% | 1.41% | 0.27% | 0.06% | 0.01% |
6 USC |
100% | 60% | 26.23% | 12.81% | 4.75% | 1.95% | 0.71% |
11 Kansas St |
100% | 40% | 14.58% | 5.8% | 1.76% | 0.59% | 0.17% |
7 Gonzaga |
100% | 42.5% | 17.65% | 6.78% | 2.12% | 0.73% | 0.22% |
10 Davidson |
100% | 57.5% | 27.29% | 12.22% | 4.45% | 1.79% | 0.64% |
2 Georgetown |
100% | 84.95% | 51.97% | 27.19% | 11.5% | 5.47% | 2.32% |
15 Umbc |
100% | 15.05% | 3.09% | 0.52% | 0.06% | 0.01% | 0% |
Team | R64 | R32 | R16 | R8 | Final 4 | Champ | WIN |
1 Memphis | 100% | 96.68% | 76.67% | 58.75% | 41.39% | 25.5% | 14.82% |
16 Texas Arlington |
100% | 3.32% | 0.58% | 0.09% | 0.01% | 0% | 0% |
8 Mississippi St |
100% | 48.5% | 10.83% | 4.85% | 1.78% | 0.53% | 0.14% |
9 Oregon |
100% | 51.5% | 11.92% | 5.52% | 2.09% | 0.64% | 0.18% |
5 Michigan St |
100% | 66.8% | 37.36% | 12.62% | 5.67% | 2.09% | 0.71% |
12 Temple |
100% | 33.2% | 13.09% | 2.92% | 0.89% | 0.22% | 0.05% |
4 Pittsburgh |
100% | 69.3% | 38.52% | 13.01% | 5.84% | 2.16% | 0.73% |
13 Oral Roberts |
100% | 30.7% | 11.04% | 2.25% | 0.64% | 0.14% | 0.03% |
Team | R64 | R32 | R16 | R8 | Final 4 | Champ | WIN |
3 Stanford |
100% | 82.7% | 50.88% | 28.21% | 12.56% | 5.54% | 2.26% |
14 Cornell |
100% | 17.3% | 4.29% | 0.93% | 0.14% | 0.02% | 0% |
6 Marquette |
100% | 72.23% | 36.28% | 18.17% | 7.27% | 2.85% | 1.03% |
11 Kentucky |
100% | 27.77% | 8.56% | 2.53% | 0.56% | 0.12% | 0.02% |
7 Miami Fla |
100% | 50.5% | 16.64% | 5.97% | 1.67% | 0.45% | 0.11% |
10 St Marys CA |
100% | 49.5% | 16.15% | 5.73% | 1.58% | 0.42% | 0.1% |
2 Texas |
100% | 90.4% | 65.14% | 38.18% | 17.9% | 8.33% | 3.61% |
15 Austin Peay |
100% | 9.6% | 2.07% | 0.29% | 0.03% | 0% | 0% |
Team | R64 | R32 | R16 | R8 | Final 4 | Champ | WIN |
1 UCLA |
100% | 99.42% | 75.62% | 56.88% | 37.44% | 22.95% | 13.04% |
16 Miss Valley St |
100% | 0.58% | 0.02% | 0% | 0% | 0% | 0% |
8 BYU |
100% | 46% | 10.66% | 4.98% | 1.81% | 0.59% | 0.17% |
9 Texas A&M |
100% | 54% | 13.7% | 6.93% | 2.76% | 0.98% | 0.31% |
5 Drake |
100% | 64.8% | 40.47% | 14.64% | 6.62% | 2.69% | 0.98% |
12 Western KY |
100% | 35.2% | 16.91% | 4.27% | 1.38% | 0.4% | 0.1% |
4 Connecticut |
100% | 76.97% | 37.43% | 11.66% | 4.59% | 1.61% | 0.5% |
13 San Diego |
100% | 23.03% | 5.19% | 0.64% | 0.11% | 0.02% | 0% |
Team | R64 | R32 | R16 | R8 | Final 4 | Champ | WIN |
3 Xavier |
100% | 76.43% | 45.03% | 18.23% | 7.28% | 2.9% | 1.04% |
14 Georgia |
100% | 23.57% | 6.87% | 1.3% | 0.24% | 0.04% | 0.01% |
6 Purdue |
100% | 56% | 28.19% | 10.01% | 3.49% | 1.22% | 0.38% |
11 Baylor |
100% | 44% | 19.9% | 6.18% | 1.89% | 0.57% | 0.15% |
7 West Virginia |
100% | 51% | 17.62% | 9.29% | 3.45% | 1.28% | 0.42% |
10 Arizona |
100% | 49% | 16.62% | 8.6% | 3.13% | 1.14% | 0.37% |
2 Duke |
100% | 90.4% | 63.82% | 45.95% | 25.76% | 14.61% | 7.61% |
15 Belmont |
100% | 9.6% | 1.95% | 0.43% | 0.06% | 0.01% | 0% |
Thank you much for posting Team Rankings’ tables! Will you be posting updated tables as we go through each round?
Ironman, I just copied the bottom table from Team Rankings, adding a little color to make it more readable. I assume that they’ll be updating their numbers as the tournament progresses. I hand’t intended to reproduce those updates here, but will give brief reports after each weekend on who’s leading among the Econbrowser pool in the quest for honor and/or glory.
But I’ll bet you could do a much cooler job with updating and making creative use of those Team Rankings numbers than I could.
Sadly, I cannot – sure, I could make the data in the tables dance, but I don’t have a good solution to keep up with the volume of games as the results come in. Given my limited time, anything I would do would be hopelessly out of date!