## Tuesday, April 9, 2013

### Congratulations to the Machine March Madness Winner

Posted by Danny Tarlow
Well, after another exciting March Madness tournament, Louisville emerged as the winner of March Madness, and Ryan Boesch emerged as the winner of Machine March Madness, with his algorithm beating out the field of 22 other machine competitors and all the human baselines. Congratulations, Ryan!

I asked him a few questions, which he answers below:

1. What inspired you to compete in the Machine March Madness competition?

Last year I finished a class on Convex Optimization during the winter quarter and was planning to take a Machine Learning class in the spring quarter. I was looking for a project to apply what I had learned. I saw this competition and submitted a last minute bracket.

2. What do you attribute your win to? What is your model best at?

The win was of course very lucky. Basketball games are random in nature so to find which model is actually the best it would require many years of tournaments. One tournament is not statistically significant.

There is nothing particularly special about my model. I used Danny's model, only I fit the parameters using convex optimization instead of batched gradient decent.

3. What do you think the most promising direction(s) towards improving your model would be?

Most Promising: My current model simply matches teams and sees which has the higher predicted score. It doesn't account for difficult of previously played games in the tournament. For example, say team 1 has a 51% chance to win the first round and also 51% chance to win the second round against team 2. If team 2 has a 95% chance of winning the first round then they are more likely to make it to round 3 even though they only have a 49% chance to beat team 1 in the second round. This is taken into account in Nate Silver's picks for example.

Second Most Promising: When in a pool with other competitors the goal is no longer to maximize your expected score, but instead to maximize your expected chance of winning. These two optimizations do not always result in the same picks. I may consider taking this into account in future years. I found this paper on Nate Silver's blog which analyzes this idea.

4. What advice would you give to future competitors?

Be wary of over fitting your model.

5. What would you change about the competition in future years?

We should try to get out and advertise for the competition earlier and to a broader audience to maximize participation.