Your verification ID is: guDlT7MCuIOFFHSbB3jPFN5QLaQ Big Computing: Sabermetrics Seminar

Monday, May 23, 2011

Sabermetrics Seminar

I went to the Sabermetrics Seminar at Harvard this weekend. It was a charity event, and all the speakers came and talked on their own dime. I just want to thank those speakers for giving up their time for such a great cause.

The Seminar itself was an eye opening experience for me. The last seminar I went to was the R/Finance in Chicago. That Seminar, like most that I go to, is for hard core statisticians and computer scientists. I believe of the hundreds of attendees to R/finance I am one of the few without a PhD.  The presentations with the possible few exceptions of JD Long's honoring of Dr Suess were of a highly technicial level. The Sabermatrics Seminar was totally different. The audience varied from the Head of the Harvard Statistics Department and an eminent physicist to people with very limited mathamatical education. The presentations also ran the gambit from something that would be taught in a high school physics class to some fairly high level stuff. The great unifier in the room was these people loved baseball and where using mathamatics to expand their understanding of the game and increase their enjoyment. One Speaker, Dan Duquette, former GM of the Boston Red Sox, reminded us of the words of Flippe Alou to "remember to enjoy the game". Tom Tippet, Director of Baseball Information Systems, gave a great Q&A on the state of Sabermetrics in MLB today. I have included a link to a summary of the seminar here.

Sabermetrics is different than the other fields I work in. In Pharma, the models are widely shared, but the data is highly confidential. In Finance the models are confidential, but the data is basically public. MLB analysts seem to strongly guard both their models and there data viewing both as propietary. While I think this makes it a great opportunity for consulting, I believe it may hinder the rate of refinement. Kaggle has shown in a very public way that open collaboration on data and models yields astounding improvements in prediction.


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