Machine Learning for Sports Prediction

Ever since my college days, I’ve been interested in using machine learning for sports prediction. However, among my research and engineering colleagues, I don’t run into very many people who share that interest.

About a year ago, I became acquainted with Bryan. Bryan contacted me because he’d read about my Zoltar prediction system for American NFL football. Bryan had created a prediction system for NCAA American college basketball.

Bryan and I decided to see if we could find other engineers who shared our interest in the intersection of machine learning and sports. So we organized an informal get-together. The idea was to have anyone who was interested show up to an empty room during the lunch break at the 2017 Microsoft “Machine Learning, Analytics, and Data Science” conference in June (an internal event for employees only).

So we sent out an email message that said basically, “Show up if you’re interested and we’ll sit around and chat.” Bryan and I had no idea of what would happen — we could well be sitting in an empty room, just the two of us, staring at each other.

Well, that’s not what happened. At 12:00 noon, the room quickly filled to capacity — probably about 220 people. Our original plan of just having everyone chat was out of the question so I did a quick 5-minute talk about the three research journals, and the four conferences, related to sports technology. Bryan did a 5-minute talk on his basketball prediction system.

So we found some people who are interested in sports and machine learning. But I just don’t know how to make good use of this interest. To do something during work time, we’d have to get sign-off from senior leadership, and presumably that would only happen if the activity benefited our company. To do something outside of work hours (such as my Zoltar and Bryan’s basketball system), is really tough because nobody has any time at all outside of work hours.

Some possibilities running through my mind are to schedule a talk every few weeks (from me, Bryan, and anyone else) to keep interest alive, or maybe try to organize a micro-conference (either standalone or attached to an existing conference), or try to engage with an external company (maybe a sports data collection company or a fantasy sports company), or, . . . well, I just don’t know. Dang! I know there’s something good that could happen, but I can’t quite figure out what is it.

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2 Responses to Machine Learning for Sports Prediction

  1. BoilingCoder says:

    sport is a game, a game is game theory
    Game theory is sir John Nash, inventor of Nash Equilibrium.
    Could you make use of the Nash’s Equilibrium to optimize machine learning (weight adjustments or training or decision making) ?

    • I’m pretty familiar with Game Theory from my college days. Many years ago I looked at using Game Theory for prediction — I don’t remember what my conclusions were. But if I had time, investigating Game Theory for predictions would be very interesting.

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