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Thread: MIT Sloan Sports Analytics Conference

  1. #16
    Mon chou Choo vaticanplum's Avatar
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    Re: MIT Sloan Sports Analytics Conference

    Rick, can you tell us more about any of the panels? Some of these look so fascinating -- injury analytics, science of team chemistry. There's a whole panel about luck. WHAT DO SABR PEOPLE SAY ABOUT LUCK?? The mind boggles.
    There is no such thing as a pitching prospect.

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  3. #17
    Stat Wanker Hodiernus RedsManRick's Avatar
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    Re: MIT Sloan Sports Analytics Conference

    Quote Originally Posted by vaticanplum View Post
    Rick, can you tell us more about any of the panels? Some of these look so fascinating -- injury analytics, science of team chemistry. There's a whole panel about luck. WHAT DO SABR PEOPLE SAY ABOUT LUCK?? The mind boggles.
    You know, the quality of discourse regarding luck wasn't really any more advanced than it is around here. The big theme of the conference was more about the need for analysts to learn 2 things:

    1. That their models are incomplete. They may do a great job at predicting what they're trying to predict, but they often predict an incomplete picture of what the coach has to consider.

    2. How to better communicate to different people in the organization. Nobody cares about your data; use the data to make a compelling case in a way that your audience will understand and value.
    Games are won on run differential -- scoring more than your opponent. Runs are runs, scored or prevented they all count the same. Worry about scoring more and allowing fewer, not which positions contribute to which side of the equation or how "consistent" you are at your current level of performance.

  4. #18
    Mon chou Choo vaticanplum's Avatar
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    Re: MIT Sloan Sports Analytics Conference

    Quote Originally Posted by RedsManRick View Post
    You know, the quality of discourse regarding luck wasn't really any more advanced than it is around here. The big theme of the conference was more about the need for analysts to learn 2 things:

    1. That their models are incomplete. They may do a great job at predicting what they're trying to predict, but they often predict an incomplete picture of what the coach has to consider.

    2. How to better communicate to different people in the organization. Nobody cares about your data; use the data to make a compelling case in a way that your audience will understand and value.
    Very interesting. I'll tell you what, I know that not everybody "buys into" sabermetrics, but the tendency of its purveyors toward self-awareness and their role in the bigger picture of things is something everyone could learn from.
    There is no such thing as a pitching prospect.


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