Automated Machine Learning: A Game-Changer for Sports

DR Automated Machine Learning A Game Changer for Sportswebinar Resource card v1.0

As we learned from Brad Pitt in Moneyball, data analytics has always been a huge part of the professional sports industry. Data gives coaches and managers clear insight into anything from determining player talent and ability, optimizing match-ups, and more.
With all the available data sources these days, however – Statcast, NBA Player Tracking, PITCHf/x, NFL Next Gen Stats, FIELDf/x, HITf/x, etc. – it can be nearly impossible to analyze all of the available data to get meaningful, actionable insights that can improve performance outcomes. There are a myriad of additional challenges to applying machine learning to sports analytics as well, including the lack of available data scientists and the long time horizons of traditional data science projects.
However, DataRobot has thrown a curveball to the sports analytics practice in the form of automated machine learning.

DR Automated Machine Learning A Game Changer for Sportswebinar Resource card v1.0

You'll Discover:

  • The potential for machine learning to help managers and coaches make sense of the wealth of available player and game data
  • The challenges associated with implementing machine learning initiatives and how automated machine learning helps sports analysts overcome them
  • An example case of using automated machine learning for catcher pitch framing in baseball


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    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
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    General Manager – Torque Data at Virgin Australia

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    VP of Strategy Analytics, LendingTree

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    Deena Narayanaswamy

    Head of Data Insights, Avant

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.