Sports analytics: How 'Moneyball' meets big data (gallery)

Sports analytics: How 'Moneyball' meets big data (gallery)

Summary: Bill James and Billy Beane have led the way for sports teams to make strategic decisions based on analyzing data rather than watching the actual games or players.

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  • Injury list

    The English Rugby Union's frequent champion Leicester Tigers are using IBM's predictive analytics software to assess injury risks and then deliver training programs for players at risk. The Tigers are hoping analytics can keep players on the field longer.

    IBM has developed software which is designed to measure fatigue levels and game intensity. The Tigers will also crunch physical and biological data from its 45 players. In addition, the Tigers plan to use big data to measure psychological factors such as stress levels, social issues and environmental stress.

    IBM's software will also be used to gauge the performance for its under-19 academy feeder teams and choose players accordingly.

    Caption: Larry Dignan

    Photo: Wikipedia

  • Kevin Mongeon is the principal owner at The Sports Analytics Institute and shows how sports analytics can impact on winning and losing in his blog, "More Hockey Data"? Unlike baseball where specific actions show measureable results, hockey is played in a continous flow making the game more difficult to put into an analysis on paper.

    Mongeon says additional data is needed to discover a statistical path to a winning season. He needs statistical models that can examine a player's abilities even under different scenerios.

    Photo: Wikipedia

     

  • An NBA study by Jenna Wiens, Guha Balakrishnan, Joel Brooks, and John Guttag from MIT examines the offensive/defensive strategy about whether it's better to crash the boards for an offensive rebound or lay back and play defense.

    This detailed analysis developed the Crash Index and Retreat Index to determine which philosophy gives a team the opportunity to score more points. The study found that when a team made a big effort for the offensive rebound, it gained more than passivly staying back on defense. The study does note that it does not take player personel into account.

    Credit: Wikipedia

Topics: Big Data, Data Management

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