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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  • 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

  • John Parolin, Statistics Analyst, ESPN Stats and Analysis is part of a team that recorded every single play in the NFL 2012 regular season and playoffs. For this year's Super Bowl, the easy finding was that major mid-season changes, offensive coordinator for the Ravens and quarterback for the 49ers, led both teams through the playoffs. The Ravens rush/pass ratio turned from 40 percent to 49 percent after the change, while the 49ers new quarterback, Colin Kaepernick, experienced great success with the zone-read option where he determined the play based on the actions of an unblocked linebacker.

    ESPN found that the one team, Atlanta Falcons, had overplayed Kapernick's running ability in the zone-read option, and held him to just 21-yards rushing — and almost led them to an upset of the 49ers. The Ravens successful defense of the zone-read option, in the first half anyway, was one of the keys to their victory.

    Photo: Wikipedia

  • Damien Demaj, Geospatial Product Engineer at ESRI analyzed the Olympic Gold Medal tennis match between Roger Federer and Andy Murray. He studied "the spatial variation of serve patterns" in his project, "Using Spatial Analytics to Study Spatio-temporal Patterns in Sport."

    Demaj's analysis focused on the placement and bounces of each serve in the match. He found that the location where the server was standing, the service patterns, and the importance of that particular point in the match were keys to understanding the game. For example, in the ad court Federer's spacial service cluster went left most of the time with a wide spread while in the deuce court he was more accurate. Murray's clusters were more focused and favored the right side of the court. Murray won the match: 6-2, 6-1, 6-4.

    Talk about detailed analysis, here's one of the tools he designed, "The sequence of bounces then allowed us to create Euclidean lines between p1 (x1,y1) and p2 (x2,y2), p2 (x2,y2) and p3 (x3,y3), p3 (x3,y3) and p4 (x4,y4) etc in each court location."  

    Credit: Wikipedia

Topics: Big Data, Data Management

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