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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  • 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

  • Ed Feng, founder of, tells how to make it in the sports analytics world. Any number of people can sift through data on their own computers but few are able to go to the next step and find a full-time job in sports analytics.

    While expertise in numbers crunching and being adept with the use of social media will help, Feng says that there's no substitute for "real human interaction." That means being able to shake someone's hand or looking at them straight in the eye, according to Feng. He suggests attending conferences such as the Sloan Sports Analytics Conference where you can show off your stuff.


Topics: Big Data, Data Management

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