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Math Software Entertainment Technology

Buffalo Bills Going the Moneyball Route With Analytics 94

Posted by Soulskill
from the math-wins-games dept.
Nerval's Lobster writes "Can data-analytics software win a Super Bowl? That's what the Buffalo Bills are betting on: the NFL team will create an analytics department to crunch player data, building on a model already well established in professional baseball and basketball. 'We are going to create and establish a very robust football analytics operation that we layer into our entire operation moving forward,' Buffalo Bills president Russ Brandon recently told The Buffalo News. 'That's something that's very important to me and the future of the franchise.' The increased use of analytics in other sports, he added, led him to make the decision: 'We've seen it in the NBA. We've seen it more in baseball. It's starting to spruce its head a little bit in football, and I feel we're missing the target if we don't invest in that area of our operation, and we will.'"
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Buffalo Bills Going the Moneyball Route With Analytics

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  • Re:Too late? (Score:5, Insightful)

    by Trepidity (597) <delirium-slashdot AT hackish DOT org> on Wednesday January 02, 2013 @05:05PM (#42454851)

    It's catching on a bit later in sports other than baseball due to the difficulty (until recently) of collecting fine-grained statistics in many sports. Baseball is fairly discrete: it operates pitch by pitch, with a lot of down time in between. So a large number of relevant statistics can be tallied by hand, which is why we have piles of statistics dating back decades. For every pitch, you can mark down whether it was a strike or ball, whether the batter swung, where in the field it went to if hit, what the fielder did with it, what the runners did in response, etc.

    For football (and even more so, soccer), a lot of the relevant information you'd get from watching replays is more "continuous" and harder to extract manually. Traditional statistics did measured things like passing completion percentage and yards gained by a running back, but they didn't collect data that could be used to quantify things like the quality of an offensive line, or of blockers, except indirectly through overall team performance. Now a lot more of that information is being automatically tallied using computer-vision algorithms churning through digitized camera footage.

  • by ranton (36917) on Wednesday January 02, 2013 @05:29PM (#42455187)

    Their success will likely depend on how much effort they put into collecting data. If all they look at is the same statistics you can find at CBS Sports, Football Outsiders, etc. then it will probably not help at all. But if they really get serious about data collection, who knows how much insight they could gain.

    There are about 130 plays per game, and 256 games per year. That is 33,280 plays to analyze each year. That would increase to about 135k if you include Division 1-A college games. If you had two guys spend 15 minutes analyzing each play (2 guys to reduce errors) then it would take 20 full time employees to do this each year. More if you want to get more immediate results after each week. There are plenty of ex-athletes that couldn't make the pros and are intelligent enough for the work. Probably somewhere around $2 million per year in salary ($500k if you only look at professional games).

    Just think of all the information you could gain. The first team to get this right could probably greatly improve their overall defenses and their offensive lines (positions that are very hard to rate with stats). I wonder how many teams know how many seconds thier offensive tackles can block an average defensive lineman, adjusted for their quarterback's mobility on each play, and any number of other mitigating factors.

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