Can Big Data Tackle Football with Machine Learning?

Mockup of telestrator diagram. Based on Image:UT Longhorn football – handoff to Melton in Big12 championship game.JPG by Johntex.

In the public mind, the movie Moneyball captures the fusion of sports and statistics for competitive advantage.

MLB and the NBA have embraced the video analysis of computer vision and the data crunching of machine learning for some time.

Now, the NFL could be next. Derrick Harris at Gigaom offers insights on this trend as reported on by Judy Battista for the New York Times.

Most intriguing is the idea that NFLers have not embraced these technologies as readily as their pro sports peers because the complexity of the game defies decoupling teamwork into discrete actions readily attributed to individual players.

Think of offensive linemen working together to protect quarterbacks and running backs. Or defensive linebackers who don’t get many tackles, but make it difficult for the offense to execute.

For this reason, many NFL coaches prefer to assess players based on how they look on film.

Over time, however, this resistance to analytics is likely to fade as machine learning based applications that use computer vision prove their value and become easier to use.

In fact, it could simply be a matter of identifying more subtle metrics to extract and analyze that previously evaded human detection.

For example, during the 2012 playoffs, the Wall Street Journal’s John Letzig reported on how MLB used motion-analysis software from Sportvision Inc. to quantify an outfielder’s ability to get a jump on fielding fly balls.

Given the rich data complexity of football, it’s hard to imagine coaches not eating up algorithm-powered, in-situ forecasts that take player stats, weather and game scenarios into account and identify those variables most likely to influence what happens next.

Or team management not angling for competitive advantage at the lowest possible cost by pinpointing those overlooked, game-deciding metrics that don’t correlate with salary levels like fourth down conversions (just like on-base percentage was the key focus in Moneyball).

In other instances, humans can request recalibration of the algorithms so video tracking models ignore what they consider to be noise and add additional factors they view as pivotal.

We reported on how Zest Finance continually improved the accuracy of its credit underwriting assessments in the payday lending market by taking this approach with respect to 70,000 variables.

Part of football’s very appeal is its complexity and the many inter-dependencies that make it tick. And so it’s a natural for the video scrutiny and data mining that computer vision and machine learning make possible.

Are you involved in an activity where many individuals come together to form a whole greater than the sum of its parts?

How could analysis of its finer points of interaction unlock hidden value in your business?

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