Derrick Harris of GigaOM reports, "When it comes to using data to determine how to build a team or manage a game, the National Football League appears years behind its professional sports brethren such as Major League Baseball and the National Basketball Association. But perhaps the increasing popularity of machine learning can change that by helping NFL teams make more sense of their very complex datasets. Delving deep into the world of computer science might sound like overkill, but professional football is big business in America, and an analytic edge off the field might be just as important as athletic or strategic edges on the field. Heck, it might help create them."
Harris goes on, "Companies of all sorts are increasingly using machine learning algorithms (and related techniques) to detect patterns and correlations among complex datasets, and there’s a growing number of software products hitting the market that either incorporate machine learning or are built entirely upon it. It stands to reason that NFL teams might consider giving these techniques and technologies a chance, too. Machine learning seems ideally suited to analyzing football data because of the complexity of the incredible number of variables that teams have to consider. Rather than requiring human beings to hypothesize and test, for example, how a mix of player statistics, combine performance, situational data and weather affect a given outcome, the algorithms can step up and find out whatever variables actually matter most."
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