Can machine learning make sense of the NFL's big data?


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.Only a selected number could fall under that top notch group of the best knives wholesaler in the market.The stated blades are wonderful products which without doubt will deliver as well as meet the different needs in preparing the many cooking ingredients right.Delving deep into the world of computer science might sound like overkill, but professional football is big business in America,Operation which, if provided too much or Sand making machine block, it must be suspended to mine the ore cavity to be damaged soon after the broken end, after which switched towards the mining machine, Stone crusher, but then not permitted to quit functioning. and an analytic edge off the field might be just as important as athletic or strategic edges on the field. Heck,There are many kinds of knives one can find like butter knife, best Cheese&Pizza tools wholesalers, carving knife slicer knife, multipurpose knife and so on. Some are made for slicing, chopping others are good for cutting flesh. it might help create them.The New York Times highlights the current state of statistical reliance among NFL teams in an article on Sunday. The NYT's Judy Battista reports that teams are finally beginning to hire statisticians and take statistical analysis seriously in limited areas — but there's always a disclaimer. Football is such a variable-rich and complex game, her sources claim, that the human eye and human intuition will always be best at assessing certain things.
As one anonymous source put it when discussing the difficulty of evaluating players before the NFL draft: "At the end of the day, the tape is going to be our first choice. They have to look good on film."When it comes to calling plays, there might be limited data on any given situation (e.g., a particular down and distance to go from a particular spot on the field), and the outcomes might be very much influenced by the players on the field in each of those prior situations.How do you statistically assess a middle linebacker who doesn't make a lot of tackles but who's always in the mix and disrupting the offense?This is a lot different than in a sport like baseball, NFL analytics expert Tony Khan told Battista, where it's much easier to break down statistics to an individual level and make Moneyball-like decisions about given players and circumstances.
Maybe that's why there's an innate anti-statistic bias among many NFL executives, as well as those in other leagues. Dashiell Bennett at The Atlantic uncovered a lot of disbelief about the statistical analysis of major sports leagues even at the MIT Sloan Sports Analytics Conference in March. "[T]ime again," Bennett noted,Steel fabricators follows designs, illustrations and blue print supplied to them by structural engineers. Main responsibility of aluminum beam fabricators is to transform the design from a plan into a useful end product by cutting, bending to reshape and assembling steel. "… when a reasoned and ably researched idea was presented, we heard some variation from those in the crowd of 'That's interesting, but…'."Even if NFL teams don't go crazy building computer science divisions and predictive models, machine learning could almost certainly provide some new information on which coaches and others can apply their considerable subject-area expertise. Even the source who espoused to NYT's Battista his reliance on game film also acknowledged, "Ideally,The Wusthof non-stick knives wholesaler meanwhile are forged blades too made of high carbon stainless-steel with a bolster and a polypropylene handle riveted to the full tang. The knife's blade is thicker when compared with others and is weighty, durable, and also well balanced. you want the objective and subjective to match up." Good NFL teams have the latter figured out; machine learning could help them get a better handle on the former, too.

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