A.I. perfectly predicted last year’s
Competitive sports are ultimately numbers games. Whether it’s a gymnast racking up points on a balance beam, a tennis player acing her opponent, or a football team scoring on a last second Hail Mary, all matches are won and lost by numbers. There are upsets, comebacks, and situations when the losing team still seems to outperform the other — but, even then, victory distills into digits.
As such, it’s obvious that many sports lend themselves nicely to the type of mathematical analyses that let keen-eyed statisticians predict outcomes — maybe even exact scores — just by crunching a bunch of numbers. After all, that’s the basis of sports betting, and it’s helped baseball managers craft winning teams on a tight budget just by considering little more than batting average, runs batted in, and stolen bases.
But what happens when artificial intelligence algorithms are used to do what they do best — pick out patterns in data that human eyes typically can’t catch? Could these algorithms undermine the “house” and turn sports betting on its head?
The prediction predicament
Athletic events are chock full of juicy data that A.I. developers would probably love to feed to their algorithms. A power forward’s field goal percentage, a running back’s rushing yards, and a midfielder’s assists are obvious example of this kind of data.
But there are other, less apparent data that can give a more nuanced and complete overview, for example, a player’s “sweet spot” (where he most often makes his shots) or the paths and distance a given player travels during a game.
But not all sports are created equal when it comes to quantity and quality of data. Some, like baseball, readily release their players’ stats. Tennis organizations, on the other hand, collect high-resolution datasets that include things as specific as ball trajectory throughout a match, making the sport rife for predictive analysis.
Article Credit: Digital Trends
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