Comparing NBA Stars to their Statistical Contemporaries: An Application of Machine Learning

Golden State Warriors' Stephen Curry reacts after making a 3-point shot against the Chicago Bulls during the second half of an NBA basketball game Friday, Nov. 20, 2015, in Oakland, Calif. Warriors won 106-94. (AP Photo/Tony Avelar)

It’s human nature to compare, and nowhere is that tendency more present than with sports. We have an almost debilitating dependency on analyzing players by comparing them to others. And well, if we’re gonna do that, may as well do it in as informed a way as possible. Men lie, women lie, numbers don’t.

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On Analytics, the Hip Hop Era, and the Return of Basketball

Tannehill

Did you know Ryan Tannehill’s box score from last Sunday pegs him as roughly the greatest quarterback in NFL history? He went 18 of 19 for 282 yards and 4 touchdowns. The NFL world was abuzz over him setting the record for most consecutive completions. On the flip side, did you know that 248 of those yards came after the catch? Tannehill himself only technically threw for 34 yards on 19 attempts. That’s an obscene amount of screens and flares and quick slants. It’s also emblematic of what has started to separate the NFL from the NBA.

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The Flaw in the Models, or How’d Brazil get beat down that badly?

Football-Brazil-v-Germany-FIFA-World-Cup-Brazil-2014-Semi-Final-Estadio-Mineirao-Belo-Horizonte-Brazil

Let’s get this out of the way. Based on Nate Silver’s models, Brazil had a 1 in 4000 chance of losing by six goals. By that same model, Brazil had a 65% chance of victory, even without Neymar and Thiago Silva [1]. And there is no statistical model I would trust more than something Nate Silver created (John Hollinger being a not-too-close second).

Even if you take the reasoning that statistical models don’t really care about the difference at the extremes, that 1-in-4000 is not that distinguishable from 1-in-400, this beatdown was still an all time outlier, as in this was one of the most unexpected scorelines in history.

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