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Superbowl Predictions

ESPN recently published a list of “expert” predictions for Superbowl 50.  Seventy writers, analysts, and pundits predicted the final score of the upcoming game between the Carolina Panthers and the Denver Broncos.  I thought it might be fun to crowdsource a single prediction from this group of experts.

Below is a histogram showing the predicted difference between Carolina’s score and Denver’s score.  The distribution looks fairly normal (symmetric and unimodal).

Super Bowl 50 Predictions

The average difference is 6.15 points, with a standard deviation of 7.1 points.  Since we are looking at Carolina’s score – Denver’s score, these predictors clearly favor Carolina to win, by nearly a touchdown.

This second histogram shows the predicted total points scored in the game.  The average is 44 points, with a standard deviation of 5.7 points.

Super Bowl 50 Predictions -- Total Points

Combining the two statistics, let’s say that the group of ESPN experts predict a final score of Carolina 25 – Denver 19.  We’ll find out just how good their predictions are tomorrow!

[See the full list of ESPN expert predictions here.]


  1. Amy Hogan says:

    Love graphically seeing the point difference gap at 0 points.

  2. But the thing is that 25 is a very unlikely final team score, and 19 is rather unlikely as well. (See, for instance,

    So I don’t think an expert would be picking 25–19 as the most likely final score — but perhaps if the “cost function” were built to get “close” to the final score with maximum likelihood. Interesting question of statistics of joint distributions with discreteness…

    • MrHonner says:

      I think the most interesting aspect of all this is quantifying the quality of the prediction after-the-fact. As you point out, this prediction is unlikely to be exactly correct, but it may end up being closest on a metric (or metrics) we design to evaluate the picks.

  3. Wahaj says:

    As a digression, I would recommend the book Superforecasting by Philip E. Tetlock and Dan Gardner. It’s interesting how many “non-experts” there are, capable of making better predictions, consistently, concerning score/point distributions on a number of popularly forecasted topics. Historically the forecasts of these so called “experts” go unchecked (primarily due to their credentials), where most of their predictions are outperformed by random walks.

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