Game Theory and College Sports

Game Theory and College Sports

If we learned anything from Moneyball, it’s that there’s more to sports than meets the eye. When people, like recruiters and coaches and fans, focus on individual human elements, like star players and aging standouts, they’re likely to lose sight of the forest for the trees. But there are underlying statistics which reveal how any one player is likely to play. These have been proven to be most visible in a game like baseball, with its short plays, firm positions, rare injuries, and endless repetition. In many ways, baseball is the the most similar sport to chess, at least among sports where you work up a sweat. But are other sports able to be analyzed in the same way?

And if so, what would be the point? Well, there are many applications which a knowledge of game theory and statistical analysis would lend some advantages. For one, recruitment would be aided greatly, as was seen in the aforementioned bestseller/blockbuster. By being able to see what kind of plays and behavior one can expect from a specific player, while accounting for all of the other inputs and outputs the system has at play, one can have a much better idea about a player’s usefulness for a team. Of course, not every sport is as cut and dried as baseball for making these determinations.

Let’s take football for example. For one, plays are more chaotic than they are in baseball. Injuries are more common, there’s more room for improvisation and team-specific patterns of play, and the whole structure of the game is more disordered by design. Still, there are insights that one can draw when looking at the statistical history of gameplay from 30,000 feet, so to speak. Legendary sports bettor Jon Price is one of the ones doing this work, offering up the best college football betting odds you’ll find anywhere, at least without earning your own master’s degree in statistical analysis.

From numbers like these we learn a number of thing. For one, different sports have different levels of consistency that their numbers hold to statistical patterns and suggestions. Correlation doesn’t equal causation, goes the old phrase, and that is no more so than in the world of sports. But it’s more true in some sports than in others. As in the examples above, baseball works really well, as does golf. Careers and matches go somewhat according to the suggestion of statistical models. But in games like rugby and football, things are much more open to guess, though not completely.

Still, in even the most orderly, statistical game, like tennis or chess (as mentioned above), the statistics can’t predict the future. They can only yield what is likely to happen, not what will happen. This likelihood becomes a lot fuzzier as you descend into the meatier sports, which is why a lot of people like games like football. They defy statistical analysis, even though they can be somewhat contained within them. There’s still a lot of room for the gut, for feelings-based decisions, and it’s how some people who make a living as sports bettors and college football sports professionals do it. It’s more art than science.