See Are the Best Gamblers Skilled, or Just Lucky? From poker to horse racing, the statistics involved in coming out on top by Adam Kucharski author of the book The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling by Adam Kucharski. Excerpt:
"Take horse races. Predicting events at a racetrack is a messy process. All sorts of factors could influence a horse’s performance in a race, from past experience to track conditions. To pin down which factors are useful, syndicates need to collect reliable, repeated observations about races. Hong Kong was the closest American gambler Bill Benter could find to a laboratory setup, with the same horses racing on a regular basis on the same tracks in similar conditions.
Using his statistical model, Benter identified factors that could lead to successful race predictions. He found that some came out as more important than others. In his early analysis, for example, the model said that the number of races a horse had previously run was a crucial factor when making predictions. In fact, it was more important than almost any other factor. Maybe the finding isn’t all that surprising. We might expect horses that have run more races to be used to the terrain and less intimidated by their opponents.
It’s easy to think up explanations for observed results. Given a statement that seems intuitive, we can convince ourselves why that should be the case, and why we shouldn’t be surprised at the result. This can be a problem when making predictions. By creating an explanation, we’re assuming that one process has directly caused another. Horses in Hong Kong win because they are familiar with the terrain, and they are familiar with it because they have run lots of races. But just because two things are apparently related — like probability of winning and number of races run — it doesn’t mean that one directly causes the other.
An often-quoted mantra in the world of statistics is that “correlation does not imply causation.” Take the wine budget of Cambridge colleges. It turns out that the amount of money each Cambridge college spent on wine in the 2012-2013 academic year was positively correlated with students’ exam results during the same period. The more the colleges spent on wine, the better the results generally were.
Similar curiosities appear in other places, too. Countries that consume lots of chocolate win more Nobel prizes. When ice cream sales rise in New York, so does the city’s murder rate. Of course, buying ice cream doesn’t make us homicidal, just as eating chocolate is unlikely to turn us into Nobel-quality researchers, and drinking wine won’t make us better at exams.
In each of these cases, a separate underlying factor could explain the pattern. For Cambridge colleges, it could be wealth, which would influence both wine spending and exam results. Or a more complicated set of reasons could lurk behind the observations. This is why Benter doesn’t try to interpret why some factors appear to be so important in his horse racing model. The number of races a horse has run might be related to another hidden factor that directly influences performance.
Alternatively, there could be an intricate trade-off between races run and other variables — like weight and jockey experience — which Benter could never hope to distill into a neat “A causes B” conclusion. But Benter is happy to sacrifice elegance and explanation if it means having good predictions. It doesn’t matter if his factors are counterintuitive or hard to justify. The model is there to estimate the probability a certain horse will win, not to explain why it will win."
Related posts:
Great Moments In Causation Vs. Correlation: Eat more chocolate, win more Nobels? (2012)
Are Your Friends Making You Fat? (2009)
Crime and Punishment: Required Reading in My Economics Class (2007)
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