With football season just three weeks away, millions of fantasy football players are obsessively preparing for their league drafts. They’re reading every guide on the web, participating in daily mock drafts, and engaging in superstitious rituals. Some players have gone beyond the traditional approach and have turned to data science to gain that competitive edge.
Fantasy sports are a perfect subject for data science, they’re stats-heavy and the main way to win is predicting which players will have great performances. Yet just because the two are a match doesn’t guarantee that your average data scientist will be able to conquer this multi-billion dollar industry.
Three years ago Boris Chen, then a data scientist at the New York Times, published a post detailing his method of bringing machine learning to fantasy football. His goal was coming up with a way to rank players at various positions. With data from fantasypros.com, he used a clustering algorithm called Gaussian mixture model to determine a new and improved system of rankings. Chen’s problem was that most rankings “do not illustrate the true distance between players”, which led him to choose this particular model because it’s designed to account for this issue.
The following chart shows the results of Chen’s algorithm on the Quarterback position by illustrating the tiers of the players in a clear manner.
To a casual fantasy football player, this graphic provides a simple guide to selecting a quarterback, especially for those players who aren’t obvious stars.
What if you’re an expert data scientist and you’re confident enough that you can build models that will not only win leagues but actual hard cash in a play-for-pay competition like FanDuel or DraftKings? You might want to tone down your expectations.
A Bloomberg article titled “You Aren’t Good Enough To Win Money Playing Daily Fantasy Football” bluntly explains why you should get your hopes: “Only the top 1.3 percent of players finished in the green during the three months measured by the Sport Business Journal. An unrelated survey of more than 1,400 fantasy sports players conducted by Krejcik of Eilers Research this summer found that 70 percent of participants have lost money.”
Though it’s unlikely you’ll be able to quit your day job and become a full time “Fantasy Football Data Scientist”, you’ll just have to settle for bragging rights over friends and coworkers.
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