How To Apply Predictive Models to Fantasy Football
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One of the most exciting trends in fantasy football is the continued advancement of predictive analytics. Building on the data revolution in other sports as well as businesses and industries around the world, advanced data analysis and machine learning models have continued to infiltrate a world that was once hyperdependent on film study. This is something of an upset due to the sport’s nature of small sample sizes and multidimensionality, and how these things impact any and all data collected. I’ve had the luxury of working closely with many fantasy football data scientists over the last couple seasons as an editor and contributor here at RotoViz. Josh Hermsmeyer’s work on wide receivers using Air Yards data, Kevin Cole’s running back model, RotoDoc on quarterbacks,1 and Phil Watkins’ tight end model are some of the latest examples of pushing the predictive analytics envelope. From this experience, it’s become clear to me the perception model-builders trust the models as gospel is misrepresented. On the contrary, those who build models are generally the first to tell you where their models fall short, a subtle irony of conceptual alignment with what seems to be the biggest charge from skeptics. Personally, I don’t build models more advanced than weighted averaging in Excel. These guys are elbow-deep in code, and every time I read one of their posts I see a new algorithm I know nothing about.2 So in some ways it makes sense that I’m writing this post, rather than one of them.3