Data-driven revolution in Fantasy Football: Insights from the realm of Data Science
Fantasy football, a popular pastime for sports enthusiasts, has undergone a significant transformation in recent years, thanks to the rise of data science and analytics. This shift, reminiscent of the revolutionary 'Moneyball' approach in college football, has made the game more competitive, lucrative, and analytically rich.
The unpredictable nature of NFL games and the smaller sample sizes pose challenges for data science in fantasy football. While analytics can enhance accuracy, they can never guarantee outcomes. Incremental improvements are now the focus, as the industry strives to make the most of the data available.
The advent of daily fantasy sports (DFS) has accelerated this datafication. It has made fantasy football more popular, competitive, and rife with opportunities to win money. In 2013, the mainstream acceptance of data in sports decision-making began to influence fantasy football analysis.
One of the first waves of data-forward thinking in fantasy football was the analysis of usage, or opportunity. Metrics like targets, once not always recorded on stat sheets, are now considered crucial for wide receivers and tight ends. A notable metric, WOPR (weighted opportunity rating), combines and properly weights a player's target share and air yard share to provide a comprehensive measure of opportunity.
Websites like Pro Football Focus, numberFire, Football Outsiders, RotoViz, PlayerProfiler, and a few others have emerged to provide deeper insights than what news blurbs and box scores offered. These platforms have become essential resources for fantasy football enthusiasts.
ESPN, in partnership with IBM, developed a custom fantasy sports platform, including College Football, which uses the Watson platform to track player stats and predict drafts, trades, injuries, and lineups to build a winning fantasy team. The custom platform has become a staple among hardcore fantasy football players for the sheer amount of insightful data it provides.
FanDuel's team is also building models to predict how players are going to perform and creating tools on top of that. Data analysts started using data to gain an edge, parsing out what's meaningful and deriving actionable information from the data.
The use of analytics in fantasy football is not just a tool in the decision-making process, but a crucial component. Leone, a data scientist for a sports-betting company, is building a model to better determine the play-calling tendencies of offenses. This is a testament to the growing importance of data in shaping strategies.
As the industry continues to evolve, companies are developing more advanced systems, including machine learning models and predictive algorithms. While improvements have been significant, the focus is now on making tiny incremental improvements. The data-focused shift in fantasy football has undoubtedly revolutionised the game, and it will be interesting to see how it continues to evolve in the future.
