Data Science Meets Sports Science

Sports teams can be seen as businesses that make up a larger economy such as the NBA or NFL depending on your market. To survive in this market does it take money to make money… or just informed decisions? Now that we have reached an era of data abundance, most sport teams are now taking advantage. Data can be used to select next year’s draft picks without exceeding salary caps, find the most underpaid player in the NBA, and for tracking players during practice for correlations to the game day

One of the revolutionaries of Big Data in the sports world was Billy Beane, the general manager of the Oakland A’s, who developed the Moneyball Theory. As Michael Lewis, the author of Moneyball, questioned

How did one of the poorest teams in baseball, the Oakland Athletics, win so many games?
— Michael Lewis

 The Moneyball Theory was based on the combination of two key statistics, the on-base percentage and the slugging percentage which made up a new statistic all together called on-base plus slugging (OPS). Beane’s work was based on Bill James’ sabermetric theories which involve the statistical analysis of baseball records (James, 1982).

Previous scouting methods involved qualitative scout assessments of players, observing traits such as strength, full arm extension and follow through, lack of fear, aggressiveness, stride length, and speed. As a result, experience and gut feelings steered the decision making process without quantitative metrics to directly compare players.

By utilizing quantitative measures on the other hand, Billy Beane was able to stream line the drafting process and understand the risk associated with each player he brought onto his team. Due to the Oakland A’s low budget, this assurance in the drafting process was crucial.

Professional sports teams are now investing in predictive analytics for potential draft picks as well as for maintaining the players they currently have. For example baseball teams are using Motus Global's Pitching Sleeve for tracking players motions to predict when a pitcher has a high probability of injury. As explained in the video below, team managers may collect data on pitchers through out the season to track any changes in their form. NBA and NFL teams are using similar technology tracking player’s movement during games and practice to optimize intensity and performance. This new combination of data science and sports science aims to improve on the health and success of tomorrows professional sports teams.