By Albert Larcada, ESPN Stats & Information
With the quantity of data and quality of computers rising at an exponential rate, the role of analytics in all industries is growing. Stores are using analytics to increase sales, pharmaceutical companies are using analytics to develop better drugs and human resource groups are using analytics to make smarter hires. The sports industry is no different.
Professional sports organizations across the world are using analytics to make better decisions. Successful front offices like the NBA’s Oklahoma City Thunder are using new data and analysis to make decisions on which players to draft and acquire. Successful coaches like the NBA Miami Heat’s Eric Spoelstra are using advanced statistical analysis to determine what plays to run and what lineups perform best together. Some coaches, like the Atlanta Falcons’ Mike Smith, are even starting to use analytics to break old traditions when the numbers say it’s a good choice to do so. And abroad players at major professional soccer clubs like England’s Manchester City are beginning to use data to help themselves improve while on the pitch.
Media companies are beginning to use analytics as well to help tell better stories. Data visualization is a great way to summarize a large number of data points into a picture that a viewer or reader can grasp quickly. In the specific Mariano Rivera case below, plotting thousands of Rivera’s pitches illustrates that he consistently keeps the ball on the inside part of the strike zone vs left-handed hitters, which limits the damage against him.
Analytics also help media put context around big events like the effect of free throw shooting in the NBA Finals or the pros and cons of onside kicking in the NFL. These are things that help fans better understand the intricacies of the games they love.
Another way to add context to events and performances is through ranking systems and win probability tools. Player ranking systems like ESPN’s Total QBR can shed light on some of the previously unknown aspects of quarterback play in the NFL. IBM’s Keys to the Match does the same in tennis, showing fans exactly what their favorite players must do in key categories to win a particular match. Insights like these can help fans know what to look for while watching a sporting event on TV.
Sports analytics used in this way is a bit of a diversion from the traditional sports statistics seen in box scores. While box scores and recaps may give you a decent overall summary of the game, the deeper dive into the underlying data of a sporting event is what yields interesting analysis. Of course this underlying data must exist before it gets analyzed – which is why data providers such as Pitch F/x in baseball, SportVu in basketball, and Opta in soccer are becoming so popular in the sports industry. The constant enhancement of data, as well as the understanding of analysts across all sports, is crucial to help the sports analytics movement continue to move forward.
But as long as there are advantages to be gained for teams and fans who persistently want to know more, this movement does not appear to be slowing down anytime soon.
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