4 Ways Sports Business Intelligence Is Changing the Game
Professional sports management and decision-making have a long history of adhering to practices of past tradition and “gut feeling.” Deciding which players to trade, draft, train, and which strategies to use during gameplay is traditionally less about rigorous statistical or numerical analysis and more about the wisdom and collective experience of the managing team. It wasn’t until Oakland Athletics General Manager Billy Beane successfully implemented “sabermetrics” (the use of statistical analysis of baseball records, especially when evaluating baseball players) that sports business intelligence showed its face in professional sports.
In his 2003 book (and the subsequent movie) Moneyball, Michael Lewis tells the story of Billy Beane’s use of statistics to revolutionize the practice of baseball player evaluation. Rigorous statistical analysis demonstrated that on-base percentage and slugging percentage are better indicators of success than traditional methods of evaluating baseball players. They found that players with these qualities were often undervalued and that they could trade these types of players at a fraction of the cost that other teams were paying. The Athletics looked and played unlike other teams in Major League Baseball at the time, but the methods that led to their success became contagious, changing the face of baseball and professional sports itself.
In 2017, most professional sports teams have analytics experts as part of the staff. Teams will often pool data from scout notes, digitized statistics, and many other sources, prepare it, and store it in a central repository. Next, a team of analysts will conduct various forms of exploratory and structured statistical analysis that will inform managers of which players to draft, trade, and focus on. Here are a few ways in which data sports business intelligence is being used in professional sports.
On-field sensors are collecting live data from the field during games. And while a lot of sports leagues are hesitant to adopt sensors for making calls that a referee normally would, the technologies are beginning to be widely implemented.
Companies are starting to use various methods to collect data, such as radio-frequency identification (RFID) tags that attach to equipment to track movement, distance, speed, and other metrics during the game. This data can be used either during the game to inform coaches of the objective performance of their players or afterward to inform future strategic decision-making. New sensor technologies have birthed entirely new metrics that arguably are just as valuable as existing ones. One example of this is MLB’s use of Statcast, a method that allows tracking of spin rates of baseballs instead of just the traditional measure of velocity.
Wearable technologies can obviously help players and trainers stay aware of fitness targets and progress, but wearable technologies can be also be used to track, prevent, and detect injuries in players.
Data collected over the long-term can be used to set baselines for player performance. Deviations from these baselines and abnormal patterns within the data can indicate injuries or other causes of poor or altered performance to coaches and trainers. To be able to conduct these sorts of analyses when it matters, teams need to establish a good line of communication between data experts and managers. Brian Burke, lead analyst and founder of the website advancedfootballanalytics.
