How Does Data Analytics Help Basketball Teams?
Since the dawn of sporting, teams and players have been looking for competitive advantages in order to win games: basketball today is no different. Except the stakes have never been higher; as evidenced by the $3 billion spent in the first 96 hours of this year’s NBA free agency.1 So, with so much on the line and team owners looking to run their teams as efficiently as possible, coaching staffs today are required to leverage data from numerous sources to provide results.
On Court Analytics
Not surprisingly, some of the most important data for team coaches and analytic departments to look at comes from in-game statistics. After a game, coaches, scouts and analysts make decisions and evaluations based on in-game numbers. At the most basic level, a coach may believe that a player has been doing well in practice and appears to be doing a lot of good things in a short amount of time on the court. Then the coach consults with the analytics team, who tell him that the player is very efficient when on the floor and probably should get more playing time. They may take it a step further and look at the player’s stats when they are on the court with specific members of the team, giving the coach insights in order to make lineup decisions for the next game. A team’s scout may look at the same lineup information of an opposing team and report their findings to the coach, so, adjustments can be made when an opponent sends in a very efficient group of players.
In addition to looking at trends and statistics of players, NBA teams now can use player tracking data to make their teams better. A new era of data use in the NBA began when the league installed a system of cameras in every stadium that could track every movement players made during games. Now, coaches can see how closely defended players are when taking shots, what strategies other teams employ to defend ball screens, and so much more. Before this technology was employed in 2010, teams had no way of accurately analyzing team defensive rotations and were missing half of the game.2
While statistics gathered and calculated during games would seem to be the only pertinent information when analyzing a basketball player, coaches and organizations know that value can be discovered in many places outside of official games. For example, teams have begun to track sleep cycles of their players every night. Due to the grueling nature of the NBA schedule, finding time for sleep can be difficult. So, in order to ensure the health of their players, some teams require the use of take-home monitors that track sleep quality. Teams combine information like this with extensive reports on hydration during practices and nutrition to help provide a complete diagnosis of player health. Information can be shared with trainers and coaches to alert them if a player needs to rest more in a game, or requires specific accommodations after a game.
Along with data on health and wellness, teams track players’ vitals, speed and acceleration in games and practices. Using sensors attached to player’s jerseys during practices in order to track heart rates, vertical jump height and player acceleration. Armed with this information, coaches can instantly see which players are getting tired and can adjust their training accordingly.
Similar information is collected during games by the tracking cameras previously mentioned. Teams can access data on which players are the fastest in the league and who runs the farthest during the season. Using this, coaches can not only tell which players are trying their hardest, but also which players need rest.
With the looming possibility that any jump could result in injury and end a career, players need to train rigorously in order to stay healthy throughout the season. But with the constant innovation of new technology, athletes can now train smarter. Companies like Peak Performance Project, or P3, use motion capture and force plate technology to analyze a player’s biomechanics and use the information to provide personalized training.3 By seeing discrepancies in the force applied to a player’s legs, trainers can alter their mechanics to prevent injury. The industry of biomechanical analysis has saved and prolonged careers, providing an invaluable service to the organizations for which athletes play.
Coaches have an expanding network of resources to make decisions for their teams. However, the surfeit of data provides complications when trying to value a certain piece of information against another. Figuring out what matters will remain one of the biggest challenges for teams going forward and will perpetuate future spending by teams into the field of data analytics. Given the insight that a player may perform better with a certain teammate but that pair may limit another player will necessitate coaching judgement and additional analysis, not blind obedience to analytic recommendations.
If the use of data analytics in sports is of interest to you, you may want to consider pursuing a degree in this field.
1 Associated Press. “NBA teams spend $3 billion in 96 hours.” St. Louis Post-Dispatch, July 5 2016. July 25 2016. http://www.stltoday.com/sports/other/nba-teams-spend-billion-in-hours/article_53a951ee-c3d2-5cb5-ba68-1b3d056bca60.html
2 Seth Partnow. “Splitting the Basketball Atom.” Nylon Calculus, 2014. July 25 2016. http://nyloncalculus.com/2014/07/14/splitting-basketball-analytics-atom/
3 Official Company Website of Peak Performance Project, http://www.p3.md/