AI in Sports: Game On!
The extent to which artificial intelligence (AI) is spreading is evident in its increased use in sports in obvious and not-so-obvious ways.
Using technology and data in sports is not new. For decades coaches/managers and teams have been crudely divided into those whose personnel and strategic decisions are guided by “analytics” and those who are “old school,” relying mainly on experience and instincts. A dramatic increase in the amount and complexity of sports data requires more sophisticated applications associated with AI.
Health and training. Given the rapid growth of AI applications in health care, it’s not surprising that sports medicine has been an early area for AI. AI is used to predict the risks of injury, assist health diagnostics and monitoring, and establish individualized treatment for training and recovery from injury. For example, rather than only use treatment in reaction to injury, AI predictions of injury risks suggest training regimens to help prevent likely injuries. And when injuries occur, AI uses data from rehab video and wearable sensors to monitor and shift tactics to promote faster recovery.
Rule enforcement. Viewers of professional and major college sporting events have seen AI technology used in officiating decisions. VAR (video assistant referee) is used in football to judge the relationship of the football and the point at which a first down or a touchdown would be accomplished and whether a player is offsides or not. In tennis, Hawk-Eye technology is used as a definitive source of whether a ball lands inside or outside the court. In 2026, Major League Baseball will use ABS (Automated Ball/Strike) technology to judge challenges to ball and strike calls by human umpires.
Recruiting. A recent article about the uses of AI in college football describes how the University of Nebraska uses an AI agent to scour social media and other sources to identify which prospective players to pursue if they enter the transfer portal. Some schools are using AI to scout high school prospects and communicate with them.
Strategy and tactics. Teams increasingly use AI to sort through volumes of data about the play-calling tendencies of their opponents and themselves. The latter is important because teams do not want their opponents to exploit predictable tactics. In this way, AI-driven applications assist in creating game plans, but there is no evidence that it is (yet) being used to call plays or make decisions about substitutions.
Front office. College athletic departments and professional teams have been employing data scientists since the beginning of this century. These data experts are increasingly using AI tools for scheduling, budgeting, travel planning, background checks, and other redundant activities. Perhaps most interesting, AI is being used to guide the purchase of insurance on players with NIL (name-image-likeness) income guarantees. Just as AI is being used to predict likely injuries for training purposes, it is being used to identify which players are most at risk for injury and the best insurance coverage for their NIL contracts.
We can expect AI to play an even bigger role in sports as applications and agents are developed and refined, and as teams seek ways to find a competitive edge.