It is the time of the fall classic, Major League Baseball’s World Series. As the two best teams vie for the championship this year, there are some actors in the game beyond the players, coaches, umpires (or referees), and fans… namely big data, analytics, and artificial intelligence. What’s  even more interesting? These new actors are also highly prevalent in football, basketball, and hockey, and they are changing these games forever.

Sports foray into technology and data really got its start in 2002 with the Oakland Athletics. General Manager Billy Beane and Assistant GM Paul DePodesta would pioneer sabermetrics, which is a new perspective on baseball analytics. Moving away from traditional metrics, Beane and DePodesta focused instead on runs (which is how you win games.) To get runs, you need players to get on base, which meant that OBP (on base percentage) suddenly took on a lot more weight than metrics like home runs. While Oakland fell short in the 2002 playoffs, their model of sabermetrics would revolutionize how teams managed the game. New metrics like WAR (wins above replacement), exit velocity, and hard-hit rate would become the new benchmarks.

These new analytic models would reshape how teams would evaluate players, draft amateurs, and morph free agent strategy. To support these efforts, sports teams changed their staff structures from human-based player scouting to large teams of data scientist obsessed with collecting every scrap of data to unlock every possible edge, right down to which spot on the basketball floor to pass against a specific team in a definitive defense of formation. In essence, the culture of sports when from gut driven decision-making to data driven decision-making.Recommended For You

Cyborg baseball player getting ready to bat.
Cyborg baseball player DEPOSIT PHOTOS

From the front office to the field, teams are employing technology everywhere they can legally do so. (Most sports leagues provide limitations on the use of technology.) Tablets are now available for reviews by coaches and players (who are not playing) so they have the latest scenario planning. Tapping into GPS and lasers, the Los Angeles Dodgers are exploiting geolocation data to get pinpoint precise defensive positions per pitch. Using AI and machine learning, individual training programs are crafted for each player to work on every facet of their game and avoid potential injury during training, and it is based on the player’s specific physiology.

For umpires and referees, there’s a different challenge: the AI judge. In tennis, hockey, and baseball, we’re already seeing an improved accuracy on calls. Players and coaches squabble less on whether a tennis ball was in or out. The National Hockey League stopped using goal judges because cameras in the net are more effective and quicker at signaling a goal. In baseball, the PITCHf/x system has over fourteen years’ worth of data showing that umpires get the ball-strike call right only about 66% of the time. With the advancement of instant replay in every sport and improved camera technology, it may not be too long before AI referees are the norm. (Imagine a sports world with no more favoritism for superstar athletes or players feigning contact to get a penalty call.)

Fans are also benefiting from the use of technology. There are companies like Reely.ai that leverage visual recognition to identify sports highlight moments in real-time, and then produce the highlight clip instantaneously. Then there is Thrive Fantasy, a sports proposition (legal) betting company, that generates real-time situational bets as games progress. In an effort to create the ultimate fan experience, teams are focusing on personalized interaction and immersion through AI capabilities like psychographic profiling and neurolinguistics to speak the language of the fan. Plus, teams are providing machine learning apps so they can produce their own replays or look up an arcane statistic at-will.

Even team chemistry is getting a technological makeover. Using AI to evaluate player skills and personality, teams can generate models to see how well different combinations of players mesh together. Will team productivity be higher? Will the team get alone? Even the most effective type of team culture and coaching style is assessed.

A robot hand holding a baseball.
Human-machine sports collaboration DEPOSIT PHOTOS

Will there ever be AI robot players? Probably not. That would dehumanize the game and take a lot of fun out of the sports (so much less random chance.) That aside, sports have become a pioneer in emerging technology. Paving the way, in many regards, before even private industry does. Consider, using AI for team chemistry has been a precursor to companies deploying a similar model to recruit new hires and determine optimal project teams. Transportation companies need precise locations for drone deliver. Micro-location companies such as SmartPoint are adapting ideas like the Dodgers use of lasers and GPS to establish one-meter accuracy. Wondering what are the relevant metrics to build a customer base or establish a new market? Companies are tapping into the approach Oakland used with sabermetrics.

The world of “SportsTech” has forever changed the game. In turn, this has proven to be an interesting proving ground for industry to adapt and use this technology. As the World Series rolls on, perhaps it will inspire the next great corporate innovation.

Full Story: https://www.forbes.com/sites/neilsahota/2020/10/25/the-ai-lords-of-sports-how-the-sportstech-is-changing-business-world/?ss=ai#47125aa5a932

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