AI in Sports the next frontier. Except it’s already been around a while?
The evolution of AI in Sports across 5 key use cases
So this isn’t new. Artificial intelligence (AI) has been around in sports for a few years, but has especially come into it’s own over the last 12 months or so. It seems like every startup (and their uncle) claims to use AI in some form. From player tracking to injury prevention to fan experience, it really is everywhere.
So, in keeping with the trend, we’re making it the flavour of our month for June. It will be a full month of speaking with startups, industry movers and innovators working on some pretty cool products. Stay tuned for our podcast episodes dropping in June.
In the meantime, we took a stab at penning some thoughts on the topic. Presenting a quick journey of AI in Sports across 5 key use cases.
1. Player Tracking & Performance Analytics: Now, it’s for the Fans
One of the first & most common uses of AI in sports is Player Tracking & Performance Analytics. AI based algorithms can analyze data on an athlete’s activity and provide insights into their performance, all completely real-time, of course. So what’s new here? Who’s talking about it is new.
Let’s take the NBA’s journey as an example, who have been big on advanced player tracking analytics for a while.
Starting with their partnership with Stats LLC on the SportVU system way back in 2013. Then moving onto Second Spectrum who have been using AI based modeling as far back as 2016, likely even prior. Those guys were acquired by Genius Sports who today work with the NBA to bring AI based data & visualisations to the fan. Next level.
Clearly this data is no longer of interest only to teams and players, but fans as well who, today, are much more knowledgeable & technical when talking about their sports teams. From Rebounding and Steals to Usage Percentage and True Shooting Percentage, the conversation has evolved big time.
2. Injury Prevention & Recovery: ‘Load management’ quantified
You could argue that this is the most important area for athletes and sports teams today, when it comes to use of AI. Load management, Injury prevention, Optimized recovery are some of the many terms bandied about. Basically it boils down to one thing, teams pay (some) athletes millions of dollars. That’s money down the medical drain if those athletes don’t play.
The journey here is more straightforward. Earlier, coaches relied mostly on player feedback and their own instincts. Today, analyzing data, AI algorithms can identify potential injury risks long before an athlete moves into a ‘red zone’. Good examples: Liverpool FC reportedly “cut injuries by half”, using an AI system called Zone7 that analyzes multiple data points, from activity to biometrics, to identify injury risks and provide customized training programs. Zone7 provides quite a range of client case studies, proving that this form of tracking is growing in importance across sports & leagues.
3. Referee / Umpire decision making: Now for everyone!
We’ve gone from fans cursing nervous linesman to fans cursing video referees. Lolz. Yes there are definitely issues but there have been steps forward and more often than not, they get the decision right. Don’t let perfect be the enemy of better, and all that. Moving swiftly on.
Semi-Automated Offside Technology, or SOAT as it is more commonly known. We’ve already seen it at work at the 2022 World Cup.
Once it’s been on highest stage, it’s inevitable that it’ll be implemented, if not already, at every major sports league. But what about your kid’s U-14 tournament or Sunday League? We’re not quite there but it might be closer than you think.
Alternative’s to Hawk-Eye’s high-end 6-figure solutions are already available in the market. Enter low-cost VAR light solutions brought to you by AI via companies such as Playsight. Right now these solutions are still meant for less affluent Pro leagues but it’s only a matter of time before this tech gets democratized even further.
4. Fan Experience: More content, personalised.
Fan Experience is quite a generic term. And generic terms have broad interpretations so let’s make this example specific.
Golf.
It’s not practical to have a commentator covering every golfer at every hole at a major. Correction: living commentator.
Enter AI courtesy of IBM and the Masters. The historic golf tournament added an AI twist. IBM’s Watson AI offered real-time insights and analysis to players and fans using NLP and ML to generate personalized highlights, provide strategic advice, and predict shots. All sounds like pretty valuable information.
The solution for now isn’t perfect, sounded a bit drab to me, but the possibilities it opens up are super interesting is only a matter of time before it gets better.
The evolution is clear, more content available for fans in the exactly the ways they want it. Going far beyond just increased coverage. Don’t like a commentator’s accent? Change it. Looking for more excitement? Increase the emotional quotient. The only question left to ask is what are the limits? … We’ll leave that one be for now.
5. Sports Betting
Here things can get pretty funky.
Sports Betting as an industry has evolved pretty quickly. From illegal to legal in many parts of the world for starters. That brings about big changes already, especially when the stakes are this high.
AI only opens up the possibilities even further. Sportsbooks can analyse more data, offering more types of bets but with better accuracy of predictions. The house was already running a pretty loaded game but now this could just make it downright exploitative.
Except that AI can make the same data available to bettors as well, right? Tools like DeepBetting or RebelBetting and a host of others might help level the playing field a bit. Then it boils down to whose algorithm is better. We’ll let you and your wallet decide.
Yes, there are lots more. We know.
That’s just five use cases. We could have easily listed more (AI in Training, AI in Ticketing, AI in Journalism etc. etc.). but every content piece has a deadline. So instead we wanted to give you a little sample and hopefully pique your interest enough to check out what we have lined up.
So, sticking with the rising trend of AI usage in Sports, we are going to dedicate a whole month to find some cool people to speak with and feature interesting solutions.
Let’s see how many times we use the words AI. Might need AI to help with the counting.
Rishabh Jain helps with Partnerships at Berlin based SportsTechX — Data & insights about SportsTech startups and the surrounding ecosystem. You can get in touch via LinkedIn.
