Preventing Injuries and Improving Performance in Sports with Machine Learning

Author(s): Eera Bhatt

Originally published on Towards AI.

That’s right, I was inspired to write this article after the International Cricket Council (ICC) World Cup this past weekend. But admittedly, I was distracted by Google’s confetti when I tried to watch the cricket match, so I didn’t end up finishing it.

Aside from watching cricket, I also watched the film Moneyball (2003) a few weeks ago as part of an economics course. In case you haven’t seen this movie, the characters have to make lots of difficult choices about which baseball players should be kept in the team based on past data about each player’s performance.

This made me wonder how other sports decisions can be made for training players, keeping them safe, etc., especially in an era of big data and artificial intelligence to help us out.

Just to clarify, I don’t think A.I. should make every decision for any sports team — especially if it’s about safety — but it can definitely inform the choices made by coaches and other leaders in the sports realm.

So for this post, I’ll cover two common usages of artificial intelligence to inform sports decisions: player performance analysis and injury prevention.

Player performance analysis. In case you’re new to artificial intelligence, it identifies patterns based on past player data to help players figure out what strategies are most likely to make them successful in the game. This informs the players’ decisions about their future games. After looking at the data’s trends, they’ll know what specific strategies are most likely to result in a win, and they can focus on these areas during practice.

Just to be clear, the coaches still have to make decisions for their own teams. But machine learning tools can help inform their decisions by identifying patterns in the players’ movements quickly that might be hard for a human to identify alone.

For example, tools like Hawk-Eye and SportVU use A.I. to track each ball and shot so they can analyze patterns. Coaches can use this information to understand their players’ weaknesses better. So, while training their players, the coaches have a better idea of what each individual player needs to work on the most. Hence, improvement!

Injury prevention. There is no question that an athlete’s life opens doors to all sorts of risk. And while playing a sport, there’s lots of tense competition that can distract players from potential injuries or health-related issues.

But thankfully, wearable technology is being developed to help catch signs of injury or health issues before they actually happen.

For instance, players might wear biosensors that catch signs of physical stress or severe fatigue during a game. Some sensors like this can alert staff members, coaches, or other leaders just in time to prevent an injury.

Digital Athlete by NFL and AWS. By far, one of the most notable examples of machine learning’s use to help safety in sports is the Digital Athlete program. The National Football League (NFL) partnered with Amazon Web Services (AWS) to build Digital Athlete in the cloud, which uses TV footage and info from wearable sensors in football gear as data. The program learns from these large amounts of data about common injuries on the field and their causes. So, thanks to Digital Athlete’s developers, sports workers can better identify an injury risk that a player might be facing.

Just think about the difference this makes. It’s football — that one sport I never played as a kid because I was scared of injuries. Then again, I barely know how it works even now, so maybe I’m not in the position to talk about it. Anyway, back to the A.I. part.

Analyzing helmet data. Along with predicting injuries, the Digital Athlete program can improve the actual equipment used in football, especially helmets. In fact, the program contains so much data about different helmets and how they affect a player’s risk of injuring themself.

When a certain helmet position or a player’s habit is associated with a higher injury risk, according to Digital Athlete, coaches can warn their players or change their team’s overall approach to the game accordingly to make things safer.

Additionally, this cloud framework for football players also includes the Pose Estimation Model, which analyzes how players move on the field using computer vision. Perhaps I’ll cover a model like this in another post soon, but until then, look here for more details about this feature.

Conclusion. Once again, A.I. can do the busy work of analyzing large amounts of data about sports players and extracting information from them. This gives coaches and other staff a powerful resource to inform their decisions as leaders.

But for now, let’s hope that this technology brings our sports teams to even greater glory!

Further Reading:

[1] Can AI Score Big in the Future of Sports? Five Key Trends Shaping the Industry (2023). Available at: https://www.forbes.com/sites/forbestechcouncil/2023/09/27/can-ai-score-big-in-the-future-of-sports-five-key-trends-shaping-the-industry/

[2] Building a digital athlete: Using AI to rewrite the playbook on NFL player safety (2024). Available at: https://www.nfl.com/playerhealthandsafety/equipment-and-innovation/aws-partnership/building-a-digital-athlete-using-ai-to-rewrite-the-playbook-on-nfl-player-safety

[3] How England’s cricket coaches are using AI to help pick their teams, with off-spinner Charlie Dean proving a huge success against Australia (2024). Available at: https://www.dailymail.co.uk/sport/cricket/article-13425239/How-Englands-cricket-coaches-using-AI-help-pick-teams.html

[4] 10 ways AI is being used in Cricket (2024). Available at: https://shorturl.at/0G1M7

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Published via Towards AI