by Picture this: a rugby player sprints down the field with no opponent in sight, only to collapse mid-run. It’s a non-contact injury, a frustrating and often preventable setback that can sideline players for weeks or months. Rugby is a game of power, precision and relentless intensity – and it’s also a sport where injuries are rife.
But imagine a tool that can predict injuries before they happen, giving coaches the ability to intervene and keep players in the game. This is the potential endpoint of our latest research into AI and rugby injuries.
Non-contact leg injuries account for almost 50% of player absences in rugby union, often sidelining them for weeks or even months if they are serious. These injuries, such as hamstring, groin, thigh and calf strains, can be incredibly frustrating for the player and the team. They disrupt training programs, affect team selection and performance.
Previous studies often failed because they focused on single injury risk factors and missed the bigger picture. They may have looked at how isolated factors such as age, previous injuries or a player’s flexibility are associated with injury, but do not always take into account the complex interplay between these factors. It’s like trying to solve a puzzle by only looking at one piece at a time.
The reality is that an older player with poor joint flexibility who is returning from an injury, for example, is at a higher risk of injury than an older player with better flexibility and no recent injury.
Cracking the code with AI
For our latest study, we took a different approach. We collected over 1,700 weekly data points from full-time male rugby players over two seasons. These included factors we know are associated with non-contact leg injuries, including body weight, changes in training intensity, fitness parameters such as strength and cardiovascular fitness, previous injuries and performance on muscle and joint screening tests. We even looked at the soreness players felt at the start of each day, before training sessions.
We fed this information into a powerful AI system that can detect complex patterns. He sifted through all the data to find combinations of risk factors associated with players’ leg injuries.
The results were interesting. The AI model predicted serious non-contact leg injuries with 82% accuracy. So, for ten such injuries, the model would have correctly predicted eight.
The model suggests that players are at greater risk of injury when they have a combination of reduced hamstring and groin strength, reduced ankle joint flexibility, greater muscle soreness and frequent changes in training intensity.
The model used other factors – such as reduced sprint time, greater body mass and previous injuries and concussions – to predict non-contact ankle sprains with 75% accuracy. But while it also managed to predict other less serious leg injuries with similar accuracy (74%), not all injuries were predicted with certainty – for example, hamstring strains and in the groin.
An AI early warning system could provide coaches with crucial information about which players may be at risk. Think of it as a high-tech crystal ball, providing insight into potential problems before they arise and enabling proactive measures to keep players on the field.
Coaches could use this information to create tailored training programs ensuring players are constantly monitored and supported. Targeted interventions – such as exercises designed to address specific weaknesses or improve mobility – can significantly reduce the risk of injury.
In theory, by optimizing pre-season training through targeted athlete selection, our study could offer clear and practical guidelines. These simple, cost-effective tools can enable coaches and medical staff to quickly identify potential risks, providing a proactive approach to player safety and performance.
This AI-based approach isn’t just for rugby. It could be used in any sport where data can be collected. Imagine personalized training plans and injury prevention strategies for every athlete, from soccer players to gymnasts. This could transform the way athletes train and compete, helping them stay healthy and perform at their best.
For now, AI is not widely used, even in elite sport. But with the development of smart technology in watches that monitors training alongside other factors, it is conceivable that eventually it could also be rolled out to amateur athletes.
The future of injury prevention?
However, this research is only a first step. Scientists around the world are already working on ways to make these AI models even more accurate, including other risks for athletes, such as psychological factors and indicators of how the body moves. They also study how different sports may present unique combinations of risk factors that need to be considered.
By combining the precision of AI with insights from sports science and medicine, we are on the cusp of a revolution in injury prevention and performance optimization. This approach can not only improve player safety, but also unlock their full potential, redefining the way athletes engage in the sports they love. With rugby as the testing ground, this innovation could pave the way to a safer, smarter sporting future.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The authors do not work for, consult, own shares in, or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their nomination university.
