HomeNewsHow AI can predict rugby injuries before they occur

How AI can predict rugby injuries before they occur

Imagine this: a rugby player sprints across the sector with no opponent in sight, then collapses mid-run. It is a Non-contact violationa frustrating and infrequently avoidable setback that may sideline players for weeks or months. Rugby is a game of power, precision and relentless intensity – and it is usually a sport where injuries are common.

But imagine a tool that might predict injuries before they occur, giving coaches the flexibility to intervene and keep players in the sport. This is the possible end point of our latest research in AI and rugby injuries.

Contactless Injuries Leg injuries are sometimes answerable for almost 50% of player absences in rugby union Offside position Players for weeks and even months in the event that they are severe. These injuries, comparable to hamstring, groin, hamstring and calf strains, may be incredibly serious frustrating for players and team. They disrupt training schedules, affecting selection and team performance.

Previous studies have often been inadequate because they focused on risk aspects for individual injuries and ignored the larger picture. They could have examined how individual aspects comparable to age, previous injuries, or a player's flexibility relate to injuries, but don’t at all times bear in mind the complex interplay of those aspects. It's like trying to resolve a puzzle by one piece at a time.

The reality is that an older player with poor joint flexibility who’s coming back from an injury, for instance, is at higher risk of injury than an older player with higher joint flexibility and no recent injury.

Cracking the code with AI

For our latest study, we took a distinct approach. We collected greater than 1,700 weekly data points from full-time male rugby players over two seasons. These were aspects that we all know are related to non-contact Leg injuries – including body weight, changes in exercise intensity, fitness parameters comparable to strength and cardiovascular fitness, previous injuries and performance on muscle and joint screening tests. We even checked out how players felt at the beginning of every day before training sessions.

We have fed this information into a robust AI system that may do that recognize complex patterns. All data was reviewed to seek out combos of risk aspects related to leg injuries in players.

The results were interesting. The AI ​​model predicted serious non-contact leg injuries with 82% accuracy. So for each ten such injuries, the model would have accurately predicted eight.

The model suggested that players were at greater risk of injury in the event that they had a mix of reduced hamstring and groin strength, reduced ankle mobility, increased muscle soreness and frequent changes in training intensity.

The model used other aspects – comparable to a discount in sprint time, greater body mass, and former injuries and concussions – to predict non-contact ankle sprains with 75% accuracy. Although it also successfully predicted another, less serious leg injuries with similar accuracy (74%), not all injuries were reliably predicted – for instance, hamstring strains and groin strains.

Gloucester's Afolabi Fasogbon is receiving treatment for an injury.
PA images/identification

An AI early warning system could provide coaches with necessary insights into which players is likely to be in danger. Think of it as a high-tech crystal ball that gives insight into potential problems before they occur and enables proactive measures to maintain players on the sector.

Coaches could use this information to create tailored training programs that ensure players are consistently monitored and supported. Targeted interventions – comparable to exercises that focus on specific weaknesses or improve mobility – can significantly reduce the danger of injury.

In theory, our study can provide clear and practical guidelines by optimizing pre-season training through targeted athlete screening. These easy, cost-effective tools can enable coaches and medical staff to discover potential risks early, providing a proactive approach to player safety and performance.

This AI-powered approach isn't only for rugby. It might be utilized in any sport where data may be collected. Imagine personalized training plans and injury prevention strategies for each athlete, from soccer players to gymnasts. It could transform the way in which athletes train and compete, helping them stay healthy and perform at their best.

So far, AI has not yet been used across the board, even in top-level sports. But with the event of smart technology in watches that monitor exercise, amongst other aspects, it’s conceivable that in time it is going to be expanded to incorporate recreational athletes.

The way forward for injury prevention?

However, this research is simply step one. Scientists around the globe are already working to make these AI models much more accurate by incorporating other risks to athletes, comparable to: psychological aspects and indicators of how Body moves. They also examine how different sports could have unique combos of risk aspects that should be considered.

By combining the precision of AI with the insights of sports science and medicine, we’re on the verge of a revolution in injury prevention and performance optimization. This approach cannot only increase player safety, but in addition unlock their full potential and redefine the way in which athletes approach the sports they love. With rugby as a proving ground, this innovation could pave the way in which for a safer, smarter future in sport.

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Must Read