Data Analytics in Rugby

If you missed it, the Rugby World Cup has just taken place in France. The tournament was a huge success, with more nations competing than ever and South Africa holding onto the tag of world champions after a hard-fought final against New Zealand in Paris. One odd statistic before the semi-finals was that the English team was the only unbeaten team left in the tournament, so they were clearly the favorites to win! Although they only narrowly lost to South Africa by one point in the semi-final, this shows how dangerous it can be to use simple statistics in isolation. There were no statistics that could have predicted how the final game of the tournament would play out.

With that as the background, I wanted to look at how rugby uses data analytics to improve player safety, arguably the biggest issue facing the future of the sport. As with NFL football, rugby is a full-contact sport, and injuries are a regular occurrence. An added factor to the risk is that rugby players do not wear any protection like body armor or helmets, although a few rugby players wear a scrum cap. There is now pretty clear evidence pointing to the potential for long-term damage from repeated impacts to the head and the jarring effect that any hit to the body brings with it.

In recent years, Rugby has increasingly turned to data analytics to improve player safety. The physical nature of the sport, with its high-impact collisions and potential for injuries, makes it essential to use data-driven approaches to mitigate risks and protect players. Here’s how rugby uses data analytics for player safety:

1. Injury Surveillance: Teams and governing bodies collect data on injuries during matches and training sessions. This data is analyzed to identify trends, such as the types and frequencies of injuries and the circumstances in which they occur. Understanding the nature of injuries helps teams develop new strategies to reduce the risk of specific injuries, possibly changing the approach to training.
   
   
2. Player Load Monitoring: Wearable technology, such as GPS trackers and accelerometers, monitors players' workloads during training and matches. This data helps coaches and medical staff manage player fatigue and reduce the risk of overuse injuries.
   
   
3. Tackle Analytics: Data is collected on tackles, including the force and location of impacts, using sensors and video analysis. This information can help identify dangerous tackles and develop techniques to minimize the risk of head and neck injuries. One recent development has been that World Rugby has announced that they will invest €2 million in intelligent mouthguard technology. This new tech can provide in-game alerts of a high acceleration event that enables the pitch side doctors to take players off and assess their condition even if the officials and cameras have missed the event and there are no visible symptoms.
   
   
4. Concussion Assessment: Data analytics can be used to assess the impact of head injuries and concussions once identified. This includes tracking a player's recovery and making return-to-play decisions based on objective data.
   
   
5. Injury Prediction: Machine learning models can analyze a range of factors, such as player workload, injury history, and performance data, to predict injury risk for individual players. This allows teams to take preventive measures for at-risk players.
   
   
6. Scrum and Lineout Analysis: Scrums and lineouts are high-risk situations in rugby, and data analytics can help identify techniques and strategies that reduce the risk of injury in these set-piece plays.
   
   
7. Game Strategy and Tactics: Coaches and analysts use data to design game plans prioritizing player safety. This can include strategies for avoiding high-impact collisions or managing player fatigue during a match.
   
   
8. Rule Changes: Data analytics can inform rule changes and improve game laws to enhance player safety. For example, changes in tackling rules to minimize head contact have been influenced by data analysis.
   
   
9. Injury Prevention Programs: Data-driven insights are used to develop prevention programs, including strength and conditioning routines and injury-specific rehabilitation protocols.
   
   
10. Player Education: Players can benefit from data analytics by understanding their physical limits and injury risks. This knowledge can lead to improved player behavior and decision-making on the field.
    
    
11. Referee Training: Referees can use data analytics to enforce safety rules more effectively during matches and penalize dangerous play.

In summary, rugby uses data analytics to assess injury risk, monitor player performance and workload, and improve the rules and regulations of the game. World Rugby aims to improve player welfare, and using a range of data analytics takes the sport to the forefront of current technology. Using data to inform decisions related to player safety, the sport aims to reduce the risk of injuries and make rugby a safer and more sustainable sport for its athletes. This can only be good for the players and the future of the sport.

By using clean and categorized data in your business, you can also reduce the potential risks created by making decisions based on incomplete information. ProcureVue™ is an AI-driven procurement analytics company that swiftly delivers clean data, actionable insights, and immediate savings.