Beyond having high-calibre players in peak physical condition, rugby is a game that relies heavily on a game plan.

Every move, each pass, and the formation of choice contributes to the symphony between players on the pitch. Coaches have the big responsibility of reviewing footage, analysing the opposing team, and devising smart strategies based on their insights. But sports are unpredictable — and that’s what makes them challenging and exciting to watch.

So, where does AI come into play? We commonly associate AI with generative art or robots in healthcare, but what’s amazing about this blossoming technology is that it can be used in just about every industry, including sports. Major League Baseball (MLB) famously uses the AI tool Statcast to monitor statistics from pitch velocity to exit speeds. At the same time, football players have Google’s Jacquard tag embedded into their insoles to monitor movement. There’s an endless list of ways AI is making its rounds in rugby, much like how this page has a robust list of the many top free spin offers in the UK.

How Rugby Strategies Have Evolved

Like many sports, rugby’s early days were driven more by instinct and less by formal rules and tactics. There was a significant focus on intrinsic skills like passing and scrummaging, and tactical choices were made a lot more spontaneously. Eventually, rugby started coming into its own, and there was greater emphasis on formality and structure. Plenty of tactical concepts that are tried and true today were developed, and roles were more defined as well.

Then, the 80s and 90s hit, and rugby was able to take advantage of video analysis. While these tools were not nearly as nuanced and accurate as the ones we have today, they completely changed the game, making it more sophisticated. Coaches could now assess team performance and break down the game to create effective strategies for the future.

Rugby has become extremely data-driven since then — there’s data on just about everything you could think of. And with technology developing at lightspeed, the game is set to evolve and change with it.

Using AI and Machine Learning to Analyse Player Performance

Rugby wouldn’t be rugby without the players that make up the teams. Nowadays, every sport seems to employ a new machine learning or AI system that focuses on player analytics and performance data, and rugby is no exception. AI is infiltrating the Rugby Union and the National Rugby League for predictive injury analysis and to analyse player movements, providing a new level of insight that we never had before. As rugby is such a demanding sport, player health is of the utmost importance. Having a real-time look into each player’s standings means we can now fully optimise performance and equip players to enjoy longer careers.

When teams are ready to scout for new players, AI also makes the recruiting process more objective. While the human element of identifying raw talent will never go away, this extra-rich data lets teams and leagues uncover more. Decisions are no longer just based on the eyes and opinions of scouts, as there’s a new layer of hard-hitting data to back them up.

Optimizing Training Methods and the Game Plan with AI

Along with AI algorithms to monitor individual players, there are also machine learning systems in place dedicated to athlete training. And again, we’ve seen these algorithms used for other sports. Xonic Golf uses computer vision to analyse golfers’ swings and to provide personalized tips based on biomechanics, while Seattle Sport Sciences looks at football players’ foot preferences and maps a total of 23 touchpoints to understand how players interact with the ball. In turn, this detailed player data leads to robust feedback and training recommendations to enhance on-field performance.

Rugby coaches can make use of similar data and technology to monitor game situational metrics and consequently make tactical adjustments. How well is the defensive line being maintained? Are there any gaps that could be exploited? How successful is each stage of play? How agile and adaptive is the team? AI can answer all these questions with a high degree of accuracy and provide actionable solutions to address any weaknesses or opportunities.

In English Premiership Rugby, we’ve seen the illustrious Saracens F.C. use drone technology to provide a perspective of the game from new heights. Rather than the rudimentary raw video footage teams used in the past, drone footage captures perspectives beyond a limited scope. The club uses an IP tower and drone to capture training bouts and games from as many angles as possible to see exactly what’s going on. Post-match or post-training, coaches and players can review the stats and replays and alter tactics if needed.

AI’s Future in Rugby

Rugby is well-prepared to catch up with other sports when it comes to AI. Although it doesn’t top the list of sports leading in AI adoption, there are prime opportunities to enhance zone control, field positioning, and other important aspects of the game. The National League Rugby has already collaborated with AI sports recording company Veo for remote video analysis, but there’s plenty of room for more.

The purpose of training is to improve and refine your performance as a team, and visual data is an invaluable asset to those goals. AI can simulate various game scenarios so that teams can test approaches virtually before seeing how they play out on the pitch. Adjustments are also made much more dynamically, as tactics can be changed quickly in real time based on a play-by-play basis. In the near future, rugby training can benefit significantly from personalized training programs, adjusting intensity based on recovery status and focus based on individual player data.