I was recently invited to present at the Tokyo World Series sports tech conference to talk about the use of GPS in rugby, and how my team uses it for load monitoring and performance. I want to share some of that here.
GPS gets used a lot of ways with field-sport athletes, particularly in rugby union. In this post I'll explain how we at the Kintetsu Liners use GPS on a daily, weekly, monthly and yearly basis to increase performance and reduce injuries.
The kit
We use a Catapult GPS and the corresponding software, Openfield. The devices measure at 10 Hz. They are little black units that sit between the shoulder blades. Players wear them for every training session and game.
What we measure, and why
There are hundreds of variables we could pull. These are the handful I find useful for my team.
Total time
Session duration. Easy to track in hours and minutes.
Total distance
The simplest variable to monitor across the long term, measured in metres.
Metres per minute
Average speed per minute of game or training. Tells you how fast a session was.
High-speed running and HSR percent
We count high-speed running as anything over 5 m/s. We then represent it as a percentage of total running. Roughly we aim for 10% of running to be high-speed in training and games, although it varies a lot by player, position and the type of session. The number varies even more across different sports.
Max velocity and percent of top speed
What was the top speed hit in training or the game? It only needs to be for a split second to count. Measured in metres per second (some shops use kilometres per hour). Ideally we want our players hitting at least 90% of their top speed twice a week. Their 100% comes from a speed test, or their top score ever on the GPS.
Acceleration efforts and efforts per minute
Several ways to measure acceleration. We use 2 m/s/s. If you increase your speed by 2 m/s within one second, that counts as one acceleration effort. Jogging at 3 m/s and accelerating to 5 m/s? One effort. It matters because two players might cover the same distance, yet one has doubled the other's accelerations, meaning he's training harder. Efforts per minute extends the same idea relative to time.
Sprint metres (Vel B6)
Total distance covered above 6.6 m/s, generally considered sprinting and over.
Daily training session analysis
With those variables in place, we look at a session through two lenses: volume and intensity.
The first views I want are differences in speed (m/min), volume and HSR through the session, plus accelerations and decelerations. That's the feedback to coaches: how fast or intense were certain drills, did they meet the demands or targets we set.
Some sessions aren't run at high speed or high intensity. Certain players may be working on specific skills that don't require running or accelerations. The data should tell you that and not penalise it.
Weekly training patterns
Looking at how the days fluctuate from day to day rather than within a single session, an interesting pattern emerges at our club. If game day is the fifth day of the week, we have been training harder on average from an acceleration standpoint than we play. That's something we strive for, and it's nice to see it visualised.
Volume fluctuates day to day, and we front-load the week with the bulk of it.
Using the information
How do you actually use this to increase performance?
You have a clear line in the sand for what a game costs you physically. Once you know that, you can make sure training meets it. If the game is played at 70 m a minute, components of training should hit 70 m a minute or above. The peak demands of a rugby game are closer to 140 m a minute. Both numbers matter, average and peak, and both go into planning and periodisation conversations with the coaches.
The other side is monitoring variables like high-speed running or accelerations across blocks, looking to nudge them up over time so players are getting fitter and the rugby is being played at a higher speed. You compare individual players or specific drills to make sure speed has increased or a player is now working harder. That goes back to the player and gets discussed.
Example: a defensive line-speed drill in week 1 of preseason can be pulled and compared to the same drill in week 4. Has the team average improved, are we getting off the line harder. Pull an individual and compare to others in the same position. The shared formatting helps because players see it themselves and can see if they're hitting their benchmarks.
Annual load monitoring
Across a season, total volume fluctuates significantly week to week and month to month. Two weeks are never the same.
One way to monitor this is the acute-to-chronic workload ratio, popular in the last few years. Plenty of literature is available online if you want to go deep. Very basically: average workload over the last 4 weeks, compared with what you've done this week.
I should add a 2026 caveat that wasn't in the original 2020 piece. Subsequent work (Carey, Bornn and others) has shown the predictive value of acute-to-chronic ratio is weaker than originally claimed. It's still useful as a load-management heuristic, but it's not the protective tool the early literature suggested. I now treat it as one input alongside subjective wellness, soreness, and direct conversation with the player.
I use high-speed running as the variable for the acute-to-chronic ratio, since I think it's most relevant for our athletes. The graph fluctuates significantly across a year. There are periods where the ratio drops low and then spikes back up, usually after weeks off or holidays. You want to avoid the big spikes as much as possible and use the information to load-manage players returning to full training.
I put a lot of emphasis on objective data, but it's important to take in subjective data: fatigue, soreness levels, what the player tells you when you ask.