This repo is a Shiny app that you can access as a user at the following link: HERE

As sports scientists, we’re used to working with numbers and analyzing training or match data. Coaches, however, don’t need to be number experts. The goal here is to break that barrier and translate data into simple words that describe how a session felt: easy, moderate, hard...

Inspired by an idea Xavi Schelling shared years ago, this Shiny app puts it into practice. By combining distance and HSR data, we split results into quintiles and map them into a simple table — turning complex metrics into one clear word to describe each training or match.

On a practical level, the app works as follows:

1. You can always filter between two seasons for which data is available, as well as by specific training days or any range of days you choose.

2. In the “General” tab, you can see how total distance and high-speed running distance (above 18 km/h, HSR) relate to each other across all training and/or match records from players who completed the session. This already gives us an idea of the overall distribution across a whole season.

   

3. In the “Dist / HSR” tab, the relevant data is split into 5 different quintiles, allowing us to see how each session compares to all existing records.

   

4. Finally, a table is displayed that puts each training session into context, making it possible to describe it in just one word — based on the distance and HSR recorded by a player, always in comparison with the rest of the training sessions logged.

Why only Distance and HSR?

Because they’re simple and give a quick, big-picture view of a session. Of course, the same idea could be applied with other variables, or even using PCA to pick the two that explain training or match load the best.

Where do the data come from?

The data can be downloaded directly from this project. They are randomly generated to simulate the load distribution of recorded seasons. In other words, they’re example data created just for this app.

Should it be individualized by player/position?

The idea is to keep it simple. So far, the goal has been to describe how a session went in a straightforward way. Of course, more filters could be added, but that would make communication more complex.