From pro sports to amateur football: adapting football science for the general public

🇬🇧 Go to English version

Professional sports science has transformed football. Through decades of rigorous research, we know exactly what defines performance for the elite athlete. We have solid benchmarks for Champions League wingers or international midfielders.

But what about the rest of us?

Amateur football represents the vast majority of the global football ecosystem, yet it remains largely unquantified. Benchmarks developed for elite athletes, who play on perfect pitches with strict tactical structures, don't always apply to district-level football or 5-a-side.

At Footbar, we set out to fill this gap. We didn't want to reinvent the wheel, but to adapt it.

By analyzing a massive database of 1,200,380 individual sessions across 125 countries, we have created the first comprehensive performance framework specifically adapted for amateur football.

Here's how we translate elite science for the reality of your pitch.

1. Redefining Intensity: Why we adapted the sprint threshold

In elite sports science, a "sprint" is generally defined as a movement exceeding 25 km/h (for men) or 21 km/h (for women). For a professional athlete, this is a relevant threshold.

However, when we applied this high standard to our amateur data, we discovered an abnormal distribution: a significant portion of amateur players recorded zero sprints per match.

By applying elite thresholds (>25 km/h), data becomes difficult to use for comparison, as many players show 0 sprints.

This does not mean that amateur players are not giving their all; it means that the elite rule does not fit the amateur match sheet. To create a useful benchmark, we adapted the thresholds to capture the functional intensity of amateur play.

Footbar's New Amateur Standards:

• Men: >22 km/h

• Women: >19 km/h

By making this adjustment, the data returns to a normal bell curve shape. This restores the statistical usefulness of the measurement, allowing coaches and players to make meaningful comparisons within their own competitive context.

By adapting thresholds to >22 km/h (Men) and >19 km/h (Women), we restore a normal distribution, allowing for fair benchmarking.

2. The "midfield prison": Relativizing the speed of central positions

A recurring observation in our data is that central players (central midfielders) record, on average, lower maximum speeds than wide players and forwards.

Max speed by position. Wingers and forwards consistently reach higher top speeds than central players.

It is crucial not to interpret this as a physical deficit. We have identified this phenomenon as the "midfield prison".

Elite matches are often very structured, but amateur matches can be chaotic and dense. Central players operate in congested areas (like a "phone booth") where they lack the 20 to 30 meters of free space needed to reach their absolute maximum speed. The same phenomenon is observed when playing in small-sided games like 5-a-side.

Key takeaway: If you play in central areas, do not judge your intensity by your top speed. Your performance is better measured by the density of your accelerations and your short, sharp movements.

3. The "shortened passing circuit": A new look at possession

In professional play, midfielders are often the team's "engine," dominating possession statistics. However, our analysis of ball touches in amateur football revealed a different hierarchy: it is often the Central Defenders who have the highest volume of touches.

Distribution of ball touches (Men). Central Defenders (left) show the highest median involvement, while Forwards (right) have the least.

We attribute this to a "shortened passing circuit" effect. In amateur football, the midfield is a high-pressure transition zone where ball losses are frequent. As a result, teams often recycle possession through the defensive line where pressure is lower, artificially inflating the technical volume of defenders compared to elite standards.

The Footbar Benchmarking Tool

We believe every player deserves to know where they stand. By collecting this data, we have established a set of normative values that you can use to compare your performance to that of the global amateur community.

For 11-a-side Players (Seniors)

• Total Distance: Volume metrics transfer quite well from the professional world. We observe normal distributions with medians of approximately 8,000 m (Men) and 7,500 m (Women) per 90 minutes.

• Max Speed: While the distribution approaches a normal distribution, we clearly see a rebound at certain low speeds, characterized above as the "midfield prison". We therefore advise against using this metric to compare players playing in different positions.

Distribution of distance covered per 90 minutes, and max speed achieved in match (men and women comparison).

• Braking vs Sprint: Injury prevention protocols should note that amateur players perform significantly more high-intensity decelerations (braking) than accelerations. The ratio is approximately 85 decelerations to 65 accelerations per match.

Distribution of the number of accelerations and decelerations per 90 minutes (men and women comparison).

For Youth Players (U15-U19)

• Maturation Monitoring: Speed depends on age. Our data shows a linear increase in median top speed from U15 to U18, leveling off in the U19 category. A speed that is "elite" for a U15 player might only be "average" for a U19 player.

Distribution of maximum speed reached by age category

For Football School (U7-U13) and Small-Sided Games

• Workload Index: Because game parameters vary enormously among young players (field size and number of players from 5-a-side to 9-a-side, duration and number of halves...), comparing distance is misleading. Instead, we use the workload metric calculated by Footbar to track physical exertion. This shows a steady progression as children grow.

Box plots of U7-U13 workload

• 5-a-side Intensity: In small-sided games, maximum speed is truncated (capped at ~6 m/s). The game is defined by braking. We observed a ratio of 1.6 to 1 between decelerations and accelerations. We also observe a phenomenon similar to the "midfield prison": the size of the field does not allow players to reach their maximum speed, and this biases the distribution. Maximum speed is therefore not a relevant criterion for evaluation in small-sided games.

5-a-side Data: Distance covered, Max speed reached, Number of accelerations, Number of decelerations

Democratizing access to performance data

By adapting elite principles to our context, we provide a fair, accurate, and actionable framework for the everyday player.

The charts and analyses above are just a glimpse into our 19-page report. If you are a coach, a data enthusiast, or a player who wants to understand the deep mechanisms of amateur football, we invite you to read the full version (preprint).

Sign up to receive the full PDF of "Benchmarking the Grassroots Game" by email.