Bridging the Gap: How We Adapted Pro Sports Science for the Grassroots Game

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

But what about the rest of us?

The amateur sector represents the vast majority of the global football ecosystem, yet it remains largely unquantified. The benchmarks developed for elite athletes, who play on perfect pitches with strict tactical structure, do not always apply to the district level or 5-a-side football.

At Footbar, we set out to fill this void. 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 adapted specifically for amateur football.

Here is 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 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 registered zero sprints per match.

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

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

The New Footbar 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 utility of the metric, allowing coaches and players to make meaningful comparisons within their own competitive context.

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

2. The "Runway Constraint": Contextualizing speed in central roles

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

Maximum velocity 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 "Runway Constraint".

Elite matches are often highly structured, but amateur matches can be chaotic and dense. Central players operate in congested zones (like a "phone booth") where they lack the 20 to 30 meters of open space necessary to reach their absolute maximum speed. The same phenomenon is observed during small-sided practice like 5-a-side.

Takeaway: If you play centrally, 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 "Turnover Filter": A new look at possession

In the professional game, midfielders are often the "engine room," dominating possession statistics. However, our analysis of ball touches in amateur football revealed a different hierarchy: it is often 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 lowest.

We attribute this to a "Turnover Filter" effect. In amateur football, the midfield is a high-pressure transition zone where turnovers are frequent. Consequently, teams often recycle possession via the defensive line where there is less pressure, artificially inflating the technical volume of defenders compared to elite norms.

The Footbar Benchmarking Tool

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

For 11-a-Side Players (Senior)

• 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 very clearly see a rebound on certain low speeds, characterized above as the "Runway Constraint". We therefore advise against using this metric to compare players playing in different positions.

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

• 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 (comparison of men and women).

For Youth Players (U15-U19)

• Maturation Tracking: Speed is age-dependent. Our data shows a linear increase in median top speed from U15 to U18, with a plateau 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 Schools (U7-U13) and Small-Sided Football

• Workload Index: Because game parameters vary hugely among youth (pitch size and number of players from 5v5 to 9v9, duration and number of halves...), comparing distance is misleading. Instead, we use the workload metric calculated by Footbar to track physical expenditure. This shows a steady progression as children grow.

Box plots of workload U7-U13.

• Intensity of 5-a-Side (Five): On small pitches, 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 "Runway Constraint": the pitch size does not allow players to reach their maximum speed, and this biases the distribution. Maximum speed is therefore not a relevant criterion for evaluation on small pitches.

Data on 5-a-Side Football: 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 framework that is fair, accurate, and actionable for the everyday player.

The charts and analyses above are just a snapshot of 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).

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