Individualizing High-Speed Running and Sprint Thresholds in Soccer

Comparative Analysis: Relative vs. Absolute Thresholds for High-Speed Running and Sprinting in Football

In elite football, simply measuring how much an athlete runs is no longer enough to quantify workload; the real challenge lies in understanding what that run represents for their physiology. Using absolute speed thresholds (e.g., 25.2 km/h) means measuring a central defender and a winger with the same ruler, ignoring their unique physical profiles.

The Power BI dashboard link is available for download in the final section of this post.

Why use individualized thresholds for HSR and Sprinting?

The adoption of individualized thresholds arises from the need to overcome a biological limitation of absolute values: specific performance capacity. By using a fixed parameter (e.g., 25.2 km/h), we assume every athlete has the same "engine," ignoring their maximum speed and, consequently, the actual stress that a specific run imposes on their body.

Using relative thresholds means measuring intensity, not just output. As highlighted by recent scientific literature (Pimenta et al., 2024; Gualtieri & Beato, 2023), absolute thresholds fail to describe the real impact on the individual athlete, leading to systematic errors in weekly load management.

Why absolute thresholds "lie": A practical example

Imagine monitoring a training session or a match using the classic absolute sprint threshold fixed at 25.2 km/h. If we only look at meters covered above this speed, we risk drawing incorrect conclusions about our athletes' status.

Take two players with very different Maximum Speed (Vmax​) profiles:

• Athlete A (The Winger): Vmax​ of 36.0 km/h. For him, running at 25.5 km/h means working at 71% of his capacity. For the GPS, he "sprinted," but physiologically he is performing a high-intensity run (VHSR) without tapping into his speed reserve or recruiting maximal motor fibers.

• Athlete B (The Center Back): Vmax​ of 30.0 km/h. During a defensive transition, he reaches 24.8 km/h. For the absolute threshold, he performed zero sprints (0 meters). In reality, he is running at 82% of his maximum capacity. The mechanical stress and neuromuscular strain are significantly higher than Athlete A’s.

Which data can we use to individualize thresholds?

Here are the primary sources for deriving maximal speed:

• Field Tests (Sprinting Tests): Maximal 30m or 40m sprints in controlled conditions. Precise, but require optimal freshness.

• Historical Data (All-time Vmax​): The highest peak ever recorded. Risky, as it may not reflect current fitness.

• GPS Training Data: Peaks recorded during specific drills; useful but often limited by pitch size.

• Match-derived Vmax​: This is the source I have chosen for my work.

Why use Match-derived data?

In my model, Vmax​ is extracted directly from official competitions. Supported by recent research, this choice is based on three pillars:

• Maximal Motivation (Contextual Peak): Matches provide the unique emotional and tactical drive to reach true maximal intensity.

• Movement Specificity: Speed reached on the pitch accounts for surface, footwear, and football-specific changes of direction.

• Dynamic Updating: Using match data, my 75% (VHSR) and 95% (Sprint) thresholds self-calibrate. If a player sets a new record in a match, the system updates immediately, ensuring load monitoring stays faithful to their current physical state.

The Impact of Normalization (75% and 95%)

Applying these thresholds (75% for VHSR and 95% for Sprint) completely changes the picture:

• Load Precision: Athlete B finally gets credit for his effort as Very High-Speed Running, allowing the staff to see the significant load accumulated despite not "sprinting" by standard definitions.

• Real Prevention: Athlete A is monitored not by how many meters he covers above 25.2 km/h, but by how often he touches 34.2 km/h (his 95%). Only this intensity provides the protective stimulus needed for the hamstrings (Edouard et al.; Buckthorpe et al., 2019).

  
  

Why is it important to train High Speed and Sprinting in training?

Training these qualities through individualized thresholds is a physiological necessity:

1. Neuromuscular Recruitment: According to Mendez-Villanueva et al. (2011), if you don't reach at least 90-95% of Vmax​, you don't fully activate Type IIb fast-twitch fibers.

2. The "Vaccine" Effect (Injury Prevention): Regularly exposing athletes to 95% of their individual limit acts as a "vaccine" for hamstrings, strengthening the muscle in specific eccentric positions.

   
   

A Dashboard to Normalize High-Speed and Sprint Data

Following the introduction to the problem of absolute vs. normalized thresholds, we shift focus to the data analysis itself. My report utilizes match data from a full season of a professional Lega Pro team.

In every match, the peak speed of each individual player was detected. Subsequently, using the 95th Percentile (P95), we extrapolated the starting point to derive the high-speed and sprint thresholds—specifically applying percentages of 75% - 95%.

The first part of the report features a table highlighting all the data mentioned above. In particular, it compares the corresponding percentage of absolute thresholds (21 and 25 km/h) against the peak speed of the individual player, proving once again how each athlete has totally different running characteristics. This comparison is further emphasized through a line graph, where the discrepancy between individual and absolute thresholds becomes even more evident.

📚 Bibliography & Scientific References

• Pimenta, R., et al. (2024). Analyzing soccer match sprint distances: A comparison of GPS-based absolute and relative thresholds.

• Gualtieri, A., & Beato, M. (2023). Individualized vs. Absolute Speed Thresholds in Professional Soccer: A Systematic Review.

• Mendez-Villanueva, A., et al. (2011). Does player-specific sprinting performance influence match high-speed running and sprinting activity?

• Buckthorpe, M., et al. (2019). Recommendations for hamstring injury prevention in elite football. (BJSM).

• Sandford, G. N., et al. (2021). Maximal Sprint Speed and the Anaerobic Speed Reserve: Key to Logistic Training Individualization.

📁 Dashboard PowerBi | Soglie individuali di High Speed Running & Sprint