Guides2 November 2026

By Valentino Sara · Founder of Athlo · Updated 2 November 2026

Which GPS metrics to track in football

Track a short list of GPS metrics and know what each one measures: total distance, high-speed running (HSR) distance, sprint distance, accelerations and decelerations, and PlayerLoad. None has a universal threshold, and none can be compared freely between clubs or vendors, so the useful reference is your own: same system, same thresholds, over time.

What GPS measures and what it does not

GPS (or, more generally, a GNSS system) records the player’s position many times per second and derives distances, speeds and accelerations from it. It measures external load: the physical work the player did, not how their body experienced it. That is what internal load estimates, for example through RPE.

In tournament matches, optical camera tracking is also used, as at the 2022 World Cup in Qatar (Bradley, 2024). It is a different system with different errors, and its figures cannot simply be mixed with GPS figures.

The metrics that matter

  • Total distance: the most stable metric. In the Premier League, the match-to-match coefficient of variation was below 5% for total distance, against 22.6–32.3% for sprint distance depending on position (Bush et al., 2015).
  • HSR and sprint: distance above a speed threshold. They are the most sensitive to how the threshold is defined (see below).
  • Accelerations and decelerations: in a meta-analysis of 19 studies across 7 sports, football showed more high-intensity decelerations than accelerations (SMD −1.74) and more very-high-intensity ones (SMD −3.19) (Harper et al., 2019). The authors point to a lack of consensus on how these metrics are defined.
  • PlayerLoad: an arbitrary unit that depends on the manufacturer and on where the sensor sits. Within the same player it correlates almost perfectly with VO₂ and heart rate (r = 0.92–0.98), but between players the correlations ranged from trivial to moderate (r = −0.43 to 0.33), and the authors advise caution when comparing athletes (Barrett et al., 2014).

Thresholds: there is no standard

Gualtieri et al. (2023) put it plainly: to date there is no consensus on the absolute thresholds that define high-speed running and sprinting in adult football.

Men Women
HSR entry (range across studies) 14.4–21.1 km/h —
HSR, most common value 19.8 km/h 12.5 km/h
Sprint entry (range across studies) 19.8–30 km/h —
Sprint, most common value 25.2 km/h 22.5 km/h

Source: Gualtieri et al. (2023). These are the most common conventions, not a standard.

The 2022 World Cup used ≥20 km/h for high intensity and ≥25 km/h for sprinting (Bradley, 2024). Because each study and each vendor uses its own threshold, figures from one cannot be set beside figures from another without adjustment (Gualtieri et al., 2023).

Absolute or individualised? The evidence is mixed. Gualtieri et al. (2023) conclude that current evidence does not allow definitive conclusions: the absolute threshold is useful for comparing players, and the relative one is preferable for individualising high-speed exposure. In 47 semi-professional players tracked with 10 Hz GPS, the individualised threshold did not improve the association with injury, and the authors consider its efficacy unclear (Massard et al., 2021). What matters is to pick one, declare it and not change it mid-season.

What elite matches demand

Demands change with position. At the 2022 World Cup, across 722 observations, defensive midfielders and centre-backs covered 8% to 19% more total distance, while wide players covered 16% to 92% more distance at ≥20 km/h and 36% to 138% more at ≥25 km/h. Nine of the ten highest peak speeds came from wide positions (35.3–35.7 km/h) (Bradley, 2024).

The practical takeaway is that a squad average hides a lot: the reference has to be by position. And at high speed, a single match is a noisy data point: at that same World Cup, the coefficient of variation of distance at ≥25 km/h was 43–68% (Bradley, 2024).

How far to trust the data

  • Sampling rate: a systematic review of 22 studies found 10 Hz to be the most valid and reliable to date, and moving to 15 Hz gave no additional benefit (Scott et al., 2016). In what we reviewed there is no independent evidence that a higher rate is better.
  • Accelerations: above 4 m/s², the accuracy of 10 Hz is compromised, with an instantaneous velocity bias of up to −0.40 m/s (Akenhead et al., 2014).
  • Between units: with 15 Hz GPS, accelerations and decelerations were the most variable metrics; the difference between units ranged from 1% (maximal speed) to 56% (decelerations >4 m/s²), and some units recorded 2 to 6 times more events than others. A software update changed the acceleration count without changing distances or peak speed (Buchheit et al., 2014).
  • Between models: a 10 Hz and a 15 Hz GPS from different brands should not be compared with each other (Johnston et al., 2014).
  • GPS versus optical: TRACAB optical tracking showed trivial deviations in total distance (0.27–0.42%) against a reference system, but the Gen4 generation showed differences of −19% in low-speed distance and +9% in high-speed distance (Linke et al., 2020).

In practice

  1. Pick a short set: total distance, HSR, sprint and, with caution, accelerations and decelerations.
  2. Declare the threshold and keep it all season. Note which system and which software version you use.
  3. Compare against your own history and by position, not against other clubs’ or other vendors’ figures.
  4. Read high-speed peaks as a trend, not as the result of a single match.

Athlo does not integrate directly with any vendor: it works with the CSV your GPS exports. When GPS is loaded by task or period, it can derive the session total respecting how each metric aggregates. You can see how it works in GPS load.

Sources

  • Gualtieri A, Rampinini E, Dello Iacono A, Beato M (2023) “High-speed running and sprinting in professional adult soccer: current thresholds definition, match demands and training strategies” Frontiers in Sports and Active Living 5:1116293. https://doi.org/10.3389/fspor.2023.1116293
  • Bradley PS (2024) “‘Setting the Benchmark’ Part 1: The contextualised physical demands of positional roles in the FIFA World Cup Qatar 2022” Biology of Sport 41(1). https://doi.org/10.5114/biolsport.2024.131090
  • Bush M, Archer DT, Hogg R, Bradley PS (2015) “Factors influencing physical and technical variability in the English Premier League” International Journal of Sports Physiology and Performance 10(7):865–872. https://doi.org/10.1123/ijspp.2014-0484
  • Harper DJ, Carling C, Kiely J (2019) “High-intensity acceleration and deceleration demands in elite team sports competitive match play: a systematic review and meta-analysis” Sports Medicine 49:1923–1947. https://doi.org/10.1007/s40279-019-01170-1
  • Massard T, Carey DL, Whalan M, et al. (2021) “Comparison of player-dependent and independent high-speed running thresholds to model injury risk in football” Journal of Sports Sciences. https://doi.org/10.1080/02640414.2021.2006414
  • Scott MTU, Scott TJ, Kelly VG (2016) “The validity and reliability of global positioning systems in team sport: a brief review” Journal of Strength and Conditioning Research 30(5):1470–1490. https://doi.org/10.1519/jsc.0000000000001221
  • Akenhead R, French D, Thompson KG, Hayes PR (2014) “The acceleration dependent validity and reliability of 10 Hz GPS” Journal of Science and Medicine in Sport 17(5):562–566. https://doi.org/10.1016/j.jsams.2013.08.005
  • Buchheit M, Al Haddad H, Simpson BM, et al. (2014) “Monitoring accelerations with GPS in football: time to slow down?” International Journal of Sports Physiology and Performance 9(3):442–445. https://doi.org/10.1123/ijspp.2013-0187
  • Johnston RJ, Watsford ML, Kelly SJ, Pine MJ, Spurrs RW (2014) “Validity and interunit reliability of 10 Hz and 15 Hz GPS units for assessing athlete movement demands” Journal of Strength and Conditioning Research 28(6):1649–1655. https://doi.org/10.1519/jsc.0000000000000323
  • Linke D, Link D, Lames M (2020) “Football-specific validity of TRACAB’s optical video tracking systems” PLoS ONE 15(3):e0230179. https://doi.org/10.1371/journal.pone.0230179
  • Barrett S, Midgley A, Lovell R (2014) “PlayerLoad™: reliability, convergent validity, and influence of unit position during treadmill running” International Journal of Sports Physiology and Performance 9(6):945–952. https://doi.org/10.1123/ijspp.2013-0418

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