The use of GPS technology to monitor the external loads of athletes in training and competition has become almost ubiquitous, particularly in professional sport.
As GPS technology has evolved over the last decade, users now have a wealth of metrics available from which they can evaluate external load and, in conjunction with coaches, better inform the training process. In recent times, researchers have introduced concepts such as measuring distance in acceleration bands, combining acceleration and velocity data (known as ‘metabolic power’), and individualizing traditional speed zones for each player.
The volume of metrics can be overwhelming and the user faces the challenge of selecting which are most appropriate for the sporting context, and what added value an approach can bring to the interpretation of time-motion analysis data. Given the role of fitness in moderating the capacity and dose-response to external load, it would seem intuitive to evaluate the athletes’ GPS data in relation to their fitness profile.
Here we highlight the challenges and complexities involved in individualizing GPS data according to fitness characteristics, and provide some recommendations for interested users.
Industry-based research papers emanating from rugby league (Gabbett, 2015), rugby union (Clarke, Anson, & Pyne, 2015; Reardon, Tobin, & Delahunt, 2015), Australian rules football (Colby, Dawson, Heasman, Rogalski, & Gabbett, 2014) and soccer (Hunter et al., 2015; Lovell & Abt, 2013) have tailored individual players speed zones according to one or more physical characteristics. These researchers have used a wide range of physical fitness attributes to individualize speed zones, such as laboratory-derived measures of the anaerobic threshold, maximal aerobic speed, and peak sprinting speed.
Research in soccer has demonstrated that individualisation of speed thresholds can add value to the interpretation of GPS data (Hunter et al., 2015; Lovell & Abt, 2013), which is intuitive given that an athletes’ “intensity” distribution of external load is likely influenced by their own fitness capabilities. However, using laboratory-based assessments has low feasibility given the economic and logistical barriers.
Recently, the use of peak sprinting speed to prescribe multiple speed zones has become common in the research literature (Colby et al., 2014; Gabbett, 2015; Reardon et al., 2015) because of its ease of collection on the training field. Unfortunately, individualization of speed zones is just not that simple, and users are cautioned that adopting this approach could be doing more harm than good!
Take the tortoise and the hare fable as an example.
The hare is a quick, powerful athlete with a high peak speed (lets say a top speed of 35 kmh-1), but he can’t sustain it for long, as reflected by his intermittent-endurance capacity (Yo-Yo, 30:15 etc.). If we take the approach in the research literature of applying arbitrary fractions of Hare’s peak speed of say 50% for high-speed running (HSR; which by the way has no physiological rationale!), this gives us a HSR threshold of 17.5 kmh-1.
Contrast this to tortoise whom has a peak speed of just 25 kmh-1, resulting in a HSR threshold of 12.5 kmh-1. But tortoise has a comparatively higher intermittent-endurance test score, which enables her to get around the pitch efficiently; more frequently enter the high-speed zones, and recovery quicker.
When the two race, they cover the same distance but in different ways. Using peak speed alone in this way to anchor speed thresholds results in hare’s HSR being under-estimated, and tortoises over-estimated (See Hunter et al., 2015 for more detailed examples).
Using one fitness capacity to anchor multiple speed zones in this way assumes that a faster player also has a high running speed associated with their endurance capacity, and vice-versa (See Figure 1).
This erroneous information may have little impact when measured over one race, but if we want to evaluate and prescribe chronic training regimes based on this GPS data, we may incur training load errors resulting in sub-optimal performance preparation or an increased injury risk (Gabbett, 2016).
Figure 1: Depiction of the erroneous use of peak speed to anchor GPS speed thresholds in the ‘tortoise and the hare’. sIFT = final speed attained in a hypothetical intermittent-endurance fitness test.
In reality, individualizing speed thresholds is complicated by the types of tests used to determine athlete performance characteristics.
Common intermittent-endurance assessments in team sports do not enable the sports scientist or fitness coach to determine the running speeds at which athletes transition into exercise intensity domains (low, moderate, high, severe). Practitioners also need to consider how often fitness tests can be administered during busy competition schedules to account for changes in fitness due to illness, injury or training interventions.
These complexities and challenges provide significant barriers to implementation of individualized speed zones, and may help to explain the low uptake of this practice by GPS users (Akenhead & Nassis, 2015).
But individualisation doesn’t need to be so difficult. In 2013, Alberto Mendez-Villanueva and colleagues presented a practical, user-friendly, and evidence-based approach to individualized GPS analysis (Mendez-Villanueva, Buchheit, Simpson, & Bourdon, 2013).
They applied each player’s maximal aerobic speed from the VAM-EVAL field test, together with their peak speed recorded in a 40 m sprint assessment, to evaluate external load with reference to each individual’s physical capacities. This approach provided an improved representation of the players external dose to soccer matches which may be used to optimize physical programming. Additionally, the maximal aerobic speed result could be used to individualise players high-intensity interval training prescription (HIIT) using well-established training techniques (i.e. Dupont, Akakpo, & Berthoin, 2004).
Unfortunately, neither HIIT prescription nor individualization of GPS speed zones can be achieved using composite intermittent-endurance field tests performed over 20m shuttle runs, which are often heavily influenced by the athletes change of direction and acceleration capacities (Castagna et al., 2006; Berthoin et al 2014).
In summary, prescribing athlete specific speed zones can add value to interpretation of GPS data (Hunter et al., 2015; Lovell & Abt, 2013; Mendez-Villanueva et al., 2013), so long as the user is considerate of the complexities of its implementation.
Users might reflect on their physical test battery, and whether it supports a holistic approach to training prescription and evaluation of external load (readers are directed to Mendez-Villanueva & Buchheit  for further detail in this regard).
Much more research is required to determine the utility and potential added-value of individualized GPS analysis, but until we learn more, it is recommended to either use established and evidence-based procedures (see Mendez-Villanueva et al., 2013; Hunter et al., 2015), or to avoid the practice all-together.
Akenhead, R., & Nassis, G. P. (2015). Training Load and Player Monitoring in High-Level Football: Current Practice and Perceptions. International Journal of Sports Physiology and Performance. http://doi.org/10.1123/ijspp.2015-0331
Berthoin, S., Gerbeaux, M., Turpin, E., Guerrin, F., Lensel-Corbeil, G., & Vandendorpe, F. (1994). Comparison of two field tests to estimate maximum aerobic speed. Journal of Sports Sciences, 12(4), 355–362.
Clarke, A. C., Anson, J., & Pyne, D. (2015). Physiologically based GPS speed zones for evaluating running demands in Women’s Rugby Sevens. Journal of Sports Sciences, 33(11), 1101–1108.
Colby, M., Dawson, B., Heasman, J., Rogalski, B., & Gabbett, T. J. (2014). Training and game loads and injury risk in elite Australian footballers. Journal of Strength and Conditioning Research, 28(8), 2244-2252.
Castagna, C., Impellizzeri, F. M., Chamari, K., Carlomagno, D., & Rampinini, E. (2006). Aerobic fitness and yo-yo continuous and intermittent tests performances in soccer players: a correlation study. Journal of Strength and Conditioning Research, 20(2), 320-325.
Dupont, G., Akakpo, K., & Berthoin, S. (2004). The effect of in-season, high-intensity interval training in soccer players. Journal of Strength and Conditioning Research, 18(3), 584–589.
Gabbett, T. J. (2015). Use of Relative Speed Zones Increases the High-Speed Running Performed in Team Sport Match Play. Journal of Strength and Conditioning Research, 29(12), 3353–3359.
Gabbett, T. J. (2016). The training-injury prevention paradox: should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273–280.
Hunter, F., Bray, J., Towlson, C., Smith, M., Barrett, S., Madden, J., et al. (2015). Individualisation of time-motion analysis: a method comparison and case report series. International Journal of Sports Medicine, 36(1), 41–48.
Lovell, R., & Abt, G. (2013). Individualization of time-motion analysis: a case-cohort example. International Journal of Sports Physiology and Performance, 8(4), 456–458.
Mendez-Villanueva, A., & Buchheit, M. (2013). Football-specific fitness testing: adding value or confirming the evidence? Journal of Sports Sciences, 31(13), 1503–1508.
Mendez-Villanueva, A., Buchheit, M., Simpson, B., & Bourdon, P. C. (2013). Match play intensity distribution in youth soccer. International Journal of Sports Medicine, 34(2), 101-110.
Reardon, C., Tobin, D. P., & Delahunt, E. (2015). Application of Individualized Speed Thresholds to Interpret Position Specific Running Demands in Elite Professional Rugby Union: A GPS Study. PLoS ONE, 10(7), e0133410.