The application of maximal mean and critical speed and metabolic powers for the quantification of external load in soccer
Date of Award
Doctor of Philosophy
School of Medical and Health Sciences
Dr Fadi Ma’ayah
Professor Anthony Blazevich
Associate Professor Chris Abbiss
Field of Research Code
In Association football, often referred to as “soccer”, competitive match play is typically known to represent the greatest physical demand that players experience. The demands associated with match running performance may impact training outcomes and injury risk. As a result, practitioners evaluate these demands to ensure that performance is optimised. To assist in this process, global positioning systems (GPS) are typically used to quantify displacement variables and, more recently, the interaction between running speed and acceleration. However, traditional player monitoring methods tend to apply identical and somewhat arbitrary displacement, velocity and acceleration bands to describe movement “intensity”, and thus to determine individual work capacities. Using the same bands to determine individual work capacity when monitoring a team however is problematic, as this method does not provide a true reflection of work capacities relative to each individual. Therefore, the ability to determine measures of intensity specific to the physical performance of each individual could provide the practitioner with greater accuracy when evaluating physical demands.
Since physical demand (i.e. external load) varies between matches, the match-to-match variability of external loads in match play were quantified in Study 1 to determine the number of matches needed to quantify performance changes. Twenty (n=20) sub-elite soccer players from a National Premier League (NPL) team (age, 19.1 ± 1.2 y; body mass, 72.4 ± 2.7 kg) volunteered for the study. GPS data were collected during the 2017 season and 416 individual match samples were collected from 26 NPL matches (13 home and 13 away). At the end of each match, GPS data were downloaded to Team AMS proprietary software and exported to Microsoft Excel creating individual performance profiles for each competitive game. Rolling averages were analysed across 6 specific durations (1, 5, 10, 60, 300, 600 s) for each player, with a maximum value for each specific duration recorded in speed (maximal mean speed [MMS] the highest speed values found when averaged over the six durations within each match) and metabolic power (maximal mean metabolic power [MMPmet] maximal estimated metabolic power when data were averaged over the six durations within each match). Matchto- match coefficients of variation (CV) were greatest for sprint-speed running distance (36.3- 43.6%) when comparing 2 versus 10 matches. CVs for maximal mean speed (4.9-7.0%) and metabolic power (4.4-9.6%) ranged from good to moderate. Greater consistency, indicated as moderate to good reliability, was found when data were averaged over a minimum of four matches. Furthermore, a greater variability in absolute high-speed displacement measures suggests that its use as a performance indicator was not as good as maximal mean analyses. Instead, the lower variability found in MMS and MMPmet variables suggest that they could be reliable alternatives to typical displacement measures.
In Study 2, the validity and reliability of MMS, MMPmet, critical speed (CS; the speed value that defines the boundary between steady-state and non-steady state exercise) and critical metabolic power (CPmet; the metabolic power value that defines the boundary between steadystate and non-steady state exercise) estimates were compared. Twenty (n=20) sub-elite soccer players from a National Youth League (NYL) team (age, 19.1 ± 1.2 y; body mass, 72.4 ± 2.7 kg) volunteered for the current study during the 2016-17 (October-January) season. Participants completed three field-based test sessions, each separated by a week, before competing in the 8-match NYL competition. The participants wore 15-Hz GPS units (SPI HPU; GPSports Systems, Canberra, Australia) during all field test sessions and throughout NYL competition. CS and CPmet were tested for validity in two separate protocols; a straight-line running CS field test and a shuttle-running CS field test. A symmetric moving average algorithm was applied to the GPS raw data using specific time windows (i.e. 1, 5, 10, 60, 300 and 600 s) and maximal values were obtained. Additionally, these maximal values were used to derive estimates of CS and CPmet. Coefficients of variation (CV) ranged from good to moderate for speed (4-6%) and metabolic power (4-8%). Only CS and CPmet values were significantly correlated (r=0.842; 0.700) and not statistically different (P=0.066; 0.271) respectively, to values obtained in a shuttle-running critical test. GPS-derived estimates of CS and CPmet during match play were validated against values obtained in the shuttle-running CS field test only. MMS and MMPmet were again found to be reproducible measures of maximal running intensity during match play, and further indicate that maximal mean analyses are a reliable alternative to the assessment of individual player match demands. In addition, the reliable (and valid) estimates of CS and CPmet represent the boundary separating match running speed and Pmet for which physiological steady state is attainable and which is not (i.e. nonsteady state), and therefore allow practitioners to quantify an individual’s responses to match play.
In Study 3 external loads were quantified and compared between elite and sub-elite populations and between playing positions. Twenty four (n=24) elite male football players (age, 26.2 ± 5.3 y; body mass, 76.2 ± 4.8 kg) from one A-League football team (representing elite Australian football) and 20 sub-elite male outfield (i.e. any player who plays a position other than goalkeeper) football players (age, 19.1 ± 1.2 y; body mass, 72.4 ± 2.7 kg) from one National Premier League Western Australia football team (representing the highest level of youth football participation in Western Australia) volunteered to participate during their respective 2017 and 2016-2017 competitive seasons. Displacement and velocity data during NPL and ALeague matches were captured using 15 Hz GPS devices (SPI HPU GPSports Systems, Canberra, Australia). A symmetric moving average algorithm was applied to the GPS raw data using specific time windows (i.e. 1, 5, 10, 60, 300 and 600 s) and maximal values were obtained. Additionally, these maximal values were used to derive estimates of CS and CPmet. A major finding of the study was that elite players performed longer-duration efforts at greater running intensities. For example, elite players exhibited greater MMS and MMPmet values, particularly when calculated (i.e. averaged) over smaller time windows (i.e. 1 and 5 s), however only MMPmet 1 was identified as consistently different between competitions (P=<0.001-0.049). Estimates of CS in centre backs were significantly lower to all other playing positions in both levels of competition level, while CPmet of centre backs was found to be lower to full backs, wide midfielders and strikers in elite soccer as well as full backs and wide midfielders in subelite soccer. The results of this study therefore indicate that GPS-derived estimates of CS and CPmet are sensitive enough to detect differences in the classifications of match intensities between player positions.
Study 4 assessed in-season variations in external loads across a season but averaged over each mesocycle (i.e. 4 weeks). An elite football cohort (n=24; age, 26.2 ± 5.3 y; body mass, 76.2 ± 4.8 kg) was examined over a full league season in the A-League (October 2017 – April 2018) competition (27 A-League rounds and 1 final series match). Displacement and velocity data during A-League matches were captured using 15-Hz GPS devices (SPI HPU GPSports Systems, Canberra, Australia). A symmetric moving average algorithm was applied to the GPS raw data using specific time windows (i.e. 1, 5, 10, 60, 300 and 600 s) and maximal values were obtained. Additionally, these maximal values were used to derive estimates of CS and CPmet. The magnitude of differences reported when comparing mesocycle 4 (M4) to other mesocycles ranged from small to very large (ES: -1.50 to 2.20). CS recorded in M2 and M3 of wide midfielders (13.98 to 13.07 m·s-1) was the only critical value (i.e. CS or CPmet) that was significantly different (P=0.032; ES: -0.19) between consecutive mesocycles. The observed differences in MMS and MMPmet suggest that these variables are sensitive to in-season change and would be a useful in the identification of variations of maximal running speed and Pmet intensities for individual players. Small variations were found for GPS-derived CS and CPmet intensity estimates over each mesocycle. Nonetheless, the ability to quantify (monitor) variations in the match running speed and Pmet for which physiological steady state is attainable and which is not, allows practitioners to evaluate physiological indices of aerobic and anaerobic
fitness during match play.
The data presented in this thesis provide insight into positive and negative aspects of the estimation of maximal running intensities as well as physiological responses during match play. Additionally, the results offer practitioners useful alternatives to arbitrary displacement, velocity and acceleration bands that allow the comprehensive assessment of external loads to maximise performance, monitor fatigue and minimise injury risk. Finally, the results demonstrate the application of the critical power concept (i.e. CS and CPmet) in soccer, which could be used to quantify physiological responses throughout a competitive season.
Access to this thesis is embargoed until 11 Dec 2021.
Lord, C. N. (2019). The application of maximal mean and critical speed and metabolic powers for the quantification of external load in soccer. Retrieved from https://ro.ecu.edu.au/theses/2268