Comparing Maximal Mean and Critical Speed and Metabolic Powers in Elite and Sub-elite Soccer

Abstract

The quantification of maximal mean speed (MMS), maximal mean metabolic power (MMPmet), critical speed (CS) and critical metabolic power (CPmet) was conducted over full A-League (elite) and National Premier League (NPL; sub-elite) seasons. Comparisons were made between levels of soccer competition and playing positions (i. e. centre backs, full backs, central midfielders, wide midfielders and strikers). 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. Maximal mean values, particularly during smaller time windows (i. e. 1 and 5 s), were greater in A-League match play. Only MMPmet1 was identified as being consistently different between competitions (P=<0.001–0.049) in all playing positions. Significance was only observed in CS (P=0.005) and CPmet (P=0.005) of centre backs between competitions. Centre backs were identified as the least energy demanding playing position. The present findings suggests that both maximal mean and critical analyses are suitable alternatives to common absolute distance and speed assessments of match running performance during competitive matches.

Assessing Inter-Limb Asymmetries in Soccer Players: Magnitude, Direction and Association with Performance

“…Soccer is an explosive sport where players perform repeated high-intensity actions. A soccer player can cover distances of ~10000 m during competitive matches, of which ~700 m and ~130 m are classified as high speed and sprinting (Lord et al, 2020), respectively. Short accelerations and linear sprints are two of the most important movement patterns since they frequently precede goals (Faude et al, 2012).…”

“…Frequent high accelerations of 0.36-0.38 n•min-2 and high decelerations of 0.46-0.52 n•min-2 have been reported during match play (Kelly et al, 2019), whilst other skills have been shown to be of importance when scoring goals, including the ability to change the direction, to rotate, and to jump (Faude et al, 2012). Since power is a key determinant for all of these skills in soccer (Kelly Journal of Human Kinetics -volume 79/2021 http://www.johk.pl Lord et al, 2020), its assessment across a range of physical tasks is warranted.…”

Bayesian approaches for critical velocity modelling of data from intermittent efforts

“…An important observation is the field data impact estimation. Although the parameter estimates in Models ( 6) and ( 7) are in keeping with prior knowledge, there are differences between the posterior estimates and the prior means given in Equation (8). We also observe that Models ( 6) and ( 7) provide comparable and believable values.…”

Reliability and Validity of Maximal Mean and Critical Speed and Metabolic Power in Australian Youth Soccer Players

“…Researchers have previously applied the moving average method of analysis to GPS data and compared moving average windows that are associated with higher running speeds (e.g. 1, 5 and 10 s) used to calculate MMS and MMPmet values, to absolute running distances associated with high-speed and sprint-speed running ( Lord et al, 2019 ). The resulting lower match-to-match variability in MMS and MMPmet values compared to absolute high-speed and sprint-speed total distances obtained from match play suggests that the maximal mean method of analysis is an appropriate alternative to absolute distances achieved in soccer ( Lord et al, 2019 ).…”

“…This form of assessment applies a moving average to GPS data over specified time intervals and has been previously used to prescribe position-specific field-based conditioning drills in Gaelic and Australian Rules football (Delaney et al, 2017;Malone et al, 2017). While the use of moving average data analyses in soccer is in its infancy, an initial match-to-match variation study suggested that this form of analysis was associated with reduced between-match variability of running performance variables, particularly when assessed at higher speeds, compared to that of absolute distances achieved during match play (Lord et al, 2019).…”