Monitoring training in elite soccer players: Systematic bias between running speed and metabolic power data

P. Gaudino, F. M. Iaia, G. Alberti, A. J. Strudwick, Greg Atkinson, W. Gregson

    Research output: Contribution to journalArticlepeer-review

    63 Citations (Scopus)

    Abstract

    We compared measurements of high-intensity activity during field-based training sessions in elite soccer players of different playing positions. Agreement was appraised between measurements of running speed alone and predicted metabolic power derived from a combination of running speed and acceleration. Data was collected during a 10-week phase of the competitive season from 26 English Premier League outfield players using global positioning system technology. High-intensity activity was estimated using the total distance covered at speeds >14.4 km · h-1 (TS) and the equivalent metabolic power threshold of >20 W · kg-1 (TP), respectively. We selected 0.2 as the Âminimally important standardised difference between methods. Mean training session TS was 478±300 m vs. 727±338 m for TP (p<0.001). This difference was greater for central defenders (~ 85%) vs. wide defenders and attackers (~ 60%) (p<0.05). The difference between methods also decreased as the proportion of high-intensity distance within a training session increased (R2=0.43; p<0.001). We conclude that the high-intensity demands of soccer training are underestimated by traditional measurements of running speed alone, especially in training sessions or playing positions associated with less high-intensity activity. Estimations of metabolic power better inform the coach as to the true demands of a training session.

    Original languageEnglish
    Pages (from-to)963-968
    Number of pages6
    JournalInternational Journal of Sports Medicine
    Volume34
    Issue number11
    DOIs
    Publication statusPublished - 3 Apr 2013

    Fingerprint

    Dive into the research topics of 'Monitoring training in elite soccer players: Systematic bias between running speed and metabolic power data'. Together they form a unique fingerprint.

    Cite this