Relationships Between Training Load Variables in Professional Youth Football Players

Abstract

This study aims to investigate the relationship between subjective and external measures of load in professional youth football players whilst accounting for the effect of training theme or competition. Data from ratings of perceived exertion and global positioning system-derived measures of external training load were collected from 20 professional youth players (age=17.4±1.3 yrs) across a 46-week season. General characteristics of training sessions were categorised based on their proximity to match day. The underlying structure of the data was investigated with principal component analysis. An extraction criterion comprising eigenvalues >1 was used to identify which components to retain. Three components were retained for training performed three days prior to match day (80.2% of variance), with two components (72.9–89.7% of variance) retained for all other modes. Generally, the first component was represented by measures of volume (Total Distance, PlayerLoad and low intensity running) whilst the second and third components were characterised by measures of intensity. Identification of multiple components indicates that load monitoring should comprise multiple variables. Additionally, differences in the underlying structure across training days that reflected different goals suggest that effective monitoring should be specific to the demands of different session types.

Key words

training load - monitoring - session-rpe - team sports - football - workload

Publication History

Received: 30 May 2020

Accepted: 13 October 2020

Article published online:
01 December 2020

• References

• 1 Drew MK, Finch CF. The relationship between training load and injury, illness and soreness: A systematic and literature review. Sports Med 2016; 46: 861-883
• 2 Akenhead R, Nassis GP. Training load and player monitoring in high-level football: current practice and perceptions. Int J Sports Physiol Perform 2016; 11: 587-593
• 3 Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med 2014; 44: 139-147
• 4 Scott BR, Lockie RG, Knight TJ. et al. A comparison of methods to quantify the in-season training load of professional soccer players. Int J Sports Physiol Perform 2013; 8: 195-202
• 5 Weaving D, Marshall P, Earle K. et al. Combining internal and external training-load measures in professional rugby league. Int J Sports Physiol Perform 2014; 9: 905-912
• 6 Alexiou H, Coutts AJ. A comparison of methods used for quantifying internal training load in women soccer players. Int J Sports Physiol Perform 2008; 3: 320-330
• 7 Weaving D, Jones B, Marshall P. et al. Multiple measures are needed to quantify training loads in professional rugby league. Int J Sports Med 2017; 38: 735-740
• 8 Delgado-Bordonau JL, Mendez-Villanueva A. Tactical periodization: Mourinho’s best-kept secret. Soccer Journal 2012; 57: 29-34
• 9 Buchheit M, Lacome M, Cholley Y. et al. Neuromuscular responses to conditioned soccer sessions assessed via GPS-embedded accelerometers: Insights into tactical periodization. Int J Sports Physiol Perform 2018; 13: 577-583
• 10 Malone JJ, Di Michele R, Morgans R. et al. Seasonal training-load quantification in elite English premier league soccer players. Int J Sports Physiol Perform 2015; 10: 489-497
• 11 Harriss D, MacSween A, Atkinson G. Ethical standards in sport and exercise science research: 2020 update. Int J Sports Med 2019; 40: 813-817
• 12 Impellizzeri FM, Rampinini E, Coutts AJ. et al. Use of RPE-based training load in soccer. Med Sci Sports Exerc 2004; 36: 1042-1047
• 13 Foster C, Hector LL, Welsh R. et al. Effects of specific versus cross-training on running performance. Eur J Appl Physiol Occup Physiol 1995; 70: 367-372
• 14 Foster C, Florhaug JA, Franklin J. et al. A new approach to monitoring exercise training. J Strength Cond Res 2001; 15: 109-115
• 15 Weaving D, Dalton NE, Black C. et al. The same story or a unique novel? Within-participant principal-component analysis of measures of training load in professional rugby union skills training. Int J Sports Physiol Perform 2018; 13: 1175-1181
• 16 Jones RN, Greig M, Mawéné Y. et al. The influence of short-term fixture congestion on position specific match running performance and external loading patterns in English professional soccer. J Sports Sci 2019; 37: 1338-1346
• 17 Scott MT, Scott TJ, Kelly VG. The validity and reliability of global positioning systems in team sport: a brief review. J Strength Cond Res 2016; 30: 1470-1490
• 18 van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in R. J Stat Softw 2011; 45: 1-67
• 19 Tabachnick BG, Fidell LS, Ullman JB. Using Multivariate Statistics. 5th ed. Boston: Pearson Education; 2007
• 20 Bartlett MS. A note on the Multiplying factors for various χ 2 approximations. J R Stat Soc Series B Stat Methodol 1954; 16: 296-298
• 21 Hair JF, Black WC, Babin BJ. et al. Multivariate Data Analysis. Upper Saddle River, NJ: Prentice Hall; 1998
• 22 Kaiser HF. The application of electronic computers to factor analysis. Educ Psychol Meas 1960; 20: 141-151
• 23 Williams S, Trewartha G, Cross MJ. et al. Monitoring what matters: a systematic process for selecting training-load measures. Int J Sports Physiol Perform 2017; 12: S2101-S2106
• 24 Akubat I, Patel E, Barrett S. et al. Methods of monitoring the training and match load and their relationship to changes in fitness in professional youth soccer players. J Sports Sci 2012; 30: 1473-1480 DOI: 10.1080/02640414.2012.712711.
• 25 Lovell TW, Sirotic AC, Impellizzeri FM. et al. Factors affecting perception of effort (session rating of perceived exertion) during rugby league training. Int J Sports Physiol Perform 2013; 8: 62-69
• 26 Manzi V, Iellamo F, Impellizzeri F. et al. Relation between individualized training impulses and performance in distance runners. Med Sci Sports Exerc 2009; 41: 2090-2096