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F. Marcello Iaia, Rampinini Ermanno, and Jens Bangsbo

This article reviews the major physiological and performance effects of aerobic high-intensity and speed-endurance training in football, and provides insight on implementation of individual game-related physical training. Analysis and physiological measurements have revealed that modern football is highly energetically demanding, and the ability to perform repeated high-intensity work is of importance for the players. Furthermore, the most successful teams perform more high-intensity activities during a game when in possession of the ball. Hence, footballers need a high fitness level to cope with the physical demands of the game. Studies on football players have shown that 8 to 12 wk of aerobic high-intensity running training (>85% HRmax) leads to VO2max enhancement (5% to 11%), increased running economy (3% to 7%), and lower blood lactate accumulation during submaximal exercise, as well as improvements in the yo-yo intermittent recovery (YYIR) test performance (13%). Similar adaptations are observed when performing aerobic high-intensity training with small-sided games. Speed-endurance training has a positive effect on football-specific endurance, as shown by the marked improvements in the YYIR test (22% to 28%) and the ability to perform repeated sprints (~2%). In conclusion, both aerobic and speed-endurance training can be used during the season to improve high-intensity intermittent exercise performance. The type and amount of training should be game related and specific to the technical, tactical, and physical demands imposed on each player.

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Ian Rollo, Franco M. Impellizzeri, Matteo Zago, and F. Marcello Iaia

The physical-performance profiles of subelite male footballers were monitored during 6 wk of a competitive season. The same squad of players played either 1 (1G, n = 15) or 2 (2G, n = 15) competitive matches per week. On weeks 0, 3, and 6, 48 h postmatch, players completed countermovement jump (CMJ), 10- and 20-m sprints, the Yo-Yo Intermittent Recovery Test (YYIRT), and the Recovery-Stress Questionnaire. Both groups undertook 2 weekly training sessions. The 2G showed after 6 wk lower YYIRT (–11% to 3%, 90% CI –15.8% to –6.8%; P < .001) and CMJ performances (–18.7%, –21.6 to –15.9%; P = .007) and higher 10-m (4.4%, 1.8–6.9%; P = .007) and 20-m sprints values (4.7%, 2.9% to 6.4%; P < .001). No differences were found at 3 wk (.06 < P < .99). No changes over time (.169 < P < .611) and no differences time × group interactions (.370 < P < .550) were found for stress, recovery, and the Stress Recovery Index. In conclusion players’ ability to sprint, jump, and perform repeated intense exercise was impaired when playing 2 competitive matches a week over 6 wk.

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Enrico Perri, Carlo Simonelli, Alessio Rossi, Athos Trecroci, Giampietro Alberti, and F. Marcello Iaia

Purpose: To investigate the relationship between the training load (TL = rate of perceived exertion × training time) and wellness index (WI) in soccer. Methods: The WI and TL data were recorded from 28 subelite players (age = 20.9 [2.4] y; height = 181.0 [5.8] cm; body mass = 72.0 [4.4] kg) throughout the 2017/2018 season. Predictive models were constructed using a supervised machine learning method that predicts the WI according to the planned TL. The validity of our predictive model was assessed by comparing the classification’s accuracy with the one computed from a baseline that randomly assigns a class to an example by respecting the distribution of classes (B1). Results: A higher TL was reported after the games and during match day (MD)-5 and MD-4, while a higher WI was recorded on the following days (MD-6, MD-4, and MD-3, respectively). A significant correlation was reported between daily TL (TLMDi) and WI measured the day after (WIMDi+1) (r = .72, P < .001). Additionally, a similar weekly pattern seems to be repeating itself throughout the season in both TL and WI. Nevertheless, the higher accuracy of ordinal regression (39% [2%]) compared with the results obtained by baseline B1 (21% [1%]) demonstrated that the machine learning approach used in this study can predict the WI according to the TL performed the day before (MD<i). Conclusion: The machine learning technique can be used to predict the WI based on a targeted weekly TL. Such an approach may contribute to enhancing the training-induced adaptations, maximizing the players’ readiness and reducing the potential drops in performance associated with poor wellness scores.

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Paolo Gaudino, F. Marcello Iaia, Anthony J. Strudwick, Richard D. Hawkins, Giampietro Alberti, Greg Atkinson, and Warren Gregson

Purpose:

The aim of the current study was to identify the external-training-load markers that are most influential on session rating of perceived exertion (RPE) of training load (RPE-TL) during elite soccer training.

Methods:

Twenty-two elite players competing in the English Premier League were monitored. Training-load data (RPE and 10-Hz GPS integrated with a 100-Hz accelerometer) were collected during 1892 individual training sessions over an entire in-season competitive period. Expert knowledge and a collinearity r < .5 were used initially to select the external training variables for the final analysis. A multivariateadjusted within-subjects model was employed to quantify the correlations of RPE and RPE-TL (RPE × duration) with various measures of external training intensity and training load.

Results:

Total high-speed-running (HSR; >14.4 km/h) distance and number of impacts and accelerations >3 m/s2 remained in the final multivariate model (P < .001). The adjusted correlations with RPE were r = .14, r = .09, and r = .25 for HSR, impacts, and accelerations, respectively. For RPE-TL, the correlations were r = .11, r = .45, and r = .37, respectively.

Conclusions:

The external-load measures that were found to be moderately predictive of RPE-TL in soccer training were HSR distance and the number of impacts and accelerations. These findings provide new evidence to support the use of RPE-TL as a global measure of training load in elite soccer. Furthermore, understanding the influence of characteristics affecting RPE-TL may help coaches and practitioners enhance training prescription and athlete monitoring.