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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% HR_max) leads to VO_2max 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.

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.

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 (TL_MDi ) and WI measured the day after (WI_MDi+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.

Purpose: To compare the acute performance and psychophysiological responses of repeated cycling sprints to exhaustion with a short exercise-to-rest ratio (1:6), between different effort durations and inspired oxygen fractions. Methods: On separate visits, 10 active participants completed 6 repeated cycling sprint exercises to exhaustion with 3 different effort durations (5, 10, and 20 s) and 2 conditions of inspired oxygen (20.9% and 13.6%). Exercise-to-rest ratio was 1:6 for all trials (ie, 5:30, 10:60, and 20:120). Vastus lateralis muscle oxygenation (near-infrared spectroscopy), blood lactate concentration, and lower-limb and breathing discomfort, using ratings of perceived exertion, were measured. Results: Number of sprints and peak power output decreased while blood lactate increased (all P < .001) during 5:30 compared with 10:60 or 20:120. No condition or interaction effects were reported for blood lactate and exercise-related sensation. Muscle deoxyhemoglobin increased (P < .001) and total hemoglobin decreased (P = .002) during sprint with increasing sprint duration (no condition or interaction). Conclusion: During repeated-sprint exercise to exhaustion with a short exercise-to-rest ratio, the psychophysiological responses did not differ between normoxia and moderate hypoxia, probably due to an extended recovery period. It means that hypoxia did not modify repeated-sprint exercise performance with a short exercise-to-rest ratio. The sprint duration was the primary underlying factor of the observed differences in performance and muscle oxygenation reported between the repeated-sprint exercise sessions.