In order to better understand the specificity of training adaptations, we compared the effects of two different anaerobic training regimes on various types of soccer-related exercise performances. During the last 3 weeks of the competitive season, thirteen young male professional soccer players (age 18.5±1 yr, height 179.5±6.5 cm, body mass 74.3±6.5 kg) reduced the training volume by ~20% and replaced their habitual fitness conditioning work with either speed endurance production (SEP; n = 6) or speed endurance maintenance (SEM; n = 7) training, three times per wk. SEP training consisted of 6–8 reps of 20-s all-out running bouts followed by 2 min of passive recovery, whereas SEM training was characterized by 6–8 x 20-s all-out efforts interspersed with 40 s of passive recovery. SEP training reduced (p<0.01) the total time in a repeated sprint ability test (RSA_t) by 2.5%. SEM training improved the 200-m sprint performance (from 26.59±0.70 to 26.02±0.62 s, p<0.01) and had a likely beneficial impact on the percentage decrement score of the RSA test (from 4.07±1.28 to 3.55±1.01%) but induced a very likely impairment in RSA_t (from 83.81±2.37 to 84.65±2.27 s). The distance covered in the Yo-Yo Intermittent Recovery test level 2 was 10.1% (p<0.001) and 3.8% (p<0.05) higher after SEP and SEM training, respectively, with possibly greater improvements following SEP compared to SEM. No differences were observed in the 20- and 40-m sprint performances. In conclusion, these two training strategies target different determinants of soccer-related physical performance. SEP improved repeated sprint and high-intensity intermittent exercise performance, whereas SEM increased muscles’ ability to maximize fatigue tolerance and maintain speed development during both repeated all-out and continuous short-duration maximal exercises. These results provide new insight into the precise nature of a stimulus necessary to improve specific types of athletic performance in trained young soccer players.

The aim of the current study was to evaluate field-based physical performance of under 16 years (U16) elite and sub-elite soccer players. Forty elite (n = 20) and sub-elite (n = 20) soccer players were enrolled and tested for countermovement jump (CMJ), 10-m sprint and agility, in terms of sprint with 90° turns (S90), S90 with ball, Slalom, Slalom with ball, and reactive agility (RAT). Statistical and practical significant differences were observed in CMJ, S90 and RAT between elite and sub-elite. Elite players exhibited a likely better performance in Slalom, Slalom with ball and S90 with ball despite a non-significant difference by level of play. Concerning sprint ability, both groups showed similar performance in the 10-m sprint. Countermovement jump and agility tests are field-based physical assessments recommended to better distinguish between U16 elite and sub-elite soccer players.

Trecroci, A, Porcelli, S, Perri, E, Pedrali, M, Rasica, L, Alberti, G, Longo, S, and Iaia, FM. Effects of different training interventions on the recovery of physical and neuromuscular performance after a soccer match. J Strength Cond Res 34(8): 2189–2196, 2020—In competitive soccer, players are frequently required to play in periods with congested fixtures in which they have limited time to recover between matches (3–4 days). Thus, finding the most appropriate intervention strategy to limit players' neuromuscular (muscle function of lower limbs) and physical (running performance) impairments in this short period becomes crucial. The aim of the study was to examine how muscle function of knee extensors and flexors and sprint performance recovered +72 hours after match in relation to different field-based training sessions. Using a crossover design, 9 subelite players (age 17.6 ± 0.5 years, height 1.77 ± 0.02 m, body mass 66.4 ± 5.8 kg) underwent a soccer-specific training (SST) session or an active recovery regime (AR) on the second day after a match. Immediately after (0 hour) and +72 hours after match, 30-m sprint and repeated sprint ability (RSA) were assessed. Maximum isometric voluntary force (MVF) of knee extensors and flexors was determined at 120° and 90° (with 180° being full extension), respectively. SST and AR promoted similar effects on the recovery kinetics of sprint, RSA, and MVF of knee extensors (p > 0.05). However, compared with SST, AR promoted a significantly better restoration of MVF of knee flexors (p < 0.05) after +72 hours from the match. Because muscle fatigue has been related with increased hamstring injury risk, a training based on AR can be a valid intervention to promote the recovery of muscle force production of knee flexors and reduce hamstring injury risk in the postmatch period.

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.

The aim of this study was to evaluate the effects of 12-week balance and slackline training programs on physical performance and perceived enjoyment scale in young soccer players. Forty-one preadolescent soccer players were assigned to two experimental groups performing traditional balance (BLT) or slackline training (SLT), and a control group. Pre-post assessment encompassed Balance Error Scoring System (BESS), Star Excursion Balance test (SEBT), sprint with 90° turns (S90), and countermovement jump (CMJ). The rate of perceived enjoyment scale (PACES) was applied at the end of the experimental period. SLT and BLT improved similarly in BESS, SEBT and S90. No changes were detected in the CMJ. Regarding PACES score, SLT presented significantly higher values than BLT. Young athletes may benefit from a motivating training approach, thus, a designed program based on slackline drills should be preferable to improve physical performance in terms of balance and change of direction ability in preadolescent soccer players.

The use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.