The objective of this study was to compose an objective and detailed notational analysis system for 3 vs. 2GK smallsided soccer games, in which three roles are examined: attacker with ball, attacker without ball and defender. The actions and the outcome of the actions were registered for each player and in each role. Players earn points for each action and outcome according to an a priori determined scheme. Performance scores for each role are calculated as the average number of points a participant earns per trial. This notation system was tested on 19 highly talented female soccer players and validity and reliability of the system were determined. In addition, practical applications were discussed and the most important items of the notation system were determined and using only these items, a simplified notation system was proposed. The notation system has high ecological validity and can discriminate the high and low categorized players, but further development is necessary to increase the reliability of the system.
Young talented athletes that mature have an increased injury risk. Human movement scientist Alien van der Sluis studied soccer players of the talent development program of FC Groningen and tennis players of the talented development program of the Royal Dutch Lawn Tennis Federation (KNLTB). The soccer players were followed for three years around their adolescent growth spurt. In the year of their growth spurt, players have more injuries compared to the year before or the year after, and they miss more training sessions and matches. A possible cause is the different rates in which bone tissue, muscle tissue and tendon tissue adapt to the growing body. More specific, players that grow more than 0.6 cm in one month, have an increased risk for injury in the next month. Moreover, players with a late growth spurt are relatively small compared to their peers, and this leads to more injuries compared to their ‘earlier mature’ counterparts.Furthermore, tennis players high in risk-taking behavior (typical for puberty), have more injuries and players with better metacognitive skills such as monitoring, have less injuries. Players may be better capable of monitoring small physical complaints, which could help them to prevent themselves from having more severe injuries.Van der Sluis concluded that during puberty, there are specific risk factors for injuries in talented athletes. Coaches and trainers should estimate the moment of the adolescent growth spurt, to take injury preventive measures at the right moment. Monthly monitoring of length, could help to predict an increased risk of injury in periods of intensive growth. At last, it is advised to provide feedback to players high in risk-taking and to educate athletes in monitoring their own training process.
Substitution is an essential tool for a coach to influence the match. Factors like the injury of a player, required tactical changes, or underperformance of a player initiates substitutions. This study aims to predict the physical performance of individual players in an early phase of the match to provide additional information to the coach for his decision on substitutions. Tracking data of individual players, except for goalkeepers, from 302 elite soccer matches of the Dutch ‘Eredivisie’ 2018–2019 season were used to enable the prediction of the individual physical performance. The players’ physical performance is expressed in the variables distance covered, distance in speed category, and energy expenditure in power category. The individualized normalized variables were used to build machine learning models that predict whether players will achieve 100%, 95%, or 90% of their average physical performance in a match. The tree-based algorithms Random Forest and Decision Tree were applied to build the models. A simple Naïve Bayes algorithm was used as the baseline model to support the superiority of the tree-based algorithms. The machine learning technique Random Forest combined with the variable energy expenditure in the power category was the most precise. The combination of Random Forest and energy expenditure in the power category resulted in precision in predicting performance and underperformance after 15 min in a match, and the values were 0.91, 0.88, and 0.92 for the thresholds 100%, 95%, and 90%, respectively. To conclude, it is possible to predict the physical performance of individual players in an early phase of the match. These findings offer opportunities to support coaches in making more informed decisions on player substitutions in elite soccer.
