(I) This study aimed to evaluate the impact on physical demands induced by FIFA’s new rule implemented based on the number of substitutions caused by COVID-19. (II) Sixty-six matches were analysed in peak periods (microcycles of three matches in a week) in the competition period before and after the pandemic. The variables collected were organised by team (22 from LaLiga^TM SmartBank 2019–2020) for a total of 132 team records and 1077 player performance reports using a multi-camera tracking system and Mediacoach^® software. Physical performance variables were analysed in the first half, second half and whole match, thus determining the individual and collective performances of the team. (III) This study shows how, despite the increase in substitutions allowed with the new rule, physical performance increased in some variables in the congested periods (e.g., total distance run and distance run in the first and second halves). Additionally, the players’ physical performance involved in a substitution was greater than it was for players who completed the game. (IV) The new substitution rule helps to maintain and even improve physical performance. This measure could improve intensity levels in both individual and team performance. It could even safeguard the physical integrity of the players by reducing the risk of injury, as fewer players have to play the full match.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) forced a stoppage in the 2019/2020 season of LaLiga™, possibly influencing performance indicators in the return to competition. Therefore, here, we evaluated whether the stoppage due to the coronavirus 2019 disease (COVID-19) lockdown influenced physical performance compared to the start of LaLiga^TM in terms of high-intensity efforts. Using a semi-automatic, multiple-camera system, running activities during 22 matches were analyzed. We compared the first 11 matches of the season (pre-lockdown) with the 11 matches just after the restart of LaLiga™ (post-lockdown). The results showed higher (p < 0.05) performance in the pre-lockdown period compared with the post-lockdown period, including in medium-speed running (14.1–21 km/h), high-speed running (21.1–24 km/h), and sprinting speed running distances (>24 km/h). However, the number of accelerations/min and decelerations/min were significantly higher during the post-lockdown period. Therefore, we conclude that the stoppage due to the COVID-19 lockdown generated lower physical performance in the post-lockdown period compared with the pre-lockdown period, most likely due to the accumulation of matches (congested schedules).

The impact that occurs on the runner’s foot when it lands on the ground depends on numerous factors: footwear, running technique, foot strike and landing pattern, among others. However, the surface is a decisive factor that can be selected by the runner to improve their sports practice, thereby avoiding injuries. This study aimed to assess the number and magnitude of accelerations in impact (produced by the runner when their foot strikes the ground) on three different surfaces (grass, synthetic track, and concrete) in order to know how to prevent injuries. Thirty amateur runners (age 22.6 ± 2.43 years) participated in the study. They had to run consecutively on three different surfaces at the same speed, with a three axis-accelerometer placed on the sacrum and wearing their own shoes. The results showed that the running impacts differed based on the type of surface. Higher mean acceleration (MA) and mean peak acceleration (PA) in the impacts were observed on concrete compared to the other two surfaces. There were small differences for MA: 1.35 ± 0.1 g (concrete) vs. 1.30 ± 0.1 g (synthetic track) SD: 0.43 (0.33, 0.54) and 1.30 ± 0.1 g (grass) SD: 0.36 (0.25, 0.46), and small differences for PA: 3.90 ± 0.55 g (concrete) vs. 3.68 ± 0.45 g (synthetic track) SD 0.42 (0.21, 0.64) and 3.76 ± 0.48 g (grass) SD 0.27 (0.05, 0.48), implying that greater impacts were produced on concrete compared to synthetic track and grass. The number of peaks of 4 to 5 g of total acceleration was greater for concrete, showing small differences from synthetic track: SD 0.23 (−0.45, 0.9). Additionally, the number of steps was higher on synthetic track (34.90 ± 2.67), and small differences were shown compared with concrete (33.37 ± 2.95) SD 0.30 (−0.25, 0.85) and with grass (35.60 ± 3.94) SD 0.36 (−0.19, 0.91). These results may indicate a change in technique based on the terrain. Given the increasing popularity of running, participants must be trained to withstand the accelerations in impact that occur on different surfaces in order to prevent injuries.

Purpose: The aim of this study was to analyze the evolution of the four most important leagues and to identify if there are differences between the English Premier League and the rest of the European leagues. Methods: Each team was characterized according to a set of 52 variables including offensive, defensive, and buildup 10 variables that were computed from OPTA’s on-ball event records of the matches for main national leagues between the 2014 and 2018 seasons. To test the evolution of leagues, the t-SNE dimensionality reduction technique was used. To better understand the differences between leagues and teams, the most discriminating variables were obtained as a set of rules discovered by RIPPER, a machine learning algorithm. Results: The evolution of playing styles has meant that teams in the major European leagues seem to 15 be approaching homogeneity of technical-tactical behavior. Despite this, a distinction can be seen between the English teams concerning the rest of the teams in the other leagues, determined by fewer free kicks, fewer long passes but more vertical, more errors in ball control but greater success in dribbling. Conclusions: These results provide important knowledge and practical applications because of the study of the different variables and performance indicators among the best football championships

Object recognition and tracking have long been a challenge, drawing considerable attention from analysts and researchers, particularly in the realm of sports, where it plays a pivotal role in refining trajectory analysis. This study introduces a different approach, advancing the detection and tracking of soccer balls through the implementation of a semi-supervised network. Leveraging the YOLOv7 convolutional neural network, and incorporating the focal loss function, the proposed framework achieves a remarkable 95% accuracy in ball detection. This strategy outperforms previous methodologies researched in the bibliography. The integration of focal loss brings a distinctive edge to the model, improving the detection challenge for soccer balls on different fields. This pivotal modification, in tandem with the utilization of the YOLOv7 architecture, results in a marked improvement in accuracy. Following the attainment of this result, the implementation of DeepSORT enriches the study by enabling precise trajectory tracking. In the comparative analysis between versions, the efficacy of this approach is underscored, demonstrating its superiority over conventional methods with default loss function. In the Materials and Methods section, a meticulously curated dataset of soccer balls is assembled. Combining images sourced from freely available digital media with additional images from training sessions and amateur matches taken by ourselves, the dataset contains a total of 6331 images. This diverse dataset enables comprehensive testing, providing a solid foundation for evaluating the model’s performance under varying conditions, which is divided by 5731 images for supervised system and the last 600 images for semi-supervised. The results are striking, with an accuracy increase to 95% with the focal loss function. The visual representations of real-world scenarios underscore the model’s proficiency in both detection and classification tasks, further affirming its effectiveness, the impact, and the innovative approach. In the discussion, the hardware specifications employed are also touched on, any encountered errors are highlighted, and promising avenues for future research are outlined.