Previous research emphasizes the significance of key performance metrics in determining match outcomes. The purpose of this study is to enhance the understanding of success in professional soccer by analyzing the relationship between match outcomes (win, lose, draw) and various Performance Indicators extracted from the Greek soccer league, as well as to develop a regression model of success in soccer. The sample consisted of all 91 matches from the first round of the 2020–2021 season of the Greek Football League. Utilizing Kruskal–Wallis tests, significant differences were found in goals scored, shots, and shots on target, ball possession, passing metrics, touches in the penalty area, and average shot distance (p < 0.05), with winning teams having demonstrated superior performance metrics. Moreover, winning teams engaged more in positional attacks and counterattacks with shots (p < 0.05). The binary logistic regression model applied to predict match outcomes identified shots on target, counterattacks, passes metrics, offensive duels and set pieces (penalties, free kicks) as key factors influencing the likelihood of winning (p < 0.05). These findings collectively highlight the importance of effective offensive play, including goal scoring, shooting accuracy, and ball possession, in determining the outcomes of soccer matches, with the regression model offering a nuanced understanding of these relationships.

Countermovement jump (CMJ) performance analysis is vital in sports science for assessing lower-body strength and neuromuscular efficiency. This study evaluated the validity of the Enode Sensor and My Jump 3 App for measuring vertical jump heights, comparing them to those measured using the established Force Plate. Twenty-nine participants performed CMJs measured using each device. Descriptive statistics indicated mean jump heights of 48.4 ± 4.18 cm (for the Enode Sensor), 47.3 ± 4.05 cm (for My Jump 3), and 46.1 ± 4.03 cm (for the Force Plate). Reliability was confirmed via Intraclass Correlation Coefficients (ICCs), with the Enode Sensor at 0.914 and My Jump 3 at 0.968, demonstrating excellent reliability. Bland–Altman analysis showed mean biases of 2.281 cm (for the Enode Sensor) and 1.297 cm (for My Jump 3) against the Force Plate, with limits of agreement suggesting close alignment. Strong positive correlations were observed (for the Enode Sensor, r = 0.972 and for My Jump 3, r = 0.987; p < 0.001), and linear regression analysis produced R² values of 0.945 and 0.973, respectively, confirming both tools’ accuracy for vertical jump measurement. These findings indicate that although both tools are suitable for CMJ assessment, My Jump 3 demonstrated slightly superior accuracy, underscoring the potential for accessible, reliable performance monitoring in sports contexts.

Football is a sport in which athletes perform many movements using one of their legs, which is characterized as dominant. This differentiation in usage often creates asymmetries between the limbs, which has been noted to be related to the occurrence of injuries as well as negative effects on performance. To assess these asymmetries, vertical jump tests, as well as tests using an isokinetic dynamometer, were employed. The aim of this study was to examine the relationship between the asymmetries observed in the application of the single-leg countermovement jump (SLCMJ) and those observed through isokinetic strength assessment at 60°/s, 180°/s, and 300°/s in high-level youth football players. The study involved 63 elite youth football players. For statistical analysis, correlation analysis and linear regression analysis were used. The results showed a significant positive correlation between the CMJ Bilateral Asymmetry Index and the BAI Anterior Quadriceps (60°/s) (r = 0.262, p = 0.038, 95% CI [0.016, 0.479]), indicating a small-to-moderate effect size (Fisher’s z = 0.269, SE = 0.129). Additionally, a significant negative correlation was identified between the CMJ Bilateral Asymmetry Index and the BAI Posterior Hamstrings (60°/s) (r = −0.319, p = 0.011, 95% CI [−0.525, −0.077]), demonstrating a moderate inverse relationship (Fisher’s z = −0.331, SE = 0.129). The overall regression model was significant, F(6,56) = 2.42, p = 0.038, R² = 0.206, indicating that the predictors collectively explained approximately 20.6% of the variance in the CMJ Bilateral Asymmetry Index. From the findings of this study, we conclude that the SLCMJ asymmetry index cannot replace the asymmetry index from isokinetic strength tests.

The running performance of football players is influenced by their team’s formation as well as by their playing position. The creation of the external load profile provides information to the coaching staff for personalized loading of the players based on their playing positions. The aim of this study was to create the athletic running profile of high-level football players under 17 years of age (U17) in the 1-4-3-3 formation, and to investigate the differences between the playing positions in the formation. The study involved 20 football players from a football academy of a professional team. For the study, 13 league matches were used in which the team played with the 1-4-3-3 formation. Positions were classified as central defenders (CDs), side defenders (SDs), central midfielders (CMs), side midfielders (SMs), and forwards (Fs). The players’ movement patterns were captured using a GPS device and categorized into six velocity zones (first: 0.1–7.19 km/h, second: 7.2–10.99, third: 11–14.39 km/h, fourth: 14.4–19.79 km/h, fifth: 19.8–25.19 km/h, sixth: >25.2 km/h). The accelerations and decelerations recorded were those exceeding 2 m/s⁻¹. The level of statistical significance was set at p < 0.05. The results showed that CMs exhibited the greatest external load in total distance and in specific distances in the other velocity zones (p < 0.05). Forwards dominated high-intensity efforts, leading in Distance Zone 5 with SD (mean = 555 m and mean = 559 m, respectively), as well as in Distance Zone 6 (F: mean = 146 m) and in high-speed running values (mean = 701 m). Side players, particularly SD and SM, contributed dynamically through elevated high-speed running (p < 0.05) and maximum speed (p < 0.05), reflecting their role in both transitions and positional phases of play. The study’s results clearly show that the physical demands of the playing positions in the 1-4-3-3 formation differ. This difference is likely due to the different tactical roles of each playing position. This variation between playing positions emphasizes the need for individualized loading of players during the training microcycle.

This study aimed to investigate the effects of mental fatigue on physical and cognitive performance (lower-limb power, isometric and handgrip strength, and psychomotor vigilance). Twenty-two physically active young adults (12 males, 10 females; M_age = 20.82 ± 1.47) were randomly assigned to either a Mental Fatigue (MF) or Control group (CON). The MF group showed a statistically significant (p = 0.019) reduction in non-dominant handgrip strength, declining by approximately 2.3 kg (about 5%), while no such change was observed in the CON group or in dominant handgrip strength across groups. Reaction time (RT) was significantly impaired following the mental fatigue protocol: RT increased by 117.82 ms, representing an approximate 46% longer response time in the MF group (p < 0.001), whereas the CON group showed a smaller, non-significant increase of 32.82 ms (~12% longer). No significant differences were found in squat jump performance, indicating that lower-limb explosive power may be less affected by acute mental fatigue. These findings demonstrate that mental fatigue selectively impairs fine motor strength and cognitive processing speed, particularly reaction time, while gross motor power remains resilient. Understanding these effects is critical for optimizing performance in contexts requiring fine motor control and sustained attention under cognitive load.

This study presents the analysis of the UEFA Euro 2024 in Germany, focusing on the impact of the first goal on match outcomes, goal distribution between halves and quarters, and the relationship between offensive, defensive, and goalkeeping metrics and match outcomes. Moreover, a regression model is developed to identify the key factors that significantly contribute to teams’ success. The analysis of the 36 group stage matches revealed that scoring the first goal significantly increased the likelihood of a positive match outcome. There were no significant differences between goals scored in the first and second halves or per 15 min of the game. Kruskal–Wallis tests highlighted that winning teams had more assists, attempts on target and runs into the penalty area. Defensive metrics showed that winning teams recovered more balls, while goalkeeping metrics revealed that winning teams had more clean sheets. The logistic regression model identified “Attempts on Target” and “Passes into Attacking Third” as significant positive predictors of winning, while “Attempts on Target Outside Area” and “Crosses Attempted” were negative predictors. These findings offer valuable insights for coaching staff to develop strategies focusing on key performance indicators that enhance the likelihood of winning.