Objective The aim of this study was (i) provide reference data of metabolic power-based measures during professional football matches; and to (ii) analyze the between-position and between-halves differences of power-based measures during professional football matches. Methods Forty-six professional male players from two Turkish Super League teams were observed during two seasons, and 58 matches were analyzed. Total distance, equivalent distance, Low Power (LP), Intermediate Power (IP), High Power (HP), Elevated Power (EP), Max Power (MP) and power metabolic measures Pmet at different match moments were considered. Results Significant between-position differences were observed for IP (p: 0.000; d: 0.284), HP (p ≤ 0.001; d = 0.45), EP (p ≤ 0.001; d = 0.44), and for MP (p ≤ 0.001; d = 0.56), with the central defenders (CD) showing the lower values, and the central midfielders (CM) showing the higher values for the overall measures. Conclusion Power-based measures are dependent on playing positions. While the CD have lower Pmet values when compared to all positions, the CM have the greatest values. Training and recovery strategies must be ensured for CM players, especially those who have greater match participation.

Purpose The aim of this study was to analyze the within-week differences in external training intensity in different microcycles considering different playing positions in women elite volleyball players. Methods The training and match data were collected during the 2020–2021 season, which included 10 friendly matches, 41 league matches and 11 champions league matches. The players’ position, training/match duration, training/match load, local positioning system (LPS) total distance, LPS jumps, accelerations, decelerations, high metabolic load distance (HMLD), acute and chronic (AC) mean and AC ratio calculated with the rolling average (RA) method and the exponentially weighted moving average (EWMA) method, monotony and strain values were analyzed. Results All the variables except strain, Acc/Dec ratio and acute mean (RA) showed significant differences among distance to match days. Regarding the players’ positions, the only difference was found in the AC ratio (EWMA); in all microcycles, the middle blocker player showed workload values when compared with the left hitter, setter and libero. Conclusion Overall, the analysis revealed that the intensity of all performance indicators, except for strain, acc/dec and acute mean load (RA), showed significant differences among distance to match day with moderate to large effect sizes. When comparing players’ positions, the middle blocker accumulated the lowest loads. There were no significant differences among other positions.

The aim of this study was to analyse the win, draw, and loss outcomes of soccer matches with situational variables and performance indicators. Data from group stage matches spanning the ten years between the 2010/2011 and 2019/2020 seasons in the European Champions League, were used. One-way analysis of variance (ANOVA) and Tukey HSD (honestly significant difference) tests indicated performance indicators which affected the outcome of matches. K-mean clustering, with statistically significant variables, categorized the quality of the opposition into three clusters: weak, balanced, and strong. Multidimensional scaling (MDS) and decision tree analysis were applied to each of these clusters, highlighting that performance indicators of the teams differed according to the quality of their opponent. Furthermore, according to the decision tree analysis, certain performance indicators, including scoring first and shots on target, increased the chances of winning regardless of the quality of the opposition. Finally, particular performance indicators increased the chance of winning, while others decreased this, in accordance with the quality of the opposition. These findings can help coaches develop different strategies, before or during the match, based on the quality of opponents, situational variables, and performance indicators.

In this study, a hybrid model combining discrete wavelet transforms (WTs) and artificial neural networks (ANNs) is used to estimate the monthly streamflow. The WT-ANN hybrid model was developed using the Daubechies main wavelet to predict the streamflow for three gauging stations on the Çoruh river basin one month in advance, with different combinations of air temperature, precipitation, and streamflow variables, and their wavelet transformations. Four different hybrid WT-ANN models were generated and compared with four different conventional ANN models. The dataset was chronologically divided into training, validation, and testing data. The results indicated that the WT-ANN hybrid models performed better than the traditional ANN models for all three stations. Furthermore, the chronologically divided dataset was used to examine the effects of changes in hydrological data over time on model performance. In conclusion, model performances in the training period deteriorated during the validation and testing periods due to structural changes in the hydrological data.

This study aims to determine the best player in each position from among the footballers who played in the 2018 World Cup in Russia. Player statistics for those who played over 200 minutes were obtained from the FIFA official and transfermarkt.com websites. Selected performance variables were then calculated per 100 minutes and the results were normalised. Kruskal Wallis H and Bonferroni Tests were used to determine the weights of the variables before the analysis. As the variables will have different values according to the players’ positions, the weights for each position were calculated separately. Finally, the performances of the players on the basis of the variables used were ranked for each position using the TOPSIS method. A second analysis was undertaken including only those players whose ages were under 28 and goalkeepers whose ages were under 32. The purpose of this analysis was to identify players with potential that had been largely unrecognised up until the tournament. It was found that both the teams selected in this way were dominated by players from European clubs. Ninety-two percent of the top sixty players in the analysis were playing in European leagues with 85% playing in Spain, England, Italy, Germany, France or Russia.