METHODS: The rank-sum ratio (RSR) was employed to evaluate the attack, defense, and overall attacking and defensive performance between the top and bottom teams during the 2019 Men's Basketball World Cup. Additionally, an independent sample T-test was conducted to test the difference in performance indicators of attack and defense between the top eight and bottom eight teams. Spearman Rho Correlation was conducted to determine the relationship between the attacking and defensive RSR value and the final competition ranking at the 0.05 confidence level. Pearson Correlation was employed to test the relationship between the performance indicators and the attacking and defensive RSR value at the 0.05 confidence level. According to Spearman and Pearson Correlation, the indicators which contributed most to the attacking and defensive performance, as well as the correlation between attack and defense and the final ranking, can thus be determined.
RESULTS: The results showed that the attacking performance of the top eight teams was far better than the bottom eight teams in terms of average points (p = 0.000), 2-point shoot percentage (p = 0.001), 3-point shoot percentage (p = 0.003), free throw percentage (p = 0.001), turnovers (p = 0.012), and assists (p = 0.000), and there was a significant difference (p < 0.05). However, second attack (p = 0.484), fast-break (p = 0.174), and offensive rebounds (p = 0.261) showed no significant difference between the two cohorts (P > 0.05), and the offensive rebounds of the bottom eight teams were better than the top eight teams. Additionally, there was a large gap between the top eight teams and the bottom eight teams in lost points (p = 0.001) and defensive rebounds (p = 0.000), with a very significant difference (p < 0.01). However, steals (p = 0.760), blocks (p = 0.166), and fouls (p = 0.686) had no significant difference between the two cohorts (P > 0.05). Additionally, there was a very significant difference between attack RSR (p = 0.000), defense RSR (p = 0.006), and the overall attack-defense RSR (p = 0.000) of the top eight and bottom eight teams (p < 0.01), and most top teams focused on developing both attack and defense and paid attention to improve the overall attacking and defensive ability. Moreover, there was a significant relationship between the overall attack-defense performance and assists (p = 0.832), rebounds (p = 0.762), turnovers (p = 0.702), 2-point shoot percentage (p = 0.704), defensive rebounds (p = 0.809), fast-break points (p = 0.577), blocks (p = 0.600), and free throw percentage (p = 0.575).
CONCLUSIONS: This study showed that the top basketball teams focused on developing both attack and defense, and have the common characteristics of strong attack and defense. Whether it was the attack, defense, or overall attacking and defensive ability, there was a significant relationship with the final ranking. Additionally, this study showed that there were very significant differences in both attacking and defensive abilities between the top eight and bottom eight teams, as well as highlighted their respective advantages and disadvantages in attacking and defensive indicators. Besides that, this study found that performance indicators such as assists, defensive rebounds, 2P%, turnovers, FT%, fast-breaks, and blocks were the main factors that distinguish the top and bottom teams, and they had a significant relationship with overall attacking and defensive performance. The above information allows coaches and players to learn the latest developments in competitive basketball, as well as their advantages and disadvantages, to help them organize targeted training in the future.
METHODS: The data was collected in training and matches of a professional adult male soccer team during three complete seasons (2020/21-2022/2023). The sample included 6 different HCs (48.8 ± 7.4 years of age; 11.2 ± 3.9 years as a HC). The 4 weeks and 4 games before and after the replacement of HCs were analysed. External load variables were collected with Global Positioning System (GPS) devices. A logistic regression (LR) model was developed to classify the HCs' retention or dismissal. A sensitivity analysis was also conducted to determine the specific locomotive variables that could predict the likelihood of HC retention or dismissal.
RESULTS: In competition, locomotor performance was better under the dismissed HCs, whereas the new HC had better values during training. The LR model demonstrated a good prediction accuracy of 80% with a recall and precision of 85% and 78%, respectively, amongst other model performance indicators. Meters per minute in games was the only significant variable that could serve as a potential physical marker to signal performance decline and predict the potential dismissal of an HC with an odd ratio of 32.4%.
DISCUSSION: An in-depth analysis and further studies are needed to understand other factors' effects on HC replacement or retention.