How did basketball teams win EuroBasket 2015? A non-standard analysis of performance based on passes, dribbling and turnovers

Game schedule, travel demands and contextual factors influence key game-related statistics among the top European male basketball teams

This study examined the effects of game schedule, travel demands and contextual factors on team game-related statistics during a full season. The top 10 teams competing in the 2020-2021 Euroleague basketball season were included where game-related statistics from their respective national competitions and the Euroleague competition were retrieved (761 games). Hierarchical linear regression models were computed to evaluate the effects of distance travelled, game schedule and contextual factors for the previous and current games (league, season phase, opponent level, game outcome, score differential) on key performance indicators (points, shooting, rebounds, assists, turnovers, fouls). Several significant models (p < 0.05) yielded R2 values ranging from 0.05 to 0.22 with small-to-medium effect magnitudes. Analyses revealed significant associations between longer durations separating games and less free-throws being made and between further distances travelled and worse 3-point shooting, more offensive rebounds and more fouls. Regarding contextual factors, favourable outcomes for shooting, assists, steals, fouling and turnovers were significantly associated with team success. Playing higher-level teams and competing in playoffs or finals was associated with several diminished outcomes. These results emphasize the multi-factorial nature of performance in elite European basketball, with game schedule, travel and various contextual factors requiring consideration in developing holistic operational plans for teams.

Home Advantage and Away Disadvantage of Teams in Champions League: Is It Valid for All Teams and Against Every Opponent?

The home advantage (HA) is a robust phenomenon in soccer whereby the home team wins more games and scores more goals than the away team. Similarly, away disadvantage (AD) means that an away team loses more games or scores less goals than the home team. This study examines the HA and AD values of teams in the UEFA-Champions League, covering the seasons from 2003/2004 to 2021/2022, a total of 2,344 matches. Controlling for team ability differences, the study revealed significant variations in HA, ranging from 32.1% to 79.5%, while AD values ranged from 45.1% to 71.9%. The study further found that HA remained consistent for teams across both the group and knockout stages, while AD varied between these stages. Furthermore, the results suggest that, for certain teams, HA is predominantly manifested against weaker opponents, and the impact of opponent strength on HA and AD is limited.

Pre-shot combinations and game-related statistics discriminating between winners and losers depending on the game location during the NBA COVID-19 season

Basketball in performance depends on numerous factors, where a stable trend was identified by winning teams with better performances in shooting effectiveness and rebounding. However, there is a need for a better understanding of pre-shot combinations that lead to these performance trends. This study aimed to analyze NBA teams’ game-related statistics, pre-shooting combinations, and pick-and-roll differences between winning and losing teams (considering the context: playing at home, away, or in a neutral court) during the COVID-19 season. A retrospective cross-sectional study on the 2019–2020 NBA season (906 games) was carried out. Game-related statistics were gathered from the private company InStat (https://basketball.instatscout.com/). The discriminant analysis and binary logistic regression models were run in order to discriminate the most important features of winning teams depending on the game location. The results showed that defensive rebounds and three-point shooting percentage remained the most important variables that best discriminated winners and losers independently of the game location context. The main results showed that winning teams had a better shooting percentage based on three-pointers, catch-and-shot actions, cuts, pick-and-roll efficacy, and uncontested shots based on a better collective behavior after a successful space creation dynamic through a tactical functional unit. At the same time, teams would need players with the ability to clear those possessions in which the opponents force to an isolation or a contested shot. From a practical application perspective, coaches should focus on composing a team with good shooters, skilled players in isolations, and a good game-time pick-and-roll strategy.

The Distribution of Match Physical Activities Relative to the Most Demanding Scenarios in Professional Basketball Players

The purpose of this study was to examine the distribution of physical activities relative to the most demanding scenarios across playing positions during official basketball match-play. Twelve professional basketball players were monitored during twelve matches using a local positioning system. Peak physical demands were measured via total distance covered, distance covered >18 km·h^-1, and the number of accelerations and decelerations >3 m·s^-2 captured over 30- and 60-s rolling averages. The results showed that players were exposed to more than one high-demanding scenario in all variables and time epochs examined. Additionally, total distance covered presented the greatest number of moderate-demanding scenarios (40-80% of most demanding scenarios), whereas distance covered >18 km·h^-1, and accelerations and decelerations >3 m·s^-2 presented the greatest proportion of low-demanding scenarios (<40% of most demanding scenarios). Regarding positional differences, backcourt players generally experienced a higher number of scenarios than frontcourt players in most variables, especially in total distance covered. For this variable, scenarios between 20 and 70% of most demanding scenarios during the 30-s epoch (p < 0.001; ES = 0.420.78), and scenarios between 50 and 90% of most demanding scenarios during the 60-s epoch (p < 0.001; ES = 0.400.64) showed significant differences between backcourt and frontcourt players. Our data suggest that match physical activities are position-dependent and variable-dependent. In addition, peak physical demands appear to be repeated during basketball competition. These results may be considered by practitioners to complement average values and most demanding scenarios when prescribing individualized training programs to optimize team performance.

Clustering Performances in Elite Basketball Matches According to the Anthropometric Features of the Line-ups Based on Big Data Technology

The aims of this study were: 1) to conduct a descriptive analysis of the anthropometric features of the line-ups of strong teams (top 16) in the 2019 FIBA Basketball World Cup; 2) to group the line-ups mentioned above into different clusters based on their average height, weight, and body mass index (BMI); and 3) to explore the performance variables that discriminate between various line-up clusters. The play-by-play statistics were collected from 104 team objects in 67 games and 525 line-ups were analyzed using two-step cluster and discriminant analysis. Line-ups were classified into four groups: low average height and weight with middle BMI (LowH–LowW–MiddleBMI); high average height and low average weight with low BMI (HighH–LowW–LowBMI); low average height and high average weight with high BMI (LowH–HighW–HighBMI); high average height and weight with middle BMI (HighH–HighW–MiddleBMI). The results of the discriminant analysis demonstrated that LowH–LowW–MiddleBMI line-ups had the least time played and the lowest offensive rating, but the best offensive rebounds, turnovers, and fastest game pace performance; HighH–LowW–LowBMI line-ups demonstrated the best defensive rating but performed poorly with a low value of assists and a high value of turnovers; the LowH–HighW–HighBMI group achieved the best time played statistics but had the lowest number of free throws made; the HighH–HighW–MiddleBMI group had a higher number of assists and a higher offensive rating and 2-point field goal performance, while also achieving the lowest number of offensive rebounds and ball possessions. These results provide novel insights for coaches and performance analysts to better understand the technical characteristics of different line-ups in elite basketball competitions.

Does predicted age at peak height velocity explain physical performance in U13-15 basketball female players?

Background

The aims of the study were (1) to identify the physical fitness and basic anthropometric characteristics of Polish female basketball players aged 13–15 years, (2) to show the effect of maturity timing on the performance in motor tests and basic body composition parameters, (3) to identify the index that contributes most to the prediction of performance in the tests of speed, jumping ability, agility, and endurance.

Methods

The sample included 904 female Polish players (U13–15). In part 1, maturity timing category distribution were examined within across age-groups. Maturity timing was followed by grouping with respect to years before or after the observed peak high velocity (PHV): PHV0 (− 0.50 to 0.49), PHV1 (0.50 to 1.49), PHV2 (1.50 to 2.49) and PHV3 (2.50 to 3.49). In part 2, the relationship between the anthropometric variables, physical fitness performance was assessed based on maturity timing categories (ANCOVA analysis). In part 3, backward stepwise multiple regression analyse quantified the relationship between maturity timing (group of PHV) and physical performance.

Results

ANCOVA results (age, body height, and body mass as covariates) showed in the U13 female basketball players significantly higher sprinting (20 m), jumping ability and endurance tests results of the PHV1 group. Better results was observed in U14 female players in PHV1 compared to PHV2 and PHV3 in 20 m and jumping tests but opposite trend was observed for 5 m sprint and endurance test (distance covered and VO2_max). U15 basketball players from the PHV3 group were characterized by better results of jumping abilities, endurance, 10 m and 20 m sprint and agility (total, S₄) tests. Maturity timing (10 m), chronological age (5 m, 20 m, agility, SVJ, VJ, and VO_2max tests), body height (10 m), body mass (10 m, 20 m, VJ, VO_2max), and the interaction between body mass and height (SVJ) were significant (adjusted R² = 0.02–0.10; p < 0.001) predictors of motor skills.

Conclusion

Trainng content of female basketball players aged 13–15 years old should be adjusted to biological requirements especially in jumping, endurance and 20 m sprint test. The time from peak height velocity (PHV) was a significant predictor only in the 10 m sprint test.

How To Win the Basketball Euroleague? Game Performance Determining Sports Results During 2003-2016 Matches

The main aim of this study was to establish game-related statistics that determined sport results in the Basketball EuroLeague between 2003 and 2016. The study encompasses matches played by 10 teams during 13 consecutive seasons of the EuroLeague. Twenty-two offensive and defensive game related variables were registered. Calculations were performed to establish which of the variables determined performance in the Basketball EuroLeague matches within the analysed period. Based on a number of mathematical and statistical analyses, the elements of play that had the highest effect on sports success were selected. The following determinants displayed the most significant correlations with sport results in the EuroLeague within the analysed period: two-point shots made (2PT-made), two-point shot attempts (2PT-attempts), three-point shots made (3PT-made), one-point shots made (1PT-made), one-point shot attempts (1PT-attempts), assists, fouls and field goals made. The game outcome in basketball is influenced by many variables which may not always be significant in a given match. However, the continuous effort to maintain these variables at the highest possible level is advantageous over less-organised teams.