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Consideration of workload intensity and peak demands across different periods of basketball games contributes to understanding the external physical requirements of elite basketball players. Therefore, the aim of this study was to investigate the average intensity and peak demands encountered by players throughout game quarters. PlayerLoad per minute and PlayerLoad at three different time samples (30 s, 1 min, and 3 min) were used as workload metrics. A total of 14 professional elite male basketball players were monitored during 30 official games to investigate this. A linear mixed model and Cohen's d were employed to identify significant differences and quantify the effect sizes among game quarters. The results showed a significant, moderate effect in PlayerLoad per minute between Q1 vs. Q4, and a small effect between Q2 and Q3 vs. Q4. Furthermore, a small to moderate decline was observed in external peak values for PlayerLoad across game quarters. Specifically,, a significant decrease was found for the 3 min time window between Q1 and other quarters. The findings from the present study suggest that professional basketball players tend to experience fatigue or reduced physical output as the game progresses.

El objetivo del estudio fue comparar el alcance de la ventaja de jugar en casa (HA) y el porcentaje de victorias locales (HW) en función del sexo y la capacidad del equipo en las ligas españolas de baloncesto. Se analizaron datos de las primeras divisiones de baloncesto masculino y femenino a lo largo de 12 temporadas (2010-2023). HA (%) se calculó como (total de victorias en casa/total de victorias) * 100 y HW (%) como (total de victorias en casa/total de partidos en casa) * 100. Los equipos se clasificaron en mayor y menor capacidad en función de su porcentaje de victorias de la temporada mediante análisis de conglomerados. Se utilizó la prueba de Mann-Whitney para comparar la HA y la HW entre sexos y grupos de equipos. Los resultados mostraron que los hombres presentaban mayor HA y HW que las mujeres (p < 0,05). Además, los hombres presentaban mayor HA que las mujeres en los equipos de mayor y menor capacidad (p < 0,05) y mayor HW sólo en los equipos de menor capacidad (p < 0,001). Los equipos de menor capacidad tenían mayor HA que los de mayor capacidad (p < 0,001), mientras que los de mayor capacidad tenían mayor HW (p < 0,001). Estos resultados sugieren que el sexo y la habilidad del equipo influyen significativamente en los efectos de HA y HW en las ligas españolas de baloncesto. Los equipos femeninos muestran una distribución más uniforme de las victorias entre los partidos jugados en casa y fuera, mientras que los equipos de menor habilidad dependen más de los partidos jugados en casa para conseguir victorias.

Our aim in this study was to examine the impact of fans (vs. no fans), geographical location of league, and team ability on home winning percentage (HW%) or home advantage (HA) in professional European basketball. Data were collected from five prestigious professional, national basketball leagues within Europe (Spain, Germany, Italy, Greece and Israel) across 16 regular seasons (2005-2006 to 2020-2021). We conducted comparisons between matches with and without fans, location of leagues, team ability (High, Medium, Low), and combinations of these factors via non-parametric tests (e.g., Mann-Whitney tests, Kruskal-Wallis). We found significantly greater HA during matches with fans for leagues in Germany (p =.001), Italy (p =.012) and Spain (p =.002). For matches with fans, HA and HW% were significantly different between several, but not all, leagues for high (p <.05), medium (p <.05) and low (p <.02) team abilities. In conclusion, HA and HW% were enhanced by spectator attendance, with this phenomenon varying by location/region and team ability. Consideration of these multiple contextual factors may assist coaches and sport organizations to develop key strategies for enhanced team success.

Aim: To quantify and compare the external peak demands (PDs) encountered according to team’s venue (home vs. away) in under-18 years (U18), male basketball players. Methods: 12 basketball players had external load variables monitored across 16 official games using inertial movement units (IMUs). PDs were calculated across 1-min and 5-min time windows for PlayerLoad™ (PL). Linear mixed models for repeated measures were used to compare PDs for PL according to playing venue (home vs. away). Results: Regarding 1- and 5-min time windows, no significant differences (p > 0.05, small effect size) were apparent between playing venues. Conclusions: External PDs remain consistent (small effects) regardless of playing venue in U18, male basketball players. Accordingly, playing venue may not be a key determinant of external PD attained during official games.

To quantify and compare the external peak demands (PD) encountered according to game result (win vs. loss), quarter result (win vs. tie vs. loss), and quarter point difference (± difference in score) in under-18 years (U18), male basketball players. Thirteen basketball players had external load variables monitored across 9 games using local positioning system technology, including distance covered, distance covered in different intensity zones, accelerations, decelerations, and PlayerLoad™. PD were calculated across 30-s, 1-min, and 5-min time windows for each variable. Linear mixed models were used to compare PD for each variable according to game result (win vs. loss), quarter result (win vs tie vs loss), and quarter point difference (high vs. low). External PD were comparable between games that were won and lost for all variables and between quarters that were won and lost for most variables (p > 0.05, trivial-small effects). In contrast, players produced higher (p < 0.05, small effects) 1-min high-speed running distance and 5-min PlayerLoadTM in quarters that were won compared to quarters that were lost. Additionally, high quarter point differences (7.51 ± 3.75 points) elicited greater (p < 0.05, small effects) external PD (30-s PlayerLoadTM, 30-s and 5-min decelerations, and 1-min and 5-min high-speed running distance) than low quarter point differences (-2.47 ± 2.67 points). External PD remain consistent (trivial-small effects) regardless of game result, quarter result, and quarter point difference in U18, male basketball players. Accordingly, external PD attained during games may not be a key indicator of team success.

Dado que el análisis de los escenarios más exigentes (MDS) en baloncesto ha mejorado el conocimiento práctico sobre las demandas de los partidos y los posibles impactos para el proceso de entrenamiento, parece importante resumir la evidencia científica que proporciona información útil y futuras direcciones relacionadas con el MDS. Esta revisión evalúa los resultados reflejados en la literatura disponible sobre el MDS en baloncesto, sintetizando y discutiendo los datos de los artículos científicos, y luego proporcionando información relevante sobre terminología, sexo y tamaño de la muestra, categoría de competición, variables de carga de trabajo registradas, tecnología utilizada, método de cálculo, ventanas de tiempo analizadas y actividades evaluadas relacionadas con el MDS.

Our purpose in this study was to examine the impact of the COVID-19 pandemic on home advantage (HA) in sports by comparing European professional basketball games in which spectators were present (pre-pandemic) with "ghost" games in which spectators were absent (during the pandemic). A secondary objective was to analyze how team ability interacted with HA by comparing HA affects pre-pandemic and during the pandemic in the context of differing team ability levels. We used Wilcoxon and Mann-Whitney U tests to identify HA through differences in home win percentages (HW%) between pre-pandemic and pandemic games. Additionally, we ran the Kruskal-Wallis test to identify HA and HW% differences between different team ability levels (high to low). Teams had higher HA and HW% pre-pandemic than during the pandemic. In turn, low level teams presented a higher HA compared to teams at other team ability levels. Thus, low level teams benefited more from playing at their home-court. However, low level teams showed lower HW% than medium and high team ability levels, showing that team ability is more important than HA in determining game outcomes.

The main aim of this study was identifying the internal demands during competition including the impact of analysis method (i.e., average vs. peak demands). A secondary aim was to analyze the effects of playing time on the internal demands (average and peak). Methods: Twenty-two professional basketball players were monitored during 11 games. The following internal workload variables were recorded as averages (i.e., value per minute considering the entire game) and peak values (i.e., greatest 1-minute window): 1) average Heart Rate (HR), peak HR, average Respiration Rate (RR), peak RR, average Ventilation (VE), peak VE, average VO2 and peak VO2. Results: Significantly higher peak values (very large effect size) for all internal variables were evident when compared to their average demand. Concerning playing time comparison, significantly greater average values were reached for large playing time group (24.54 ± 3.23 min) for all variables compared to small playing time group (14.13 ± 3.78 min). However, non-significant differences were apparent between large playing time and small playing time group at their internal PD. Conclusions: Understanding internal load demands using averages values drastically underestimates the internal peak values of games. Thus, 14 minutes of total playing time (1.5 quarters; ~37% of a game) is enough to expose to the players to internal peak demands.

The purpose of this study was to compare external peak demands (PDs) across quarters (Q) in basketball. Thirteen elite, junior, male basketball players were monitored using electronic performance tracking systems. There were studied intervals for different time windows to determine the external PD for distance (m); player load; distance covered in four different zones; accelerations; and decelerations. A mixed linear model was run to identify differences among quarters, and the auto-correlation function was carried out to determine fluctuations across the whole game. The results showed significant differences between Q1 vs. Q2 for distance, player load, and standing–walking distance; between Q1 vs. Q3 for distance, player load, and HSR; between Q1 vs. Q4 for distance, player load, standing–walking, and HSR; and between Q3 vs. Q4 for distance and player load. These findings suggest that external PD for running-based demands (distance, player load, and high-speed running) decrease across basketball games with the most notable declines occurring between the first and fourth quarters. Nevertheless, it is important to note that non-significant differences were found between quarters for several external PD variables (jogging, running, acceleration, and deceleration) across different time windows. Findings from the present study reinforce the importance of considering specific PD variables for different functions due to the specific insight each provides.