Background: The aim of this study is to describe the peak match demands and compare them with average demands in basketball players, from an external load point of view, using different time windows. Another objective is to determine whether there are differences between positions and to provide an approach for practical applications. Methods: During this observational study, each player wore a micro technology device. We collected data from 12 male basketball players (mean ± SD: age 17.56 ± 0.67 years, height 196.17 ± 6.71 cm, body mass 90.83 ± 11.16 kg) during eight games. We analyzed intervals for different time windows using rolling averages (ROLL) to determine the peak match demands for Player Load. A separate one-way analysis of variance (ANOVA) was used to identify statistically significant differences between playing positions across different intense periods. Results: Separate one-way ANOVAs revealed statistically significant differences between 1 min, 5 min, 10 min, and full game periods for Player Load, F (3,168) = 231.80, η_p² = 0.76, large, p < 0.001. It is worth noting that guards produced a statistically significantly higher Player Load in 5 min (p < 0.01, η_p² = −0.69, moderate), 10 min (p < 0.001, η_p² = −0.90, moderate), and full game (p < 0.001, η_p² = −0.96, moderate) periods than forwards. Conclusions: The main finding is that there are significant differences between the most intense moments of a game and the average demands. This means that understanding game demands using averages drastically underestimates the peak demands of the game. This approach helps coaches and fitness coaches to prepare athletes for the most demanding periods of the game and present potential practical applications that could be implemented during training and rehabilitation sessions.

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 aim of this study was (I) to establish absolute specific velocity thresholds during basketball games using local positional system (LPS) and (II) to compare the speed profiles between various levels of competitions. The variables recorded were total distance (TD); meters per minute (m·min); real time (min); maximum speed (Km h⁻¹), distance (m), percentage distance, and percentage duration invested in four speed zones (standing–walking; jogging; running; and high-speed running). Mean and standard deviation (±SD) were calculated, and a separate one-way analysis of variance was undertaken to identify differences between competitions. TD (3188.84 ± 808.37 m) is covered by standing–walking (43.51%), jogging (36.58%), running (14.68%), and sprinting (5.23%) activities. Overall, 75.22% of the time is invested standing–walking, jogging (18.43%), running (4.77%), and sprinting (1.89%). M·min (large effect size), % duration zone 2 (moderate effect size); distance zone 4 (large effect size), and % distance zone 4 (very large effect size) are significantly higher during junior than senior. However, % distance zone 1 (large effect size) and % duration zone 1 (large effect size) were largely higher during senior competition. The findings of this study reveal that most of the distance and play time is spent during walking and standing activities. In addition, the proportion of time spent at elevated intensities is higher during junior than in senior competition.

Since the analysis of most demanding scenarios (MDS) in basketball has improved the practical knowledge about match demands and possible impacts for the training process, it seems important to summarize the scientific evidence providing useful information and future directions related to MDS. Specifically, basketball coaching and strength and conditioning staffs. This review assesses the results reflected in the available literature about the MDS in basketball, synthesizing and discussing data from 12 scientific papers, and then providing relevant insights about terminology, sex and sample size, competition category, workload variables recorded, technology used, method of calculation, time windows analyzed, and activities evaluated related to MDS. Therefore, the present narrative review would be of utility for coaches, scientists, athletes and strength and conditioning trainers exploring the current trends and future directions related to MDS in basketball.

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.

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...