The purpose of this study was to examine the impact of COVID-19 pandemic on home advantage (HA) comparing games with the presence of spectators (pre-pandemic) and during ghost games in absence of spectators (post-pandemic). A secondary twofold objective was to analyse the influence of team ability on HA by (i) comparing HA for pre- and post- pandemic and (ii) comparing different team ability levels. Wilcoxon and Mann-Whitney U tests were used to identify HA and home wins percentage (HW%) differences between pre-pandemic and post-pandemic games. Additionally, the Kruskal-Wallis test was run to identify HA and HW% differences between team ability levels (High to Low). Teams had higher HA and HW% during pre-pandemic than post-pandemic. In turn, low level teams present higher HA compared to the other team ability levels. Thus, low level teams are more benefited from playing at their home-court, resulting in a higher chance of winning comparing with playing away. However, for HW% low level teams showed lower HW% than medium and high team ability levels, showing that when a team is considerably weaker than the opponent, then this difference in ability will outweigh HA and the stronger team is likely to win both at home and away.

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.

The aim of this study was to analyse the relationship between External Load (EL) and internal load (IL). Thirteen male basketball players competing at professional level in First Spanish Division (ACB) during six friendly games throughout the 2020/2021 preseason were monitored. The EL variables collected were movement load (ML), movement intensity (MI), box score time (BST), and total duration (TD)] while IL variables monitored were heart rate (HR), respiratory rate (RR), training impulse (TRIMP) and time invested in five HR zones. Very large to almost perfect correlation (r= 0.77-0.91) exists between EL variables except TD. In addition, HR, TRIMP and RR present large to very large correlation (r= 0.55-0.79) with all EL variables except TD. Monitoring HR-based variables would present general information and an estimated prediction of players EL which could allow basketball practitioners to prioritize time invested players internal/external load.

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 PlayerLoad TM 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 PlayerLoad TM , 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.