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This study aimed to (i) compare the extent of home-court advantage (HA) and home win percentage (HW) between Spanish basketball leagues across different playing levels (i.e., leagues) and (ii) analyze the influence of team ability on HA and HW within each playing level. We gathered data for game locations and results from open online sources for all games in the First Division (ACB), Second Division (LEB Gold), and Third Division (LEB Silver) Spanish male basketball leagues between 2010-2023. The dataset included 12 seasons, 635 teams (215 First Division, 203, Second Division, and 217 Third Division), and 19,539 games (7075 First Division, 6344 Second Division, and 5520 Third Division). We calculated HA [(total home wins/total wins) * 100] and HW [(total home wins/total home games) * 100] for each team across each season. We performed Kruskal Wallis tests, calculated partial eta squared ( η p 2 ), and performed Mann-Whitney U tests with rank biserial correlation ( r b ) effect sizes and Dwass-Steel-Critchlow-Fligner pairwise comparisons to compare HA and HW between leagues and between team abilities (low, medium, and/or high) within each league. There was a significant effect for HA between leagues ( p = .01) with post hoc comparisons revealing a higher HA for the First Division than for the Third Division ( p = .01), but HW was not significantly different between leagues. In turn, variations in HA and HW between team abilities were consistent across playing levels, with HW significantly increasing in a progressive manner with higher team abilities ( p < .001) and with HA significantly lower among high-level teams compared to lower-ability team clusters ( p < .01). These findings emphasize the importance of considering playing level and team ability and interpreting HA and HW together to contextualize an apparent home advantage in Spanish basketball.

Our main aim in this study was to analyze any differences in Win Percentages at Home (HW) that might represent a Home-Court Advantage (HA) in women's professional basketball. A secondary objective was to analyze how team ability might modify the HA effect by comparing any interaction effect between HW and team ability in games played with and without fans. We collected data from first Spanish female basketball divisions, using a linear mixed model (LMM) for repeated measures to identify differences between time periods (games with fans vs. games without fans) for HA and HW. When comparing games with and without fans, we found no significant HA and HW differences ( p = .283 and p = .872, respectively). In fact, interestingly, we observed higher win values when fans were absent. Additionally, during the COVID-19 shut down stage, HA increased; but it returned to pre-pandemic levels afterward, with no significant differences between these periods ( p = .482). Similarly, while HW seemed to increase during the COVID-19 shut down period and continued improving in the post-pandemic phase, there were actually no statistically significant differences ( p = .772). Higher HA and HW were evident without fans during the pandemic compared to the pre-pandemic period when fans were present in women's professional basketball. During the post-pandemic period, HA decreased upon fans' return, while HW continued increasing. We discuss possible bases for these unexpected findings., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In basketball, game-related statistics are the most common measure of performance. However, the literature assessing their reliability is scarce. Purpose : Analyze the number of games required to obtain a good relative and absolute reliability of teams' game-related statistics. Method : A total of 884 games from the 2015-2016 to 2017-2018 seasons of the Spanish men's professional league were analyzed using all games and clustered by scoring difference. Intra-class correlation coefficient (ICC) was calculated for each variable. The number of games required to detect a change and to achieve good relative reliability was calculated using minimal detectable change and Spearman-Brown prophecy formula respectively. Results : Using all games, the results showed that the minimal number of games required in each group was 30 to detect a medium change (d > .5), 187 for a small change (d > .2), and 100 for good relative reliability (ICC ≥ .75). Using balanced and unbalanced games, the minimal number of games required in each group was respectively 31 and 30 to detect a medium change (d > .5), 190 and 188 for a small change (d > .2), and 191 and 121 for good relative reliability (ICC ≥ .75). Conclusions : The sample needs to consist of at least 30 games in each group to detect a medium size change, and at least 190 games to detect a small size change. To be able to rank teams with good reliability, at least 100 games are required when including both balanced and unbalanced games.

The purpose of the present study was to analyse men's basketball competitions, trying to identify which game-related statistics allow to discriminate winning and losing teams. The sample used corresponded to 306 games from the 2004-2005 Regular Season of the Spanish Men's Professional League. The game-related statistics gathered were: 2 and 3 points field-goals (both successful and unsuccessful), free-throws (both successful and unsuccessful), offensive and defensive rebounds, blocks, assists, fouls, turnovers and steals. The data were analysed in two groups: balanced games (final score differences equal or below 12 points) and unbalanced games (final score differences above 12 points). Discriminant analysis allowed to conclude the following: (i) in balanced games, the variable that best differentiate both groups were the defensive rebounds; (ii) in unbalanced games, the variables that discriminate between both groups were the successful 2 points field-goals, the defensive rebounds and the assists; and (iii) in all games, the statistical analysis identified two variables that discriminate winning and losing teams (defensive rebounds and assists). The defensive rebounds were the only game-related statistic that discriminates both groups in all performed analysis. Coaches and players should be aware of these different profiles in order to increase knowledge about game cognitive and motor solicitation and, therefore, to enhance specificity at the time of practice and game planning.