ArticlePloS one2024
Game location effect in game-related statistics and pre-shot combination differences between winners and losers during the basketball ACB COVID-19 season.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to assess pre-shot combinations and game-location influence, together with the traditional game-related statistics to evaluate their impact in the performance of the teams in the Asociación de Clubs de Baloncesto (ACB) Spanish basketball league. The COVID-19 season was a great opportunity to better analyse the effect of game-location among ACB-teams to evaluate the differences between winners and losers. A retrospective cross-sectional study of the 2019-2020 ACB season (137 games of the regular season and 33 games of the final stage) was carried out. Game-related statistics were gathered from InStat (https://basketball.instatscout.com/). To evaluate the key performance indicators in this context, a discriminant analysis together with a binary logistic regression were run. The main results revealed that specific variables (normalized per minute played), such as points, field-goal percentage, field goals made, defensive rebounds, assists, and contested field goals made, played a crucial role in classifying winners and losers (p<0.05). Defensive rebounds (0.29±0.05 vs 0.24±0.04, p < 0.001), and assists (0.23±0.05 vs 0.19±0.04, p = 0.042), were key performance indicators for home-court winners. Defensive rebounds (0.29±0.05 vs 0.25±0.04, p < 0.001), and field-goal percentage (48.2±5.31 vs 42.7±5.95, p = 0.009) were key performance variables for away-court winners. Neutral-court winners had better isolation efficiency (49.5±31.6 vs 33.3±31.4, p < 0.05) and contested shot percentage (48.2±6.02 vs 44.8±5.34, p < 0.05) in the context of neutral-court tournaments. Uncontested shots and pick-and-roll efficiency seem not to be so important in the ACB when compared to the (National Basketball Association) NBA. Teams should look for players who assist to good shooters, as well as the presence of specialized players proficient in defensive rebounding. In the context of neutral-court tournaments, the importance of isolations and contested-shot performance is critical.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.