https://orcid.org/0000-0002-4183-3004
Figures
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.
Citation: Bustamante-Sánchez Á, Jiménez-Saiz SL (2024) Game location effect in game-related statistics and pre-shot combination differences between winners and losers during the basketball ACB COVID-19 season. PLoS ONE 19(7): e0303908. https://doi.org/10.1371/journal.pone.0303908
Editor: Philipp Baumert, UMIT TIROL Private University for Health Sciences and Technology GmbH: UMIT TIROL Private Universitat fur Gesundheitswissenschaften und -technologie GmbH, GERMANY
Received: November 20, 2023; Accepted: May 3, 2024; Published: July 12, 2024
Copyright: © 2024 Bustamante-Sánchez, Jiménez-Saiz. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly because it is owned by a third-party organization (InStat). Data are available from the InStat company for researchers who meet the criteria for access to confidential data. InStat gave the authors permission to use their dataset for research purposes, but does not allow them to public share datasets. The authors paid for the datasets that were used in this research according to InSatat requirements. The data underlying the results presented in the study can be requested to Instat at https://basketball.instatscout.com/. Registration is a requirement for data access, through the form of the link or with an email request to support@hudl.com.
Funding: This study was funded by the Universidad Europea de Madrid in the form of grants [CIPI/19/095; CIPI/22.303] to AB-S and SLJ-S.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Traditionally, basketball performance has been assessed through game-related statistics, which evaluates the outcome of each possession until a new possession starts [1–4], although these outcomes usually depend on previous team structures and pre-shot combinations which influence success [5].
The collective behaviours which precede the outcome of a basketball possession help to better understand the playing structures in which basketball coaches should focus to maximize their team performance [6, 7]. Pick and rolls seem to remain the most used structure to end a play because they are the best offensive structure to gain a spatial advantage in a short period of time [8], but there are other pre-shot combinations that are highly linked to game-related statistics that have been traditionally considered important to secure a winning game outcome. For instance, defensive rebound is one of the most important feature of the game to control [3, 5, 9], and its importance seems to be related to the opportunity to make a transition [1, 2] and to avoid a second chance for the opponents through an offensive rebound [10]. Similarly, the field-goal percentage is a key factor to win a game [9], especially when the number of possessions is similar between teams [11]. This factor is related to performance in uncontested shots, in which elite players are supposed to score [5]. These opportunities are usually made after a successful space creation dynamic which usually defines a good collective offensive behaviour [5].
Contested shots are supposed to be part of the success of a team in a game because the opponents should defend well to avoid open shots. Not only the defender who guards the player with the ball can contest the shot during a 1on1 situation, but also the first player to reach the offensive player according to each team defensive structure. This behaviour also applies to any zone defensive strategy that usually tend to guard the ball handler as it was an 1on1 situation, while the other 4 players defend an area of the court with the aim to collapse the space of the opponents [12]. Because of this dynamic, many teams need players who play well on isolations to overcome a good defensive strategy, especially during the last seconds of each possession [12, 13].
Game location is also a variable to be considered when analysing the behaviour and performance of a team [14], because it may affect the mental and physical aspects due to a lack of time to recover before the game [15, 16], or because of the influence of the fans when the team is playing away [17]. Game location also affects differently depending on each playing position, with the guards being more efficient at home games, the forwards at away games, and the centers being the less influenced players by game location [18]. Although the game location seems to favour the home teams [19–21], its influence needs to be better explored with the inclusion of competitive games played in a neutral court, to include a control group among the traditional home versus away game-location comparisons [5, 22]. This study is significant and novel in the context of Spanish professional basketball, because it addresses the need of a control group of competitive games played in a neutral-court during the season to better assess the game-location influence.
The 2019–20 season was different from the others because of the COVID-19 pandemic and therefore the Asociación de Clubs de Baloncesto (ACB) competition (i.e., first basketball league in Spain) had to be completed in a neutral court at the end of the season. This situation brought the opportunity to analyse and compare the effect of playing home, away, and in a neutral court to better assess the game-related statistics and pre-shot combinations of Spanish professional basketball teams.
Thus, the aim of this study was to assess differences between losers and winners, considering their game-related statistics and pre-shooting combinations, including pick-and-roll performance. We also aimed to evaluate the effect of the game-location (neutral, home, away) to understand the performance indicators that influence better the outcome of a game. According to previous research, we hypothesized that winners would grab more rebounds and have better field-goal percentages; would perform better in isolations and transitions; and would have more contested shots with a more effective pick-and-roll performance.
Materials and methods
In this section we will explain the design, the sample, the procedure, and the statistical analysis that were performed to reach the aim of the study.
Design
To analyse the performance indicators related to the game result and game location, a retrospective cross-sectional study of the 2019–2020 Spanish ACB League was conducted. The study was designed in compliance with the recommendations for clinical research of the Declaration of Helsinki and approved by the Ethics Committee of the University (CIPI/19/095). The need for individual consent was waived since game data were collected from a commercially accessible provider (InStat, Moscow, Russia). The data were accessed for research purposes from September 16, 2021, to October 10, 2021.
Sample
170 games were analysed during the 2019–2020 ACB season. To assess the performance indicators in the three different locations (playing at home n = 137, away n = 137 or in a neutral court n = 33). Our selection criterion was to include in the sample all the games played by the 12 teams that participated in the ACB final stage in Valencia to compare their performance when they played at home, away, and in a neutral court during the final stage. We therefore discarded the 6 teams that were part of the 2019–2020 ACB league which did not play any official game in a neutral court.
Procedure
To gather the data, Instat basketball reports (Instat, Moscow, Russia) were used. For each game, performance indicators were classified into two groups of analysis, depending on the game result: winners and losers. These groups were subsequently categorised in subgroups depending on the game location: home, away, or in a neutral court. We used Instat dashboard filters to select all the performance indicators for the teams that were included in the sample, and to classify them into different study groups. Once the data was gathered, we exported it to an Excel file and to SPSS format to perform the statistical analysis. According to previous studies [5, 13, 21, 23–26], we selected the following performance indicators:
- Game-related statistics: possessions, points per possession, points, field goals made, field goals missed, field goals percentage, two-point field goals made, two-point field goals missed, two-point field goals percentage, three-point field goals made, three-point field goals missed, three-point field goals percentage, free throws made, free throws missed, free throws percentage, rebounds, offensive rebounds, defensive rebounds, assists, steals, turnovers, blocks, fouls, fouls drawn.
- Pre-shot combinations: transitions made, transitions missed, percentage of offensive transitions, catch and shoot made, catch and shoot missed, catch and shot percentage, catch and drive made, catch and drive missed, catch and drive percentage, post up made, post up missed, post up percentage, isolations made, isolations missed, isolation percentage, hand-off made, hand-off attempted, hand-off percentage, cuts made, cuts missed, cuts percentage, drives made, drives attempted, drives percentage.
- Pick and rolls (PnR), Pick and pops (PnP), and defensive parameters: PnR handlers made, PnR handlers missed, PnR handlers percentage, PnR rollers made, PnR rollers missed, PnR rollers percentage, PnP made, PnP missed, PnP percentage, uncontested field goals made, uncontested field goals missed, uncontested field goals percentage, contested field goals made, contested field goals missed, contested field goals percentage.
Table 1 shows the action definitions that InStat provides.
To assess the validity and reliability of the data, a subsample of 10 games were randomly selected, and were analysed by two experienced coaches. Intra-class correlations were very good (Kappa values > 0.81).
Statistical analysis
Normality assumptions were checked with the Kolmogorov-Smirnov test. Homoscedasticity assumptions were checked with the Levene test. Descriptive statistics were presented as mean and standard deviation. A factorial ANOVA test was used to compare the effect of the game result (win, lose), the effect of the game location (neutral-court, home-court, and away-court), and the interaction between the result and the game location. A Bonferroni post hoc test was used to analyse pairwise comparisons. The level of significance for all the comparisons was set at p< 0.05. The effect size was assessed by the eta squared value (η2) as specified in previous research [27]. A discriminant analysis was run to assess the main performance indicators that discriminate between the teams (winners and losers). We used the leave-one-out method to ensure the cross-validation of data, which generates the discriminant analysis n times taking out one of the games of the samples [3, 28]. We looked at the structured coefficients greater than |0.30| to select the most important variables [3, 5]. We considered the aforementioned variables to run a binary logistic regression model so we could assess the coefficients that most influence the corresponding explanatory variable, with their 95% confidence intervals (CI) [5, 29]. The IBM SPSS statistical package version 21.0 for Windows (IBM Corp., Armonk, NY) was used to analyse the data.
Results
Table 2 shows the performance differences of game-related statistics (per possession) between winners and losers. Winners had better results in field-goals made, field-goals percentage, and rebounds. Neutral-court winners had more steals, free-throws made and attempted, and fewer turnovers. Home-court winners had more 2-point field-goals made, more 3-point field-goals made, more assists, better 2-point and 3-point percentages, 3-point field-goal attempts, and fewer fouls. Away-court winners had more 2-point field-goals made, more assists, better 2-point and 3-point field-goals percentages, and fewer turnovers and fouls. The effect size for all variables was small.
Table 3 shows the performance differences in pre-shot combinations (per possession) between winning and losing teams. Neutral-court winners had more post-up missed and a better percentage of isolations. Home-court winners had better results in transitions made, catch-and-shoot made, catch-and-shoot percentage, catch-and-drive made, catch-and-drive percentage, and drives percentage, but fewer hands-off missed. Away-court winners had better results in catch-and-shoot, post-ups, posts-up percentage, cuts made, and cuts percentage, but fewer hand-offs missed. The effect size for all variables was small.
Table 4 shows the performance differences for picks and defensive indicators (per possession) between winning and losing teams. Neutral-court winners had more contested shots made and a better percentage of contested shots. Home-court winners had better pick-and-roll-handlers percentages, better pick-and-roll-rollers percentages, better contested-shots percentages, and more contested-shot made. Away-court winners had more contested-shots made, and a better contested-shots percentage, but had fewer uncontested-shot made. The effect size for all variables was small.
Table 5 shows the discriminant analysis, which was statistically significant (p < 0.05), and it could correctly classify 86.8% of the cases, as an average of the n iterations that were specified in the statistical analysis section. The model found these discriminants to classify winners and losers: points (SC = 0.511), field-goal percentage (SC = 0.424), field goals made (SC = 0.419), defensive rebounds (SC = 0.367), assists (SC = 0.332), contested field-goals made (SC = 0.304).
Table 6 shows the analysis of the binary logistic regression. Defensive rebounds (p < 0.001; OR = 3.044E13), and assists (p = 0.042; OR = 353289.356), were statistically significant for home-court winners. Defensive rebounds (p < 0.001; OR = 2.676E12), and field-goal percentage (p = 0.009; OR = 1.198) were statistically significant for away-court winners. No variables were statistically significantly associated with winning for neutral-court teams.
Discussion
The aim of this study was to assess differences between losers and winners, considering their game-related statistics and pre-shooting combinations, including pick-and-roll performance. We also aimed to evaluate the effect of the game-location (neutral, home, away) to understand the performance indicators that influence better the outcome of a game. The first hypothesis was that winners would grab more rebounds and have better field-goal percentages; the second hypothesis was that winners would perform better in isolations and transitions; and the third hypothesis was that winners would have more contested shots with a more effective pick-and-roll performance.
Not only the winners had better field-goal percentages and rebounds, but also, they were identified as key factors in the discriminant analysis, including assists and defensive rebounds too. Thus, the first hypothesis can be accepted. The importance of the field-goals, in the National Basketball Association (NBA) [5, 9, 30], and in the ACB league [31, 32], has been previously reported, since scoring has a direct influence in the game outcome. Moreover, this study highlights the importance of maintaining the ball and to obtain more possessions through more steals and fewer turnovers, together with more free-throw attempts when playing in a neutral court. The value of the assists to have more odds to win a basketball game has been traditionally defined in previous studies in Europe [20, 32] and in the NBA league [5, 33]. This fact should be related to a positive end of a possession (because an assist implies a basket) and a greater cohesion of the teammates to share the ball if an advantage has been created when the offensive players outnumber their opponents [34]. However, there is still some controversy about its importance by itself, or just because it is linked to a successful possession, because some spatial or numeric advantages could also be successfully resolved by the ball-handler if they find the opportunity to end the possession through an easy basket instead of a pass to assist a teammate [5, 35]. Notwithstanding, according to our results, assists are a key factor for both winning at home and away, but it is not so critical when playing on a neutral court. This issue has been argued previously suggesting that some variables are largely impacted by fans’ support [19]. The importance of rebounding seems to remain unaltered during the evolution of this sport, probably because its control produces a second chance to score (in offense), or the possibility to start a fast break, to get the possession of the ball again to build a potential scoring opportunity, and to deny the possibility of scoring by the opponents (in defence) [10, 36].
Considering the pre-shot combinations, the second hypothesis was partially accepted since home-team winners were better at transitions, and neutral-court winners were better at isolations. The catch and shoot performance was a key factor for success in winners both playing home and away and supports the information provided above about the importance of field-goals accuracy for winning in basketball, not only at the professional level in the NBA [5, 9] and the ACB leagues [13, 32], but also in the National Collegiate Athletic Association (NCAA) level [14]. However, this study adds some insights about the best type of shots that discriminate between winners and losers depending on the home-advantage effect. Home-court winners had better percentages in catch and drive and drive actions, which are typically actions taken by perimeter players. However, away-court winners had better percentages in post-up actions and cuts, which are usually actions that require to shoot very close to the basket, and that could be related to a need for a more physical playing style when playing away, plus a better accuracy in technical actions when playing at home. These findings are related to the defensive systems used by home and away teams, modifying the open spaces and tactical behaviours that may generate a decrease/increase in scoring opportunities [37].
Contested field-goals made was a discriminant factor for winners, and also winners had better contested-shot efficacy in all kinds of courts (neutral, home, and away). Home-court winners had better efficacy in pick-and-roll situations, both finishing with the ball handler and the rollers. Thus, the third hypothesis was partially fulfilled. The importance of pick-and-roll structures has been growing in recent years since this action provides a quick spatial advantage for the offensive team [7, 8, 38]. However, there is a difference between this study in the ACB league and previous research in the NBA [5], in which all teams had better percentages in pick-and-roll situations, independently of their game location. Apparently, this feature is not so strong in the ACB league because of the different defensive rules that apply to each competition. The NBA has the defensive three-second violation rule (illegal defence) that does not allow a defensive player to stay in the lane more than 3 consecutive seconds while not actively guarding an opponent, while this rule does not apply in the ACB league. This rule suggest better opportunities in the ACB league than in the NBA to contain pick-and-roll situations through defensive helps in the lane [11]. Field-goal percentage relies on accuracy for both contested and uncontested shots, although uncontested shots missed are a key factor in case the team is playing away. This study presented different results when compared to a previous one in the NBA [5], in which uncontested shots were key factors to win the game. As we mentioned before, the different defensive rules that apply in both leagues could be the main factor for these results that supports the importance of contested shots in the ACB league. Besides, there could be an influence of (i) a more structured defensive rotation to contest open shots [39]; or (ii) more space to defend and better athleticism in the NBA [40].
In the context of pandemic-affected seasons or neutral-court tournaments (i.e. Spanish basketball Cup, Euroleague Final Four, etc.), this research deepens the knowledge about the key performance indicators that are crucial for a win outcome. The importance of rebounding seems to be unaltered when compared to the standard home-team vs away-team game [5, 10, 36]. However, one of the strengths of our research is to also consider pre-shot combinations to better understand the context in which each action is decisive for a successful game outcome. According to our results, to get a better field-goal percentage to win a game [31, 32], is especially critical when shooting contested throws in a neutral-court tournament. These results also highlight the importance to improve the number of steals and to lower the occurrences of turnovers, especially to enhance the percentage of successful isolations in neutral-court situations [5]. If we consider these results globally, teams that play in neutral courts seem to be less influenced by the game-location atmosphere to look for a better collective behaviour, so they find it more difficult to play team-oriented pre-shot combinations [5, 17, 37]. In conclusion, coaches should consider this different behaviour and remember the importance of isolations and contested-shot performance as critical to be successful in neutral-court tournaments. These results and implications could only be extrapolated to matches without spectators that may occur in the future due to the special conditions they had during COVID-19 season.
Conclusion
In conclusion, our study sheds light on the nuanced influence of game location on performance differentials between winners and losers in the ACB COVID-19 season. The findings underscore the critical role of specific variables, including points, field-goal percentage, field goals made, defensive rebounds, assists, and contested field goals made, in distinguishing between successful and unsuccessful teams across neutral-court, home-court, and away-court settings.
Notably, our analysis reveals distinct patterns among winners depending on game location. Home-court winners exhibited heightened significance of defensive rebounds and assists, whereas away-court winners leaned more heavily on defensive rebounds and field-goal percentage as predictors of success. Intriguingly, neutral-court winners appeared to adjust their play style, favouring less team-oriented pre-shot combinations, potentially influenced by the unique game-location atmosphere. Coaches should thus consider the strategic implications of emphasizing isolations and contested shot performance in neutral-court tournaments.
Furthermore, our research highlights disparities between the ACB league and the NBA, particularly regarding the perceived importance of uncontested shots and pick-and-roll efficiency. This variance may be attributed to factors such as the three-second violation rule in the NBA, impacting defensive structures and help defence strategies differently.
Overall, our study contributes novel insights into the interplay between game location, game-related statistics, and pre-shot combinations in competitive basketball contexts, providing valuable implications for coaching strategies and player performance optimization in diverse tournament settings.
Future studies should consider the importance of the crowds in the different outcomes of neutral-court games, since not only the court, but the fans could influence each team behaviour. They should also consider the different influence that neutral game-locations may have in different competitions, levels and in women leagues and tournaments.
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