Efficacy of the "pick and roll" offense in top level European basketball teams

HoopTransformer: Advancing NBA Offensive Play Recognition with Self-Supervised Learning from Player Trajectories

BACKGROUND AND OBJECTIVE

Understanding and recognizing basketball offensive set plays, which involve intricate interactions between players, have always been regarded as challenging tasks for untrained humans, not to mention machines. In this study, our objective is to propose an artificial intelligence model that can automatically recognize offensive plays using a novel self-supervised learning approach.

METHODS

The dataset was collected by SportVU from 632 games during the 2015-2016 season of the National Basketball Association (NBA), with a total of 90,524 possessions. A multi-agent motion prediction pretraining model was built on the basis of axial-attention transformer and trained with different masking strategies: motion prediction (MP), motion reconstruction (MR), and MP + MR joint strategy. A downstream play-level classification task and similarity search were used to evaluate the models' performance.

RESULTS

The results showed that the MP + MR joint masking strategy maximized the ability of the model compared with individual masking strategies. For the classification task, the joint strategy achieved a top-1 accuracy of 81.5% and top-3 accuracy of 97.5%. In the similarity search evaluation, the joint strategy attained a top-5 accuracy of 76% and top-10 accuracy of 59%. Additionally, with the same MP + MR joint masking strategy, our HoopTransformer model outperformed the two baseline models in the classification task and similarity search.

CONCLUSION

This study presents a self-supervised learning model and demonstrates the effectiveness and potential of the model in accurately comprehending and capturing player movements and complex interactions during offensive plays.

A Basketball Big Data Platform for Box Score and Play-by-Play Data

This is the second part of a research diptych devoted to improving basketball data management in Spain. The Spanish ACB (Association of Basketball Clubs, acronym in Spanish) is the top European national competition. It attracts most of the best foreign players outside the NBA (National Basketball Association, in North America) and also accelerates the development of Spanish players who ultimately contribute to the success of the Spanish national team. However, this sporting excellence is not reciprocated by an advanced treatment of the data generated by teams and players, the so-called statistics. On the contrary, their use is still very rudimentary. An earlier article published in this journal in 2020 introduced the first open web application for interactive visualization of the box score data from three European competitions, including the ACB. Box score data refer to the data provided once the game is finished. Following the same inspiration, this new research aims to present the work carried out with more advanced data, namely, play-by-play data, which are provided as the game runs. This type of data allow us to gain greater insight into basketball performance, providing information that cannot be revealed with box score data. A new dashboard is developed to analyze play-by-play data from a number of different and novel perspectives. Furthermore, a comprehensive data platform encompassing the visualization of the ACB box score and play-by-play data is presented.

Differentiating attack-defense performance for starting and bench players during the Tokyo Olympics men's basketball competition

This study aimed to explore the differences in attack-defense performance between the top and bottom teams for starting and bench players during the Tokyo Olympics men's basketball competition, to determine the relationship between the attack-defense performance of starting and bench players and the final competition rankings, as well as with each performance indicator. The rank-sum ratio (RSR) comprehensive evaluation was employed to describe the attack-defense performance of starting and bench players. Additionally, an independent sample t-test, Spearman Rho Correlation, and Pearson Correlation were conducted to test the differences and relationships between the various variables at a 0.05 level of significance, respectively. The results indicated that the top four teams showed significant differences in the attack-defense performance of their starting players compared to the bottom four teams (p = 0.021), mainly in terms of 3-point shooting percentage (p = 0.042) and free throw shooting percentage (p = 0.044). Besides that, the attack-defense ranks of both starting players (p = 0.004, r = 0.757) and bench players (p = 0.020, r = 0.658) had a significant correlation with the final rankings. Points per game, 2-point field goal percentage, and assists had a statistically significant (p<0.01) and strong positive correlation (0.70 Collapse

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MESH Headings

• Male
• Humans
• Basketball
• Tokyo
• Athletic Performance

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Affiliation(s)

Wenping Sun Department of Sport Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Kuala Lumpur, Malaysia
ChenSoon Chee Department of Sport Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Kuala Lumpur, Malaysia
LianYee Kok Department of Sport Science, Faculty of Applied Sciences, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
FongPeng Lim Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, Kuala Lumpur, Malaysia
Shamsulariffin Samsudin Department of Sport Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Kuala Lumpur, Malaysia

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Modeling the influence of basketball players' offense roles on team performance

This study aimed to (1) use the clustering method to build a classification model based on the play-type data of basketball players, to classify native and foreign players into different offensive roles; (2) use the clustered offensive role model to investigate how different offensive roles influence team performance. The sample was drawn from 20 teams spanning five seasons (2017-2021) in the Chinese Basketball Association, comprising 823 native and 228 foreign players. The clustering results obtained fourteen offensive roles for native players and five for foreign players. Subsequent analyses revealed that the offensive roles of two native player clusters, namely N6 Spot-up Wings who Attack (OR = 3.281, p < 0.05) and N13 Bigs who Cut to the Rim (OR = 4.272, p < 0.05), significantly influenced team performance. Conversely, no significant impact was observed for foreign players. The findings of this study offer novel insights into player dynamics and offer coaches a fresh perspective on team composition.

Ball Screens in the Men's 2019 Basketball World Cup

BACKGROUND

The objective of this research was to analyse the effectiveness and search for successful patterns in ball screens in the men's 2019 Basketball World Cup.

METHODS

The sample consisted of 515 ball screens obtained in critical moments. LINCE software was used as a registration instrument by means of an observational instrument designed ad hoc. A descriptive analysis and chi-square tests (χ2) were performed with SPSS 25.0 and a T-patterns analysis with Theme 5 software.

RESULTS

The results indicate that the criteria that have the most influence on this type of action are the result of the team executing the screen (winning, losing or tying), the type of offense and the defence used on the ball screen. The most representative patterns of success tend to take place between 9-16 s of possession, with the screeners being inside players and the screened players being outside players, and it is performed in the upper areas of the court against an individual type of defence and ending with the screened player advancing towards the basket or passing to an open teammate.

CONCLUSIONS

The data obtained will enable the coaching staff to train ball screens in accordance with specific game situations.

Evaluation of differences in the performance strategies of top and bottom basketball teams utilizing rank-sum ratio comprehensive

Introduction

This study aimed to explore common characteristics among top basketball teams, differentiate attacking and defensive performance between top and bottom teams, and correlate attacking and defensive performance with final competition rankings during the 2019 Men's Basketball World Cup, as well as to determine the relationship between performance indicators and the attacking and defensive performance. In addition, the study aimed to determine the attacking and defensive level of the top and bottom eight teams and find their existing problems and shortcomings, to further improve their competitive basketball strength, and also provided valid and reliable information for coaches to conduct targeted training in the future.

Methods

The rank-sum ratio (RSR) was employed to evaluate the attack, defense, and overall attacking and defensive performance between the top and bottom teams during the 2019 Men's Basketball World Cup. Additionally, an independent sample T-test was conducted to test the difference in performance indicators of attack and defense between the top eight and bottom eight teams. Spearman Rho Correlation was conducted to determine the relationship between the attacking and defensive RSR value and the final competition ranking at the 0.05 confidence level. Pearson Correlation was employed to test the relationship between the performance indicators and the attacking and defensive RSR value at the 0.05 confidence level. According to Spearman and Pearson Correlation, the indicators which contributed most to the attacking and defensive performance, as well as the correlation between attack and defense and the final ranking, can thus be determined.

Results

The results showed that the attacking performance of the top eight teams was far better than the bottom eight teams in terms of average points (p = 0.000), 2-point shoot percentage (p = 0.001), 3-point shoot percentage (p = 0.003), free throw percentage (p = 0.001), turnovers (p = 0.012), and assists (p = 0.000), and there was a significant difference (p < 0.05). However, second attack (p = 0.484), fast-break (p = 0.174), and offensive rebounds (p = 0.261) showed no significant difference between the two cohorts (P > 0.05), and the offensive rebounds of the bottom eight teams were better than the top eight teams. Additionally, there was a large gap between the top eight teams and the bottom eight teams in lost points (p = 0.001) and defensive rebounds (p = 0.000), with a very significant difference (p < 0.01). However, steals (p = 0.760), blocks (p = 0.166), and fouls (p = 0.686) had no significant difference between the two cohorts (P > 0.05). Additionally, there was a very significant difference between attack RSR (p = 0.000), defense RSR (p = 0.006), and the overall attack-defense RSR (p = 0.000) of the top eight and bottom eight teams (p < 0.01), and most top teams focused on developing both attack and defense and paid attention to improve the overall attacking and defensive ability. Moreover, there was a significant relationship between the overall attack-defense performance and assists (p = 0.832), rebounds (p = 0.762), turnovers (p = 0.702), 2-point shoot percentage (p = 0.704), defensive rebounds (p = 0.809), fast-break points (p = 0.577), blocks (p = 0.600), and free throw percentage (p = 0.575).

Conclusions

This study showed that the top basketball teams focused on developing both attack and defense, and have the common characteristics of strong attack and defense. Whether it was the attack, defense, or overall attacking and defensive ability, there was a significant relationship with the final ranking. Additionally, this study showed that there were very significant differences in both attacking and defensive abilities between the top eight and bottom eight teams, as well as highlighted their respective advantages and disadvantages in attacking and defensive indicators. Besides that, this study found that performance indicators such as assists, defensive rebounds, 2P%, turnovers, FT%, fast-breaks, and blocks were the main factors that distinguish the top and bottom teams, and they had a significant relationship with overall attacking and defensive performance. The above information allows coaches and players to learn the latest developments in competitive basketball, as well as their advantages and disadvantages, to help them organize targeted training in the future.

The Pick-and-Roll in Basketball From Deep Interviews of Elite Coaches: A Mixed Method Approach From Polar Coordinate Analysis

Pick-and-roll is the most widespread cooperative action among high-level basketball teams and the most applied strategy by coaches to gain an advantage over the rival team. During pick-and-roll, opposing teams perform antagonistic actions based on goals that are expressed in offensive and defensive tactics. The aim of this study is to examine the approaches of high-level coaches on the offensive and defensive dynamics emerging in matches of a basketball elite team during an entire season of the Spanish Asociación de Clubes de Baloncesto (ACB) league. To this end, we used a mixed-methods approach based on systematic observation of verbatim transcripts of interviews conducted with six high-level coaches about the pick-and-roll dynamics that emerged in matches of the Unicaja Málaga team during an entire season of the ACB league. The observational design was nomothetic, punctual, and multidimensional. The choice of this methodology is justified since we developed an ad hoc indirect observation tool to evaluate the coaches’ perspective on this dynamic. Once the intra-observer reliability of the instrument was confirmed, we performed a polar coordinate analysis to identify the significant relationships between the coaches’ evaluations and the offensive and defensive pick-and-roll elements that supported such verbal behaviors. The results highlight the presence of various offensive and defensive aspects of pick-and-roll (n = 2224) emerging in the Unicaja team that were significantly associated with positive and negative evaluations of the coaches. The interview confirms that coach 1 and his staff were less confident in options that pick-and-roll offer, which is also reflected in the record of screens made and simulated, than coach 3. This study shows that the application of mixed methods, by analysis of the polar coordinate of the coding carried out on responses of a systematized interview, has proven to be an effective strategy in obtaining relevant information on the expert knowledge of the elite coaches on the influence of pick-and-roll on tactical actions in basketball.

A Log-Linear Analysis of Efficiency in Wheelchair Basketball According to Player Classification

Within the framework of observational methodology, we conducted a log-linear analysis to study efficiency in relation to players’ functional ability in wheelchair basketball by analyzing associations between the position of the last pass before a shot, a shot position, and a shot result (favorable or unfavorable). The observation sample contained all offensive sequences ending in a shot by the US men’s wheelchair basketball team, who won the gold medal at the 2016 Paralympic Games in Rio de Janeiro. Data were annotated and coded in the Lince software program and their reliability confirmed by measuring interobserver agreement using Cohen’s kappa statistics. Log-linear modeling showed that the models generated to explain offensive sequences ending in a shot by players with greater functional ability were closest to models observed for professional basketball players and were the only models to show significant effects in terms of efficiency.

Player Profiling and Monitoring in Basketball: A Delphi Study of the Most Important Non-Game Performance Indicators from the Perspective of Elite Athlete Coaches

BACKGROUND

Little is known about which indicators of performance elite athlete coaches (i.e., professional coaches who coach at the national or international levels) consider to be important for basketball.

OBJECTIVE

Using a Delphi procedure, the aim of this study was to identify the non-game performance indicators elite athlete coaches consider to be important for the recruitment/selection of basketball players.

METHODS

Ninety elite athlete coaches (basketball coaches (n = 71) and strength/conditioning coaches (n = 19) who coached men (n = 60), women (n = 23), or both (n = 7)), employed in 23 countries across six continents, participated in a three-round online Delphi survey. Round 1 asked coaches to identify the non-game performance indicators (i.e., measures other than game statistics) they currently used (or would like to use) for player recruitment/selection, with common indicators combined into single indicators. Round 2 asked coaches to rate the importance of each performance indicator using a Likert scale (range: 0 = no importance whatsoever to 10 = extremely important). Round 3 asked coaches to identify the single best test measure for each indicator rated ≥ 6 (i.e., important to extremely important) in Round 2. Results were reported descriptively.

RESULTS

A total of 608 responses (344 after removal of duplicates) were reported in Round 1, which were collapsed into 35 indicators, all of which were rated as 'important' in Round 2. Psychological and game intelligence indicators were typically rated as very important to extremely important (i.e., median = 9), with physical fitness and movement skills typically rated as very important (i.e., median = 8). For most indicators, coach observation was identified as the best test measure, with unique objective performance/anthropometric tests identified for all physical fitness indicators.

CONCLUSION

This study identified a range of psychological, game intelligence, physical fitness, and movement skill indicators that were considered by elite athlete coaches to be important to extremely important for the recruitment/selection of basketball players. These findings may inform the development of a basketball-specific test battery for recruiting/selecting and monitoring players.

Effect of Ball Screen and One-on-One on the Level of Opposition and Effectiveness of Shots in the ACB

In light of the importance of shots on the final result in basketball matches, this research aims to reveal the predictive ability of variables associated with different game actions (ball screen, one-on-one near the basket and one-on-one far away from the basket), shot zone (near to or far from the basket) and players' position (outside or inside) of the success of shots and of the type of shot, defended or not defended, in three seasons (2014 to 2016) of the Copa del Rey in the ACB (Asociación de Clubes de Baloncesto) League in Spain. Observational methodology was used, and an ad hoc instrument was created using Lince software. The study was conducted on a total of 7695 game actions, of which an analysis of 877 shots was made using CHAID (Chi-squared Automatic Interaction Detection) classification trees. The results obtained in this research have led us to the following conclusions: (a) the most important variable in the success of shots is to find "open shots", regardless of the game action, (b) one-on-one actions outside and inside the zone have a clear connection to the finishes of the plays and end up in defended shots, (c) shots by ball screen lead to more open shots than in one-on-one and (d) play with passes may facilitate to find "open shots". These results can be of interest for coaches to prepare their teams in the best possible way, but they must be interpreted with caution as the sample was very specific (Copa del Rey in the ACB League) and, therefore, it is necessary to continue investigating this topic in other competition contexts.

How To Win the Basketball Euroleague? Game Performance Determining Sports Results During 2003-2016 Matches

The main aim of this study was to establish game-related statistics that determined sport results in the Basketball EuroLeague between 2003 and 2016. The study encompasses matches played by 10 teams during 13 consecutive seasons of the EuroLeague. Twenty-two offensive and defensive game related variables were registered. Calculations were performed to establish which of the variables determined performance in the Basketball EuroLeague matches within the analysed period. Based on a number of mathematical and statistical analyses, the elements of play that had the highest effect on sports success were selected. The following determinants displayed the most significant correlations with sport results in the EuroLeague within the analysed period: two-point shots made (2PT-made), two-point shot attempts (2PT-attempts), three-point shots made (3PT-made), one-point shots made (1PT-made), one-point shot attempts (1PT-attempts), assists, fouls and field goals made. The game outcome in basketball is influenced by many variables which may not always be significant in a given match. However, the continuous effort to maintain these variables at the highest possible level is advantageous over less-organised teams.

Determinants of Dribbling and Passing Skills in Competitive Games of Women's Basketball

This work aimed to identify the influence of selected endogenous (internal load) and exogenous (possession duration, game quarter, and defensive pressure) factors in natural game conditions on the efficiency of dribbling and passing skills. Dribbling and passing skills were assessed during four games of U19 female basketball players and five games of senior (2nd division) female basketball players. In total, 551 dribbling and 914 passing executions were evaluated. Binary logistic regression identified defensive pressure of the opponent as a predictor of dribbling and passing skills effectivity. When the defensive pressure of the opponent was medium, the chances for the ineffective pass were 1.997 times more likely (95% CI; 1.179-3.383), as it is at the minimum defensive pressure. When the defensive pressure of the opponent was high, the chances for ineffective dribbling were 7.45 times more likely (95% CI; 3.672-15.113) and for ineffective pass were 8.419 times more likely (95% CI; 4.6-15.409), as it is at minimum defensive pressure. The game quarter and the internal load were not identified as the predictors influencing the dribbling and passing effectivity. Possession duration was also an insignificant predictor of dribbling and passing skills effectivity. However, the passing skill effectivity decreases when the shot clock is winding down. These findings confirm the importance of transferring game situations into the training process. Coaches should take into account these factors when they want to stimulate determinants of player performance in a balanced and systematic way.

Prediction of Offensive Possession Ends in Elite Basketball Teams

In basketball, the end of the ball possession has been described as one of the most important determinants of successful offensive play by a team. The present study aimed to: (i) investigate outcomes according to the play types of ends of the ball possession; (ii) find the most efficient ball possessions during the game; (iii) predict most efficient ends of the ball possession by time in an elite basketball competition. The sample was composed of 38,640 situations of ends of the ball possession from 240 games of the 2017-2018 regular season of the men's Euroleague that were quantitatively analyzed. According to the results, the predictive model can be used in modern basketball. The most efficient ends of the ball possession are the 2-point field goals on the fast break (78.2%), cuts (64.8%), pick and roll (P&R) screener (61.5%), and transition and offensive rebound (57.4%) situations. This information allows a better collective understanding of basketball, and it could be a great tool to use for coaches to prove which tactical solutions are to be considered when improving offense and defense strategies. It also contributes to the design of precise practice tasks of the coach that improve the game.

Methods of performance analysis in team invasion sports: A systematic review

The objective of this review was to systematically describe the traditional and contemporary data capture and analytic methods employed in performance analysis research in team invasion sports, evaluate the practicality of these methods, and formulate practical recommendations on methods for analysing tactics and strategies in team invasion sports. A systematic search of the databases SPORTDiscus, Web of Science, Scopus, MEDLINE and PubMed was performed. Keywords addressed performance analysis methods and team invasion sports, with all other disciplines of sports science excluded. A total of 537 articles were included in the review and six main themes of research identified. Themes included game actions, dynamic game actions, movement patterns, collective team behaviours, social network analysis and game styles. Performance analysis research has predominantly focused on identifying key performance indicators related to success by analysing differences in game actions between successful and less successful teams. However, these measures are outcome-focused and only provide limited insight into winning team's strategy. Team invasion sports are now viewed as dynamic, complex systems with opposing teams as interacting parts. Strategies and tactics should be analysed using a holistic process-orientated approach by recording dynamic actions, collective team behaviours and passing networks, and viewing them in game styles.

Analyse der Interaktionen im direkten Block als vorbereitende und abschließende Angriffshandlung in zufällig ausgewählten Hauptrundenspielen der easyCredit Basketball Bundesliga 2017/18

Der direkte Block (engl. „ball screen“, meist als Pick & Roll mit Abrollen des Blockstellers zum Korb ausgeführt) besitzt seit vielen Jahren einen hohen Stellenwert im Leistungsbasketball und wird in Positions- und Schnellangriffen gegen Mann-Mann- sowie Ball-Raum-Verteidigungsvarianten eingesetzt. Die vorliegende Studie analysiert die Häufigkeiten und Erfolgsquoten direkter Blocks zur Vorbereitung finaler Abschlusshandlungen und als direkte Abschlusshandlung anhand einer Zufallsstichprobe der Hauptrundenspiele der easyCredit Basketball Bundesliga 2017/18. Insgesamt 2555 Angriffssequenzen wurden mit einem an Remmert angelehnten Kategoriensystem mit Hilfe der Spielanalysesoftware utilius vs® detailliert beschrieben und quantitativ ausgewertet. Mit 19,9 % der Abschlusshandlungen und 26,4 % der vorbereitenden Abschlusshandlungen ist der quantitative Anteil der Blocks im Vergleich zu aktuellen internationalen Studien (siehe „Problemstellung“) niedrig. Die Erfolgsquote liegt jedoch mit durchschnittlich 1,22 Punkten pro Angriffsversuch (PPA) höher als die 1,16 PPA der 1‑gegen-1-Abschlüsse. 86,7 % der direkten Blocks zum Angriffsabschluss werden als aktive Blocks gestellt und sind mit 1,25 PPA überdurchschnittlich erfolgreich. Das Über-den-Block-Gehen und Switch sind die häufigsten Verteidigungsvarianten gegen direkte Blocks, erstere ist mit nur 1,08 zugelassenen PPA deutlich erfolgreicher. Die häufigste offensive Lösung im direkten Block ist der Wurf des Ballbesitzers, der mit 1,13 PPA jedoch nur mäßig lukrativ ist. Durchbruch und Abrollen sind mit 1,28 und 1,31 PPA effektiver. Gewinnerteams erzielen durchschnittlich 1,33 PPA aus direkten Blocks zum Angriffsabschluss, Verliererteams nur 1,10 PPA. Sie sind damit deutlich effektiver und verteidigen erfolgreicher.

The Basketball Pass: A Systematic Review

The aim of this study was to review and organise current literature about the basketball pass and find the main factors that influence its learning skills and performance. Thirty-seven studies were included after the screening process. The documents were classified into main research topics. This review identified the following conclusions: (i) the assessment of passing performance should be made under uncertain and variable conditions to obtain information on players’ responses to competitive scenarios, (ii) it is advisable to incorporate new and random activities to facilitate the transference of learning to the competition, (iii) it is recommended to include overwhelming factors during the practice to minimise the effect of pressure and choking, (iv) optimal physical conditioning is essential to maintain passing performance during a basketball game, (v) small sided games and changing environments stand as the best training situations to improve passing skills. Furthermore, limited information is available about biomechanical aspects and physical conditioning training programs to improve passing skills in basketball. Likewise, there is sparse data on passing skills development in children.

Use of Machine Learning to Automate the Identification of Basketball Strategies Using Whole Team Player Tracking Data

The use of machine learning to identify and classify offensive and defensive strategies in team sports through spatio-temporal tracking data has received significant interest recently in the literature and the global sport industry. This paper focuses on data-driven defensive strategy learning in basketball. Most research to date on basketball strategy learning has focused on offensive effectiveness and is based on the interaction between the on-ball player and principle on-ball defender, thereby ignoring the contribution of the remaining players. Furthermore, most sports analytical systems that provide play-by-play data is heavily biased towards offensive metrics such as passes, dribbles, and shots. The aim of the current study was to use machine learning to classify the different defensive strategies basketball players adopt when deviating from their initial defensive action. An analytical model was developed to recognise the one-on-one (matched) relationships of the players, which is utilised to automatically identify any change of defensive strategy. A classification model is developed based on a player and ball tracking dataset from National Basketball Association (NBA) game play to classify the adopted defensive strategy against pick-and-roll play. The methodology described is the first to analyse the defensive strategy of all in-game players (both on-ball players and off-ball players). The cross-validation results indicate that the proposed technique for automatic defensive strategy identification can achieve up to 69% accuracy of classification. Machine learning techniques, such as the one adopted here, have the potential to enable a deeper understanding of player decision making and defensive game strategies in basketball and other sports, by leveraging the player and ball tracking data.

Development of a Knockout Competition in Basketball: A Study of the Spanish Copa del Rey

Performance in basketball has been widely studied with regard to the results of the game using competition statistics. Few studies have analyzed the play process from a dynamic viewpoint regarding tactical actions and styles of play. The general objective of this research was to analyze the development in the styles of play of the teams participating in the Spanish Copa del Rey by studying the development of ball possessions (from start to finish) and the styles of play (attack and defense phases). The specific aim was to identify the relations between how possessions end and the style of play, as well as the relation between the duration of possession and the action of shooting and efficacy of the possession. All the matches corresponding to the Spanish Copa del Rey in basketball in the seasons 2015/2016, 2016/2017, and 2017/2018 were analyzed, comprising a total of 3,865 possessions. To this end, two groups of variables were characterized, which made it possible to define play, the development of possession (start and finish), and the style of play (attack and defense phase). An exploratory and descriptive analysis of the situational variables, development of possession, and style of play was carried out to characterize the competition. The Chi-squared test and Cramer's V coefficient were used to estimate the association among the categorical variables, interpreting the association among the categories using contingency tables. The results show a greater number of attacks in the final stages of the matches, with short possessions that end in baskets or rebounds, and positional attacks and individual half-court defenses predominating. There were more shots in positional attacks and more fouls in transitions. It also was apparent that the competition is developing from 1 year to another. The Spanish Copa del Rey competition changes from season to season, revealing slight modifications in the teams' styles of play, although there is stability in the fundamental play parameters, like the predominance of man-to-man defense, the duration of the attacks, and the use of screens. The style of play conditions the finalization of the possession.

Successful and Unsuccessful Offensive Sequences Ending in a Shot in Professional and Elite Under-16 Basketball

Following observational methodology, we analyzed successful and unsuccessful offensive attacks by professional and elite under-16 (U16) basketball players in Spain using an adapted ad hoc observation instrument designed to study efficiency in basketball. We identified both similarities and differences between how players from both categories built their attacks. The synchronic statistical analysis based on frequency counts showed that shots were more efficient in professional basketball and that U16 basketball was less static and had a higher frequency of fast breaks. Diachronic analysis, which consisted of T-pattern detection using Theme software, allowed us to identify characteristic successful and unsuccessful offensive sequences in professional and elite U16 basketball. These results have practical implications as they can be used to design training drills and prepare for competitions in U16 and professional basketball.