What is the difference between a winning and a losing team: insights from Euroleague basketball

Çene, Erhan

doi:10.1080/24748668.2018.1446234

Cited by 49 publications

(48 citation statements)

References 16 publications

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“…The second sport in AI application was basketball. Most of the studies on performance were in the “technical and tactical analysis” area [20, 22–25, 41–43, 55, 62], whereas one study was on the “physical, technical, and tactical analysis” [21]. Their focus justifies the recognized importance of physical, technical, and tactical analysis for basketball success [79, 80].…”

Section: Discussionmentioning

confidence: 99%

“…Our analysis showed that the AI techniques or methods used to predict sporting performance ( n = 11 studies) were with youth academy (9%), semi-professional (9%), and professional (82%) basketball players. The methods reported were as follows: on each artificial neural network (55%) [20–25], on decision tree classifier (27%) [41–43], and 9% each of Markov process [55] and support vector machine + decision tree classifier [62]. For injury risk assessment, the studies were based on “training process/knee injury causes” [66] and “heart defect detection” [64].…”

Section: Discussionmentioning

confidence: 99%

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Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Claudino

Capanema

Souza³

et al. 2019

Sports Med - Open

208

117

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Background The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using. Methods Systematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes. Results Fifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI. Conclusions The results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods. Electronic supplementary material The online version of this article (10.1186/s40798-019-0202-3) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Claudino

Capanema

Souza³

et al. 2019

Sports Med - Open

208

117

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“…A video analysis approach was also adopted to assess the following game-related statistics similarly to previous studies [9,11,12,15,16]: field goals attempted, field goals made (number and percentage), 2-point attempted, 2-point made (number and percentage), free throws attempted, free throws made (number and percentage), total rebounds, offensive rebounds (number and percentage), defensive rebounds (number and percentage), assists, turnovers. It is important to note that unlike in traditional basketball, in 3x3 basketball 2-point shots refer to those executed outside the arc, while shots scored within the arc are considered as 1-point shots.…”

Section: Methodsmentioning

confidence: 99%

“…A recent study using the magnitude-based inference approach showed that winning teams performed substantially higher percentages of 3-point, free throw, defensive rebound and steals compared to losing teams in male collegiate players’ close basketball games [9]. Furthermore, defensive rebounds, steals, turnovers, shooting percentage and offensive rebounds have been identified through a Bayesian model averaging approach as parameters discriminating winning and losing teams in Euroleague basketball [12]. However, while game-related statistics can provide a global view of a team’s efficacy, derived performance indicators such as effective field-goal percentage, offensive rebounding percentage, recovered balls per possession and free throw rate might allow a more accurate prediction of team success [13,14].…”

Section: Introductionmentioning

confidence: 99%

Performance profile and game-related statistics of FIBA 3x3 Basketball World Cup 2017

Conte¹,

Straigis²,

Clemente³

et al. 2019

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The study aimed to characterize the performance profile of 3x3 basketball and particularly to assess: a) the differences between games in live time (LT) and stoppage time (ST) phases and their ratio, and b) the game-related statistics and derived game indicators differentiating between winning and losing teams. Eight games [quarterfinals, semifinals and finals (1 st and 3 rd place)] of the FIBA 3x3 basketball world cup (Serbia, 17 th -21 st June 2017) were analysed. The LT and ST phases were categorized into three phase durations: 1-20 s, 21-40 s, >40 s. The LT/ST ratio was calculated. The game-related statistics and derived parameters were assessed through video-based notational analysis methods, and differences between winning and losing teams were calculated using a mixed linear model. The results revealed no statistically significant differences in the distribution of LT and ST phases between games, with an LT/ST ratio of 0.92±0.13. Moreover, winning teams showed a significantly higher (p<0.05) number of free throws made and attempted, team offensive ratings, and recovered balls per possession compared to losing teams. Conversely, winning teams revealed significantly lower (p<0.05) values for turnover, rebound (offensive, defensive and total), offensive rebound percentage and team defensive rating compared to losing teams. These results provide coaches and practitioners with novel and applied information regarding the performance profile of 3x3 basketball to optimize training sessions, which should be characterized by short live time phases and a work-to-rest ratio ~1 and focused on developing the ability to avoid turnovers and increase the recovered balls per possession, minimizing the scoring possibilities for the opponent team.

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“…The FIBA regulations define eight game-related statistics: field goals, free throws, rebounds, turnovers, assists, steals, blocked shots, and fouls. Game-related statistics have been studied to scientifically illustrate the games in various categories: the Olympic Games for men 3,4,5 and women 6 , FIBA Basketball World Cup for men 7 , professional leagues for men 8,9,10 and women 11,12,13 , and international tournaments for youth athletes 14,15 . In addition, game-related statistics have been analyzed in various contexts such as effects of rule changes 16,17 , age and sex differences 18,19 , and relationship with physical fitness 20 .…”

Section: Introductionmentioning

confidence: 99%

Are regional differences in basketball already established in under-18 games?

Madarame

2018

Motriz: rev. educ. fis.

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To identify regional differences in basketball games for under-age categories, this study analyzed game-related statistics of four continental championships for under-18 (U18) men. Methods: A total of 149 games from each continental championship for U18 men held in 2016 were analyzed. A one-way analysis of variance followed by a Bonferroni post hoc test was performed to assess differences in game-related statistics between the continents (Europe, Africa, America, Asia). A discriminant analysis was performed to identify game-related statistics which discriminate among the continents. Results: The U18 African championship, which was characterized by a high number of free throws, was clearly discriminated from any other U18 continental championship. Among the other U18 continental championships excluding the African championship, the biggest difference was observed between the European and the Asian championships. The U18 European championship was characterized by a low number of possessions and a high number of assists, whereas the U18 Asian championship was characterized by a high number of possessions and a low number of assists. Both possessions and assists showed high numbers in the U18 American championship. Conclusion: This study identified regional differences in basketball games among four continental championships for U18 men held in 2016. It is suggested that basketball games for under-age categories are played in a different manner in each region of the world. The information derived from this study will help players and coaches of under-age national teams prepare for international competitions.

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What is the difference between a winning and a losing team: insights from Euroleague basketball

Cited by 49 publications

References 16 publications

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Performance profile and game-related statistics of FIBA 3x3 Basketball World Cup 2017

Are regional differences in basketball already established in under-18 games?

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