Examining the Turkish Men’s Professional Basketball Team’s Success According to Game-related Statistics with Discriminant Analysis

Doğan, İlkay; Ersöz, Yasin

doi:10.1080/24748668.2016.11868931

Cited by 16 publications

(27 citation statements)

References 8 publications

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“…• Quality of the team and opposition. The teams that entered into the play-off season were defined as strong teams/oppositions and the teams that did not enter into the next phase were classified as weak teams/oppositions (Doğan et al, 2016;Ibáñez et al, 2008;Sampaio, Lago, et al, 2010). • Match outcome (win and lose).…”

Section: Situational Variablesmentioning

confidence: 99%

“…Therefore, some studies have used different methods to evaluate and compare the quality of teams and their opponents, such as multi-criteria assessing methods like TOPSIS and AHP (Dadelo, Turskis, Zavadskas, & Dadeliene, 2014;Kiani Mavi, Kiani Mavi, & Kiani, 2012) or cluster analysis (Marcelino, Mesquita, & Sampaio, 2011;Sampaio, Drinkwater, et al, 2010). Besides, teams' quality has either been subjectively categorised as "successful" or "unsuccessful" according to their standings or winning percentage within a particular tournament (Gómez, Ibáñez, Parejo, & Furley, 2017;Ibáñez et al, 2008;Pollard & Gómez, 2007;Sampaio, Ibáñez, Lorenzo, & Gómez, 2006) or classified as "strong" or "weak" and "top half " or "bottom half " based on symmetric division of end-of-season classification (Doğan, Işik, & Ersöz, 2016;Sampaio, Lago, Casais, & Leite, 2010). However, there is less available research about exploring the difference of technical and physical performances of players according to the specific position between different levels of teams.…”

Section: Introductionmentioning

confidence: 99%

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Players’ technical and physical performance profiles and game-to-game variation in NBA

Zhang

Lorenzo

Gómez

et al. 2017

International Journal of Performance Analysis in Sport

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The aim of this study was to (i) identify technical and physical performances of basketballers according to playing position in strong and weak teams, and (ii) describe variability in game-togame performance according to game outcome, location, quality of teams and opposition. Performance-related variables of all the 699 matches of regular season 2015-2016 in the National Basketball Association were analysed. All the comparisons were performed using magnitude-based inferences. As could be expected, results showed that technical and physical performances differed between players of strong and weak teams. In technical aspect, forwards and centres from strong teams made more three-point field goals, but fewer two-point field goals, than their counterparts from weak teams. Interestingly, forwards and guards from strong teams covered shorter distances and lower speeds than their peers from weak teams. In addition, the threepoint field goals made and attempted presented high variability. Game location generally had no significant impact on the variability of players' performance. Guards exhibited relatively lower variability in technical and physical variables in comparison with players from other positions. Exploring the difference and variability of technical and physical performances of basketballers allows fine-tuning of practice and game plans in order to build up optimal winning strategies.

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Section: Situational Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Players’ technical and physical performance profiles and game-to-game variation in NBA

Zhang

Lorenzo

Gómez

et al. 2017

International Journal of Performance Analysis in Sport

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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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“…When studying the structure of schoolchildren's motor preparedness, it is effective to use multidimensional methods of mathematical statistics, such as factor and discriminant analyses (Ivashchenko, 2016;Lopatiev, Ivashchenko, Khudolii, Pjanylo, Chernenko, & Yermakova, 2017). Discriminant analysis also proved effective for studying the training process of athletes (Doğan, Işik, & Ersöz, 2016;Miarka, Fukuda, Del Vecchio, & Franchini, 2016).…”

Section: Introductionmentioning

confidence: 99%

Movement Coordination: Identification of Development Peculiarities in Girls and Boys Aged 11-13

Іващенко

Худолій

Єрмаков

et al. 2018

Teor. metod. fiz. vihov.

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The study objective is to determine the possibility of identifying the state of coordination abilities development in girls and boys aged 11-13 using the methodology of multidimensional statistics. Materials and methods: Study participants. The study involved girls: 11 (n=20), 12 (n=23), 13 (n=19) years old; boys: 11 (n=21), 12 (n=20), 13 (n=19) years old. Study organization. The paper used analysis and generalization of scientific literature data, testing, discriminant analysis. Testing procedure. The testing program included well-known tests (Liakh, 2000; Serhiienko, 2001; Ivashchenko, 2016). Study results: The first canonical function explains 65.7% of the results variation, the second one – 25.1%, the third – 4.8%, which indicates their informative value. The coefficients of canonical correlation indicate the prognostic value of these functions. The first canonical discriminant function is most substantially related to the results of tests 4 (r=0.526), 14 (r=0.377), 2 (r=-0.306): therefore, a significant difference between the girls and boys aged 11-13 is observed in the development level of relative and speed strength, movement coordination. The structure coefficients of the second canonical discriminant function indicate that the function is most substantially related to variables 2 (r=0.502), 16 (r=0.434), 9 (r=0.379), 17 (r=0.357): so, a significant difference between the girls aged 12-13 and boys aged 11-13 is observed in speed strength, vestibular stability, and anthropometric data. Conclusions: The discriminant models characterize both age- and gender-related peculiarities of movement coordination development in the girls and boys aged 11-13. The data obtained show significant differences between the groups of girls and boys aged 11-13 both in the structure of coordination abilities development and the level of preparedness, and highlight the need to conduct separate physical education classes for girls and boys. Different programs should be used to develop coordination abilities in boys and girls aged 11-13.

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Examining the Turkish Men’s Professional Basketball Team’s Success According to Game-related Statistics with Discriminant Analysis

Cited by 16 publications

References 8 publications

Players’ technical and physical performance profiles and game-to-game variation in NBA

Players’ technical and physical performance profiles and game-to-game variation in NBA

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

Movement Coordination: Identification of Development Peculiarities in Girls and Boys Aged 11-13

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