Analysis of Team Relationship using Self-Organizing Map for University Volleyball Players

Takemura, Yasunori; Oda, Kyoko; Ono, Michiyoshi

doi:10.2991/jrnal.2018.5.3.12

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…It can be used for team sports relationships and adaptation to a game position. Takemura, and colleagues (Takemura et al, 2018;Takemura et al, 2014) considered physical and psychological factors for clustering players' features in the team sports of volleyball and rugby by developing clustering modeling using SOM for a team relationship map. In rugby, studies (Croft et al, 2015;Zheng et al, 2020) proved that SOM helps identify essential relationships among crucial indicators in discrete data.…”

Section: Introductionmentioning

confidence: 99%

Performance prediction of taekwondo athletes using machine learning

Abhariana,

Abdolvand,

Abharianc

et al. 2024

Preprint

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Purpose The purpose of this paper was analyzing the performance of elite taekwondo athletes with machine learning approaches through achieving three objectives: (1) clustering the performance of elite taekwondo athletes into four clusters of excellent to poor performance, (2) determining the most effective physical characteristics for performance in this sport, and (3) predicting their medal-winning in the world competitions. Design/methodology/approach Descriptive and predictive models were employed based on the National Olympic Academy dataset of Iranian taekwondo athletes’ physical fitness and anthropometric records which were collected during 1996–2019. Findings In the female (999 records) and male (1560 records) datasets, SOM-kmeans and SOM-spectral clustering algorithms (with average test efficiency of Silhouette as 80%, 79% and Davies-Bouldin as 20%, 34%) were applied and 4 clusters have been obtained based on different physical functions of taekwondo athletes and the number of medals allocated in each cluster. Semi-supervised learning model using the CPLE-Learning algorithm in both female and male datasets demonstrated the possibility of winning medals in the competitions. The accuracy of predicting gold, silver and bronze medals in female dataset were 68%, 59% and 73%, and in the male dataset have been found 58%, 61% and 54%, respectively. Originality/value The results indicate that machine learning algorithms’ ability in sports performance analysis provides valuable information for the management of taekwondo athletes and better planning to prepare them physically.

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Section: Introductionmentioning

confidence: 99%

Performance prediction of taekwondo athletes using machine learning

Abhariana,

Abdolvand,

Abharianc

et al. 2024

Preprint

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“…To cluster the athletes by psychological characteristics, two clustering algorithms, self-organizing map (SOM) (Kohonen, 1995) and Ward's method (Ward, 1963) are used. SOM also has been used for the analysis of the relationship between roles in team sports (Takemura et al, 2014(Takemura et al, , 2018. SOM is one of the artificial neural networks and Ward's method is one of the hierarchical clustering methods.…”

Section: Introductionmentioning

confidence: 99%

Psychological Analysis of Rugby Players Based on Self-Organizing Map (SOM) and Ward’s Method

Zheng¹,

Hatachi²,

Masuda³

et al. 2020

APE

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This research aimed to analyze the mental level of different players. The targets of the questionnaire were administered to 32 male elite rugby players. Two different types of clustering analysis algorithms: SOM and Ward's method were used for analysis. Results of SOM and Ward's method were checked with each other. The two different types of clustering methods were a good match with each other. Furthermore, the results of SOM made clearer visualization than Ward's method and could help us find more information about the features and relationships of each player. Although SOM had not been seen a lot used for analyzing in sports, this study confirmed that SOM is a very effective analysis method for the sports area.

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