Evaluation and Analysis of Relationship between Roles and Individual’s Role Adaptation in Team Sports Using SOM

Takemura, Yasunori; Yokoyama, Muneyuki; Omori, Sho; Shimosaka, Ryo

doi:10.1007/978-3-319-05515-2_9

Cited by 3 publications

(10 citation statements)

References 4 publications

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“…To evaluate the performance of the SOM algorithm, individually, at first, only x input data sets (only physical data sets) are used same as previous works [8]. Figure 4 shows the feature maps used for the 2017 physical data sets.…”

Section: Resultsmentioning

confidence: 99%

“…The N volleyball club in 2017 comprised 22 students (There are same members as physical data. ).Psychological features measured were those of the Diagnostic Inventory of Psychological Competitive Ability for Athletes (DIPCA.3) [8]. DIPCA.3 measures 12 types of psychological ability in a 48-item questionnaire.…”

Section: Describe the Psychological Data And Analysis Using Statisticsmentioning

confidence: 99%

“…The analysis of physical and psychological features indicates that the conventional statistical method cannot reveal player adaption to positions in team sports and relationships between positions [8,9]. We therefore propose a new method using SOMs.…”

Section: Proposal Algorithm Using Sommentioning

confidence: 99%

“…However, does it follow that good decisions are made? Previous research has not clarified athlete adaptation to positions and relationships between positions considering both physical and psychological features [8].…”

mentioning

confidence: 99%

“…We consider the two main types of features, which are physical and psychological features, and introduce the concept of using an algorithm to cluster player features in team sports. In previous works, we developed clustering algorithm using self-organizing maps (SOM) for team relationship map [8,9]. Previous work is adapted in university rugby players.…”

mentioning

confidence: 99%

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

Takemura¹,

Oda²,

Ono³

2018

JRNAL

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In Japan, sports efforts are actively being carried out to host the 2020 Olympic Games. Especially in the field of sports science, researches on ergonomics, development of sports equipment and pattern recognition technology using artificial intelligence are actively researched. The budget of Japanese sports in 2016 is 32.4 billion yen, and considering that the budget for 10 years ago was 19 billion yen, it is a doubling momentum.

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Section: Resultsmentioning

confidence: 99%

Section: Describe the Psychological Data And Analysis Using Statisticsmentioning

confidence: 99%

Section: Proposal Algorithm Using Sommentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Takemura¹,

Oda²,

Ono³

2018

JRNAL

Self Cite

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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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A Consideration on Emotional Topology: Verbal Data Processing and Representation Applied to Athlete Statements

Aoki¹,

Hirahara²,

Kato³

et al. 2018

PSYCH

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In order to analyze emotional state and movement for desirable life, we considered a topological analysis of mind for the representative people whom were, in this case, athletes. The utterances were acquired from online articles that included statements they made during interviews. The sampled data was processed with TF-IDF, BLSOM, and fuzzy clustering technique. The resultant mapping provides a typical emotional state and movement in the extracted axes so that one can discuss the athletes' psychological transition on this map. The proposed procedure was significantly effective at presenting the player's emotional interest and motivation.

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Evaluation and Analysis of Relationship between Roles and Individual’s Role Adaptation in Team Sports Using SOM

Cited by 3 publications

References 4 publications

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

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

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

A Consideration on Emotional Topology: Verbal Data Processing and Representation Applied to Athlete Statements

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