@article{oai:shizusan.repo.nii.ac.jp:00001915,
 author = {徐, 広孝 and 中西, 健一郎 and 青木, 優 and Jo, Hirotaka and Nakanishi, Kenichiro and Aoki, Masaru},
 issue = {2},
 journal = {スポーツと人間 : 静岡産業大学論集, Sport and human beings : Journal of Shizuoka Sangyo University},
 month = {Feb},
 note = {Previous studies on skill evaluation and scale construction using game performance data of soccer 
are based on the premise that the data reflect the skills required for soccer offense. However, no 
studies have verified whether Japanese professional level (J.League) game performance data reflects 
the skills required for soccer offense. Although Factor analysis can be used as a method to confirm 
this problem, there is a problem that it is difficult to properly use hyperparameters such as factor 
extraction method, rotation method, and number of factors in exploratory factor analysis. Therefore, 
in practice, inefficient analysis work is repeated. Against this background, in this study, we first 
developed a program (PAHFA: a Program that Analyzes all Hyperparameters in Factor Analysis) 
that is practically effective in exploratory factor analysis, and verified its practicality. Then, using 
the game performance big data (45 variables, 147,032 plays) of offense in J.League referenced to the 
previous research, the purpose was to elucidate the factor structure by exploratory factor analysis. 
As a result, it became clear that PAHFA has practicality. In addition, the game performance big 
data in offense of J.League was composed of 14 variables and 6 factors, and its content validity was 
confirmed. However, this factor structure is not a completely simple structure, and a task remains 
to elucidate a more robust factor structure.},
 pages = {65--84},
 title = {Jリーグの攻撃プレーにおける ゲーム・パフォーマンス・ビッグデータの因子構造 － 探索的因子分析における実用的プログラムを適用して －},
 volume = {6},
 year = {2022},
 yomi = {ジョ, ヒロタカ and ナカニシ, ケンイチロウ and アオキ, マサル}
}