인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
PURPOSE : Environmental factors such as temperature, humidity, wind speed, and altitude can significantly influence athletic performance. This study aimed to quantitatively analyze environmental characteristics from the KBO, NPB, and MLB, applying statistical methods to identify team-level environmental profiles and to provide baseline evidence toward the development of a standardized performance metric in baseball.
METHODS : Data from the 2024 regular season were collected, including travel distance, stadium characteristics, and weather variables. After preprocessing and feature aggregation, highly correlated variables were removed, resulting in 13 final variables. Data were standardized, and principal component analysis (PCA) was applied for dimensionality reduction. K-means clustering was then performed to classify teams into distinct environmental profiles, with the optimal number of clusters validated using the elbow method and silhouette scores.
RESULTS : Three clusters were identified: (1) MLB teams characterized by long travel distances and high weather variability, (2) KBO and NPB teams with shorter travel distances and humid conditions, and (3) dome and retractable-roof stadium teams representing highly stable and controlled environments. Dome stadiums provided the most stable environmental baseline across leagues.
CONCLUSIONS : Cluster analysis revealed distinct environmental profiles among professional baseball leagues. Travel distance and weather variability emerged as key environmental factors influencing performance, while dome settings served as a stable reference point for cross-league comparisons. Incorporating environmental variables into evaluative frameworks can enhance performance standardization and support more accurate assessments of player adaptability and potential challenges in international transfers.
METHODS : Data from the 2024 regular season were collected, including travel distance, stadium characteristics, and weather variables. After preprocessing and feature aggregation, highly correlated variables were removed, resulting in 13 final variables. Data were standardized, and principal component analysis (PCA) was applied for dimensionality reduction. K-means clustering was then performed to classify teams into distinct environmental profiles, with the optimal number of clusters validated using the elbow method and silhouette scores.
RESULTS : Three clusters were identified: (1) MLB teams characterized by long travel distances and high weather variability, (2) KBO and NPB teams with shorter travel distances and humid conditions, and (3) dome and retractable-roof stadium teams representing highly stable and controlled environments. Dome stadiums provided the most stable environmental baseline across leagues.
CONCLUSIONS : Cluster analysis revealed distinct environmental profiles among professional baseball leagues. Travel distance and weather variability emerged as key environmental factors influencing performance, while dome settings served as a stable reference point for cross-league comparisons. Incorporating environmental variables into evaluative frameworks can enhance performance standardization and support more accurate assessments of player adaptability and potential challenges in international transfers.
본문·목차
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-095600832
