자료포락분석(DEA)을 활용한 대학 배드민턴 선수의 경기력 효율성 평가
Evaluating the Performance Efficiency of University Badminton Players Using Data Envelopment Analysis (DEA)
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This study evaluated the relative efficiency of collegiate men’s singles badminton players using an output-oriented Data Envelopment Analysis (DEA). Inputs were total matches played, sets per win (Avg), and third-set loss rate; outputs were win rate, maximum advancement round (normalized Avg), 2–0 win rate, comeback win rate, and third-set win rate. Using official 2024 Korea Badminton Association records, 92 players were analyzed under VRS (BCC) and CRS (CCR), with super-efficiency also computed. Descriptive results showed marked heterogeneity (e.g., matches 2–17; win rate 0.333– 0.846). Many players were efficient under VRS (efficiency = 1.000), while some showed scale efficiency < 1 under CRS, indicating participation volume/structure effects. Higher 2–0 win rates and fewer sets per win aligned with better win and round outcomes, whereas a higher third-set loss rate was detrimental. The framework—combining round normalization and multi-indicator outputs—offers an objective tool for player assessment and selection.
Abstract
This study evaluated the relative efficiency of collegiate men’s singles badminton players using an output-oriented Data Envelopment Analysis (DEA). Inputs were total matches played, sets per win (Avg), and third-set loss rate; outputs were win rate, maximum advancement round (normalized Avg), 2–0 win rate, comeback win rate, and third-set win rate. Using official 2024 Korea Badminton Association records, 92 players were analyzed under VRS (BCC) and CRS (CCR), with super-efficiency also computed. Descriptive results showed marked heterogeneity (e.g., matches 2–17; win rate 0.333– 0.846). Many players were efficient under VRS (efficiency = 1.000), while some showed scale efficiency < 1 under CRS, indicating participation volume/structure effects. Higher 2–0 win rates and fewer sets per win aligned with better win and round outcomes, whereas a higher third-set loss rate was detrimental. The framework—combining round normalization and multi-indicator outputs—offers an objective tool for player assessment and selection.