以灰關聯分析優化排球運動員選材之輔助模式

目的：本研究主要目的在優化排球團隊選擇球員之輔助系統。方法：運用灰關聯分析（grey relational analysis, GRA），針對2020－2021臺灣企業甲級排球聯賽（Top Volleyball League, TVL）男子組參賽之66名球員1,443筆表現數據，先透過迴歸分析確認12項準則對於勝率具有良好的解釋力，接著計算出各項準則之權重及個人綜合績效值，然後將球員依位置加以排名及組成最佳團隊。結果：扣球得分及舉球失誤之權重最高；最佳7名球員中有4名球員被GRA及TVL評審選中（K = .521），具有適度的相關；最後雙倍擴展至14名球員，僅有1名TVL最佳球員未入選。結論：建議透過GRA提供之全面性綜合績效值，排球團隊可以更客觀地選材，組成最佳團隊。

關鍵字

排球選材；排球綜合績效評估

並列摘要

Purpose: This study aimed to optimize the player selection model for a volleyball team. Method: Grey relational analysis (GRA) was deployed to generate weightings for 12 volleyball skill indicators and overall performance appraisal, rank each player by position and formulate the best team by analyzing 66 players and 1,433 initially classified data played in 2020-2021 Top Volleyball League (TVL) season in Taiwan. Prior to the GRA, we adopted the regression analysis to confirm these indicators with high explanatory power for the winning percentage. Results: The highest weighting included spike points and set errors. Secondly, four of the best seven players were selected by both GRA and TVL committees, implying a moderate consistency with K = .521. Lastly, only one of the best seven players selected by the TVL committee was not included in the double expansion 14-player roster. Conclusion: These results suggested that GRA could provide a holistic performance appraisal for player selection for a volleyball team.

並列關鍵字

volleyball information system (VIS) ； volleyball performance appraisal

參考文獻

李建毅（2006）。我國優秀男子排球選手得分技術競技能力與比賽成績表現之關聯研究。排球教練科學，8，52-73。https://doi.org/10.6 948/svc.200606.0052

胡林煥（2014）。運用資料包絡分析法評估企業排球聯賽選手績效表現。運動教練科學，33，23-38。 https://doi.org/10.6194/scs.2014.33. 03

陳一進（2004）排球得分方式與比賽成績應用灰關聯分析之探討。大專體育，70，149-154。https://doi.org/10.6162/srr.2004.70.24

張恩崇（2011）。以灰色關聯分析探討排球比賽得分因素與成績之關聯性。體育學報，44(2)，275-289。https://doi.org/10.6222/pej.440 2.201106.0908

嚴雅婷（2019）。球隊的技術表現型態：2018企業甲級男女排球聯賽之主成分分析。運動教練科學，54，17-29。https://doi.org/10.6194/scs.201906_(54).0003

主題瀏覽