研究生: |
張亞森 Zhang, Ya-Sen |
---|---|
論文名稱: |
從攻守數據分析籃球比賽之勝負,以奧運、世界盃為例 Analyzing the Outcome of Basketball Games from BOXSCORE, Taking the Olympics and the World Cup as Examples |
指導教授: |
朱文增
Chu, Wen-Tseng |
口試委員: |
李建興
Li, Jian-Xing 黃煜 Huang, Yu 朱文增 Chu, Wen-Tseng |
口試日期: | 2023/06/09 |
學位類別: |
碩士 Master |
系所名稱: |
運動休閒與餐旅管理研究所 Graduate Institute of Sport, Leisure and Hospitality Management |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 173 |
中文關鍵詞: | 奧運會 、世界盃 、攻守數據分析 、球權 、籃球 |
英文關鍵詞: | Olympics, Basketball World Cup, Statistical Analysis, Possession, Basketball |
研究方法: | 次級資料分析 |
DOI URL: | http://doi.org/10.6345/NTNU202301622 |
論文種類: | 學術論文 |
相關次數: | 點閱:160 下載:0 |
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奧運會與世界盃被視為體育界最高等級的短期賽事,球隊必須在較短的時間內磨合,而教練亦須參考攻守數據分析,瞭解球隊優勢與劣勢,並以此設計相應的戰術。對於競爭激烈的短期賽事而言,奧運會與世界盃的攻守數據分析則最具代表性。目的:瞭解2016里約奧運會、2020東京奧運會,2014世界盃、2019世界盃單節與全場比賽之攻守數據。並探討攻守數據在單節比賽與全場比賽不同進攻節奏的差異,以及攻守數據影響比賽勝負的情形。方法:描述性統計、k平均數集群分析、獨立樣本t檢定、單因子變異數分析與邏輯斯迴歸分析。結果:攻守數據在不同進攻節奏下產生顯著差異,顯示出在不同的節奏下單節與全場比賽表現存在差異,而這些差異也進一步呈現出現代籃球節奏逐漸加快的趨勢。防守籃板、抄截、失誤轉換得分以及最大領先分數則會顯著影響比賽勝負。這些數據不僅反應了球隊的表現,更關係著在場上的選擇與執行,從而決定了比賽的最終結果。結論:透過更多的防守籃板、抄截以及利用對手失誤,能夠打出更多進攻機會,藉由靠近籃框的攻擊,有效提升得分效率,並透過拉開分數來提高獲勝機會。應全面提升球員的個人技術、強化團隊默契、挑選適合球員,並注重攻守數據分析應用和健康管理,以上這些方法將有助於幫助國家隊在短期且競爭激烈的比賽中獲得勝利。
The Olympic Games and the World Cup are regarded as the pinnacle short-term sporting events in the world of sports, with each country sending their best athletes to compete, intensifying the level of competition in these events. Teams must quickly integrate within a shorter period of time, and coaches must refer to offensive and defensive data analysis to understand the team's strengths and weaknesses, designing corresponding tactics based on this analysis. For fiercely competitive short-term events, the offensive and defensive data analysis of the Olympic Games and the World Cup is most representative. Purpose:The aim of this study was to comprehend the offensive and defensive data from the 2016 Rio Olympics, 2020 Tokyo Olympics, 2014 World Cup, and 2019 World Cup. Furthermore, it sought to explore the disparities in offensive and defensive data between single quarters and entire games, as well as the impact of these data on match outcomes. Method:Various statistical techniques were employed, including descriptive statistics, k-means cluster analysis, independent samples t-tests, one-way analysis of variance (ANOVA), and logistic regression analysis. Result:The findings indicated substantial disparities in offensive and defensive data under different offensive tempos. This suggested differences in performance between single quarters and full games under varying rhythms, and these variances further exhibited the prevailing trend of modern basketball games becoming progressively faster-paced. Defensive rebounds, steals, points scored from turnovers, and the largest lead score significantly influenced match outcomes. These data not only mirrored team performance but also influenced decision-making and execution on the court, ultimately determining the culmination of the match. Conclusion:Generating more offensive opportunities through increased defensive rebounds, steals, and capitalizing on opponent turnovers allowed teams to enhance scoring efficiency. Employing an attacking strategy near the basket effectively boosted scoring efficiency and widening the point differential to heighten the likelihood of victory. To excel in short-term and fiercely competitive matches, a comprehensive approach encompassing players' individual skill enhancement, fortified team cohesion, appropriate player selection, and a focus on the application of offensive and defensive data analysis and health management was essential. These methods contributed to aiding national teams in securing triumphs in short-term and highly competitive matches.
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