從資料科學觀點探討籃球運動數據分析之應用

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2023

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資料科學能夠使人們將雜亂無章的數據與資料轉換成為有意義的資訊,以供決策之用。在籃球運動中,局部定位系統便是能夠將運動員的表現進行量化之儀器。因此本研究將用資料科學觀點探討局部定位系統在籃球運動數據分析之應用情形為何。研究方法分為量化與質性,量化部分透過局部定位系統監控球員在四節比賽中的跑動里程與Player Load,以描述性統計、單因子變異數分析、相依樣本t檢定分析球員在不同防守策略、不同位置、不同節次中各項運動表現數據之差異;質性部分則是針對籃球教練進行半結構式訪談,以了解系統之實質效益。研究結果顯示,球員平均跑動里程最高為第一節,平均Player Load最高則為第四節,不同位置間前鋒經歷最高平均跑動里程與Player Load,最低則為後衛;差異分析呈現,後衛與前鋒在跑動里程呈現顯著差異,球員在第一節與第二節之跑動里程顯著高於第三節;局部定位系統所提供之體能負荷分析可使教練將其用於模擬正式比賽之強度、控制訓練量、疲勞監控、以及監測球員努力程度;在技戰術分析之層面則是可以提供球員在場上的位置、跑動情形、與面積。然而,局部定位系統未來需要新增追蹤球之功能,優化面積計算、變換方向、跳耀、與碰撞之指標上監測的準確度。在實際應用上則是透過長期追蹤與監測選手,方能使系統功效最大化。基於上述結果,建議未來不管是研究方向亦或是實務方面皆可以長期運用系統,以進行運動表現監測與分析。
Data science enable people to transform disorderly unstructured data into meaningful information with an aim to making appropriate decision. In the game of basketball, local positioning system (LPS) can be used to quantify athletic performance Therefore, the main purpose of this study was utilizing the viewpoint of data science to discover how to apply LPS to basketball data analysis. The research method is divided into quantitative section and qualitative section. In terms of quantitative section, data were collected from the players of National Taiwan Normal University women basketball team. Total distance and player load were measured using LPS in one match. Descriptive statistics, single-factor variance analysis, and dependent sample t-tests were used to test the differences in athletic performance between defensive strategies, game quarters, and playing positions. With regard to qualitative section, semi-structured interviews were conducted in order to explore whether the LPS can offer substantial benefits to basketball coaches. The results shows that all players presented highest relative distance covered in the first quarter and highest player load in the forth quarter. Forwards presented highest relative distance covered and player load compared to other positions, and guards presented lowest relative distance covered and player load compared to other positions. The relative distance of forwards was significantly greater than that of guards. The relative distance of first and second quarter was greater than that of third quarter. The analysis of physical demands provided by LPS enable coaches to stimulate the intensity of basketball match, control the volume of training, and monitor players’ fatigue as well as players’ effort. In terms of the technical and tactical analysis, LPS can present player’s position on the court, moving pattern, and spaces among players. Nonetheless, LPS should add the function of tracking ball movements and optimize the accuracy of monitoring the calculation of space, change of direction, jumping, and collision in the future. Furthermore, tracking and monitoring athletic performance over a great period of time can maximize the efficiency of LPS. Hence, based on the aforementioned, it is recommended that LPS should be used as monitoring and analyzing athletic performance regularly in both practical and research aspects in the future.

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大數據, 追蹤系統, 運動表現分析, big data, tracking system, performance analysis

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