簡易檢索 / 詳目顯示

研究生: 許軒瑜
Hsu, Hsuan-Yu
論文名稱: 從資料科學觀點探討籃球運動數據分析之應用
The Application on Basketball Data Analysis of the Data Science Viewpoint
指導教授: 陳美燕
Chen, Mei-Yen
梁嘉音
Liang, Chia-Yin
口試委員: 李逸驊
Li, Yi-Hua
陳美燕
Chen, Mei-Yen
梁嘉音
Liang, Chia-Yin
口試日期: 2021/06/12
學位類別: 碩士
Master
系所名稱: 體育與運動科學系
Department of Physical Education and Sport Sciences
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 100
中文關鍵詞: 大數據追蹤系統運動表現分析
英文關鍵詞: big data, tracking system, performance analysis
研究方法: 半結構式訪談法量化研究
DOI URL: http://doi.org/10.6345/NTNU202301625
論文種類: 學術論文
相關次數: 點閱:166下載:23
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 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.

    目 次 謝辭 i 中文摘要 iii 英文摘要 v 目次 vii 表次 xi 圖次 xiii 第壹章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第三節 研究問題 3 第四節 研究重要性 3 第五節 名詞釋義 5 第貳章 文獻探討 7 第一節 資料科學之相關研究 7 第二節 運動數據分析之相關研究 9 第三節 局部定位系統之相關研究 15 第五節 本章總結 21 第參章 研究方法 23 第一節 研究架構與流程 23 第二節 研究對象 26 第三節 研究工具 27 第四節 資料處理與分析 30 第五節 研究信實度 32 第六節 研究倫理 34 第肆章 結果與討論 37 第一節 球員運動表現數據 37 第二節 球員運動表現數據之差異 43 第三節 局部定位系統在體能負荷分析之應用 55 第四節 局部定位系統應用於籃球技戰術分析 66 第五節 局部定位系統當前限制與未來展望 77 第伍章 結論與建議 85 第一節 結論 85 第二節 建議 88 第三節 研究限制 90 參考文獻 91 附錄一 訪談同意書 99 附錄二 訪談大綱 (教練) 100

    方麒堯、相子元 (2020)。局部定位系統應用於運動員追蹤。2020年7月22日,取自https://www.sportscience.com.tw/article/detail/%E5%B1%80%E9%83%A8%E5%AE%9A%E4%BD%8D%E7%B3%BB%E7%B5%B1%E6%87%89%E7%94%A8%E6%96%BC%E9%81%8B%E5%8B%95%E5%93%A1%E8%BF%BD%E8%B9%A4

    方麒堯、相子元 (2020)。運動員追蹤系統。2020年5月13日,取自https://www.sportscience.com.tw/article/detail/%E9%81%8B%E5%8B%95%E5%93%A1%E8%BF%BD%E8%B9%A4%E7%B3%BB%E7%B5%B1

    王文科、王智弘 (2014)。教育研究法。台灣五南圖書出版股份有限公司。

    李逸驊、蔡琪揚、陳韋翰、黃冠勛、戴沁琳(2019)。穿戴加速規測量大專籃球聯賽公開男生組第一級隊伍之運動強度。大專體育學刊,21(4),342-352。

    林文斌、葉劭緯、楊鎮浯 (2017)。 從數據科學觀點探討中華職棒球員與球團效率管理。體育學報,50(S), 91-107。

    林薏如 (2015)。大數據時代襲捲,美國MBA開設資料科學相關課程。2015年1月6日,取自天下雜誌。取自 https://www.cw.com.tw/article/5063520
    科技部 (2018)。精準運動科學研究專案計畫。2018年4月27日,取自https://www.most.gov.tw/folksonomy/detail?article_uid=ca77e278-b376-46c1-b622-ee3c826cf6d5&menu_id=9aa56881-8df0-4eb6-a5a7-32a2f72826ff&l=ch

    高麗娟、黃光獻 (2014)。體育運動質性資料分析方法。體育學報,47(2),159-177。

    許懷中、黃致豪 (2017) 。以大數據分析球員技術面表現, 對戰組合與中華職棒歷年票房之相關性。體育學報,50(S),79-90。

    陳向明 (2002)。社會科學質的研究。五南圖書出版股份有限公司。

    程紹同 (2016)。運動產業 4.0 時代之大數據新思維。 運動管理,(33),19-44。

    鈕文英 (2015)。研究方法與論文寫作 (第二版)。臺北市:雙葉書廊。

    潘淑滿 (2003)。質性研究:理論與應用。臺北市:心理出版社。

    鄧碧珍、戴沁琳、錢薇娟、蔡琪揚、陳仕佳 (2021)。以運動員追蹤系統量化大專籃球員半場及全場攻防之身體負荷量。華人運動生物力學期刊,18(1),19-25。

    錢薇娟、蔡琪揚、方麒堯、陳韋翰 (2020)。局部定位系統結合IMU在量測籃球運動表現與負荷之應用。運動表現期刊,7(1),29-44。

    羅莉雯 (2019)。以迴歸樹預測美國職棒大聯盟各球隊的年度勝率及晉級季後賽之名單。淡江大學大數據分析與商業智慧碩士學位學程學位論文,1-60。

    Abdelkrim, N. B., El Fazaa, S., & El Ati, J. (2007). Time–motion analysis and physiological data of elite under-19-year-old basketball players during competition. British Journal of Sports Medicine, 41(2), 69-75.

    Akerkar, R., & Sajja, P. S. (2016). Intelligent techniques for data science. Cham: Springer International Publishing.

    Bastida-Castillo, A., Gómez-Carmona, C. D., la Cruz-Sánchez, D., Reche-Royo, X., Ibáñez, S. J., & Pino Ortega, J. (2019). Accuracy and inter-unit reliability of ultra-wide-band tracking system in indoor exercise. Applied Sciences, 9(5), 939.

    Becca L. (2014). Universities increasing programs for scientists. Retrieved from https://www.wallstreetandtech.com/careers/universities-increasing-programs-for-data-scientists/d/d-id/1318139.html

    Bojanova, I. (2014). It enhances football at world cup 2014. IT Professional, 16(4), 12-17.

    Brito, Â., Roriz, P., Silva, P., Duarte, R., & Garganta, J. (2017). Effects of pitch surface and playing position on external load activity profiles and technical demands of young soccer players in match play. International Journal of Performance Analysis in Sport, 17(6), 92-918.

    Cady, F. (2017). The data science handbook. John Wiley & Sons.

    Cao, L. (2017). Data science: A comprehensive overview. ACM Computing Surveys (CSUR), 50(3), 1-42.

    Cummins, C., Orr, R., O’Connor, H., & West, C. (2013). Global positioning systems (GPS) and microtechnology sensors in team sports: A systematic review. Sports Medicine, 43(10), 1025-1042.

    Davenport, T. H., Patil, D. J., & Scientist, D. (2012). The sexiest job of the 21st century. Harvard Business Review, 9.

    Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745-766.

    Douglas, A. S., & Kennedy, C. R. (2020). Tracking In-Match Movement Demands Using Local Positioning System in World-Class Men's Ice Hockey. The Journal of Strength & Conditioning Research, 34(3), 639-646.

    Ely, M., Anzul, M., Freidman, T., Garner, D., & McCormack-Steinmetz, A. (1991). Doing qualitative research: Circles within circles (Vol. 3). Psychology Press.
    Forbes business (2020). https://www.forbes.com/business

    Frencken, W. G. P., & Lemmink, K. A. P. M. (2008). Team kinematics of small-sided soccer games: A systematic approach. In Science and football VI (pp. 187-192). Routledge.

    Frencken, W., Lemmink, K., Delleman, N., & Visscher, C. (2011). Oscillations of centroid position and surface area of soccer teams in small-sided games. European Journal of Sport Science, 11(4), 215-223.

    Frencken, W., Van Der Plaats, J., Visscher, C., & Lemmink, K. (2013). Size matters: Pitch dimensions constrain interactive team behaviour in soccer. Journal of Systems Science and Complexity, 26(1), 85-93.

    García, F., Vázquez-Guerrero, J., Castellano, J., Casals, M., & Schelling, X. (2020). Differences in physical demands between game quarters and playing positions on professional basketball players during official competition. Journal of Sports Science & Medicine, 19(2), 256.

    George, G., Osinga, E. C., Lavie, D., & Scott, B. A. (2016). Big data and data science methods for management research. Academy of Management Journal, 59(5), 1493–1507.

    Guba, E. G. (1981). Criteria for assessing the trustworthiness of naturalistic inquiries. Ectj, 29(2), 75.

    Hasan, H. S., Hussein, M., Saad, S. M., & Dzahir, M. A. M. (2018). An Overview of Local Positioning System: Technologies, Techniques and Applications. International Journal of Engineering & Technology, 7(3.25), 1-5.

    Hodder, R. W., Ball, K. A., & Serpiello, F. R. (2020). Criterion Validity of Catapult ClearSky T6 Local Positioning System for Measuring Inter-Unit Distance. Sensors, 20(13), 3693.

    Hoppe, M. W., Baumgart, C., Polglaze, T., & Freiwald, J. (2018). Validity and reliability of GPS and LPS for measuring distances covered and sprint mechanical properties in team sports. PloS one, 13(2), e0192708.

    Huang, K. , & Chen, K. (2011). Multilayer perceptron for prediction of 2006 world cup football game. Advances in Artificial Neural Systems, 2011 , 1–8.

    Huang, K. Y. , & Chang, W. L. (2010). A neural network method for prediction of 2006 World Cup Football game. The 2010 international joint conference on neural networks (IJCNN) (pp. 1–8). IEEE.

    Jennings, D., Cormack, S. J., Coutts, A. J., & Aughey, R. J. (2012). GPS analysis of an international field hockey tournament. International Journal of Sports Physiology and Performance, 7(3), 224-231.

    Kempton, T., Sirotic, A. C., & Coutts, A. J. (2017). A comparison of physical and technical performance profiles between successful and less-successful professional rugby league teams. International Journal of Sports Physiology and Performance, 12(4), 520-526.

    Leser, R., Moser, B., Hoch, T., Stögerer, J., Kellermayr, G., Reinsch, S., & Baca, A. (2015). Expert-oriented modelling of a 1vs1-situation in football. International Journal of Performance Analysis in Sport, 15(3), 949-966.

    Lewis, M. (2004). Moneyball: The art of winning an unfair game. New York, NY: WW Norton & Company.

    Maxcy, J., & Drayer, J. (2014). Sports analytics: Advancing decision making through technology and data. Institute for Business and Information Technology, Fox School of Business, Temple University.

    Mikalonytė, R., & Paulauskas, R. (2020). The changes in physical demands between game parts of junior female handball players. Pedagogika, 80-95.

    Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis: An Expanded Sourcebook. Thousand Oaks, CA: Sage Publications, Inc.

    Miljković, D., Gajić, L., Kovačević, A., & Konjović, Z. (2010, September). The use of data mining for basketball matches outcomes prediction. In IEEE 8th International Symposium on Intelligent Systems and Informatics (pp. 309-312). IEEE.

    Patton, M. Q. (1999). Enhancing the quality and credibility of qualitative analysis. Health Services Research, 34(5 Pt 2), 1189.

    Patton, M. Q. (2002). Qualitative research & evaluation methods. California: Sage Publications, Inc.

    Pino-Ortega, J., Rojas-Valverde, D., Gómez-Carmona, C. D., Bastida-Castillo, A., Hernández-Belmonte, A., García-Rubio, J., & Ibáñez, S. J. (2019). Impact of contextual factors on external load during a congested-fixture tournament in elite U’18 basketball players. Frontiers in Psychology, 10, 1100.

    Portes, R., Jiménez, S. L., Navarro, R. M., Scanlan, A. T., & Gómez, M. Á. (2020). Comparing the External Loads Encountered during Competition between Elite, Junior Male and Female Basketball Players. International Journal of Environmental Research and Public Health, 17(4), 1456.

    Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.

    Rico-González, M., Los Arcos, A., Clemente, F. M., Rojas-Valverde, D., & Pino-Ortega, J. (2020). Accuracy and Reliability of Local Positioning Systems for Measuring Sport Movement Patterns in Stadium-Scale: A Systematic Review. Applied Sciences, 10(17), 5994.

    Rico-González, M., Los Arcos, A., Nakamura, F. Y., Moura, F. A., & Pino-Ortega, J. (2020). The use of technology and sampling frequency to measure variables of tactical positioning in team sports: A systematic review. Research in Sports Medicine, 28(2), 279-292.

    Rico-González, M., Pino-Ortega, J., Nakamura, F. Y., Arruda Moura, F., Rojas-Valverde, D., & Los Arcos, A. (2020). Past, present, and future of the technological tracking methods to assess tactical variables in team sports: A systematic review. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 234(4), 281-290.

    Roell, M., Roecker, K., Gehring, D., Mahler, H., & Gollhofer, A. (2018). Player monitoring in indoor team sports: concurrent validity of inertial measurement units to quantify average and peak acceleration values. Frontiers in physiology, 9, 141.

    Scanlan, A., Dascombe, B., & Reaburn, P. (2011). A comparison of the activity demands of elite and sub-elite Australian men's basketball competition. Journal of Sports Sciences, 29(11), 1153-1160.

    Serrano, C., Felipe, J. L., Garcia-Unanue, J., Ibañez, E., Hernando, E., Gallardo, L., & Sanchez-Sanchez, J. (2020). Local Positioning System Analysis of Physical Demands during Official Matches in the Spanish Futsal League. Sensors, 20(17), 4860.

    Shapiro, S. L., & Drayer, J. (2012). A new age of demand-based pricing: An examination of dynamic ticket pricing and secondary market prices in Major League Baseball. Journal of Sport Management, 26(6), 532-546.

    Shapiro, S. L., & Drayer, J. (2014). An examination of dynamic ticket pricing and secondary market price determinants in Major League Baseball. Sport Management Review, 17(2), 145-159.

    Trapero, J., Sosa Marín, C., Zhang, S., Portes, R., Gómez-Ruano, M. Á., Bonal, J., & Lorenzo Calvo, A. (2019). Comparison of the Movement Characteristics Based on Position-Specific Between Semi-Elite and Elite Basketball Players. Revista de psicología del deporte, 28(3), 0140-145.

    Vazquez-Guerrero, J., Fernández-Valdés, B., Jones, B., Moras, G., Reche, X., & Sampaio, J. (2019). Changes in physical demands between game quarters of U18 elite official basketball games. Plos One, 14(9), e0221818.

    Vazquez-Guerrero, J., Reche, X., Cos, F., Casamichana, D., & Sampaio, J. (2020). Changes in external load when modifying rules of 5-on-5 scrimmage situations in elite basketball. The Journal of Strength & Conditioning Research, 34(11), 3217-3224.

    Vieira, L. H. P., Aquino, R., Moura, F. A., de Barros, R. M., Arpini, V. M., Oliveira, L. P., & Santiago, P. R. (2019). Team dynamics, running, and skill-related performances of Brazilian U11 to professional soccer players during official matches. The Journal of Strength & Conditioning Research, 33(8), 2202-2216.

    Yang, J. B., & Lu, C. H. (2012). Predicting NBA championship by learning from history data. Proceedings of Artificial Intelligence and Machine Learning for Engineering Design.

    下載圖示
    QR CODE