Constructing a Gaming Model for Professional Tennis Players Using the C5.0 Algorithm
Source abstract
Abstract
Professional tennis players have their own habits of tactics and play. However, players’ shortcomings can be corrected by constantly practicing professional techniques and by tactical analysis. Therefore, this study aimed to develop a two-stage, expert decision-making system for tennis matches. The first stage consisted in dividing the court area and defining the technical classification of the net. Tennis coaches were invited to assess tennis players’ skills on the competition court, dividing it into 48 areas on both sides of the court centerline and identifying the skills used by the players. In the second stage, a classification model was developed, and the score, hitting habits, and tennis skills of the players, Roger Federer and Rafael Nadal, over 10 matches, played from 2007 to 2019, were analyzed and classified using notational analysis and the C5.0 decision tree algorithm. The results show that the two players’ highest scored techniques were the forehand stroke in the backcourt and the backhand stroke in the half court. Thus, using this expert decision-making system, our data can provide other players with imaginary training objects from two of the top players in the world to be used during training and can allow the accumulation of experience for players through continuous simulation and training analysis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sports Analytics and Performance · Sports Performance and Training · Sports Dynamics and Biomechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Recreational & Graduate Institute of Recreational Sport Management, National Taiwan University of Sport, Taichung City 404, Taiwan
Funding
MOE Teaching Practice Research Program
References (44)
Show source-provided references
1. Roetert, E.P., and Kovacs, M. (2020). Tennis Anatomy, Human Kinetics. [2nd ed.].
2. Micki, S. (2022, August 04). The Four Styles of Tennis Play and Strategies to Beat Them. Available online: https://howtheyplay.com/individual-sports/Tennis-Playing-Styles-and-Strategies-to-Beat-Them.
3. Anthropometric and biomotor factors that determine the ability of field tennis service
4. Tactical analysis in tennis: From its origins to the present
5. The differential performance of tennis professionals of different levels in serve and return games
6. ATP Stats (2022, August 04). Performance Zone. Available online: https://www.atptour.com/en/stats/performance-zone.
7. The relationship between match statistics and top 100 ranking in professional men’s tennis
8. Study on the effect of shoulder training on the mechanics of tennis serve speed through video analysis10.32604/mcb.2021.017050 ↗
9. van Daalen, M. (2019). Teaching Tennis Volume 3: The Development of Champions, Author Solutions Inc.
10. Movement characteristics of elite tennis players on hard courts with respect to the direction of ground strokes
11. A notational analysis of elite tennis strategy10.1080/026404101300036299 ↗
12. Notational analysis in female Grand Slam tennis competitions10.26582/k.53.1.18 ↗
13. Match analysis of elite players during paddle tennis competition
14. Hizan, H., Whipp, P.R., and Reid, M. (2010). Validation of match notation (a coding system) in tennis. J. Quant. Anal. Sports, 6.10.2202/1559-0410.1223 ↗
15. Monitoring hitting load in tennis using inertial sensors and machine Learning10.1123/ijspp.2016-0683 ↗
16. Analysis of serving and receiving techniques for world-class badminton women’s single player Tzu-ying Dai
17. Table tennis match analysis: A review10.1080/02640414.2018.1450073 ↗
18. Towards the use of multidimensional performance indicators in football small-sided games: The effects of pitch orientation10.1080/02640414.2018.1543834 ↗
19. Constraints on visual exploration of youth football players during 11v11 match-play: The influence of playing role, pitch position and phase of play10.1080/02640414.2020.1723375 ↗
20. Machine learning in men’s professional football: Current applications and future directions for improving attacking play10.1177/1747954119879350 ↗
21. Capobianco, G., Giacomo, U.D., Mercaldo, F., and Santone, A. (2018, January 10–13). A formal methodology for notational analysis and real-time decision support in sport environment. In Proceedings 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.10.1109/BigData.2018.8621892 ↗
22. Novak, A.R., Bennett, K.J.M., Pluss, M.A., and Fransen, J. (2019). Performance analysis in esports: Part 1—The validity and reliability of match statistics and notational analysis in League of Legends. SportRxiv.10.31236/osf.io/sm3nj ↗
23. Investigating the most important aspect of elite grass court tennis: Short points10.1177/1747954121999593 ↗
24. Use of decision tree model in sport management10.1123/cssm.2017-0036 ↗
25. Application of C4.5 decision tree for scholarship evaluations10.1016/j.procs.2019.04.027 ↗
26. Analyzing basketball games by a support vector machines with decision tree model10.1007/s00521-016-2321-9 ↗
27. Sport analytics for cricket game results using machine learning: An experimental study10.1016/j.aci.2019.11.006 ↗
28. Who should sign a professional baseball contract? Quantifying the financial opportunity costs of major league draftees10.1177/1527002520939615 ↗
29. A Decision Support System for Scheduling the Canadian Football League10.1287/inte.1110.0561 ↗
30. Empirical study on relationship between sports analytics and success in regular season and postseason in major league baseball10.3233/JSA-190269 ↗
31. Predictive data analytics for contract renewals: A decision support tool for managerial decision-making10.1108/JEIM-12-2019-0375 ↗
32. Applying the chi-square test to improve the performance of the decision tree for classification by taking baseball database as an example
33. Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., and Liotta, A. (2019, January 10–12). Table tennis stroke recognition based on body sensor network. Proceedings of the Internet and Distributed Computing Systems, 12th International Conference, IDCS 2019, Naples, Italy.10.1007/978-3-030-34914-1 ↗
34. Accurate recognition of player identity and stroke performance in table tennis using a smart wristband10.1109/JSEN.2021.3060914 ↗
35. Wang, X., Huang, Y., Zhong, J., Zhu, Y., Tang, Q., Wang, M., and Li, S. (2021, January 27). Tennis posture classification and recognition based on an improved KNN. Proceedings of the SPIE, 12th International Conference on Graphics and Image Processing (ICGIP 2020), Xi’an, China.
36. A decision tree framework for shot classification of field sports videos10.1007/s11227-020-03155-8 ↗
37. Classification of tennis video types based on machine learning technology10.1155/2021/2055703 ↗
38. USTA (2022, August 04). Tennis Terms & Words to Know. Available online: https://www.usta.com/en/home/improve/tips-and-instruction/national/tennis-terms-definitions.html.
39. Qiu, Y.R. (2020). Construct a Model of Tennis Players’ Competition. [Master’s Thesis, National Taiwan University of Sport].
40. Holsti, O.R. (1969). Content Analysis for the Social Sciences and Humanities, Addison-Wesley.
41. Content analysis in mass communication: Assessment and reporting of intercoder reliability10.1111/j.1468-2958.2002.tb00826.x ↗
42. Quinlan, J.R. (2022, August 04). Data Mining Tools See5 and C5.0. Available online: https://www.rulequest.com/see5-info.html.
43. A decision-theoretic generalization of on-line learning and an application to boosting
44. Yobero, C. (2022, August 04). Determining Creditworthiness for Loan Applications Using C5.0 Decision Trees. Available online: https://rpubs.com/cyobero/C50.