{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T14:53:37Z","timestamp":1754146417998,"version":"3.41.2"},"reference-count":44,"publisher":"National Library of Serbia","issue":"3","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>This paper presents a novel artificial intelligence system that integrates deep learning-driven decision tree ensemble algorithms (DLDDTEA) for table tennis match analysis. By analyzing videos of professional matches featuring Lin Yun-Ju and Ma Long, the system extracts key insights into player techniques, hitting positions, and scoring outcomes. DLDDTEA processes the video data and constructs a predictive model to determine optimal serve positions and estimate point win\/loss probabilities within the first three exchanges. The results revealed distinct serve strategies and techniques: Lin Yun-Ju favors backhands, whereas Ma Long prefers forehands. Based on these findings, this study offers specific training and strategic recommendations for both players. Thus, the proposed system offers a comprehensive framework for table tennis match analysis, enabling players to gain a deeper understanding of their strengths and weaknesses, ultimately facilitating the development of more effective training and competitive strategies.<\/jats:p>","DOI":"10.2298\/csis241030040c","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T02:50:59Z","timestamp":1747363859000},"page":"1061-1080","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning-driven decision tree ensembles for table tennis: analyzing serve strategies and first-three-stroke outcomes"],"prefix":"10.2298","volume":"22","author":[{"given":"Che-Wei","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Recreational Sport, National Taiwan University of Sport, North Dist., Taichung City, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng-Hsiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Sport Information and Communication, National Taiwan University of Sport, North Dist., Taichung City, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng-Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Recreational Sport, National Taiwan University of Sport, North Dist., Taichung City, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing-Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Sport Information and Communication, National Taiwan University of Sport, North Dist., Taichung City, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Wang, J.: Comparison of Table Tennis Serve and Return Characteristics in the London and the Rio Olympics. 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(2020)","DOI":"10.30827\/Digibug.63715"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Zhou, X.: Explanation and Verification of The Rules of Attack in Table Tennis Tactics. BMC Sports Science, Medicine and Rehabilitation. Vol. 14, No. 1, 6. (2022)","DOI":"10.1186\/s13102-022-00396-3"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Guarnieri, A., Presta, V., Gobbi, G., Ramazzina, I., Condello, G., Malagoli Lanzoni, I.: Notational Analysis of Wheelchair Paralympic Table Tennis Matches. International Journal of Environmental Research and Public Health. Vol. 20, No. 5, 3779. (2023)","DOI":"10.3390\/ijerph20053779"},{"key":"ref13","unstructured":"Huang, J. C.: Analysis of Players\u2019 Hitting Techniques on Tennis Courts Made of Different Materials, Physical Education Journal. Vol. 12, 225-240. (1990)"},{"key":"ref14","unstructured":"Jiang, Z. G.: Research on Men's Tennis Singles Skills and Winning and Losing Factors in Taiwan, Physical Education Journal. 34, 79-92. (2003)"},{"key":"ref15","unstructured":"Gambhir, M.: \u201cMatch Analysis\u201d Using Notational Analysis and Data Analytics in Table Tennis with Interactive Visualization, In Proceedings Book of the 16th ITTF Sports Science Congress. International Table Tennis Federation, Budapest, Hungary, 286-299. (2019)"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Malagoli Lanzoni, I., Cortesi, M., Russo, G., Bankosz, Z., Winiarski, S., Bartolomei, S.: Playing Style of Women and Men Elite Table Tennis Players. International Journal of Performance Analysis in Sport. Vol. 24, No. 5, 495-509. 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(2020)","DOI":"10.1080\/02640414.2020.1723375"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Herold, M., Goes, F., Nopp, S., Bauer, P., Thompson, C., Meyer, T.: Machine Learning in Men\u2019s Professional Football: Current Applications and Future Directions for Improving Attacking Play. International Journal of Sports Science & Coaching. Vol. 14, No. 6, 798-817. (2019)","DOI":"10.1177\/1747954119879350"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Sigari, M. H., Sureshjani, S. A., Soltanian-Zadeh, H.: Sport Video Classification Using an Ensemble Classifier. In 2011 7th Iranian Conference on Machine Vision and Image Processing, Tehran, Iran. 1-4. (2011)","DOI":"10.1109\/IranianMVIP.2011.6121538"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Kostuk, K. J., Willoughby, K. A.: A Decision Support System for Scheduling the Canadian Football League. Interfaces. Vol. 42, No. 3, 286-295. (2012)","DOI":"10.1287\/inte.1110.0561"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Pai, P. F., ChangLiao, L. H., Lin, K. P.: Analyzing Basketball Games by a Support Vector Machines with Decision Tree Model. Neural Computing & Applications. Vol. 28, No. 12, 4159-4167. (2017)","DOI":"10.1007\/s00521-016-2321-9"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Mumcu, C., Mahoney, K.: Use of Decision Tree Model in Sport Management. Case Studies in Sport Management. Vol. 7, No. 1, 1-3. (2018)","DOI":"10.1123\/cssm.2017-0036"},{"key":"ref24","unstructured":"\u00c7ene, E., Parim, C., \u00d6zkan, B.: Comparing the Performance of Basketball Players with Decision Trees and TOPSIS. International Journal of Data Science and Applications. Vol. 1, No. 1, 21-28. (2018)"},{"key":"ref25","unstructured":"Y\u0131ld\u0131z, B.F.: Applying Decision Tree Techniques to Classify European Football Teams. Journal of Soft Computing and Artificial Intelligence. Vol. 1, No. 2, 86-91. (2020)"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Gu, Z., He, C.: Application of Fuzzy Decision Tree Algorithm Based on Mobile Computing in Sports Fitness Member Management. Wireless Communications and Mobile Computing. Vol. No. 1, 4632722. (2021)","DOI":"10.1155\/2021\/4632722"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Tsai, Y. H., Wu, S. K., Yu, S. S., Tsai, M. H.: Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification. Applied Sciences. Vol. 12, No. 16, 8052. (2022)","DOI":"10.3390\/app12168052"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Sadhu, S., Biswas, S., Sarkar, D., Sarkar, P.P.: A Comparison Between Different Classifiers for Tennis Match Result Prediction. Malaysian Journal of Computer Science. Vol. 32, No. 2, 97-111. (2019)","DOI":"10.22452\/mjcs.vol32no2.2"},{"key":"ref29","unstructured":"Chiang, H. H., Hsieh, C. H., Xiao, S. H., Lin, C. Y., Tsai, M. H.: Analysis of Swimming Strokes of 200 Meters Individual Medley for Japanese Swimmers Using a Decision Tree, Sports & Exercise Research, 21(1), 17-29. (2019)"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Madinabeitia, I., P\u00e9rez, B., Gomez-Ruano, M.\u00c1., C\u00e1rdenas, D.: Determination of Basketball Players\u2019 High-Performance Profiles in The Spanish League. International Journal of Performance Analysis in Sport. Vol. 23, No. 2, 83-96. (2023)","DOI":"10.1080\/24748668.2023.2183460"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Zuccolotto, P., Sandri, M., Manisera, M.: Spatial Performance Analysis in Basketball with CART, Random Forest and Extremely Randomized Trees. Annals of Operations Research. Vol. 325, No. 1, 495-519. (2023)","DOI":"10.1007\/s10479-022-04784-3"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Papageorgiou, G., Sarlis, V., Tjortjis, C.: Evaluating the Effectiveness of Machine Learning Models for Performance Forecasting in Basketball: A Comparative Study. Knowledge and Information Systems. Vol. 66, No. 7, 4333-4375. (2024)","DOI":"10.1007\/s10115-024-02092-9"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Mat Sanusi, K.A., Mitri, D.D., Limbu, B., Klemke, R.: Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks. Sensors. Vol. 21, No. 9, 3121. (2021)","DOI":"10.3390\/s21093121"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Liu, Q., Ding, H.: Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence. Frontiers in Neurorobotics. Vol. 16, 820028. (2022)","DOI":"10.3389\/fnbot.2022.820028"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Qiao, F.: Application of Deep Learning in Automatic Detection of Technical and Tactical Indicators of Table Tennis. PLOS ONE. Vol. 16, No. 3, e0245259. (2021)","DOI":"10.1371\/journal.pone.0245259"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Song, H., Li, Y., Zou, X., Hu, P., Liu, T.: Elite Male Table Tennis Matches Diagnosis Using SHAP and a Hybrid LSTM-BPNN Algorithm. Scientific Reports. Vol. 13, No. 1, 11533. (2023)","DOI":"10.1038\/s41598-023-37746-1"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"Liu, J. W., Hsu, M. H., Lai, C. L., Wu, S. K.: Using Video Analysis and Artificial Neural Network to Explore Association Rules and Influence Scenarios in Elite Table Tennis Matches. The Journal of Supercomputing. Vol. 80, No. 4, 5472-5489. (2024)","DOI":"10.1007\/s11227-023-05684-4"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Li, H., Ali, S.G., Zhang, J., Sheng, B., Li, P., Jung, Y., Wang, J., Yang, P., Lu, P., Muhammad, K., Mao, L.: Video-based Table Tennis Tracking and Trajectory Prediction Using Convolutional Neural Networks. Fractals. Vol. 30, No. 05, 2240156. (2022)","DOI":"10.1142\/S0218348X22401569"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Yanan, P., Jilong, Y., Heng, Z.: Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data. Sensors. Vol. 21, No. 19, 6685. (2021)","DOI":"10.3390\/s21196685"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Chang, C. W., Qiu, Y. R.: Constructing a Gaming Model for Professional Tennis Players Using the C5.0 Algorithm. Applied Sciences. Vol. 12, No. 16, 8222. (2022)","DOI":"10.3390\/app12168222"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Chiu, C. H., Ke, S. W., Tsai, C. F., Lin, W. C., Huang, M. W., Ko, Y. H.: Deep Learning Based Decision Tree Ensembles for Incomplete Medical Datasets. Technology and Health Care, Vol. 32, No. 1, 75-87. (2024)","DOI":"10.3233\/THC-220514"},{"key":"ref42","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhou, Z., Zou, Q.: Analysis System for Table Tennis Techniques and Tactics Using Data Mining. Soft Computing. Vol. 27, No. 19, 14269-14284. (2023)","DOI":"10.1007\/s00500-023-09082-z"},{"key":"ref43","unstructured":"Holsti, O.R.: Content Analysis for the Social Sciences and Humanities. Addison-Wesley Pub. Co, Reading, Massachusetts, USA. (1969)."},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"Riffe, D., Lacy, S., Fico, F., Watson, B.: Analyzing Media Messages: Using Quantitative Content Analysis in Research. Routledge, New York, USA. 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