Evaluating Tennis Player Performance Based on Biomechanical Variables Using Multi Criteria Decision Making Techniques

Authors

  • Rafid Habib Qadoori University of Diyala
  • Qahtan M.Yas University of Diyala
  • Safaa A. Ismaeel University of Diyala
  • Majid Habeb Qadoori University of Diyala

DOI:

https://doi.org/10.47134/jpo.v3i2.2242

Keywords:

Tennis Player, Biomechanical Variables, MCDM Techniques, Triangular Fuzzy Numbers, AHP Technique, TOPSIS Technique, MOORA Technique

Abstract

In tennis, choosing the right player can be extremely difficult because their performance in a match depends on many biomechanical criteria. Thus, this study aims to establish a framework to help coaches select the best tennis players based on a set of biomechanical parameters.  Tennis involves specific strategies that require players to execute serves accurately and professionally to get more points. Consequently, there are biomechanical criteria that evaluate each player's performance.  Multi-criteria decision-making (MCDM) techniques have provided optimal solutions to various problems. The methodology proposed in this study incorporates several decision-making techniques in two stages: the first involves calculating the weights of the selected criteria by combining triangular fuzzy numbers with the Analytical Hierarchy Process (AHP) technique. The second stage is to select the appropriate alternative using the TOPSIS technique, which focuses on the shortest geometric distance of the alternative from the positive ideal solution and the farthest geometric distance from the negative ideal solution. Additionally, the MOORA technique was applied, based on two attributes: beneficial value and non-beneficial value. The study’s findings indicated that the first attempt (AT-1) was the best alternative in both techniques. Spearman correlation coefficient was employed to assess the relationship between the two techniques, and a sensitivity analysis was conducted based on the ranking results derived from both techniques.

References

Al-Azzawi, A. A. M., Talal, M. L., Khadhim, B. J., & Yas, Q. M. (2023). Selecting Optimal Educational Boards Based on a Decision Support Approach. International Journal on Technical and Physical Problems of Engineering, 15(1), 345–351.

Alazzawi, A. (2025). A Group Decision-Making for Selecting Multi-Deep Face Recognition Models A Group Decision-Making for Selecting Multi-Deep Face Recognition Models. Iraqi Journal for Computer Science and Mathematics, 6(2), 1–21.

Ali, K., & Al-Hameed, A. (2022). Spearman’s correlation coefficient in statistical analysis. Int. J. Nonlinear Anal. Appl, 13(May 2021), 2008–6822. http://dx.doi.org/10.22075/ijnaa.2022.6079

Almarashi, A. M., Daniyal, M., & Jamal, F. (2024). A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player’s performance. BMC Sports Science, Medicine and Rehabilitation, 7, 1–11. https://doi.org/10.1186/s13102-024-00815-7

Brito, A. V, Fonseca, P., Costa, M. J., Cardoso, R., Santos, C. C., Fernandez-fernandez, J., & Fernandes, R. J. (2024). The Influence of Kinematics on Tennis Serve Speed: An In-Depth Analysis Using Xsens MVN Biomech Link Technology. Bioengineering, 11(971), 1–15.

Brocherie, F., & Dinu, D. (2022). Biomechanical estimation of tennis serve using inertial sensors: A case study. Frontiers in Sports and Active Living, 4(5), 1–8.

Caroline, M., Sorel, A., Touzard, P., Bideau, B., Degroot, H., & Kulpa, R. (2021). Influence of the forehand stance on knee biomechanics: implications for potential injury risks in tennis players. Journal of Sports Sciences, 39(9), 992–1000.

Colomar, J., Corbi, F., Brich, Q., & Baiget, E. (2022). Determinant Physical Factors of Tennis Serve Velocity: A Brief Review. International Journal of Sports Physiology and Performance, 17, 1159–1169.

F. M. Jumaah, A. A. Zaidan, B. B. Zaidan, R. Bahbibi, M. Y. Qahtan, and A. S. (2018). Technique for order performance by similarity to ideal solution for solving complex situations in multi-criteria optimization of the tracking channels of GPS baseband telecommunication receivers. Telecommunication Systems, 68, 425–443.

Hraste, M., & Jelaska, I. (2024). Tennis performance priorities for all-court player using the Analytic Hierarchy Process method. Acta Kinesiologica, 18(2), 1–6.

Id, J. L., & Muehlbauer, T. (2022). Physical fitness and stroke performance in healthy tennis players with different competition levels: A systematic review and. PLoS ONE, 1–15. https://doi.org/10.1371/journal.pone.0269516

Jassim Al-Shamary, A. K., Yas, Q. M., Badr, A. M., Shalabi, R. Al, & Aldulaimi, S. H. (2022). Multi Criteria Decision Making Technique for Evaluation and Selection performance Large Scale Data of Composite Materials. 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022, 96–103. https://doi.org/10.1109/ICETSIS55481.2022.9888862

Li, Z., & Yin, Y. (2024). A Quantitative Assessment Model for Momentum in Tennis Based on Exponential Decay and EWM-TOPSIS Method. Academic Journal of Computing & Information Science, 7(5), 136–142. https://doi.org/10.25236/AJCIS.2024.070518

Liang, Z., Wu, J., Yu, J., & Ying, S. (2023). Comparison and analysis of the biomechanics of the lower limbs of female tennis players of different levels in foot-up serve. Frontiers in Physiology, 14(February), 1–11. https://doi.org/10.3389/fphys.2023.1125240

Martin, C. (2018). Biomechanics of the Tennis Serve. Tennis Medicine, 1–14.

Saaty, T. L. (2001). Fundamentals of the Analytic Hierarchy Process. The Analytic Hierarchy Process in Natural Resource and Environmental Decision Making, 15–35.

Saaty, T. L. (2012). How to make a decision. International Series in Operations Research and Management Science, 175, 1–21. https://doi.org/10.1007/978-1-4614-3597-6_1

Setyawan, H., Purwanto, S., Indra, E. N., Prayudho, S., Pavlovic, R., Nowak, A. M., Susanto, N., Darmawan, A., Shidiq, A. P., Padang, U. N., & Islam, U. (2024). The Differences Result in Serve Skill of Junior Tennis Players Assessed Based on Gender and Age. Retos, 54, 272–278.

Shahzadi, G., Luqman, A., & Ali Al-Shamiri, M. M. (2022). The Extended MOORA Method Based on Fermatean Fuzzy Information. Mathematical Problems in Engineering, 2022(1), 7595872.

Temesi, J., Szádoczki, Z., & Bozóki, S. (2024). Incomplete pairwise comparison matrices: Ranking top women tennis players. Journal of the Operational Research Society, 75(1), 145–157. https://doi.org/10.1080/01605682.2023.2180447

Xiao, W., Geok, S. K., Bai, X., Bu, T., Rozilee, M., & Wazir, N. (2022). Effect of Exercise Training on Physical Fitness Among Young Tennis Players: A Systematic Review. Frontiers in Public Health, 10, 1–10. https://doi.org/10.3389/fpubh.2022.843021

Yas, Q. M., Ibrahim, D. S., & Mohammed, K. G. (2023). Academic Ranking of Diyala University Using Hybrid Decision-Making Approach. In AIP Conference Proceedings, 2834(1).

Yas, Q. M., Adday, B. N., & Abed, A. S. (2021). Evaluation Multi Diabetes Mellitus Symptoms by Integrated Fuzzy-based MCDM Approach. Turkish Journal of Computer and Mathematics Education, 12(13), 4069–4082.

Yas, Q. M., & Zaidan, F. K. (2023). A NEW HYBRID MULTI-CRITERIA DECISION APPROACH FOR EVALUATING AND BENCHMARKING VACCINES. International Journal on Technical and Physical Problems of Engineering, 15(54), 33–38.

Yi, Q., Liu, Z., Liu, X., Wang, Y., & Li, R. (2024). The development strategies of amateur table tennis matches in China based on the SWOT-AHP model: a case study in Shanghai. Scientific Reports, 1–11. https://doi.org/10.1038/s41598-024-62334-2

Yu, H., & Hutson, A. D. (2024). A robust Spearman correlation coefficient permutation test. Communications in Statistics - Theory and Methods, 53(6), 2141–2153. https://doi.org/10.1080/03610926.2022.2121144

Zadeh, L. A. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 100(1999), 9–34.

Zaidan, A. A. Z. B. B., Albahri, M. A. A. O. S., & Qahtan, A. S. A. M. Y. (2019). Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Computing and Applications, 3. https://doi.org/10.1007/s00521-019-04325-3

Zhang, S. F., Liu, S. Y., & Zhai, R. H. (2011). An extended GRA method for MCDM with interval-valued triangular fuzzy assessments and unknown weights. Computers & Industrial Engineering, 61(4), 1336–1341.

Zhang, S., & Mao, H. (2021). Optimization analysis of tennis players’ physical fitness index based on data mining and mobile computing. Wireless Communications and Mobile Computing, 1, 9838477. https://doi.org/10.1155/2021/9838477

Zhang, X., & Yang, G. (2023). The Biomechanical Analysis on the Tennis Batting Angle Selection Under Deep Learning. IEEE Access, 11(September), 97758–97768. https://doi.org/10.1109/ACCESS.2023.3313167

Downloads

Published

2025-12-11

How to Cite

Qadoori, R. H., Yas, Q., Ismaeel, S., & Qadoori , M. (2025). Evaluating Tennis Player Performance Based on Biomechanical Variables Using Multi Criteria Decision Making Techniques. Pubmedia Jurnal Pendidikan Olahraga, 3(2), 17. https://doi.org/10.47134/jpo.v3i2.2242

Issue

Section

Articles

Similar Articles

<< < 1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.