Mathematical Equations Associated with Spline Interpolation for Determining and Analyzing Tennis Shots Trajectory and Velocity

Authors

  • Marius Alin BACIU Faculty of Physical Education and Sport, Babeș-Bolyai University, Cluj-Napoca, Romania. *Corresponding author: marius.baciu@ubbcluj.ro https://orcid.org/0009-0007-5811-1378
  • Alina Paula APOSTU Faculty of Physical Education and Sport, Babeș-Bolyai University, Cluj-Napoca, Romania. https://orcid.org/0000-0002-2731-6940
  • Radu-Tiberiu ȘERBAN Faculty of Physical Education and Sport, Babeș-Bolyai University, Cluj-Napoca, Romania. https://orcid.org/0000-0002-7342-5080
  • Andrei-Cătălin BRISC Faculty of Physical Education and Sport, Babeș-Bolyai University, Cluj-Napoca, Romania.

Keywords:

tennis, biomechanics, spline interpolation, trajectory, velocity

Abstract

Aim: This study investigated the application of spline interpolation for analyzing tennis ball trajectories and velocities. The motivation stemmed from the growing role of technology in sports performance and the need for quantitative tools that enable precise technical evaluation. The objective was to demonstrate that mathematical equations derived from spline interpolation can accurately describe shots trajectories and velocities, providing useful feedback for coaches and athletes. Material and Methods: A 15-year-old male tennis player executed 10 shots, including forehand (flat and topspin), backhand (flat and topspin), and serves. Distances and ball heights were measured manually at fixed points (0, 5, 10 m, landing), with times recorded via stopwatch. Curve Expert Professional was used to generate polynomial and trigonometric functions for trajectory modeling. Results: The analysis identified differences in maximum height and velocity between shot types. Topspin shots showed higher arcs and lower velocities, while flat shots produced flatter trajectories and higher speeds. Serves revealed distinct parameters influenced by toss height and angle. Equations reflected these dynamics and allowed interpolation of intermediate points (see Table 1, Figure 1). Discussion: The findings confirmed the suitability of spline interpolation, consistent with prior research (Cross, 1999b; Elliot et al., 2003). Despite limitations related to manual measurement and a single participant, the method proved reliable. Broader applications could integrate motion capture and sensors. Conclusions: Spline interpolation represents a feasible and effective approach for shot analysis in tennis, offering coaches a practical, science-based tool for technical optimization.

Article history: Received 2025 November 09; Revised 2026 January 08; Accepted 2026 January 09; Available online 2026 March 30; Available print 2026 April 30.

AUTHOR CONTRIBUTIONS
Andrei-Cătălin Brisc contributed with data collection, conceptualization, draft writing.
Marius Alin Baciu and Alina Paula Apostu contributed with supervision, methodology design and manuscript review, Radu-Tiberiu Șerban contributed to the writing and formatting of the manuscript. All authors have read and agreed to the published version of the manuscript.

CONFLICT OF INTEREST
The authors declare no conflict of interest.

ACKNOWLEDGEMENT
The authors acknowledge the support of the Faculty of Physical Education and Sport, Babeș-Bolyai University, Cluj-Napoca.

References

Baciu, M. A. (n.d.). Biomechanics of tennis strokes. Cluj-Napoca: UBB Press.

Balla, Béla József & Hantiu, Iacob. (2019). Physical Exercise Program to Reduce Trunk Asymmetry in Adolescence. Studia Universitatis Babeş-Bolyai Educatio Artis Gymnasticae, 64, https://doi.org/10.24193/subbeag.64(2).12

Berry, D. (2020). A people’s history of tennis. Pluto Press.

Borisova, O. (2012). Tennis: History and the present. Pedagogics, Psychology, Medical Biological Problems of Physical Training and Sports, (12), 119-124. https://doi.org/10.6084/m9.figshare.97379

Christensen, J., Rasmussen, J., Halkon, B., & Koike, S. (2016). The development of a methodology to determine the relationship in grip size and pressure to racket head speed in a tennis forehand stroke. Procedia Engineering, 147, 787-792. https://doi.org/10.1016/j.proeng.2016.06.317

Cross, R. (1999a). Dynamic properties of tennis balls. Sports Engineering, 2(1), 23-33. https://doi.org/10.1046/j.1460-2687.1999.00019.x

Cross, R. (1999b). The sweet spot of a tennis racket. Sports Engineering, 2(2), 85–97. https://doi.org/10.1046/j.1460-2687.1999.00011.x

Cross, R. (2003). Measurements of the horizontal and vertical speeds of tennis courts. Sports Engineering, 6, 95-111. https://doi.org/10.1007/BF02903531

Elliot, B., Reid, M., & Crespo, M. (2003). Technique development in tennis stroke production. London: ITF.

Gomboș, Leon & Alexandru Andrei, Gherman & Patrascu, Adrian & Radu, Paul. (2017). Postural balance and 7-meter throw’s accuracy in handball. Timisoara Physical Education and Rehabilitation Journal, 10, 103-108. Doi: 10.1515/tperj-2017-0025

Gordon, B., & Dapena, J. (2006). Contributions of joint rotations to racquet speed in tennis serves. Journal of Sports Sciences, 24(1), 31–49. https://doi.org/10.1080/02640410400022045

International Tennis Federation. (2025). Official rules of tennis. London: ITF.

Mora, S., & Knottenbelt, W. (2017). Deep learning for domain-specific action recognition in tennis. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 170-178). https://doi.org/10.1109/CVPRW.2017.27

Pop, S., Chihaia, O. (2015). Measures to prevent injuries in the performance rugby. Studia Universitatis Babeș-Bolyai Educatio Artis Gymnasticae, 60(3), 67 – 72.

Roșculeț, A. (2022). Modern training methods in tennis. Bucharest: Didactica Press.

Siedentop, D., Hastie, P., & van der Mars, H. (2019). Complete guide to sport education (3rd ed.). Champaign, IL: Human Kinetics.

Sprigings, E., Marshall, R., Elliott, B., & Jennings, L. (1994). A three-dimensional kinematic method for determining the effectiveness of arm segment rotations in producing racquet-head speed. Journal of Biomechanics, 27(3), 245-254. https://doi.org/10.1016/0021-9290(94)90001-9

Tamborrino, C., Falini, A., & Mazzia, F. (2024). Empirical density estimation based on spline quasi-interpolation with applications to copulas clustering modeling. Journal of Computational and Applied Mathematics, 452, 116131. https://doi.org/10.1016/j.cam.2024.116131

Downloads

Published

2026-04-09

How to Cite

BACIU, M. A., APOSTU, A. P., ȘERBAN, R.-T., & BRISC, A.-C. (2026). Mathematical Equations Associated with Spline Interpolation for Determining and Analyzing Tennis Shots Trajectory and Velocity. Studia Universitatis Babeş-Bolyai Educatio Artis Gymnasticae, 71(1), 129–136. Retrieved from https://studia.reviste.ubbcluj.ro/index.php/subbeducatio/article/view/10215

Issue

Section

Articles

Most read articles by the same author(s)

Similar Articles

1 2 3 4 > >> 

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