Maximizing ROI in Youth Tennis: A Multi-Stage Modeling Approach for Data-Driven Investment Decisions

Kangyue Zhu

doi:10.47297/taposatWSP2633-456946.20250610

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Maximizing ROI in Youth Tennis: A Multi-Stage Modeling Approach for Data-Driven Investment Decisions

Volume 6, Issue 10

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Maximizing ROI in Youth Tennis: A Multi-Stage Modeling Approach for Data-Driven Investment Decisions
Maximizing ROI in Youth Tennis: A Multi-Stage Modeling Approach for Data-Driven Investment Decisions
The High School Affiliated Renmin University of China, Beijing 100086, China
Vol. 6, Issue 10, Pages: 234-241(2025)
Published：30 October 2025
DOI：10.47297/taposatWSP2633-456946.20250610
Accepted：

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Zhu Kangyue.Maximizing ROI in Youth Tennis: A Multi-Stage Modeling Approach for Data-Driven Investment Decisions[J].Theory and Practice of Science and Technology,2025,06(10):234-241.
DOI：

Zhu Kangyue.Maximizing ROI in Youth Tennis: A Multi-Stage Modeling Approach for Data-Driven Investment Decisions[J].Theory and Practice of Science and Technology,2025,06(10):234-241. DOI： 10.47297/taposatWSP2633-456946.20250610.

摘要

Abstract

This study develops an optimization decision model to enhance the development of young tennis players and maximize investment returns. A stage classification model using the Random Forest Classifier predicts athletes' development stages (S0 - S4) based on seven-dimensional features

including training time and competition level. The proposed two-stage optimization framework first adjusts controllable variables to help athletes progress to the next stage while estimating intermediate variables through regression models. The second stage employs a multi-objective optimization model that incorporates actual costs and aims to balance consistency with economic feasibility

under realistic constraints like weekly training limits. This method combines machine learning and optimization techniques to offer personalized

cost-effective development suggestions for tennis families with limited resources

demonstrating strong interpretability and operability.

关键词

Keywords

references

Cˆot´e, Jean and Matthew Vierimaa (2014). "The developmental model of sport participation: 15 years after its first conceptualization". Science & sports 29, S63–S69.

Federation, International Tennis (Feb. 2025). ITF World Tennis Tour Juniors Entry Fees FAQ . Online doc- ument, ITF. Accessed: 2025-09-14. URL: https://www.itftennis.com/media/14141/entry- fee- faq- wttj.pdf.

Friedman, Jerome H (2001). "Greedy function approximation: A gradient boosting machine". Annals of statistics , pp. 1189 – 1232 .

Geurts, Pierre, Damien Ernst, and Louis Wehenkel (2014). "Extremely randomized trees". arXiv preprint arXiv:1407.7502 . URL: https://arxiv.org/abs/1407.7502.

Gulbin, Jason P et al. (2013). "An integrated framework for the optimisation of sport and athlete development: A practitioner approach". Journal of sports sciences 31.12, pp. 1319 – 1331 .

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman (2009). The Elements of Statistical Learning . 2nd. Springer.

Ke, Guolin et al. (2017). "LightGBM: A highly efficient gradient boosting decision tree". Advances in Neural Information Processing Systems . Vol. 30.

Tennis123 (2025). Tennis Tournament Listings and Registration Platform . Accessed: 2025-09-14. URL: https: //www.tennis123.net/match.

Tikhonov, A. N. and V. Y. Arsenin (1977). Solutions of ill-posed problems . Washington, DC: Winston and Sons.

Vicente, L.N. et al. (1996). "Mathematical Programming with Equilibrium Constraints". Macmillan Ency-clopedia of Operations Research and Management Science .

Zou, Hui and Trevor Hastie (2005). "Regularization and variable selection via the elastic net". Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67.2, pp. 301 – 320 .

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