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Time-Series Forecasting in Sports: Using LSTM and GRU for Stadium Attendance Prediction Cover

Time-Series Forecasting in Sports: Using LSTM and GRU for Stadium Attendance Prediction

By:
Yu PangORCID  
Open Access
|May 2025

References

  1. Abumohsen, M., Owda, A. Y., & Owda, M. (2023). Electrical load forecasting using LSTM, GRU, and RNN algorithms. Energies, 16(5), 2283.
  2. Baimbridge, M., Cameron, S., & Dawson, P. (1996). Satellite television and the demand for football: A whole new ball game? Scottish Journal of Political Economy, 43(3), 317–333.
  3. Borland, J., & MacDonald, R. (2003). Demand for sport. Oxford Review of Economic Policy, 19(4), 478–502.
  4. Busari, G. A., & Lim, D. H. (2021). Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance. Computers & Chemical Engineering, 155, article 107513.
  5. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. ArXiv Preprint arXiv:1406.1078.
  6. Cho, M., Kim, C., Jung, K., & Jung, H. (2022). Water level prediction model applying a long short-term memory (LSTM)– gated recurrent unit (GRU) method for flood prediction. Water, 14(14), 2221.
  7. DeSchriver, T. D., Rascher, D. A., & Shapiro, S. L. (2016). If we build it, will they come? Examining the effect of expansion teams and soccer-specific stadiums on Major League Soccer attendance. Sport, Business and Management: An International Journal, 6(2), 205–227. https://doi.org/10.1108/SBM-05-2014-0025">https://doi.org/10.1108/SBM-05-2014-0025
  8. Du, P., Wang, Y., Liao, C., & Xian, T. (2022). Sports games attendance forecast using machine learning. 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA). https://doi.org/10.1109/ICDSCA56264.2022.9987748">https://doi.org/10.1109/ICDSCA56264.2022.9987748
  9. Hall, J., O’Mahony, B., & Vieceli, J. (2010). An empirical model of attendance factors at major sporting events. Special Issue on Event Studies, 29(2), 328–334. https://doi.org/10.1016/j.ijhm.2009.10.011">https://doi.org/10.1016/j.ijhm.2009.10.011
  10. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
  11. Karg, A., Nguyen, J., & McDonald, H. (2021). Understanding season ticket holder attendance decisions. Journal of Sport Management, 35(3), 239–253.
  12. Mueller, S. Q. (2020). Pre- and within-season attendance forecasting in major league baseball: A random forest approach. Applied Economics, 52(41), 4512–4528. https://doi.org/10.1080/00036846.2020.1736502">https://doi.org/10.1080/00036846.2020.1736502
  13. Pang, Y., & Wang, F. (2024). Forecasting stadium attendance using machine learning models: A case of the national football league. Studia Sportiva, 18(2), 147–164.
  14. Park, J., & Park, S. (2017). A study on prediction of attendance in Korean Baseball League using artificial neural network. KIPS Transactions on Software and Data Engineering, 6(12), 565–572.
  15. Paul, R. J., Ehrlich, J. A., & Losak, J. (2021). Expanding upon the weather: Cloud cover and barometric pressure as determinants of attendance for NFL games. Managerial Finance, 47(6), 749–759. https://doi.org/10.1108/MF-06-2020-0295">https://doi.org/10.1108/MF-06-2020-0295
  16. Paul, R. J., & Weinbach, A. P. (2011). NFL bettor biases and price setting: Further tests of the Levitt hypothesis of sportsbook behaviour. Applied Economics Letters, 18(2), 193–197. https://doi.org/10.1080/13504850903508242">https://doi.org/10.1080/13504850903508242
  17. Şahin, M., & Erol, R. (2018). Prediction of attendance demand in European football games: Comparison of ANFIS, fuzzy logic, and ANN. Computational Intelligence and Neuroscience, 1, article 714872. https://doi.org/10.1155/2018/5714872">https://doi.org/10.1155/2018/5714872
  18. Şahin, M., & Uçar, M. (2022). Prediction of sports attendance: A comparative analysis. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 236(2), 106–123. https://doi.org/10.1177/1754337120983135">https://doi.org/10.1177/1754337120983135
  19. Schreyer, D., & Ansari, P. (2021). Stadium attendance demand research: A scoping review. Journal of Sports Economics, 23, 749–788. https://doi.org/10.1177/15270025211000404">https://doi.org/10.1177/15270025211000404
  20. Spenner, E. L., Fenn, A., & Crooker, J. (2004). The demand for NFL attendance: A rational addiction model. Colorado College Economics and Business Working Paper No. 200401. https://doi.org/10.2139/ssrn.611661">https://doi.org/10.2139/ssrn.611661
  21. Sun, X., Wang, Y., & Khan, J. (2023). Hybrid LSTM and GAN model for action recognition and prediction of lawn tennis sport activities. Soft Computing, 27(23), 18093–18112. https://doi.org/10.1007/s00500-023-09215-4">https://doi.org/10.1007/s00500-023-09215-4
  22. Wakefield, K. L., & Sloan, H. J. (1995). The effects of team loyalty and selected stadium factors on spectator attendance. Journal of Sport Management, 9(2), 153–172. https://doi.org/10.1123/jsm.9.2.153">https://doi.org/10.1123/jsm.9.2.153
  23. Yeung, C., Sit, T., & Fujii, K. (2025). Transformer-based neural marked spatio temporal point process model for analyzing football match events. Applied Intelligence, 55(5), 335. https://doi.org/10.1007/s10489-024-05996-9">https://doi.org/10.1007/s10489-024-05996-9
  24. Zhang, Q., Zhang, X., Hu, H., Li, C., Lin, Y., & Ma, R. (2022). Sports match prediction model for training and exercise using attention-based LSTM network. Digital Communications and Networks, 8(4), 508–515.
  25. Zhou, Z.-H. (2021). Machine learning. Springer Nature.
DOI: https://doi.org/10.2478/pcssr-2025-0027 | Journal eISSN: 1899-4849 | Journal ISSN: 2081-2221
Language: English
Page range: 25 - 35
Submitted on: Feb 21, 2025
Accepted on: Apr 11, 2025
Published on: May 15, 2025
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2025 Yu Pang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.