Uneingeschränkter Zugang

A Study on Improving Tactical Strategies in Sports Games Using Optimization Algorithms

   | 10. Apr. 2024

Zitieren

Chen, X., Jiang, J. Y., Jin, K., Zhou, Y., Liu, M., Brantingham, P. J., & Wang, W. (2022, October). ReLiable: Offline Reinforcement Learning for Tactical Strategies in Professional Basketball Games. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 3023-3032). Search in Google Scholar

Osaba, E., Villar-Rodriguez, E., Del Ser, J., Nebro, A. J., Molina, D., LaTorre, A., Suganthan, P. N., Coello, C. A. C., & Herrera, F. (2021). A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm and Evolutionary Computation, 64, 100888. Search in Google Scholar

McCullough, B. P., Orr, M., & Kellison, T. (2020). Sport ecology: Conceptualizing an emerging subdiscipline within sport management. Journal of Sport Management, 34(6), 509-520. Search in Google Scholar

Goud, P. S. H. V., Roopa, Y. M., & Padmaja, B. (2019, March). Player performance analysis in sports: with fusion of machine learning and wearable technology. In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) (pp. 600-603). IEEE. Search in Google Scholar

Beal, R., Chalkiadakis, G., Norman, T. J., & Ramchurn, S. D. (2020). Optimising game tactics for football. arXiv preprint arXiv:2003.10294. Search in Google Scholar

Gouveia, É. R., Gouveia, B. R., Marques, A., Kliegel, M., Rodrigues, A. J., Prudente, J., Lopes, H., & Ihle, A. (2019). The effectiveness of a tactical games approach in the teaching of invasion games. Journal of Physical Education and Sport, 19, 962-970. Search in Google Scholar

Suzuki, G., Takahashi, S., Ogawa, T., & Haseyama, M. (2019). Team tactics estimation in soccer videos based on a deep extreme learning machine and characteristics of the tactics. Ieee Access, 7, 153238-153248. Search in Google Scholar

Chmait, N., & Westerbeek, H. (2021). Artificial intelligence and machine learning in sport research: An introduction for non-data scientists. Frontiers in Sports and Active Living, 3, 363. Search in Google Scholar

Godbout, P., & Gréhaigne, J. F. (2022). Regulation of tactical learning in team sports–The case of the tactical-decision learning model. Physical Education and Sport Pedagogy, 27(3), 215-230. Search in Google Scholar

Huang, Y. C., Liao, I. N., Chen, C. H., İk, T. U., & Peng, W. C. (2019, September). Tracknet: A deep learning network for tracking high-speed and tiny objects in sports applications. In 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1-8). IEEE. Search in Google Scholar

Rahimian, P., Oroojlooy, A., & Toka, L. (2021, October). Towards optimized actions in critical situations of soccer games with deep reinforcement learning. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-12). IEEE. Search in Google Scholar

Wang, Z., Long, C., Cong, G., & Ju, C. (2019, July). Effective and efficient sports play retrieval with deep representation learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 499-509). Search in Google Scholar

Yiğit, A. T., Samak, B., & Kaya, T. (2020). Football player value assessment using machine learning techniques. In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019 (pp. 289-297). Springer International Publishing. Search in Google Scholar

Nadikattu, R. R. (2020). Implementation of new ways of artificial intelligence in sports. Journal of Xidian University, 14(5), 5983-5997. Search in Google Scholar

Liu, Z. (2020, February). Application of artificial intelligence technology in basketball games. In IOP Conference Series: Materials Science and Engineering (Vol. 750, No. 1, p. 012093). IOP Publishing. Search in Google Scholar

Ma, H., & Pang, X. (2019). Research and analysis of sport medical data processing algorithms based on deep learning and Internet of Things. IEEE Access, 7, 118839-118849. Search in Google Scholar

Yeh, R. A., Schwing, A. G., Huang, J., & Murphy, K. (2019). Diverse generation for multi-agent sports games. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4610-4619). Search in Google Scholar

Tian, C., De Silva, V., Caine, M., & Swanson, S. (2019). Use of machine learning to automate the identification of basketball strategies using whole team player tracking data. Applied Sciences, 10(1), 24. Search in Google Scholar

Song, H., Montenegro-Marin, C. E., & Krishnamoorthy, S. (2021). Secure prediction and assessment of sports injuries using deep learning based convolutional neural network. Journal of Ambient Intelligence and Humanized Computing, 12, 3399-3410. Search in Google Scholar

Liu, H., Hou, W., Emolyn, I., & Liu, Y. (2023). Building a prediction model of college students’ sports behavior based on machine learning method: combining the characteristics of sports learning interest and sports autonomy. Scientific Reports, 13(1), 15628. Search in Google Scholar

Lu, C. J., Lee, T. S., Wang, C. C., & Chen, W. J. (2021). Improving sports outcome prediction process using integrating adaptive weighted features and machine learning techniques. Processes, 9(9), 1563. Search in Google Scholar

eISSN:
2444-8656
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
Volume Open
Fachgebiete der Zeitschrift:
Biologie, andere, Mathematik, Angewandte Mathematik, Allgemeines, Physik