Awesome Computational Intelligence in Sports

We are curating awesome research and approaches to CI in Sports!

This repository serves as a list of knowledge for researchers working in Computational Intelligence in Sports. The list mainly comprises methods based on evolutionary algorithms, artificial neural networks, fuzzy systems, and swarm intelligence algorithms[^1]. The research citations were done with Mendeley in the MLA 8th edition format. The list includes books, scientific literature, datasets, and software from Computational Intelligence in Sports.

[^1]: Several included research papers are only partially based on these methods but are essential, especially for interdisciplinary research.

Contents

• Books 📚
• Review papers 📃
• Research papers 🔬
• Dissertation or thesis 📒
• Tutorials 📖
• Perspectives 📰
• Datasets 📊
  • Basketball 🏀
  • Cycling 🚲
  • Soccer ⚽
  • Track and field 🏃‍
  • Triathlon 🥉
  • Wrestling 🤼‍♀️
• Benchmarks 🧪
• Software 💻
• Web applications 🌐

Books 📚

• Begg, Rezaul, and Marimuthu Palaniswami. “Computational Intelligence for Movement Sciences.” Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, edited by Rezaul Begg and Marimuthu Palaniswami, IGI Global, 2006, doi:10.4018/978-1-59140-836-9.

• Dindorf, Carlo, et al. “Artificial Intelligence in Sports, Movement, and Health.” Springer, 2024, doi:10.1007/978-3-031-67256-9.

• Fister, Iztok, et al. “Computational intelligence in sports.” Edited by Yew Lim, Meng-Hiot Soon Ong, vol. 22, Springer International Publishing, 2019, doi:10.1007/978-3-030-03490-0.

• Menges, Tessa. “Advancing Endurance Sports with Artificial Intelligence: Application-Focused Perspectives.” Artificial Intelligence in Sports, Movement, and Health, edited by Carlo Dindorf et al., Springer Nature Switzerland, 2024, pp. 31–48, doi:10.1007/978-3-031-67256-9_3.

Review papers 📃

• Beal, Ryan, et al. “Artificial intelligence for team sports: a survey.” The Knowledge Engineering Review, vol. 34, Cambridge University Press, 2019, doi:10.1017/S0269888919000225.

• Bonidia, Robson P., et al. “Data Mining in Sports: A Systematic Review.” IEEE Latin America Transactions, vol. 16, no. 1, IEEE Computer Society, Jan. 2018, pp. 232–39, doi:10.1109/TLA.2018.8291478.

• Bonidia, Robson P., et al. “Computational Intelligence in Sports: A Systematic Literature Review.” Advances in Human-Computer Interaction, vol. 2018, Hindawi Limited, Oct. 2018, pp. 1–13, doi:10.1155/2018/3426178.

• Bunker, Rory, and Teo Susnjak. “The Application of Machine Learning Techniques for Predicting Match Results in Team Sport: A Review.” Journal of Artificial Intelligence Research, vol. 73, AI Access Foundation, Apr. 2022, pp. 1285–322, doi:10.1613/JAIR.1.13509.

• Cardenas Hernandez, Fernando Pedro, et al. “Beyond Hard Workout: A Multimodal Framework for Personalised Running Training with Immersive Technologies.” British Journal of Educational Technology, doi:10.1111/bjet.13445.

• Cruz, Maria I., et al. “Advancements in Performance Monitoring: A Systematic Review of Sensor Technologies in Rowing and Canoeing Biomechanics.” Sports, vol. 12, no. 9, 2024, doi:10.3390/sports12090254.

• Farrokhi, Alireza, et al. “Application of Internet of Things and Artificial Intelligence for Smart Fitness: A Survey.” Computer Networks, vol. 189, Elsevier, Apr. 2021, p. 107859, doi:10.1016/j.comnet.2021.107859.

• Fister Jr, Iztok, et al. “Computational Intelligence in Sports: Challenges and Opportunities within a New Research Domain.” Applied Mathematics and Computation, vol. 262, Elsevier, July 2015, pp. 178–86, doi:10.1016/j.amc.2015.04.004.

• Frangoudes, Fotos, et al. “Assessing Human Motion During Exercise Using Machine Learning: A Literature Review.” IEEE Access, vol. 10, 2022, pp. 86874–903, doi:10.1109/ACCESS.2022.3198935.

• Gámez Díaz, R.; Yu, Q.; Ding, Y.; Laamarti, F.; El Saddik, A. “Digital Twin Coaching for Physical Activities: A Survey.“ Sensors 2020, 20, 5936, doi:10.3390/s20205936.

• H. Pascual, X. M. Bruin, A. Alonso, J. Cerd`a, “A systematic review on human modeling: Digging into human digital twin implementations.“, arXiv preprint, doi:arXiv:2302.03593.

• Jud, Matthias, and Stefan Thalmann. “AI in Digital Sports Coaching – a Systematic Review.” Managing Sport and Leisure, Routledge, 2025, pp. 1–17, doi:10.1080/23750472.2024.2449016.

• Krstić, Dušan, et al. “The Application and Impact of Artificial Intelligence on Sports Performance Improvement: A Systematic Literature Review.” 2023 4th International Conference on Communications, Information, Electronic and Energy Systems (CIEES), IEEE, 2023, pp. 1–8, doi:10.1109/CIEES58940.2023.10378750.

• Lai, Daniel T. H., et al. “Computational Intelligence in Gait Research: A Perspective on Current Applications and Future Challenges.” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 5, 2009, pp. 687–702, doi:10.1109/TITB.2009.2022913.

• Lygouras, Dimosthenis, and Avgoustos Tsinakos. “The Use of Immersive Technologies in Karate Training: A Scoping Review.” Multimodal Technologies and Interaction, vol. 8, no. 4, 2024, doi:10.3390/mti8040027.

• Milasi, Sadegh Fatahi, et al. “Unlocking the Potential: A Comprehensive Meta-Synthesis of Internet of Things in the Sports Industry.” Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, vol., no., p. 17543371241229520, doi:10.1177/17543371241229521.

• Nalbant, Kemal Gökhan, and Sevgi Aydın. “Literature Review on the Relationship between Artificial Intelligence Technologies with Digital Sports Marketing and Sports Management.” Indonesian Journal of Sport Management, vol. 2, no. 2, Oct. 2022, pp. 135–43, doi:10.31949/ijsm.v2i2.2876.

• Rajšp, Alen, and Iztok Jr. Fister. “A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training.” Applied Sciences, vol. 10, no. 9, Multidisciplinary Digital Publishing Institute, Apr. 2020, p. 3013, doi:10.3390/app10093013.

• Song, Yu (Wolf). “Human Digital Twin, the Development and Impact on Design.” Journal of Computing and Information Science in Engineering, vol. 23, no. 6, Dec. 2023, doi:10.1115/1.4063132.

• Stessens, Loes, et al. “Physical Performance Estimation in Practice: A Systematic Review of Advancements in Performance Prediction and Modeling in Cycling.” International Journal of Sports Science & Coaching, vol., no., p. 17479541241262384, doi:10.1177/17479541241262385.

• Szot, Tomasz. “Evolution of Sport Wearable Global Navigation Satellite Systems’ Receivers: A Look at the Garmin Forerunner Series.” Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, doi:10.1177/17543371241237319.

• Wakelam, Edward, et al. “The Collection, Analysis and Exploitation of Footballer Attributes: A Systematic Review.” Journal of Sports Analytics, vol. 8, no. 1, IOS Press, Jan. 2022, pp. 31–67, doi:10.3233/JSA-200554.

• Yang, Luyao, et al. “Intelligent Wearable Systems: Opportunities and Challenges in Health and Sports.” ACM Comput. Surv., vol. 56, no. 7, Association for Computing Machinery, Apr. 2024, doi:10.1145/3648469.

Research papers 🔬

• Adeyemo, Victor Elijah, et al. “Identification of Pattern Mining Algorithm for Rugby League Players Positional Groups Separation Based on Movement Patterns.” ArXiv, Feb. 2023, p. 2023, http://arxiv.org/abs/2302.14058.

• Ariyaratne, M. K. A., and R. M. Silva. “Meta-Heuristics Meet Sports: A Systematic Review from the Viewpoint of Nature Inspired Algorithms.” International Journal of Computer Science in Sport, vol. 21, no. 1, Mar. 2022, pp. 49–92, doi:10.2478/ijcss-2022-0003.

• Attigala, D. A., et al. “Intelligent Trainer for Athletes Using Machine Learning.” 2019 International Conference on Computing, Power and Communication Technologies (GUCON), 2019, pp. 898–903.

• Barshan, Billur, and M. C. Yuksek. “Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units.” The Computer Journal, vol. 57, no. 11, Oxford University Press, Nov. 2014, pp. 1649–67, doi:10.1093/comjnl/bxt075.

• Boillet, Alice, Laurent A. Messonnier, and Caroline Cohen. "Individualized physiology-based digital twin model for sports performance prediction: a reinterpretation of the Margaria–Morton model." Scientific Reports 14, no. 1 (2024): 5470, doi:10.1038/s41598-024-56042-0.

• Carey, David L., et al. “Optimizing Preseason Training Loads in Australian Football.” International Journal of Sports Physiology and Performance, vol. 13, no. 2, Human Kinetics, Feb. 2018, pp. 194–99, doi:10.1123/ijspp.2016-0695.

• Chacoma, Andrés, and Orlando V Billoni. “Simple Mechanism Rules the Dynamics of Volleyball.” ArXiv, Feb. 2022, http://arxiv.org/abs/2202.13765.

• Chen, Shuxi, et al. “Detecting Sports Fatigue from Speech by Support Vector Machine.” 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), IEEE, 2016, pp. 96–99, doi:10.1109/ICCSN.2016.7586626.

• Cintia, Paolo, and Luca Pappalardo. “Coach2vec: Autoencoding the Playing Style of Soccer Coaches“. Arxiv, June 2021, doi:10.48550/arxiv.2106.15444. Preprint.

• Connor, Mark, et al. “Optimising Team Sport Training Plans with Grammatical Evolution.” 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., June 2019, pp. 2474–81, doi:10.1109/CEC.2019.8790369.

• Connor, Mark, et al. “Adaptive Athlete Training Plan Generation: An Intelligent Control Systems Approach.” Journal of Science and Medicine in Sport, vol. 25, no. 4, Elsevier, Apr. 2022, pp. 351–55, doi:10.1016/j.jsams.2021.10.011.

• Cui, Dandan, et al. “Using Case-Based Causal Reasoning to Provide Explainable Counterfactual Diagnosis in Personalized Sprint Training.” Case-Based Reasoning Research and Development, edited by Juan A. Recio-Garcia et al., Springer Nature Switzerland, 2024, pp. 418–29, doi:10.1007/978-3-031-63646-2_27.

• De Prisco, Roberto, et al. “Providing Music Service in Ambient Intelligence: experiments with gym users.” Expert Systems with Applications, vol. 177, Pergamon, Sept. 2021, p. 114951, doi:10.1016/j.eswa.2021.114951.

• Deng, Huijian, et al. “Prediction of Sports Aggression Behavior and Analysis of Sports Intervention Based on Swarm Intelligence Model.” Scientific Programming, vol. 2022, Hindawi Limited, 2022, doi:10.1155/2022/2479939.

• Díaz, Rogelio Gámez, Fedwa Laamarti, and Abdulmotaleb El Saddik. "DTCoach: your digital twin coach on the edge during COVID-19 and beyond." IEEE Instrumentation & Measurement Magazine 24, no. 6 (2021): 22-28, doi:10.1109/MIM.2021.9513635.

• Ding, Xianqiong, et al. “Sports Training Model Based on GA Optimized Neural Network.” Proceedings - 2020 13th International Conference on Intelligent Computation Technology and Automation, ICICTA 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 227–30, doi:10.1109/ICICTA51737.2020.00055.

• Eriksson, Rikard, et al. “Generating Weekly Training Plans in the Style of a Professional Swimming Coach Using Genetic Algorithms and Random Trees.” Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference, edited by Arnold Baca et al., Springer, Cham, 2022, pp. 61–68, doi:10.1007/978-3-030-99333-7_9.

• Farrokhi, Alireza, et al. “A Decision Tree-Based Smart Fitness Framework in IoT.” SN Computer Science, vol. 3, no. 1, Springer, Jan. 2022, p. 2, doi:10.1007/s42979-021-00940-x.

• Feely, Ciara, et al. “A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12877 LNAI, Springer, Cham, 2021, pp. 79–93, doi:10.1007/978-3-030-86957-1_6.

• Feely, Ciara, et al. “Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations.” Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, ACM, 2023, pp. 183–93, doi:10.1145/3565472.3592952.

• Ferencsik, Dorina K., and Erika B. Varga. “Cycling Activity Dataset Creation and Application for Feedback Giving.” Acta Marisiensis. Seria Technologica, vol. 18, no. 2, Walter de Gruyter GmbH, Dec. 2021, pp. 29–35, doi:10.2478/AMSET-2021-0015.

• Fialho, Gabriel, et al. “Predicting Sports Results with Artificial Intelligence – A Proposal Framework for Soccer Games.” Procedia Computer Science, vol. 164, Elsevier, Jan. 2019, pp. 131–36, doi:10.1016/j.procs.2019.12.164.

• Fidelis, J. Vijay, and E. Karthikeyan. “Player Management in Soccer Using Particle Swarm Optimization.” 4th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques, ICEECCOT 2019, Institute of Electrical and Electronics Engineers Inc., Dec. 2019, pp. 303–08, doi:10.1109/ICEECCOT46775.2019.9114599.

• Fister, Dušan, et al. “Visualization of cycling training.” Proceedings of the StuCoSReC: 3rd Student Computer Science Research Conference, Koper, Slovenia. 2016.

• Fister, Iztok, et al. “Framework for Planning the Training Sessions in Triathlon.” Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, 2018, pp. 1829–34, doi:10.1145/3205651.3208242.

• Fister, Iztok, et al. “Planning the Sports Training Sessions with the Bat Algorithm.” Neurocomputing, vol. 149, no. PB, Elsevier, Feb. 2015, pp. 993–1002, doi:10.1016/J.NEUCOM.2014.07.034.

• Fister, Iztok, et al. “Synthetic Data Augmentation of Cycling Sport Training Datasets.” Lecture Notes in Networks and Systems, vol. 371, Springer Science and Business Media Deutschland GmbH, 2022, pp. 65–74, doi:10.1007/978-3-030-93247-3_7.

• Fister Jr., Iztok. “The Relevance of Nature-Inspired Metaheuristic Algorithms in Smart Sport Training.” International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI'2020), edited by Jemal H Abawajy et al., Springer International Publishing, 2021, pp. 1–8, doi:10.1007/978-3-030-80216-5_1.

• Fister Jr., Iztok., et al. “Adaptation of Sport Training Plans by Swarm Intelligence.” Recent Advances in Soft Computing, edited by Radek Matoušek, Springer International Publishing, 2019, pp. 56–67, doi:10.1007/978-3-319-97888-8_5.

• Fister Jr, Iztok, et al. “New Perspectives in the Development of the Artificial Sport Trainer.” Applied Sciences, vol. 11, no. 23, Multidisciplinary Digital Publishing Institute, Dec. 2021, p. 11452, doi:10.3390/app112311452.

• Fister Jr, Iztok , et al. “SportyDataGen: An Online Generator of Endurance Sports Activity Collections.” Proceedings of the Central European Conference on Information and Intelligent Systems, Faculty of Organization and Informatics, University of Zagreb, 2018, pp. 171–78.

• Fister Jr, Iztok, et al. “The Importance of Monitoring and Maintaining Data in Sports Training Process.” Proceedings of the 8th Conference for Youth Sport, 2016.

• Fister Jr, Iztok, et al. “Topology-Based Generation of Sport Training Sessions.” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, Springer Science and Business Media Deutschland GmbH, Jan. 2021, pp. 667–78, doi:10.1007/s12652-020-02048-1.

• Fister Jr, Iztok, et al. “On Deploying the Artificial Sport Trainer into Practice.” 2021 8th International Conference on Soft Computing and Machine Intelligence, ISCMI 2021, Institute of Electrical and Electronics Engineers Inc., Sept. 2021, pp. 21–26, doi:ISCMI53840.2021.9654817.

• Fister Jr, Iztok, and Iztok Fister. “Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence.” Modeling and Optimization in Science and Technologies, vol. 10, Springer, Cham, 2017, pp. 79–94, doi:10.1007/978-3-319-50920-4_4.

• Fister Jr, Iztok, et al. “Population-Based Metaheuristics for Planning Interval Training Sessions in Mountain Biking.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11655 LNCS, Springer, Cham, 2019, pp. 70–79, doi:10.1007/978-3-030-26369-0_7.

• Fister Jr, Iztok, et al. “Discovering Dependencies among Mined Association Rules with Population-Based Metaheuristics.” Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, 2019, pp. 1668–74, doi:10.1145/3319619.3326833.

• Frevel, Nicolas, et al. “The Impact of Technology on Sports – A Prospective Study.” Technological Forecasting and Social Change, vol. 182, Sept. 2022, p. 121838. ScienceDirect, doi:10.1016/j.techfore.2022.121838.

• Hrovat, Goran, et al. “Interestingness Measure for Mining Sequential Patterns in Sports.” Journal of Intelligent & Fuzzy Systems, vol. 29, no. 5, Jan. 2015, pp. 1981–94, doi:10.3233/IFS-151676.

• He, Liqin, et al. “Decision Support System for Effective Action Recognition of Track and Field Sports Using Ant Colony Optimization.” Soft Computing, Mar. 2023, pp. 1–11, doi:10.1007/s00500-023-07967-7.

• Hliš, Tilen, et. al. "Path Overlap Detection and Property Graph Construction from TCX Data for Advanced Analysis." 2025 IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics (SAMI), Stará Lesná, Slovakia, 2025, pp. 000489-000494, doi: 10.1109/SAMI63904.2025.10883235.

• Kipp, Kristof, et al. “Use of Machine Learning to Model Volume Load Effects on Changes in Jump Performance.” International Journal of Sports Physiology and Performance, vol. 15, no. 2, Human Kinetics, Feb. 2020, pp. 285–87, doi:10.1123/IJSPP.2019-0009.

• Kumyaito, Nattapon, et al. “Planning a Sports Training Program Using Adaptive Particle Swarm Optimization with Emphasis on Physiological Constraints.” BMC Research Notes, vol. 11, no. 1, Dec. 2018, p. 9, doi:10.1186/s13104-017-3120-9.

• Joshi, Ketan, et al. “Robust Sports Image Classification Using InceptionV3 and Neural Networks.” Procedia Computer Science, vol. 167, Elsevier, Jan. 2020, pp. 2374–81, doi:10.1016/j.procs.2020.03.290.

• Langaroudi, Milad Keshtkar, and Mohammad Reza Yamaghani. “Sports Result Prediction Based on Machine Learning and Computational Intelligence Approaches: A Survey.” Journal of Advances in Computer Engineering and Technology, vol. 5, no. 1, 2019, pp. 27–36.

• Lee, Geon Ju, et al. “Exploiting Weighted Association Rule Mining for Indicating Synergic Formation Tactics in Soccer Teams.” Concurrency and Computation: Practice and Experience, 2021, p. e6221, doi:10.1002/CPE.6221.

• Li, Gang, and Tongzhou Zhao. “Approach of Intelligence Question-Answering System Based on Physical Fitness Knowledge Graph.” 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE), IEEE, 2021, pp. 191–95, doi:10.1109/RCAE53607.2021.9638824.

• Liu, Runze, et al. “SmartDampener: An Open Source Platform for Sport Analytics in Tennis.” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 8, no. 3, Association for Computing Machinery, Sept. 2024, doi:10.1145/3678507.

• Liu, Yu, et al. “Design and Implementation of Concurrent Optimization Schemes for Sports Health Prediction Platform.” 2018 7th International Conference on Digital Home (ICDH), IEEE, 2018, pp. 208–12, doi:10.1109/ICDH.2018.00044.

• Lopez-Gomez, Julio Alberto, et al. “A Feature-Weighting Approach Using Metaheuristic Algorithms to Evaluate the Performance of Handball Goalkeepers.” IEEE Access, 2022, pp. 1–1, doi:10.1109/ACCESS.2022.3156120.

• López-Serrano, Carlos, et al. “Contextualizing Evaluation of Performance in Volleyball: Introducing Contextual Individual Contribution Coefficients to Assess Technical Actions.” Perceptual and Motor Skills, vol. 130, no. 6, Dec. 2023, pp. 2663–84, doi:10.1177/00315125231212592.

• Lukac, Luka, et al. “A Minimalistic Toolbox for Extracting Features from Sport Activity Files.” 2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES), IEEE, 2021, pp. 000121–26, doi:10.1109/INES52918.2021.9512927.

• Lukač, Luka, et al. “Digital Twin in Sport: From an Idea to Realization.” Applied Sciences, vol. 12, no. 24, Dec. 2022, p. 12741, doi:10.3390/app122412741.

• Masagca, Ramon Carlo. “The AI Coach: A 5-Week AI-Generated Calisthenics Training Program on Health-Related Physical Fitness Components of Untrained Collegiate Students.” Journal of Human Sport and Exercise , vol. 20, no. 1, 2024, pp. 39–56, doi:10.55860/13v7e679.

• Matabuena, Marcos, and Rosana Rodríguez-López. “An Improved Version of the Classical Banister Model to Predict Changes in Physical Condition.” Bulletin of Mathematical Biology, vol. 81, no. 6, Springer New York LLC, June 2019, pp. 1867–84, doi:10.1007/S11538-019-00588-Y.

• Moutaouakil, Karim El, et al. “Quadratic Programming and Triangular Numbers Ranking to an Optimal Moroccan Diet with Minimal Glycemic Load.” Statistics, Optimization & Information Computing, vol. 11, no. 1, 1, Jan. 2023, pp. 85–94. iapress.org, doi:10.19139/soic-2310-5070-1541.

• Mutijarsa, Kusprasapta, et al. “Heart Rate Prediction Based on Cycling Cadence Using Feedforward Neural Network.” 2016 International Conference on Computer, Control, Informatics and Its Applications (IC3INA), IEEE, 2016, pp. 72–76, doi:10.1109/IC3INA.2016.7863026.

• Nikitina, Marina A. “Development of a Personalized Diet Using Structural Optimization.” Society 5.0: Cyber-Solutions for Human-Centric Technologies, edited by Alla G. Kravets et al., Springer Nature Switzerland, 2023, pp. 43–52. doi:https://doi.org/10.1007/978-3-031-35875-3_4.

• Novatchkov, Hristo, and Arnold Baca. “Artificial Intelligence in Sports on the Example of Weight Training.” Journal of Sports Science & Medicine, vol. 12, no. 1, Dept. of Sports Medicine, Medical Faculty of Uludag University, Mar. 2013, pp. 27–37, pmid:24149722.

• Novatchkov, Hristo, and Arnold Baca. “Fuzzy Logic in Sports: A Review and an Illustrative Case Study in the Field of Strength Training.” International Journal of Computer Applications, vol. 71, no. 6, 2013, pp. 8–14.

• Ofoghi, Bahadorreza, et al. “Modelling and Analysing Track Cycling Omnium Performances Using Statistical and Machine Learning Techniques.” Journal of Sports Sciences, vol. 31, no. 9, Routledge, May 2013, pp. 954–62, doi:10.1080/02640414.2012.757344.

• Pappalardo, Luca, et al. “PlayeRank: Data-Driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach.” ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 5, ACM PUB27 New York, NY, USA, Sept. 2019, pp. 1–27, doi:10.1145/3343172.

• Podgorelec, Vili, et al. “Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization.” Applied Sciences, vol. 10, no. 23, Multidisciplinary Digital Publishing Institute, Nov. 2020, p. 8494, doi:10.3390/app10238494.

• Rajsp, Alen, and Iztok Jr Fister. “A Modified Evolutionary Algorithm for Generating the Cycling Training Routes.” IEEE Access, vol. 10, 2022, pp. 109743–59, doi:10.1109/ACCESS.2022.3214997.

• Rajšp, Alen, and Iztok Jr Fister. “Discovering the Influence of Interruptions in Cycling Training: A Data Science Study.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12745 LNCS, Springer, Cham, 2021, pp. 420–432, doi:10.1007/978-3-030-77970-2_32.

• Rajšp, Alen, et al. “Preprocessing of Roads in OpenStreetMap Based Geographic Data on a Property Graph.” Proceedings of the Central European Conference on Information and Intelligent Systems, Faculty of Organization and Informatics, University of Zagreb, 2921, pp. 193–199.

• Rajšp, Alen, Marjan Heričko, and Iztok Fister Jr. “The use of Gamification in Smart Sport Training.”Proceedings of the Central European Conference on Information and Intelligent Systems, Faculty of Organization and Informatics, University of Zagreb, pp. 113-120

• Rauter, Samo. “New Approach for Planning the Mountain Bike Training with Virtual Coach.” Trends in Sport Sciences, vol. 2, no. 25, 2018, pp. 69–74, doi:10.23829/TSS.2018.25.2-2.

• Rodríguez-Gallego, Laura, et al. “Assessment of Feedback Devices for Performance Monitoring in Master's Swimmers.” International Journal of Performance Analysis in Sport, vol. 22, no. 5, Sept. 2022, pp. 701–14, doi:10.1080/24748668.2023.2181556.

• Sakabe, Hibiki, and Yohei Nakada. “Computational Method for Determining Optimal Dribbling Routes in Basketball.” 2022 IEEE Eighth International Conference on Multimedia Big Data (BigMM), 2022, pp. 107–08. IEEE Xplore, doi:10.1109/BigMM55396.2022.00024.

• Sakabe, Hibiki, and Yohei Nakada. “Enhanced Method for Computing Optimal Dribbling Routes Using Tracking Data in Basketball.” 2023 IEEE Ninth Multimedia Big Data (BigMM), 2023, pp. 11–18, doi:10.1109/BigMM59094.2023.00009.

• Schaefer, David, et al. “Training Plan Evolution Based on Training Models.” 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), IEEE, 2015, pp. 1–8, doi:10.1109/INISTA.2015.7276739.

• Sen, Anik, et al. “Sequence Recognition of Indoor Tennis Actions Using Transfer Learning and Long Short-Term Memory.” Frontiers of Computer Vision, 28th International Workshop, IW-FCV 2022, edited by Kazuhiko Sumi et al., Springer, Cham, 2022, pp. 312–24, doi:10.1007/978-3-031-06381-7_22.

• Silacci, Alessandro, et al. “Designing an E-Coach to Tailor Training Plans for Road Cyclists.” Advances in Intelligent Systems and Computing, vol. 1026, Springer, Cham, 2020, pp. 671–77, doi:10.1007/978-3-030-27928-8_102.

• Silacci, Alessandro, et al. “Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study.” Applied Sciences, vol. 11, no. 1, Multidisciplinary Digital Publishing Institute, Dec. 2020, p. 313, doi:10.3390/app11010313.

• Skublewska-Paszkowska, Maria, et al. “Tennis Patterns Recognition Based on a Novel Tennis Dataset – 3DTennisDS.” Advances in Science and Technology Research Journal, vol. 18, no. 6, 2024, pp. 159–76, doi:10.12913/22998624/191264.

• Smyth, Barry, et al. “Recommendations for Marathon Runners: On the Application of Recommender Systems and Machine Learning to Support Recreational Marathon Runners.” User Modeling and User-Adapted Interaction, Springer, Aug. 2021, pp. 1–52, doi:10.1007/s11257-021-09299-3.

• Stöckl, Michael, and Stuart Morgan. “Visualization and Analysis of Spatial Characteristics of Attacks in Field Hockey.” International Journal of Performance Analysis in Sport, vol. 13, no. 1, Apr. 2013, pp. 160–78, doi:10.1080/24748668.2013.11868639.

• Teikari, Petteri, and Aleksandra Pietrusz. “Precision Strength Training: Data-Driven Artificial Intelligence Approach to Strength and Conditioning.” SportRxiv, 2021, doi:10.31236/OSF.IO/W734A. Preprint.

• Thorsen, Ola, et al. “Can Machine Learning Help Reveal the Competitive Advantage of Elite Beach Volleyball Players?” Swedish Artificial Intelligence Society, 2024, pp. 57–66, doi:10.3384/ecp208007.

• Van Bulck, David, et al. “Result-Based Talent Identification in Road Cycling: Discovering the next Eddy Merckx.” Annals of Operations Research, Springer, Oct. 2021, pp. 1–18, doi:10.1007/s10479-021-04280-0.

• Wang, Zhen, et al. “Quantum Photonics Advancements Enhancing Health and Sports Performance.” Optical and Quantum Electronics, vol. 56, no. 3, Mar. 2024, pp. 1–12, doi:10.1007/s11082-023-05917-z.

• Xiong, Shengyao, and Xinwei Li. “Intelligent Strategy of Internet of Things Computing in Badminton Sports Activities.” Wireless Communications and Mobile Computing, edited by Venkateswaran N, vol. 2022, Oct. 2022, pp. 1–9, doi:10.1155/2022/9409151.

• Yashiro, Kotaro, and Yohei Nakada. “Fast Implementation for Computational Method of Optimum Attacking Play in Rugby Sevens.” Modeling, Simulation and Optimization, edited by Biplab Das et al., Springer Nature, 2022, pp. 97–109. Springer Link, doi:10.1007/978-981-19-0836-1_8.

• Zaib, Ali, and Muhammad Talal Ahmad. “Research on Biomechanical Analysis of Football Player Using Information Technology in Sports Field.” Revista de Psicología Del Deporte (Journal of Sport Psychology), vol. 31, no. 3, Oct. 2022, pp. 21–30.

• Zhang, Juwei, et al. “The Relationship between Measurement and Evaluation in Physical Education Teaching Based on Intelligent Analysis and Sensor Data Mining.” Journal of Intelligent & Fuzzy Systems, no. Preprint, IOS Press, pp. 1–16, doi:10.3233/JIFS-235410.

• Zhang, Ying, et al. “Research on Interactive Sports Game Experience in Physical Training System Based on Digital Entertainment Technology and Sensor Devices.” Entertainment Computing, 2024, p. 100866, doi:10.1016/j.entcom.2024.100866.

• Zhang, Yuwang, and Yuan Zhang. “Sports Training System Based on Convolutional Neural Networks and Data Mining.” Computational Intelligence and Neuroscience, vol. 2021, Hindawi Limited, 2021, doi:10.1155/2021/1331759.

• Zhou, Haisheng, and Yang Li. “Design of Intelligent Analysis System of Basketball Skilled Movement Based on Data Mining Technology.” 2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering (ECICE), IEEE, 2023, pp. 457–59, doi:10.1109/ECICE59523.2023.10383045.

• Zhu, Dan, et al. “A Perspective on Rhythmic Gymnastics Performance Analysis Powered by Intelligent Fabric.” Advanced Fiber Materials, Oct. 2022, doi:10.1007/s42765-022-00197-w.

• Znika, I., and A. Radovan. “Personal Physical Fitness Modeling through Real-Time Predictive Models.” 2024 47th MIPRO ICT and Electronics Convention (MIPRO), 2024, pp. 157–62, doi:10.1109/MIPRO60963.2024.10569604.

Dissertation or thesis 📒

• Gámez Díaz, Rogelio. "Digital Twin Coaching for Edge Computing Using Deep Learning Based 2D Pose Estimation." PhD diss., Université d'Ottawa/University of Ottawa, 2021, doi:10.20381/ruor-26229.

• Laamarti, Fedwa. "Towards Standardized Digital Twins for Health, Sport, and Well-being." PhD diss., Université d'Ottawa/University of Ottawa, 2019, doi:10.20381/ruor-23746.

• Murillo Burford, Esteban. “Predicting Cycling Performance Using Machine Learning.” Wake Forest University Graduate School of Arts and Sciences, 2020.

• Eriksson, Rikard, and Johan Nicander. “Automated Generation of Training Programs for Swimmers Generating Weekly Training Plans in the Style of a Professional Swimming Coach Using Genetic Algorithms and Random Trees.” Chalmers University of Technology, 2021, doi:20.500.12380/302927.

Tutorials 📖

• Bock, Marius, et al. “Tutorial on Deep Learning for Human Activity Recognition.” Oct. 2021, doi:10.48550/arxiv.2110.06663.

Perspectives 📰

• Chmait, Nader, and Hans Westerbeek. “Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-Data Scientists.” Frontiers in Sports and Active Living, vol. 3, Frontiers Media S.A., Dec. 2021, p. 363, doi:10.3389/fspor.2021.682287.

• Reis, Felipe J. J., et al. “Artificial Intelligence and Machine Learning Approaches in Sports: Concepts, Applications, Challenges, and Future Perspectives.” Brazilian Journal of Physical Therapy, vol. 28, no. 3, 2024, p. 101083, doi:10.1016/j.bjpt.2024.101083.

Datasets 📊

• Rico-Garcia, Mateo, et al. “Vertical Jump Data from Inertial and Optical Motion Tracking Systems.” Data, vol. 7, no. 8, Aug. 2022, p. 116, doi:10.3390/data7080116.
• Romagnoli, Sofia, et al. “Sport DB 2.0: A New Database of Data Acquired by Wearable and Portable Devices While Practicing Sport.” 2023 Computing in Cardiology (CinC), vol. 50, 2023, pp. 1–4, doi:10.22489/CinC.2023.067.

Basketball 🏀

• Hoelzemann, Alexander, et al. “Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors.” ArXiv, May 2023, doi:10.48550/arXiv.2305.13124.

Cycling 🚲

• Fister Jr., et al. “A collection of sport activity datasets for data analysis and data mining 2017a.” Technical report 2017a, University of Maribor, 2017

• Iztok Fister Jr., Samo Rauter, Dušan Fister, Iztok Fister. “A collection of sport activity datasets with an emphasis on powermeter data.” Technical report, University of Maribor, November 2017

• Rajšp, Alen, and Iztok Fister Jr. “Neo4j Graph Dataset of Cycling Paths in Slovenia.” Data in Brief, vol. 48, June 2023, p. 109251, doi:10.1016/j.dib.2023.109251.

• Samo Rauter, Iztok Fister Jr., Iztok Fister. “A collection of sport activity files for data analysis and data mining 2016a.” Technical report 0101, University of Ljubljana and University of Maribor 2016a, 2016

• Iztok Fister Jr., Samo Rauter, Dušan Fister, Iztok Fister. “A collection of sport activity datasets for data analysis and data mining 2016b.” Technical report 2016b, University of Maribor, 2016

• Samo Rauter, Iztok Fister Jr., Iztok Fister. “A collection of sport activity files for data analysis and data mining.” Ver 12 05, University of Maribor, 2015

Soccer ⚽️

• Rouissi, Mehdi, et al. “Data concerning isometric lower limb strength of dominant versus not-dominant leg in young elite soccer players.” (2018).

• Pappalardo, Luca, et al. “A public data set of spatio-temporal match events in soccer competitions.” Scientific data 6.1 (2019): 1-15.

• Slimani, Maamer, Armin Paravlić, and Nicola Luigi Bragazzi. “Data concerning the effect of plyometric training on jump performance in soccer players: A meta-analysis.” Data in brief 15 (2017): 324-334.

Track and field 🏃‍

• Aguilera-Castells, Joan, et al. “Correlational data concerning body centre of mass acceleration, muscle activity, and forces exerted during a suspended lunge under different stability conditions in high-standard track and field athletes.” Data in brief 28 (2020): 104912.

Triathlon 🥉

• Iztok Fister Jr., Dušan Fister. “A collection of IRONMAN, IRONMAN 70.3 and Ultra-triathlon race results.”, version 0.1, Technical Report 0110, 2016

Wrestling 🤼‍♀️

• Okagbue, Hilary I., et al. “Statistical analysis of frequencies of opponents׳ eliminations in Royal Rumble wrestling matches.”, 1988–2018.” Data in brief 19 (2018): 1458-1465.

Benchmarks 🧪

• Tcx test files - A collection of the sports activity (tcx) test files for benchmarking the parsers

Software 💻

• ast-monitor - A wearable Raspberry Pi computer for cyclists.
• ast-tdl - Training Description Language.
• gpx - Process GPX Files into R Data Structures.
• gpxpy - A simple Python library for parsing and manipulating GPX files.
• openant - ANT and ANT-FS Python Library.
• python-tcxparser - Simple parser for Garmin TCX files.
• sport-activities-features - A minimalistic toolbox for extracting features from sport activity files written in Python.
• tcxread - A parser for TCX files.
• tcxreader - Reader / parser for Garmin's TCX file format.
• TCXWriter - Library for writing/creating TCX files on Arduino & ESP32 devices
• tcx2gpx - Python package for converting tcx GPS files to gpx files.
• TCXReader.jl - Julia package designed for parsing TCX files.
• tcx-ls - CLI application for viewing TCX file information.
• tcxr - A simple TCX file reader written in R.

Web applications 🌐

Cite us

Fister Jr., I. (2023). firefly-cpp/awesome-computational-intelligence-in-sports: 1.0 (1.0). Zenodo. https://doi.org/10.5281/zenodo.10431418