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. 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.

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

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• 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.

• 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.

• 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, 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.

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• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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/COVER.

• 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, Iztok Jr, 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.

• 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, 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.

• 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.

• 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.

• 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. 193–99.

• 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.

• 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.

• 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.

• 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.

• 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, 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.

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• 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.

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• 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.

• 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.

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

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• 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.
• tcxreader - Reader / parser for Garmin's TCX file format.
• tcx2gpx - Python package for converting tcx GPS files to gpx files.