Chapter 30: Further Reading

Books

Soccer Analytics and Data Science

  • Sumpter, D. (2016). Soccermatics: Mathematical Adventures in the Beautiful Game. Bloomsbury Publishing. An accessible introduction to mathematical modeling in soccer, covering expected goals, network analysis, and game theory.

  • Anderson, C., & Sally, D. (2013). The Numbers Game: Why Everything You Know About Soccer Is Wrong. Penguin Books. A foundational text on data-driven thinking in soccer, covering topics from goal-scoring to managerial decision-making.

  • Kuper, S., & Szymanski, S. (2018). Soccernomics (5th ed.). Nation Books. Covers the economics and statistics of soccer with an emphasis on transfer markets, wage bills, and competitive advantage.

  • Tippett, J. (2019). The Expected Goals Philosophy. Self-published. A detailed exploration of the expected goals framework and its applications in betting, scouting, and tactical analysis.

Data Science and Machine Learning

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2023). An Introduction to Statistical Learning (2nd ed.). Springer. Essential reference for the statistical and machine learning methods used throughout this textbook.

  • Raschka, S., Liu, Y., & Mirjalili, V. (2022). Machine Learning with PyTorch and Scikit-Learn. Packt Publishing. Practical guide to implementing ML models relevant to sports analytics.

  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Comprehensive treatment of Bayesian methods, which are particularly relevant to small-sample sports analytics problems.

Ethics and AI

  • O'Neil, C. (2016). Weapons of Math Destruction. Crown. Essential reading on algorithmic bias and the societal impact of data-driven decision-making.

  • Floridi, L. (2023). The Ethics of Artificial Intelligence. Oxford University Press. Comprehensive philosophical framework for AI ethics applicable to sports analytics contexts.

  • Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs. While not sports-specific, provides critical context for understanding data collection, privacy, and consent in the digital age.

Academic Papers

Foundational Soccer Analytics

  • Pappalardo, L., et al. (2019). "A public data set of spatio-temporal match events in soccer competitions." Scientific Data, 6(1), 236. The paper accompanying one of the most important open soccer datasets.

  • Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2019). "Actions Speak Louder than Goals: Valuing Player Actions in Soccer." Proceedings of KDD 2019. Introduces the VAEP framework for action valuation.

  • Fernandez, J., & Bornn, L. (2018). "Wide Open Spaces: A statistical technique for measuring space creation in professional soccer." MIT Sloan Sports Analytics Conference. Seminal paper on spatial analysis using tracking data.

  • Spearman, W. (2018). "Beyond Expected Goals." MIT Sloan Sports Analytics Conference. Extends xG concepts using tracking data to model expected possession value.

  • Power, P., Ruiz, H., Wei, X., & Lucey, P. (2017). "Not All Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data." KDD 2017. Framework for evaluating pass quality using positional data.

Computer Vision and Tracking

  • Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021). "OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields." IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172--186. Foundational paper on multi-person pose estimation.

  • Scott, A., Uchida, I., Onishi, M., Kameda, Y., Fukui, K., & Fujii, K. (2022). "SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos." CVPR 2022 Workshop. Recent advances in automated soccer tracking.

  • Theiner, J., Gritz, W., & Ewerth, R. (2022). "Extraction of Positional Player Data from Broadcast Soccer Videos." WACV 2022. Methods for extracting tracking data from broadcast footage, relevant to democratization.

Machine Learning in Sports

  • Rein, R., & Memmert, D. (2016). "Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science." SpringerPlus, 5(1), 1410. Survey of challenges and opportunities at the intersection of data science and soccer.

  • Yam, D. (2019). "A Framework for Evaluating Actions in Soccer." Journal of Sports Analytics, 5(4), 285--297. Comparative analysis of action evaluation frameworks.

  • Dick, U., & Brefeld, U. (2019). "Learning to Rate Player Positioning in Soccer." Big Data, 7(1), 71--82. Machine learning approaches to evaluating off-ball positioning.

Ethics and Fairness in Sports Data

  • Mehrabi, N., Morstatter, F., Saxena, N., Lippman, K., & Galstyan, A. (2021). "A Survey on Bias and Fairness in Machine Learning." ACM Computing Surveys, 54(6), 1--35. Comprehensive survey of fairness definitions and bias mitigation techniques.

  • Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MIT Press. Textbook-length treatment of algorithmic fairness, freely available online.

  • Wachter, S., Mittelstadt, B., & Russell, C. (2021). "Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and EU Data Protection Law." Computer Law & Security Review, 41, 105567. Legal perspective on algorithmic fairness relevant to European sports contexts.

Open Data Resources

Open-Source Tools

Conferences and Events

  • MIT Sloan Sports Analytics Conference: Annual conference featuring cutting-edge sports analytics research. sloansportsconference.com
  • StatsBomb Conference: Annual event focused specifically on soccer analytics. statsbomb.com/conference
  • OptaPro Forum: Analytics event organized by Opta (Stats Perform). optasportspro.com
  • ECML-PKDD Sports Analytics Workshop: Academic workshop on machine learning in sports. Held annually in conjunction with the ECML-PKDD conference.
  • KDD Sports Analytics Workshop: Workshop at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
  • IJCAI Workshop on AI in Sports: Workshop at the International Joint Conference on Artificial Intelligence.

Industry Reports and White Papers

  • FIFA. (2023). "FIFA Football Technology Innovation Programme: Annual Report." Overview of technological developments sanctioned by FIFA.
  • FIFPRO. (2024). "Player Data Rights: A Framework for the Digital Age." Position paper on player data governance.
  • UEFA. (2023). "The European Club Footballing Landscape." Annual benchmarking report with data on analytics adoption.
  • Deloitte. (Annual). "Football Money League." Annual review of the economics of professional football, increasingly featuring analytics-related insights.

Podcasts and Media

  • The Analyst Podcast --- Regular discussions on soccer analytics trends and methods.
  • Tifo Football --- Accessible tactical and analytical content on YouTube.
  • Zonal Marking (Michael Cox) --- Tactical analysis blog and book (Zonal Marking: The Making of Modern European Football).
  • Between the Posts --- Analytical blog covering tactical and statistical analysis.
  • The Double Pivot --- Podcast focused on data and analytics in soccer.
  • Measureables --- Podcast covering sports analytics across multiple sports.

Online Learning

  • Friends of Tracking: Free YouTube course covering tracking data analysis, expected goals, pitch control, and more.
  • DataCamp / Coursera / edX: Multiple courses on data science and machine learning applicable to sports analytics.
  • FC Python: Tutorials specifically designed for soccer analytics in Python. fcpython.com
  • McKay Johns (YouTube): Tutorials on soccer data visualization and analysis.
  • Barça Innovation Hub: Educational resources from FC Barcelona's analytics division.