Appendix H: Bibliography

This appendix provides the complete reference list for the textbook, organized by category. References are listed alphabetically by first author within each section.

Books

Anderson, C., & Sally, D. (2013). The Numbers Game: Why Everything You Know About Soccer Is Wrong. Penguin Books.

Barton, G. (2020). Don't Be Fooled by Randomness in Football. Self-published.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Brettschneider, L. (2020). Data Analytics in Football: Positional Data Collection, Modelling and Analysis. Routledge.

Chollet, F. (2021). Deep Learning with Python (2nd ed.). Manning Publications.

Cox, M. (2017). The Mixer: The Story of Premier League Tactics, from Route One to False Nines. HarperCollins.

Cox, M. (2019). Zonal Marking: The Making of Modern European Football. HarperCollins.

Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Statistical Methods for Rates and Proportions (3rd ed.). Wiley.

Floridi, L. (2023). The Ethics of Artificial Intelligence. Oxford University Press.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Hamilton, H. (2022). Expected Goals: The Story of How Data Conquered Football and Changed the Game Forever. Mudlark.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2023). An Introduction to Statistical Learning (2nd ed.). Springer.

Knaflic, C. N. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley.

Kuper, S., & Szymanski, S. (2022). Soccernomics (5th ed.). Nation Books.

Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. W. W. Norton.

McKinney, W. (2022). Python for Data Analysis (3rd ed.). O'Reilly Media.

O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.

Provost, F., & Fawcett, T. (2013). Data Science for Business. O'Reilly Media.

Raschka, S., Liu, Y., & Mirjalili, V. (2022). Machine Learning with PyTorch and Scikit-Learn. Packt Publishing.

Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.

Sumpter, D. (2016). Soccermatics: Mathematical Adventures in the Beautiful Game. Bloomsbury Publishing.

Tippett, J. (2019). The Expected Goals Philosophy. Self-published.

Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press.

Wilson, J. (2008). Inverting the Pyramid: The History of Football Tactics. Orion Publishing.

Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.

Academic Papers

Soccer Analytics and Metrics

Anzer, G., & Bauer, P. (2021). "A Goal Scoring Probability Model for Shots Based on Synchronized Positional and Event Data in Football (Soccer)." Frontiers in Sports and Active Living, 3, 624475.

Bransen, L., & Van Haaren, J. (2019). "Measuring Football Players' On-the-Ball Contributions from Passes during Games." Machine Learning and Data Mining for Sports Analytics (ECML/PKDD Workshop).

Bransen, L., Van Haaren, J., van den Broek, G.,"; Davy, A., & Davis, J. (2019). "Choke or Shine? Quantifying Soccer Players' Abilities to Perform Under Mental Pressure." MIT Sloan Sports Analytics Conference.

Caley, M. (2015). "Premier League Projections and New Expected Goals." Cartilage Free Captain Blog.

Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2019). "Actions Speak Louder than Goals: Valuing Player Actions in Soccer." Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1851--1861.

Fernandez, J., & Bornn, L. (2018). "Wide Open Spaces: A Statistical Technique for Measuring Space Creation in Professional Soccer." MIT Sloan Sports Analytics Conference.

Fernandez, J., Bornn, L., & Cervone, D. (2021). "A Framework for the Fine-Grained Evaluation of the Instantaneous Expected Value of Soccer Possessions." Machine Learning, 110, 1389--1427.

Fernandez-Navarro, J., Fradua, L., Zubillaga, A., Ford, P. R., & McRobert, A. P. (2016). "Evaluating the Effectiveness of Styles of Play in Elite Soccer." Journal of Sports Sciences, 34(16), 1545--1552.

Goes, F. R., Kempe, M., Meerhoff, L. A., & Lemmink, K. A. (2019). "Not Every Pass Can Succeed: A Framework for Measuring Passing Difficulty in Soccer Matches." Journal of Sports Sciences, 37(14), 1662--1670.

Liu, G., & Schulte, O. (2018). "Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation." arXiv preprint arXiv:1805.11088.

Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., & Giannotti, F. (2019). "A Public Data Set of Spatio-Temporal Match Events in Soccer Competitions." Scientific Data, 6(1), 236.

Pena, J. L., & Touchette, H. (2012). "A Network Theory Analysis of Football Strategies." arXiv preprint arXiv:1206.6904.

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." Proceedings of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1605--1613.

Rathke, A. (2017). "An Examination of Expected Goals and Shot Efficiency in Soccer." Journal of Human Sport and Exercise, 12(2), 514--529.

Rudd, S. (2011). "A Framework for Tactical Analysis and Individual Offensive Production Assessment in Soccer Using Markov Chains." New England Symposium on Statistics in Sports.

Singh, K. (2019). "Introducing Expected Threat (xT)." Karun Singh Blog.

Spearman, W. (2018). "Beyond Expected Goals." MIT Sloan Sports Analytics Conference.

Spearman, W., Basye, A., Dick, G., Hotovy, R., & Pop, P. (2017). "Physics-Based Modeling of Pass Probabilities in Soccer." MIT Sloan Sports Analytics Conference.

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.

Cioppa, A., Deliege, A., Giancola, S., Ghanem, B., Van Droogenbroeck, M., Gade, R., & Moeslund, T. B. (2022). "SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.

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 on Computer Vision in Sports.

Theiner, J., Gritz, W., & Ewerth, R. (2022). "Extraction of Positional Player Data from Broadcast Soccer Videos." IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Machine Learning and Network Analysis

Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MIT Press.

Dick, U., & Brefeld, U. (2019). "Learning to Rate Player Positioning in Soccer." Big Data, 7(1), 71--82.

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.

Niculescu-Mizil, A., & Caruana, R. (2005). "Predicting Good Probabilities with Supervised Learning." Proceedings of the 22nd International Conference on Machine Learning, 625--632.

Platt, J. C. (1999). "Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods." Advances in Large Margin Classifiers, 61--74. MIT Press.

Rein, R., & Memmert, D. (2016). "Big Data and Tactical Analysis in Elite Soccer: Future Challenges and Opportunities for Sports Science." SpringerPlus, 5(1), 1410.

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.

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

Sports Science and Injury Prevention

Gabbett, T. J. (2016). "The Training-Injury Prevention Paradox: Should Athletes Be Training Smarter and Harder?" British Journal of Sports Medicine, 50(5), 273--280.

Hägglund, M., Waldén, M., Magnusson, H., Kristenson, K., Bengtsson, H., & Ekstrand, J. (2013). "Injuries Affect Team Performance Negatively in Professional Football: An 11-Year Follow-Up of the UEFA Champions League Injury Study." British Journal of Sports Medicine, 47(12), 738--742.

Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernandez, J., & Medina, D. (2018). "Effective Injury Forecasting in Soccer with GPS Training Data and Machine Learning." PLoS ONE, 13(7), e0201264.

Windt, J., & Gabbett, T. J. (2017). "How Do Training and Competition Workloads Relate to Injury? The Workload-Injury Aetiology Model." British Journal of Sports Medicine, 51(5), 428--435.

Conference Proceedings

Brier, G. W. (1950). "Verification of Forecasts Expressed in Terms of Probability." Monthly Weather Review, 78(1), 1--3.

Cervone, D., D'Amour, A., Bornn, L., & Goldsberry, K. (2016). "A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes." Journal of the American Statistical Association, 111(514), 585--599.

Impect. (2019). "Packing: A New Way of Analyzing Football." Impect GmbH Technical Report.

Lucey, P., Bialkowski, A., Monfort, M., Carr, P., & Matthews, I. (2015). "Quality vs Quantity: Improved Shot Prediction in Soccer Using Strategic Features from Spatiotemporal Data." MIT Sloan Sports Analytics Conference.

Shaw, L., & Sudarshan, M. (2020). "The Right Place at the Right Time: Advanced Off-Ball Metrics for Exploiting an Opponent's Defensive Weaknesses in Soccer." Friends of Tracking / MIT Sloan Sports Analytics Conference.

Online Resources

Open Data and Tools

FBref / Sports Reference. Football Reference. https://fbref.com

Friends of Tracking. YouTube educational series. https://www.youtube.com/c/FriendsofTracking

kloppy (PySport). Standardized soccer data loading. https://github.com/PySport/kloppy

Metrica Sports. Sample tracking data. https://github.com/metrica-sports/sample-data

mplsoccer (Andrew Rowlinson). Soccer visualization library. https://github.com/andrewRowlinson/mplsoccer

socceraction (ML-KULeuven). Action valuation framework. https://github.com/ML-KULeuven/socceraction

StatsBomb. Open data repository. https://github.com/statsbomb/open-data

statsbombpy. Python API for StatsBomb data. https://github.com/statsbomb/statsbombpy

Understat. Expected goals data. https://understat.com

Blogs and Media

Between the Posts. https://betweentheposts.net

FC Python. Soccer analytics tutorials. https://fcpython.com

StatsBomb Articles. https://statsbomb.com/articles

The Analyst. https://theanalyst.com

Tifo Football. YouTube tactical analysis. https://www.youtube.com/c/TifoFootball

Industry Reports

Deloitte. (Annual). Football Money League. Deloitte Sports Business Group.

FIFA. (2023). FIFA Football Technology Innovation Programme: Annual Report.

FIFPRO. (2024). Player Data Rights: A Framework for the Digital Age. Position Paper.

UEFA. (2023). The European Club Footballing Landscape. UEFA Benchmarking Report.

Conferences

ECML-PKDD Workshop on Machine Learning and Data Mining for Sports Analytics. Annual.

KDD Workshop on Large-Scale Sports Analytics. Annual.

MIT Sloan Sports Analytics Conference. Annual. https://www.sloansportsconference.com

OptaPro Forum / Stats Perform. Annual. https://www.optasportspro.com

StatsBomb Conference. Annual. https://statsbomb.com/conference

Software and Libraries

Harris, C. R., et al. (2020). "Array Programming with NumPy." Nature, 585, 357--362.

Hunter, J. D. (2007). "Matplotlib: A 2D Graphics Environment." Computing in Science & Engineering, 9(3), 90--95.

McKinney, W. (2010). "Data Structures for Statistical Computing in Python." Proceedings of the 9th Python in Science Conference, 56--61.

Pedregosa, F., et al. (2011). "Scikit-learn: Machine Learning in Python." Journal of Machine Learning Research, 12, 2825--2830.

Virtanen, P., et al. (2020). "SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python." Nature Methods, 17, 261--272.