Chapter 11: Further Reading

Overview

This curated collection provides resources for deepening your understanding of possession analysis, territorial control, and pressing metrics. Materials range from foundational research to cutting-edge analytics applications.

Foundational Research

Academic Papers

  1. Mackay, N. (2017). Predicting goal probabilities for possessions in football. MIT Sloan Sports Analytics Conference. - Foundational work on possession value - Probability modeling for possession outcomes - Framework for efficiency analysis

  2. Fernandez, J., & Bornn, L. (2018). Wide open spaces: A statistical technique for measuring space creation in professional soccer. MIT Sloan Sports Analytics Conference. - Spatial control models - Pitch control probability estimation - Applications to possession analysis

  3. Spearman, W. (2018). Beyond expected goals. MIT Sloan Sports Analytics Conference. - Expected Possession Value framework - Integration with possession analysis - Action valuation approaches

  4. Power, P., et al. (2017). Not all passes are created equal: Objectively measuring the risk and reward of passes in soccer. KDD 2017. - Pass valuation methods - Risk-reward in possession - Network integration

  5. Fernandez-Navarro, J., et al. (2016). Attacking and defensive styles of play in soccer: Analysis of Spanish and English elite teams. Journal of Sports Sciences, 34(24), 2195-2204. - Style classification methodology - Possession pattern analysis - Cross-league comparisons

Pressing and Defensive Analysis

Research

  1. Low, B., et al. (2021). Exploring the effects of deep-learning based high press in football. International Journal of Sports Science & Coaching, 16(6), 1423-1435. - High pressing analysis - Machine learning applications - Effectiveness metrics

  2. Andrienko, G., et al. (2017). Visual analysis of pressure in football. Data Mining and Knowledge Discovery, 31(6), 1793-1839. - Pressure event analysis - Visualization techniques - Spatial-temporal methods

  3. Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science. SpringerPlus, 5(1), 1410. - Tactical analysis frameworks - Big data applications - Future directions

Industry Publications

  1. StatsBomb. PPDA and Defensive Metrics. https://statsbomb.com/ - Practical PPDA applications - Industry methodology - Case studies

  2. Twelve Football. Pressing Intensity Analysis. https://twelve.football/

    • Advanced pressing metrics
    • Team comparison tools
    • Visual examples

Spatial and Territorial Control

Research Papers

  1. Kim, S. (2004). Voronoi analysis of a soccer game. Nonlinear Analysis: Modelling and Control, 9(3), 233-240.

    • Voronoi tessellation for space control
    • Mathematical foundations
    • Soccer applications

  2. Taki, T., & Hasegawa, J. (2000). Visualization of dominant region in team games and its application to teamwork analysis. Computer Graphics International.

    • Dominant region calculation
    • Team coordination analysis
    • Visualization methods

  3. Lucey, P., et al. (2014). Quality vs quantity: Improved shot prediction in soccer using strategic features from spatiotemporal data. MIT Sloan Sports Analytics Conference.

    • Spatial feature importance
    • Shot prediction integration
    • Possession context

Books and Chapters

  1. Memmert, D., & Raabe, D. (2018). Data Analytics in Football. Routledge.

    • Comprehensive analytics overview
    • Possession chapter coverage
    • Practical applications

  2. Stein, M., et al. (2018). Visual soccer analytics: Understanding the characteristics of collective team movement based on feature-driven analysis and abstraction. In Sports Analytics (pp. 107-131). Springer.

    • Visual analytics methods
    • Team movement patterns
    • Abstraction techniques

Practical Guides and Tutorials

Online Resources

  1. Friends of Tracking. Possession and Territory Tutorials. https://github.com/Friends-of-Tracking-Data-FoTD

    • Video tutorials
    • Python implementations
    • Step-by-step guides

  2. McKay Johns. Possession Analysis Tutorials. https://www.youtube.com/c/McKayJohns

    • Practical Python coding
    • StatsBomb data usage
    • Visualization examples

  3. FC Python. Territorial Control Guides. https://fcpython.com/

    • Beginner-friendly tutorials
    • Code examples
    • Soccer context

Documentation

  1. Mplsoccer Documentation. Heatmaps and Zones. https://mplsoccer.readthedocs.io/

    • Visualization tools
    • Zone plotting
    • Pitch drawing

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

    • Free event data
    • Data specifications
    • Example analyses

Historical and Tactical Context

Books

  1. Wilson, J. (2013). Inverting the Pyramid: The History of Soccer Tactics. Nation Books.

    • Evolution of possession tactics
    • Total Football origins
    • Tiki-taka development

  2. Perarnau, M. (2014). Pep Confidential: The Inside Story of Pep Guardiola's First Season at Bayern Munich. Arena Sport.

    • Possession philosophy
    • Tactical implementation
    • High pressing origins

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

    • English tactical evolution
    • Possession debates
    • Style comparisons

  4. Biermann, C. (2019). Football Hackers: The Science and Art of a Data Revolution. Blink Publishing.

    • Modern analytics practices
    • Possession metrics development
    • Industry insights

Advanced Analytics

Machine Learning and Modeling

  1. Decroos, T., et al. (2019). Actions speak louder than goals: Valuing player actions in soccer. KDD 2019.

    • VAEP framework
    • Possession action valuation
    • Player contribution

  2. Robberechts, P., & Davis, J. (2020). How data availability affects the ability to learn good xG models. Machine Learning and Data Mining for Sports Analytics.

    • Model quality considerations
    • Data requirements
    • Practical implications

  3. Bransen, L., & Van Haaren, J. (2018). Measuring football players' on-the-ball contributions from passes during games. Machine Learning and Data Mining for Sports Analytics.

    • Pass contribution metrics
    • Player evaluation
    • Possession impact

Tracking Data Integration

  1. Fernandez, J., & Bornn, L. (2021). SoccerMap: A deep learning architecture for visually-interpretable analysis in soccer. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.

    • Deep learning for soccer
    • Spatial analysis
    • Interpretable models

  2. Spearman, W., et al. (2017). Physics-based modeling of pass probabilities in soccer. MIT Sloan Sports Analytics Conference.

    • Physics-based models
    • Pass success prediction
    • Spatial integration

Industry Reports and Resources

Technical Reports

  1. FIFA Technical Study Group. (2018). 2018 FIFA World Cup Russia Technical Report.

    • Tournament analysis
    • Possession patterns
    • Tactical trends

  2. UEFA Technical Reports.

    • Champions League analysis
    • European tactical trends
    • Possession statistics

Analytics Platforms

  1. Opta Sports. https://www.optasports.com/

    • Industry data provider
    • Possession metrics
    • Event data

  2. Wyscout. https://wyscout.com/

    • Video and data platform
    • Possession analysis tools
    • Professional usage

  3. InStat Football. https://instatsport.com/

    • Event data provider
    • Analytical tools
    • Visualization

Blogs and Regular Publications

  1. StatsBomb Blog. https://statsbomb.com/articles/

    • Regular analytical content
    • Possession features
    • Methodology explanations

  2. The Athletic Tactics Coverage. https://theathletic.com/

    • Quality journalism
    • Data-driven analysis
    • Tactical breakdowns

  3. Tifo Football. https://www.youtube.com/c/TifoFootball

    • Video analysis
    • Tactical explanations
    • Accessible content

Conferences and Workshops

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

    • Annual research presentations
    • Possession research
    • Industry networking

  2. OptaPro Forum.

    • Analytics conference
    • Industry presentations
    • Methodology papers

  3. StatsBomb Conference.

    • Annual event
    • Technical presentations
    • Networking opportunities

Software and Tools

Python Libraries

  1. Pandas. https://pandas.pydata.org/

    • Data manipulation
    • Event processing
    • Analysis foundation

  2. SciPy. https://scipy.org/

    • Scientific computing
    • Statistical analysis
    • Kernel density estimation

  3. Scikit-learn. https://scikit-learn.org/

    • Machine learning
    • Clustering
    • Classification

Visualization

  1. Matplotlib. https://matplotlib.org/

    • Core plotting
    • Customization
    • Publication quality

  2. Seaborn. https://seaborn.pydata.org/

    • Statistical visualization
    • Heatmaps
    • Distribution plots

Beginners

Start with: #16-18 (Tutorials), #21 (Wilson), #35-37 (Blogs)

Intermediate

Progress to: #1-4 (Research papers), #9-10 (Industry), #14 (Memmert)

Advanced

Explore: #6-8 (Pressing research), #25-29 (ML applications)

Practitioners

Focus on: #9-10 (Industry), #30-34 (Platforms), #38-40 (Conferences)

Citation Guidelines

When citing possession analysis in academic or professional work:

For methodology: Cite foundational papers (#1-5)

For pressing metrics: Reference industry standards (#9-10)

For implementation: Cite libraries and tutorials (#16-20)

For context: Include tactical background (#21-24)

Last updated: 2024. Links verified at time of publication.