Chapter 10: Further Reading

Overview

This curated collection provides resources for deepening your understanding of passing network analysis in soccer. Materials range from foundational network science to cutting-edge sports analytics applications.

Foundational Network Science

Textbooks

  1. Newman, M. E. J. (2018). Networks (2nd ed.). Oxford University Press. - Comprehensive introduction to network science - Mathematical foundations of centrality and clustering - Essential for understanding underlying theory

  2. Barabási, A.-L. (2016). Network Science. Cambridge University Press. - Freely available online: http://networksciencebook.com - Visual, accessible introduction - Excellent coverage of real-world network phenomena

  3. Easley, D., & Kleinberg, J. (2010). Networks, Crowds, and Markets. Cambridge University Press. - Game-theoretic perspective on networks - Available free online from authors - Chapter on network structure and dynamics

  4. Kolaczyk, E. D., & Csárdi, G. (2014). Statistical Analysis of Network Data with R. Springer. - Practical implementation focus - R-based but concepts transfer to Python - Statistical inference for networks

Key Papers

  1. Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215-239. - Classic paper defining centrality concepts - Foundation for betweenness and closeness measures - Essential historical context

  2. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684), 440-442. - Introduced clustering coefficient concept - Small-world network properties - Highly cited foundational work

  3. Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab. - Original PageRank paper - Foundation for eigenvector-based centrality - Historical document from Google founders

Soccer-Specific Network Analysis

Academic Papers

  1. Grund, T. U. (2012). Network structure and team performance: The case of English Premier League soccer teams. Social Networks, 34(4), 682-690. - Pioneering application to soccer - Links network properties to outcomes - Statistical methodology for sports networks

  2. Pena, J. L., & Touchette, H. (2012). A network theory analysis of football strategies. arXiv preprint arXiv:1206.6904. - Applies network theory to tactical analysis - Compares national team styles - Accessible introduction to the field

  3. Clemente, F. M., Martins, F. M. L., & Mendes, R. S. (2016). Social network analysis applied to team sports analysis. SpringerBriefs in Applied Sciences and Technology.

    • Book-length treatment of sports networks
    • Covers soccer, basketball, volleyball
    • Practical methodology guidance

  4. Buldu, J. M., Busquets, J., Echegoyen, I., & Seirullo, F. (2019). Using network science to analyse football passing networks: Dynamics, space, time, and the multilayer nature of the game. Frontiers in Psychology, 10, 1900.

    • Comprehensive review of network methods
    • Temporal and spatial extensions
    • FC Barcelona focus with elite insights

  5. Yamamoto, Y., & Yokoyama, K. (2011). Common and unique network dynamics in football games. PloS One, 6(12), e29638.

    • Dynamic network analysis
    • Team coordination patterns
    • Novel temporal methodology

  6. Clemente, F. M., et al. (2015). General network analysis of national soccer teams in FIFA World Cup 2014. International Journal of Performance Analysis in Sport, 15(1), 80-96.

    • World Cup application
    • Cross-team comparison
    • Accessible methodology

  7. Gudmundsson, J., & Horton, M. (2017). Spatio-temporal analysis of team sports. ACM Computing Surveys, 50(2), 1-34.

    • Broader spatial-temporal context
    • Connects passing networks to tracking data
    • Technical survey of methods

  8. Gama, J., et al. (2014). Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport, 14(3), 692-708.

    • Attacking phase focus
    • Intra-team dynamics
    • Practical coaching applications

Technical Implementation

Python Libraries

  1. NetworkX Documentation https://networkx.org/documentation/stable/

    • Official reference for Python network analysis
    • Extensive algorithm implementations
    • Tutorial materials included

  2. Mplsoccer Documentation https://mplsoccer.readthedocs.io/

    • Soccer-specific Python visualization
    • Pitch drawing and network plotting
    • StatsBomb data integration

  3. StatsBomb Python API https://github.com/statsbomb/statsbombpy

    • Official StatsBomb data access
    • Free competition data
    • Example notebooks

Tutorials and Notebooks

  1. Friends of Tracking: Passing Networks Tutorial https://github.com/Friends-of-Tracking-Data-FoTD

    • Video tutorial series
    • Jupyter notebook implementations
    • Step-by-step guidance

  2. McKay Johns: Network Analysis with StatsBomb Data https://www.youtube.com/c/McKayJohns

    • YouTube tutorials
    • Practical Python implementations
    • Real-world examples

  3. FC Python: Passing Network Visualization https://fcpython.com/

    • Beginner-friendly tutorials
    • Soccer analytics focus
    • Code examples provided

Advanced Topics

Temporal Networks

  1. Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97-125.

    • Foundation for time-varying networks
    • Mathematical framework
    • Applications beyond sports

  2. Cintia, P., et al. (2015). Network-based identification of the key players in the passing game of soccer teams. Proceedings of the 2015 ACM SIGMOD Workshop on Network Analysis.

    • Dynamic network identification
    • Key player detection algorithms
    • Match phase analysis

Machine Learning on Networks

  1. Hamilton, W. L. (2020). Graph Representation Learning. Morgan & Claypool.

    • Neural networks for graphs
    • Node embeddings
    • Modern deep learning approaches

  2. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. ICLR 2017.

    • Graph neural networks
    • Foundation for advanced methods
    • Potential soccer applications

Multilayer Networks

  1. Boccaletti, S., et al. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1-122.
    • Theoretical framework for complex networks
    • Multiple relationship types
    • Advanced structural analysis

Industry and Applied Resources

Professional Analysis

  1. Twelve Football Blog https://twelve.football/blog

    • Industry-leading analytics company
    • Technical blog posts
    • Network analysis examples

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

    • Data provider insights
    • Methodology explanations
    • Case studies

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

    • Quality tactical journalism
    • Network visualizations used
    • Expert analysis context

Conferences and Presentations

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

    • Annual research presentations
    • Paper archive available
    • Industry-academic intersection

  2. OptaPro Forum

    • Annual analytics conference
    • Industry presentations
    • Network methodology papers

  3. European Conference on Machine Learning (ECML) Sports Analytics Workshop

    • Academic sports analytics focus
    • Peer-reviewed research
    • Technical depth

Historical and Contextual Reading

Soccer Tactics History

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

    • Tactical evolution context
    • Historical formation analysis
    • Understanding why networks differ

  2. Honigstein, R. (2015). Das Reboot: How German Football Reinvented Itself and Conquered the World. Yellow Jersey.

    • German football revolution
    • Data-driven development context
    • National team transformation

Analytics Industry

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

    • Soccer analytics introduction
    • Statistical thinking for soccer
    • Accessible for general readers

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

    • Modern analytics practices
    • Industry insider perspective
    • Network analysis mentions

Software and Tools

Network Visualization

  1. Gephi https://gephi.org/

    • Open-source network visualization
    • Interactive exploration
    • Large network handling

  2. Cytoscape https://cytoscape.org/

    • Network analysis platform
    • Plugin ecosystem
    • Publication-quality graphics

Data Sources

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

    • Free event data
    • Multiple competitions
    • Well-documented format

  2. Wyscout Data

    • Commercial provider
    • Extensive coverage
    • Industry standard

  3. Understat https://understat.com/

    • Free xG and shot data
    • Top 5 leagues
    • API available

Practical Exercises

  1. Kaggle: Soccer Event Data https://www.kaggle.com/datasets

    • Practice datasets
    • Community notebooks
    • Competition entries

  2. Google Colab Network Analysis Notebooks

    • Free computational resources
    • Pre-configured environments
    • Shareable analyses

Beginners

Start with: #2 (Barabási), #19-21 (Tutorials), #35 (Numbers Game)

Intermediate

Progress to: #8-10 (Soccer papers), #16-18 (Documentation), #27-28 (Industry blogs)

Advanced

Explore: #1 (Newman), #11 (Buldu), #22-26 (Advanced topics)

Practitioners

Focus on: #27-29 (Industry), #30-31 (Conferences), #39-41 (Data sources)

Citation Guidelines

When citing network analysis in academic or professional work:

For methodology: Cite foundational papers (#5-7) and soccer-specific methodology (#8-10)

For implementation: Reference NetworkX (#16) and data sources (#39-41)

For context: Include broader soccer analytics references (#35-36)

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