Chapter 6: Further Reading

An annotated bibliography for deeper exploration of spatial analysis on the soccer pitch.

Coordinate Systems and Data Standards

StatsBomb. "Open Data Specification." https://github.com/statsbomb/open-data The official documentation for StatsBomb's open event data, including coordinate system definitions, JSON schema, and sample datasets. Essential reference for anyone working with StatsBomb data.

FIFA. "EPTS (Electronic Performance and Tracking Systems) Standard Data Format." The FIFA specification for tracking data format, including the centre-origin coordinate system. Available through FIFA's technology programme documentation. Defines the standard used by multiple tracking data providers.

IFAB. "Laws of the Game." https://www.theifab.com/laws The authoritative source for pitch dimensions, markings, and regulations. Section 1 ("The Field of Play") provides the official dimensional constraints referenced in Section 6.1.

Spatial Analysis and Visualization

Mplsoccer Documentation. https://mplsoccer.readthedocs.io/ Comprehensive documentation for the mplsoccer Python library, which provides pitch drawing, scatter plots, heat maps, Voronoi diagrams, and many other soccer-specific visualizations. Includes tutorials and gallery examples.

Rathke, A. (2017). "An Examination of Expected Goals and Shot Efficiency in Soccer." Journal of Human Sport and Exercise, 12(2S), S514-S529. Early academic work connecting shot location (spatial coordinates) to goal probability. Demonstrates why accurate coordinate handling is foundational to expected goals modeling.

Fernandez, J. & Bornn, L. (2018). "Wide Open Spaces: A Statistical Technique for Measuring Space Generation in Professional Soccer." MIT Sloan Sports Analytics Conference. A landmark paper that introduces methods for quantifying space creation using player tracking coordinates. Proposes spatial influence models that extend beyond simple Voronoi tessellation.

Kernel Density Estimation

Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall. The classic reference on kernel density estimation, including bandwidth selection methods (Silverman's rule of thumb) and kernel function properties. Still the gold standard introduction to KDE theory.

Scott, D. W. (2015). Multivariate Density Estimation: Theory, Practice, and Visualization. 2nd ed. Wiley. A thorough treatment of multivariate density estimation, including bivariate KDE with anisotropic bandwidths. Chapter 6 covers bandwidth selection in detail.

Scipy Documentation. "scipy.stats.gaussian_kde." https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gaussian_kde.html API reference for the Gaussian KDE implementation used throughout this chapter. Includes examples of bandwidth selection and evaluation.

Pitch Control and Spatial Models

Spearman, W. (2018). "Beyond Expected Goals." MIT Sloan Sports Analytics Conference. Introduces a physics-based pitch control model that incorporates player velocity and acceleration. One of the foundational papers in tracking-data analytics.

Fernandez, J. & Bornn, L. (2018). "Wide Open Spaces: A Statistical Technique for Measuring Space Generation in Professional Soccer." MIT Sloan Sports Analytics Conference. Defines spatial influence functions based on player position, velocity, and distance to the ball. Provides the conceptual framework for the pitch control discussion in Section 6.6.

Spearman, W., Basye, A., Dick, G., Hotovy, R., & Pop, P. (2017). "Physics-Based Modeling of Pass Probabilities in Soccer." MIT Sloan Sports Analytics Conference. An earlier paper by Spearman that models passing probabilities using time-to-intercept calculations -- the physical foundation for velocity-aware pitch control.

Brefeld, U., Davis, J., Van Haaren, J., & Zimmermann, A. (Eds.) (2019). Machine Learning and Data Mining for Sports Analytics. Springer. Conference proceedings covering a range of spatial analytics topics in sports, including pitch control variants, zone-based models, and spatial clustering approaches.

Zone Models and Tactical Analysis

Tippett, J. (2019). The Expected Goals Philosophy. Self-published. An accessible introduction to expected goals that includes discussion of pitch zones, shot locations, and spatial value. Good for readers who want to connect the coordinate system ideas in this chapter to xG modeling.

Maric, R. (2020). "Half-Spaces and Positional Play." Spielverlagerung.com. A detailed tactical analysis of the half-space concept referenced in Section 6.3.3. Explains the German tactical school's five-channel model with match examples.

Pollard, R. & Reep, C. (1997). "Measuring the Effectiveness of Playing Strategies at Soccer." The Statistician, 46(4), 541-550. An early statistical study of pitch zones and their relationship to goal-scoring. Introduced concepts similar to Zone 14 and demonstrated the value of spatial segmentation.

Voronoi Tessellation

Okabe, A., Boots, B., Sugihara, K., & Chiu, S. N. (2009). Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. 2nd ed. Wiley. The definitive reference on Voronoi diagrams, covering theory, algorithms, and applications. Chapter 1 provides the mathematical foundation used in Section 6.6.2.

Kim, S. (2004). "Voronoi Analysis of a Soccer Game." Nonlinear Analysis: Modelling and Control, 9(3), 233-240. An early application of Voronoi tessellation to soccer, computing dominant regions and analyzing player positioning during matches.

Python Tools and Libraries

Harris, C. R., et al. (2020). "Array programming with NumPy." Nature, 585(7825), 357-362. The NumPy library reference. NumPy arrays underpin every coordinate computation in this chapter.

Hunter, J. D. (2007). "Matplotlib: A 2D Graphics Environment." Computing in Science & Engineering, 9(3), 90-95. The foundational reference for matplotlib, on which mplsoccer is built.

Virtanen, P., et al. (2020). "SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python." Nature Methods, 17(3), 261-272. Reference for SciPy, which provides gaussian_kde, Voronoi, ConvexHull, and other spatial algorithms used in this chapter.

Online Resources and Tutorials

Friends of Tracking (YouTube channel). https://www.youtube.com/channel/UCUBFJYcag8j2rm_9HkrrA7w Video tutorials by Laurie Shaw, David Sumpter, and others covering pitch control models, tracking data analysis, and spatial visualization. The pitch control tutorial series directly complements Section 6.6.

McKinnon, R. "Exploring Pitch Control." Towards Data Science. A blog-post walkthrough of implementing Spearman's pitch control model in Python. Includes code and visualizations.

Sumpter, D. (2022). Twelve Yards: The Art and Psychology of the Perfect Penalty. Faber & Faber. While focused on penalties, this book contains accessible explanations of spatial reasoning in soccer, including shot angle calculations and coordinate-based analysis.