Further Reading: Player Performance Metrics

This annotated bibliography provides resources for deeper exploration of topics introduced in Chapter 15.

Essential Books

The Expected Goals Philosophy

Tippett, James (2019)

A comprehensive guide to expected goals models and their applications to player and team evaluation. Chapters 4-6 cover individual player assessment using xG and xA, including detailed discussions of finishing skill versus luck and the proper use of per-90 normalization. Essential reading for understanding the foundation of modern player metrics.

Best for: Understanding xG-based player evaluation in depth

Soccermatics

Sumpter, David (2016)

Applies mathematical modeling to soccer, with chapters on network theory, spatial analysis, and probability that directly support the player profiling and similarity methods in this chapter. Chapter 9 on player recruitment is particularly relevant.

Best for: Readers who want mathematical rigor alongside intuition

Football Hackers

Biermann, Christoph (2019)

Profiles how clubs and individuals have used data to evaluate players. The chapters on Brentford's recruitment model and Ralf Rangnick's player profiling system illustrate real-world applications of the metrics and methods discussed in this chapter.

Best for: Understanding how player metrics are used inside professional clubs

Smart Money: How the World's Best Sports Bettors Beat the Bookies Out of Billions

Keogh, Brendan and Rose, Daniel (2013)

While focused on sports betting, the chapters on valuation and market efficiency provide a framework for understanding how player metrics translate into transfer market decisions.

Best for: Connecting player evaluation to economic value

Moneyball

Lewis, Michael (2003)

The foundational text on evidence-based player evaluation in professional sport. Though set in baseball, the core principles of identifying undervalued skills through metrics, overcoming traditional scouting biases, and building competitive advantage through data apply directly to soccer player evaluation.

Best for: Understanding the philosophy behind metric-driven recruitment

Academic Papers

Player Evaluation and Rating Systems

"Actions Speak Louder than Goals: Valuing Player Actions in Soccer" Decroos, Tom; Bransen, Lotte; Van Haaren, Jan; Davis, Jesse (2019). Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Introduces the VAEP (Valuing Actions by Estimating Probabilities) framework, which assigns a value to every on-ball action based on its impact on scoring and conceding probabilities. This paper provides the theoretical foundation for action-based player ratings that go far beyond goals and assists.

"A Framework for the Fine-Grained Evaluation of the Instantaneous Expected Value of Soccer Possessions" Fernandez, Javier; Bornn, Luke; Cervone, Dan (2019). Machine Learning, 108, 1389-1416.

Develops expected possession value models that can attribute value to every player action within a possession chain. Directly relevant to building the multi-dimensional player profiles discussed in Section 15.6.

Aging and Development

"Aging Curves for Major League Soccer" Dendir, Seife (2016). Journal of Sports Analytics, 2(1), 17-28.

One of the few rigorous studies of aging curves specific to soccer. Uses panel data methods to estimate position-specific peak ages while controlling for survivorship bias. The methodology section is an excellent guide for implementing the delta method described in Section 15.4.

"The Age-Productivity Gradient: Evidence from a Sample of F1 Drivers" Castellucci, Fabrizio; Padula, Mario; Pica, Giovanni (2011). Labour Economics, 18(4), 464-473.

While focused on Formula 1, this paper provides rigorous econometric methods for estimating age-performance curves with selection corrections. The Heckman selection model approach is directly transferable to soccer aging curve analysis.

"Peak Performance and Age Among Superathletes" Fair, Ray (2007). Journal of Quantitative Analysis in Sports, 3(1).

A cross-sport study of peak performance ages that provides methodological templates for separating physical and skill components of aging. Useful background for understanding why different metrics peak at different ages.

Similarity and Clustering

"Identifying Play Styles of Football Players Based on Match Event Data" Decroos, Tom; Van Haaren, Jan; Davis, Jesse (2018). Proceedings of the Machine Learning and Data Mining for Sports Analytics Workshop.

Applies non-negative matrix factorization to identify player play styles from event data. Proposes a data-driven alternative to position labels, identifying roles such as "box crasher," "dribbling winger," and "deep-lying playmaker" from statistical signatures.

"A Multi-Dimensional Evaluation Framework for Player Recruitment in Football" Pappalardo, Luca; Cintia, Paolo (2018). Machine Learning and Data Mining for Sports Analytics Workshop.

Proposes a comprehensive player evaluation framework using multiple metrics, dimensionality reduction, and similarity computation. Includes practical advice on building recruitment shortlists---directly aligned with Sections 15.6 and 15.7.

"Clustering Football Players by Playing Style" Lago-Penas, Carlos; Lago-Ballesteros, Joaquin; Rey, Ezequiel (2011). Journal of Sports Sciences, 29(13), 1421-1430.

An early study applying cluster analysis to identify playing styles among professional soccer players. Uses match statistics to group players into archetypes, demonstrating the approach described in Section 15.7.3.

Goalkeeper Evaluation

"Evaluating Soccer Player Goalkeeping: A Bayesian Approach" Schuckers, Michael; Pasquali, Amy; Curro, James (2012). Journal of Quantitative Analysis in Sports, 8(2).

Applies Bayesian methods to goalkeeper evaluation, addressing the small-sample challenges inherent in shot-stopping analysis. Directly relevant to the discussion of PSxG-GA and Bayesian shrinkage in Sections 15.2.1 and 15.3.3.

Online Resources

Websites and Blogs

StatsBomb Blog (statsbomb.com) Industry-leading articles on player evaluation methodology. Their series on radar charts, player similarity, and xG-based assessment are essential reading. The open data repository enables hands-on implementation of all methods in this chapter.

FBref (fbref.com) Comprehensive player statistics powered by StatsBomb data. The per-90 tables, percentile ranks, and scouting reports for individual players directly implement the concepts in Sections 15.1-15.3.

Mackay Analytics (mackayanalytics.nl) Technical blog focused on player evaluation methodology, including detailed posts on aging curves, similarity models, and radar chart design.

Between the Posts (betweentheposts.net) Specializes in goalkeeper analytics, including detailed PSxG models and goalkeeper rating systems relevant to Section 15.2.1.

The Athletic (theathletic.com) Subscription required. Employs analysts who produce accessible player evaluation content, including per-90 leaderboards, radar chart profiles, and age curve discussions.

Video Resources

Friends of Tracking (YouTube) Academic lecture series covering player evaluation, similarity models, and aging curves. Lectures by William Spearman and Laurie Shaw are particularly relevant.

StatsBomb Conference Recordings (YouTube) Annual conference presentations on player evaluation methodology from industry practitioners.

McKay Johns / Fanalytics (YouTube) Tutorials on building radar charts, computing similarity scores, and implementing player profiling in Python.

Communities

Twitter/X #SoccerAnalytics Active community where analysts share radar charts, player comparisons, and methodological discussions. Follow practitioners at major clubs and data providers for cutting-edge approaches.

Reddit r/SoccerAnalytics Discussion forum for methodology questions, career advice, and analysis sharing. Good source for peer feedback on player evaluation projects.

Technical References

Cosine Similarity and Distance Metrics

"Introduction to Information Retrieval" Manning, Christopher; Raghavan, Prabhakar; Schutze, Hinrich (2008). Cambridge University Press.

The standard textbook on similarity and distance metrics in high-dimensional spaces. Chapter 8 on scoring and vector space models provides the mathematical foundation for the cosine similarity computations in Section 15.7.

Clustering Algorithms

"An Introduction to Statistical Learning" James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert (2021). Springer.

Chapter 14 on unsupervised learning covers K-Means clustering, hierarchical clustering, and PCA with clear explanations and R/Python examples. Essential background for the player archetype clustering in Section 15.7.3.

Radar Chart Design

"The Visual Display of Quantitative Information" Tufte, Edward (1983). Graphics Press.

While Tufte is famously skeptical of radar charts, his principles of data visualization inform better design: maximizing data-ink ratio, avoiding chart junk, and ensuring honest representation of the data.

Recommended Reading Sequence

For Beginners: 1. FBref player pages (explore per-90 tables and percentile ranks) 2. The Expected Goals Philosophy Chapters 4-6 3. StatsBomb blog articles on radar charts 4. Friends of Tracking lecture on player evaluation

For Technical Readers: 1. Decroos et al. (2019) on VAEP 2. Dendir (2016) on aging curves 3. Manning et al. (2008) Chapter 8 on similarity metrics 4. James et al. (2021) Chapter 14 on clustering

For Practitioners: 1. Football Hackers (organizational context) 2. Pappalardo & Cintia (2018) on recruitment frameworks 3. StatsBomb Conference recordings on player evaluation 4. Build a portfolio project using StatsBomb open data

Chapter-Specific Deep Dives

Continue to Chapter 16: Team Performance Analysis