Further Reading: Introduction to Football Analytics

Annotated bibliography for deeper exploration of Chapter 1 topics

Essential Reading

Foundational Texts

Romer, D. (2006). "Do Firms Maximize? Evidence from Professional Football." Journal of Political Economy, 114(2), 340-365.

The landmark academic paper demonstrating that NFL teams are systematically too conservative on fourth down. Romer applied dynamic programming to show that going for it on fourth down is often the higher expected-value decision. This paper is the intellectual foundation for the fourth-down revolution discussed in this chapter. Accessible to readers with basic economics background.

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

While focused on baseball, this book catalyzed the sports analytics revolution across all sports. The narrative of the Oakland A's using statistical analysis to compete with richer teams resonated far beyond baseball. Essential reading for understanding the cultural context of how analytics entered professional sports.

Alamar, B. (2013). Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers. Columbia University Press.

A comprehensive introduction to sports analytics across multiple sports. Chapters on football cover team evaluation, player evaluation, and game strategy. More academic than Moneyball but accessible to practitioners. Good overview of methodological foundations.

Football-Specific Resources

Public Analysis and Writing

Football Outsiders (footballoutsiders.com)

Founded in 2003 by Aaron Schatz, Football Outsiders pioneered public football analytics. Their annual Football Outsiders Almanac provides deep statistical analysis of every NFL team. Their DVOA metric remains influential. The website offers free articles and premium content. Essential for understanding how analytics can be communicated to mainstream audiences.

The Athletic's Football Analytics Coverage

Multiple writers at The Athletic (Ben Baldwin, Seth Walder, and others) produce accessible analytics-driven football content. Baldwin's fourth-down charts have become widely referenced. Subscription required, but high-quality writing that bridges analytical rigor and readability.

Open Source Football (opensourcefootball.com)

A collaborative blog featuring R and Python tutorials for football analytics. Excellent for learning practical skills while seeing how concepts from this textbook are applied in practice. Free and community-driven.

Technical Foundations

Statistics and Data Science

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer. (Free online)

The go-to introduction to modern statistical learning methods. While not sports-specific, the techniques covered (regression, classification, resampling methods) are the foundation for Part IV of this textbook. The free online version includes R labs; a Python version is also available.

VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly. (Free online)

Comprehensive guide to Python's data science ecosystem: NumPy, Pandas, Matplotlib, and Scikit-learn. Essential reference for the programming we'll do throughout this textbook. Available free at jakevdp.github.io/PythonDataScienceHandbook/.

Football-Specific Technical Work

nflfastR Documentation and Tutorials

The nflfastR package (R) and nfl_data_py (Python) provide access to play-by-play data and Expected Points models. Their documentation includes tutorials that demonstrate concepts from Chapters 2-5. Essential practical resource.

NFL Big Data Bowl Competition Materials

Annual competition materials (available on Kaggle) include tracking data samples and winning submissions. Excellent for seeing how advanced practitioners approach football analytics problems. Submissions from 2018-present cover topics from route running to tackle probability.

Academic Research

Journals and Conferences

Journal of Quantitative Analysis in Sports

The primary academic journal for sports analytics research. Football papers cover topics including draft pick valuation, salary cap optimization, and player evaluation. Technical, peer-reviewed research that pushes the frontier of the field.

MIT Sloan Sports Analytics Conference

Annual conference featuring research presentations and industry panels. Papers and presentation videos are freely available online. The football track covers both academic research and industry applications. Excellent for understanding cutting-edge developments.

Selected Academic Papers

Burke, B. (2019). "DeepQB: Deep Learning for Quarterback Evaluation." MIT Sloan Sports Analytics Conference.

Early application of deep learning to quarterback evaluation using tracking data. Illustrates how advanced methods from Chapter 26 are being applied to football.

Yurko, R., Ventura, S., & Horowitz, M. (2019). "Going Deep: Models for Continuous-Time Within-Play Valuation of Game Outcomes in American Football." Journal of Quantitative Analysis in Sports.

Technical paper on continuous-time win probability models. Provides mathematical foundations for the win probability concepts in Chapter 15.

Eager, E. & Steele, C. (2020). "Evaluating NFL Draft Prospects." Journal of Sports Analytics.

Analysis of which college statistics predict NFL success. Relevant to Chapter 21 on draft evaluation.

Career Development

Breaking Into the Field

McIntyre, K. (2020). "How to Get a Job in Sports Analytics." The Athletic.

Practical advice from someone who has hired analysts. Covers portfolio building, networking, and interview preparation. Realistic about the challenges of breaking into the field.

Sports Analytics World Podcast

Regular interviews with working sports analysts across multiple sports. Valuable for understanding different career paths and what hiring managers look for.

Ongoing Learning

R/NFLAnalysis and r/NFLstatheads (Reddit)

Active communities discussing football analytics. Good for staying current with public analysis and asking questions. Quality varies, but active participants include published researchers.

Twitter/X Football Analytics Community

Many prominent football analysts are active on Twitter (Ben Baldwin @benbbaldwin, Seth Walder @SethWalder, Timo Riske @PFF_Moo). Following these accounts provides a real-time view of current analysis and debates.

Historical Context

The Evolution of Football Strategy

Brown, C. (2012). The Essential Smart Football. Self-published.

Chris Brown's analysis of football strategy and innovation. Provides context for how coaches think about the game, which is essential for analysts who want to communicate effectively with football people.

Billick, B. & Peterson, J. (2001). Developing an Offensive Game Plan. Coaches Choice.

Written by a Super Bowl-winning head coach, this book reveals how offensive coordinators think about game planning. Understanding this perspective helps analysts frame their work in ways that resonate with coaches.

The Analytics Movement

Silver, N. (2012). The Signal and the Noise. Penguin.

Nate Silver's book on prediction covers sports (including football) among other domains. The chapter on sports forecasting addresses many themes from this textbook, including the challenge of small samples and the importance of uncertainty quantification.

Reading Schedule Suggestion

Before Chapter 2: - Skim nflfastR/nfl_data_py documentation - Read one Football Outsiders article

Before Part II (Player Analytics): - Read Romer (2006) abstract and introduction - Explore Big Data Bowl winning submissions

Before Part IV (Predictive Modeling): - Begin Introduction to Statistical Learning (Chapters 1-3) - Read one academic paper from JQAS

Throughout the Course: - Follow 2-3 analysts on Twitter - Read one Football Outsiders or Athletic article per week

Good analysts never stop learning. These resources provide pathways for continued development well beyond this textbook.