Coming soon · Book Profile
Sports Analytics in Practice with R
A hands-on, chapter-by-chapter guide to applying R programming and analytics techniques—from data visualization to machine learning, NLP, optimization, and exploratory analysis—across diverse sports contexts.
A profile of this book is on the way.
What it’s about
Sports Analytics in Practice with R teaches aspiring and practicing data scientists how to use R for real analytical problems using publicly available, outcome-known sports data. Because sports data is accessible and outcomes are public, it serves as an ideal learning ground for techniques that transfer far beyond sports. Each standalone chapter demonstrates a distinct method—geospatial baseball analysis, football-draft classification and clustering, logistic regression to explain basketball wins, cricket fan-sentiment NLP, fantasy-football lineup optimization, and opponent-scouting exploratory analysis—using varied datasets including Paralympic, women's, and international sports. The book deliberately favors clarity over code optimization, positioning analytics as a supplement to (not replacement for) human judgment, framing decisions as 'human over the loop.' Readers finish with a portfolio of reusable analytical tools and the conceptual grounding to extend them to any sport or domain.
The through-line
- Who it’s for
- An aspiring or practicing data scientist or sports fan who wants to apply analytics skills to real, outcome-known data.
- The problem
- They lack practical, end-to-end examples of how to obtain, clean, visualize, and model data using R. They feel intimidated by programming and unsure whether analytics has real value in domains where experts dismiss it.
- The plan
- Install R and RStudio and learn foundational objects, functions, and data types.
- Master visualization best practices and both static and interactive plots.
- Apply supervised and unsupervised models to player evaluation and outcome explanation.
- Use NLP to analyze fan sentiment and engagement in social media/forums.
- Employ simulation and linear optimization for lineup decisions.
- The payoff
- The reader confidently obtains, cleans, visualizes, and models sports data in R. · They produce persuasive data narratives that inform coaches, executives, and fans. · They can extend and adapt techniques across sports and non-sports domains.
See our guide
Additional reading
- Professional Practice in Sport Performance Analysis · Andrew D. Butterworth (Editor)
Cited frequently as a key resource for understanding the role of the analyst within interdisciplinary teams, career development, and the application of emerging technologies like multimedia profiles.
- Qualitative Diagnosis of Human Movement · Duane V. Knudson
Presented as the foundational text for the four-phase process of analyzing and coaching technique, a key component of performance analysis.
- Various works on coach recall and notational analysis · Ian M. Franks (often with M. Hughes or G. Miller)
The work of Franks is cited throughout as providing the original rationale for performance analysis (e.g., poor coach recall) and for foundational concepts in notational system design.
- The Book: Playing the Percentages in Baseball · Tango, T. M., Lichtman, M. G., and Dolphin, A. E.
Cited as a detailed treatment of how analytic methods can be used to answer specific questions about baseball strategy, representing a deep application of the book's principles.
- Scorecasting: The Hidden Influences Behind How Sports Are Played and Games Are Won · Moskowitz, T. J., and Wertheim, L. J.
Recommended for showing how analytic methods can be used to address a wide range of sports issues, similar to the scope of the author's own book.
- Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football · Winston, W. L.
Referenced for its applications of analytic methods to a variety of sports, including football-specific examples like expected points.
- The Drunkard's Walk: How Randomness Rules Our Lives · Mlodinow, L.
Suggested for its excellent non-technical description of the intuition behind probability and randomness, which is a foundational concept of the book.
- Statistical Methods for the Social Sciences · Agresti, A., and Finlay, B.
The author's favorite introductory statistics text, recommended for its wide range of topics and emphasis on applications closely related to those in sports.
- Baseball between the Numbers: Why Everything You Know About the Game Is Wrong · Keri, J. (Ed.)
Mentioned as a collection of interesting essays where analytic methods are used to study specific sports issues, such as comparing players from different eras.
- The Reflective Practitioner: How Professionals Think in Action · Donald A. Schon
Provides the foundational theory for experiential learning and reflective practice, which is presented in Chapter 5 as a crucial method for coaches to develop their knowledge.