Chapter 28 Further Reading: Building an Analytics Department

Books

Football Analytics and Data-Driven Management

  1. Anderson, C., & Sally, D. (2013). The Numbers Game: Why Everything You Know About Football Is Wrong. Penguin Books. - A foundational text on applying statistical thinking to football, covering topics from match prediction to the economics of the transfer market. Essential reading for anyone building an analytics function.

  2. Biermann, C. (2019). Football Hackers: The Science and Art of a Data Revolution. Blink Publishing. - Profiles the individuals and clubs at the forefront of football's data revolution, including detailed accounts of analytics operations at clubs like Brentford, FC Midtjylland, and others.

  3. Tippett, J. (2019). The Expected Goals Philosophy. Independently published. - A practical guide to understanding and applying expected goals models, with insights into how data-driven clubs have used xG to gain competitive advantage.

  4. Kuper, S., & Szymanski, S. (2018). Soccernomics. Nation Books (5th edition). - The economics of football through an analytical lens, including chapters on player valuation, transfer market efficiency, and the financial impact of data-driven management.

  5. Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. W. W. Norton. - While focused on baseball, this remains the seminal account of how data-driven decision-making can disrupt professional sports. Required reading for understanding the cultural dynamics of analytics adoption.

  6. Ankersen, R. (2012). The Gold Mine Effect: Crack the Secrets of High Performance. Icon Books. - Written by the chairman of FC Midtjylland, this book explores the environments and cultures that produce elite performers, providing context for the data-driven approach FCM later adopted.

Analytics Management and Organizational Design

  1. Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Press. - The definitive guide to building an analytics-driven organization. Although not sports-specific, the frameworks for organizational design, talent management, and stakeholder engagement are directly applicable.

  2. Davenport, T. H., & Harris, J. G. (2017). Competing on Analytics: Updated, with a New Introduction. Harvard Business Review Press. - Updated edition reflecting the evolution of analytics from competitive advantage to competitive necessity, with new case studies and frameworks.

  3. Provost, F., & Fawcett, T. (2013). Data Science for Business. O'Reilly Media. - Excellent primer on the principles of data science from a business perspective, useful for analytics leaders who need to bridge the gap between technical teams and organizational stakeholders.

  4. Patil, D. J., & Mason, H. (2015). Data Driven: Creating a Data Culture. O'Reilly Media.

    • A concise guide to building data-driven organizational cultures, covering topics from hiring to stakeholder management to measuring impact.

  5. Laursen, G. H. N., & Thorlund, J. (2010). Business Analytics for Managers: Taking Business Intelligence Beyond Reporting. Wiley.

    • Practical guide to managing analytics functions, including governance frameworks, ROI measurement, and organizational design patterns.

Academic Papers and Research

Sports Analytics Organization and Management

  1. Alamar, B., & Mehrotra, V. (2011). "Beyond Moneyball: The Rapidly Evolving World of Sports Analytics." Analytics Magazine, September/October.

    • Overview of the expanding scope of sports analytics beyond player valuation to include operations, fan engagement, and organizational design.

  2. Rein, R., & Memmert, D. (2016). "Big Data and Tactical Analysis in Elite Soccer: Future Challenges and Opportunities for Sports Science." SpringerPlus, 5(1), 1410.

    • Discusses the implications of big data for tactical analysis in football, including organizational requirements for effective implementation.

  3. Goes, F. R., Meerhoff, L. A., et al. (2021). "Unlocking the Potential of Big Data to Support Tactical Performance Analysis in Professional Soccer." European Journal of Sport Science, 21(4), 481-496.

    • Examines the practical challenges of implementing big data analytics in professional football, including infrastructure requirements and workflow design.

  4. Morgulev, E., Azar, O. H., & Lidor, R. (2018). "Sports Analytics and the Big-Data Era." International Journal of Data Science and Analytics, 5(4), 213-222.

    • Reviews the state of sports analytics, including discussion of organizational structures and technology requirements.

Decision-Making and Organizational Behavior

  1. Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A Flaw in Human Judgment. Little, Brown Spark.

    • Explores the role of noise (unwanted variability) in professional judgment, with direct implications for why structured analytical processes improve decision-making in football.

  2. Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown.

    • Research on what makes some people better forecasters than others, with applications to the prediction tasks central to football analytics (match outcomes, player development, transfer success).

Industry Reports and Whitepapers

  1. 21st Club (various years). Research publications on club management, analytics adoption, and organizational best practices.

    • Industry-specific research from one of the leading football consultancies, covering topics from manager selection to recruitment efficiency.

  2. FIFA / Big Count Reports. Global football participation and industry statistics.

    • Useful context for understanding the scale and economics of professional football worldwide.

  3. Deloitte Annual Review of Football Finance.

    • Comprehensive financial analysis of the football industry, providing context for analytics budget decisions and ROI calculations.

Online Resources and Communities

Blogs and Websites

  1. StatsBomb (statsbomb.com)

    • Industry-leading analytics company whose blog and public research set the standard for football analytics communication. Their data specification documents are invaluable for understanding data architecture.

  2. The Athletic --- Analytics Coverage

    • Premium sports journalism with regular, high-quality football analytics content, including profiles of analytics departments and practitioners.

  3. Soccerment (soccerment.com)

    • Football analytics content including research papers, data visualizations, and industry commentary.

  4. Football Slices / McKay Johns

    • Data visualization and analytics content focused on making complex analysis accessible and visually compelling.

Conferences and Events

  1. OptaPro Analytics Forum (now Stats Perform)

    • Annual conference bringing together football analytics practitioners, researchers, and industry leaders. Presentations are often published online.

  2. StatsBomb Conference

    • Annual event featuring cutting-edge football analytics research and practitioner presentations.

  3. MIT Sloan Sports Analytics Conference

    • The largest sports analytics conference globally, featuring football research alongside other sports. Proceedings available online.

  4. SSAC (Sports Science and Analytics Conference) --- Various regional events

    • Smaller, focused events that provide networking opportunities and practical insights.

Podcasts

  1. The Scouted Football Podcast --- In-depth tactical and analytical football discussion.

  2. Tifo Football Podcast --- Accessible football analytics and tactical analysis.

  3. Double Pivot Podcast --- Analytics-focused discussion covering methodology and industry developments.

  4. Expected Value --- Sports analytics industry discussion including career advice and organizational insights.

Technical Resources

Programming and Data Science

  1. McKinney, W. (2022). Python for Data Analysis. O'Reilly Media (3rd edition).

    • The definitive guide to pandas and data analysis in Python, essential for any football analytics practitioner.

  2. VanderPlas, J. (2023). Python Data Science Handbook. O'Reilly Media (2nd edition).

    • Comprehensive coverage of the Python data science stack including NumPy, pandas, matplotlib, and scikit-learn.

  3. Klosterman, M. (2020). Friends of Tracking YouTube Series.

    • Video tutorials on football analytics using Python, covering event data, tracking data, and model building.

Data Engineering and Infrastructure

  1. Reis, J., & Housley, M. (2022). Fundamentals of Data Engineering. O'Reilly Media.

    • Comprehensive guide to building data infrastructure, directly applicable to the technology stack discussion in Section 28.3.

  2. Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit. Wiley (3rd edition).

    • Foundational text on data warehouse design, relevant to building the centralized data repositories described in this chapter.

For readers new to the topic, we suggest the following sequence:

  1. Start with Moneyball (Lewis) and The Numbers Game (Anderson & Sally) for foundational context
  2. Read Football Hackers (Biermann) for industry-specific case studies
  3. Study Competing on Analytics (Davenport & Harris) for organizational frameworks
  4. Explore Data Driven (Patil & Mason) for practical culture-building advice
  5. Follow StatsBomb and OptaPro/Stats Perform content for current industry developments
  6. Attend at least one analytics conference (in person or virtually) for networking and emerging trends

For practitioners already working in football analytics, we recommend focusing on the organizational behavior and management literature (items 7-11, 16-17), as the technical skills are typically stronger than the organizational and communication skills in most analytics teams.