Abstract and foreword

I began writing this book during the COVID-19 pandemic. It began as an instruction manual for interns or trainees before tools like Anthropic’s Claude Code arrived on the scene. I wanted to give individuals just starting out in thier careers a practical introduction to the work of analytics in a sports organization. Generative A.I. has now revolutionized analytics work, but it is creating a gap that I think makes this book even more important than it would have been back in 2020. Understanding what you are doing is more important than ever, but these tools make analytics tasks a commodity. Strong programming and analytics skills are no longer a strategic moat for your career and are more accessible than ever. However, The goal of this text remains the same. The purpose is to help you understand the work, ask better questions, build useful analyses, and connect those analyses to business decisions.

A.I. has made a bigger difference in the technical work of analytics than anything I have seen in my career and I used both Claude and Codex to help clean and refine this text. However, all examples come from actual projects that were put together before these tools were available. I predict fundamental shifts in how organizations are structured. A.I. isn’t an I.T. issue, it is an org structure issue. There is a famous quote:

The future is already here, it just isn’t evenly distributed. - William Gibson

This is true of A.I. in sports organizations. Some teams are already using it to improve their work, some are not, all will eventually. While our business outcomes will not be dictated based on how well it is implemented, the teams that use it well will have a competitive advantage. The teams that don’t will be at a disadvantage. If nothing else, this book can give you a foundation for building business systems using these powerful new tools.

If you want to start with applied work, begin with chapter 5. The first four chapters build the foundation: how analytics fits into strategy, what the data looks like, how to explore it, and how to frame projects so the work answers a business question. The later chapters apply those ideas to segmentation, pricing, lead scoring, promotions, marketing mix modeling, analyzing media data, research, and operations.

This book was originally written for someone early in a sports business career who wants to work near analytics, strategy, business intelligence, CRM, or technology. That audience is still central. It is also written for managers and practitioners in ticketing, marketing, sponsorship, finance, research, operations, and service who need to use analytics without necessarily becoming full-time analysts. These functions overlap in practice. A good analyst needs business context, and a good manager needs enough analytical fluency to know what is possible, what is credible, and what is not worth doing.

Analytics work is always in flux as technologies become more powerful. Cloud data platforms, modern BI tools, machine-learning libraries, and generative AI have made many technical tasks easier to start. That does not make the work automatic. The hard parts are still defining the problem, understanding the data, choosing a reasonable method, checking whether the answer makes business sense, and communicating the result clearly. The person who can connect those pieces is valuable in any department.

This text serves four primary purposes:

1. To give you an analytically grounded toolbox that you can build upon
2. To teach you about how a sports team’s business works
3. To demonstrate how to apply this knowledge to real problems to achieve desired outcomes
4. To build a reference manual of solutions to common problems

The first three items are the foundation of analytics-supported strategy. You need experience to build effective strategy, but this book should give you a practical head start. You will not become an expert by reading it once. You should, however, become more comfortable with the types of problems sports organizations face, the kinds of data used to solve them, and the tradeoffs that shape analytical work.

If all you have is a hammer, everything looks like a nail.

This is the famous Law of the Instrument.¹ Analytics and data are not the answer to every question. Sometimes the right answer is a conversation with a sales manager, a better operating procedure, a cleaner customer record, a clearer offer, or a decision to stop measuring something that does not matter. Since this book shows how to do the work, it includes granular material. The details are intentional. They help you see how an analysis is built instead of only seeing the final chart.

Analytics is still a black box to many managers. It should not be. Consider this quote: “Expert Power is influence wielded as a result of expertise, special skill, or knowledge.” (Stephen P. Robbins 2012) Understanding analytical tools and techniques gives you a degree of expert power. If you manage analytical work without understanding the basics, you may struggle to evaluate recommendations, scope projects, or explain why a decision was made. You do not need to know every algorithm. You do need enough fluency to participate intelligently.

You do not need to be a software engineer to benefit from this book, but you will get more from it if you are willing to learn basic scripting. The examples are written in R (R Core Team 2025). R remains a strong language for statistics, visualization, and teaching data analysis. Python, SQL, spreadsheet tools, and BI platforms are also common in sports organizations. The specific tool matters less than the habit: structure the data, write reproducible work when possible, check the output, and explain the decision.

The R Language (my favorite tool although I use Python in conjunction with it) is excellent in some ways and unusual in others. It has idiosyncrasies that make it operate differently than many programming languages. If a concept is unfamiliar, do not stop. Keep moving, run the examples, and return to the details as needed. The point is not to memorize syntax. The point is to understand the workflow well enough to adapt it.

The book also teaches a little bit about the business of a professional sports team. Sports is unusual because many outcomes are outside the control of business staff. Wins, injuries, star players, weather, schedule quality, local competition, media attention, and brand history all affect demand. Your strategies need to account for that reality. If your team is rebuilding, if the schedule is weak, or if the market is changing, your sales and marketing strategy should change too.

We will frame strategy around goals. A club may care about ticket revenue, renewal rate, attendance, sponsorship value, fan experience, operating efficiency, media reach, or franchise valuation. These goals interact. A decision that helps short-term revenue may hurt long-term loyalty. A decision that improves fan experience may require operational investment. Analytics should help clarify those tradeoffs.

We are not going to cover every form of corporate strategy. We will not spend much time on ownership structures, media-rights entities, league governance, legal arrangements, or every possible business-development opportunity. Instead, we will focus on the practical strategy that connects the club to its fans and customers: selling, marketing, pricing, researching, servicing, and operating better.

From that perspective, we can create a provisional definition of strategy in this context:

Strategy is the discipline of coordinating decisions, systems, and resources so the organization can achieve its business goals more effectively.

That definition is intentionally practical. If you are skeptical of anything you find here, that is good. Analysts should demand to be convinced. You do not need to understand every mathematical detail before using a technique, but you do need to know what the technique is for, what assumptions it makes, and how it can mislead you. Think of an analyst or strategist as a mechanic for the business systems of a club. If everything always worked, you would not need them.

Chapter 1 covers the relationship between analytics, strategy, and technology. It also explains the distinction between Business Intelligence and Analytics and introduces a maturity model for building analytical capability.

Chapter 2 introduces the data used throughout the book by creating it. The formats resemble data you may encounter in the wild: customer records, ticketing data, demographic data, secondary-market data, and sales activity. The examples use an imaginary professional baseball team, but the lessons apply across sports and many other businesses.

Chapter 3 covers data exploration. You will build common graphs, summarize data, and learn how to look for structure before jumping to conclusions. BI tools such as Tableau, Power BI, Looker, and Qlik are useful, but code gives you reproducibility and flexibility that point-and-click tools often cannot match.

Chapter 4 explains how to frame projects. This is one of the most important skills in the book. A well-framed question saves time, reduces confusion, and gives the analysis a better chance of influencing a decision.

Chapter 5 demonstrates several methods for building customer segmentation schemes. Segmentation is central to integrated sales, marketing, service, and research strategy. You will also confront one of the most common analytical problems: missing data.

Chapter 6 covers pricing and forecasting. Pricing is complex, and many pricing systems are now vendor-supported or automated. You still need to understand the logic behind price recommendations, demand estimates, and forecasts. This chapter also goes deeper into regression.

Chapter 7 demonstrates methods for Lead Scoring. Lead scoring extends segmentation and helps sales and marketing teams prioritize effort. The chapter discusses common techniques and demonstrates a machine-learning model.

Chapter 8 looks at promotions. Promotions are a critical marketing tool, but they are easy to misread. This chapter discusses marketing strategy, offer design, and the difficulty of attributing sales to marketing activity.

Chapter 9 covers marketing mix modeling. When you cannot follow individual customers from an ad to a purchase, you can still measure marketing from the top down: regress sales on spend by channel, accounting for carryover and diminishing returns, to estimate what each channel returns and where the next dollar should go.

Chapter 10 covers analyzing media data. Media rights are among a club’s largest revenue lines, and the audience data comes in two very different forms: panel-based linear-television ratings from Nielsen and census-level direct-to-consumer streaming and subscription data. The chapter explains what each metric means, how to aggregate it, and how to interpret it without adding up numbers that should never be summed.

Chapter 11 covers research methods. Research can be tedious, but it is fundamental to strategy. This chapter introduces concepts such as survey design, hypothesis testing, and sampling. If you take one idea from the chapter, take this one: sampling matters, and it is easy to do poorly.

Chapter 12 covers operations. Simulation and queuing are introduced through stadium and ballpark problems. These methods are useful because operations problems often involve systems with many moving parts.

This book cannot be comprehensive. It is heavily concerned with sales and marketing because those functions sit close to the fan relationship and to several major revenue streams: tickets, concessions, retail, media, and sponsorship. If you do not understand how the club attracts, serves, retains, and monetizes fans, higher-order analytics projects will have a weak foundation.

Many related topics receive only limited attention: social data, retail, media-rights valuation, staffing, food and beverage pricing, sponsorship valuation, CRM strategy, gate entry, parking, digital rights, and league-level technology constraints. Those topics matter. They are also large enough to deserve their own treatments. The aim here is to give you a strong foundation that transfers across roles.

Finally, this book will age. Some tools, vendors, and workflows will change. That is unavoidable. The durable lessons are more basic: define the problem, understand the data, choose a method that fits, validate the result, communicate clearly, and connect the work to a decision. If you learn those habits, you will be able to adapt as the analytics space changes.

I hope this text gives you a deeper understanding of sports business analytics and enough practical confidence to improve the work around you.

Book License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Just give credit where it is due. Nothing is new, everything is built on those that came before you. We aren’t trying to advance theory here, just to show you how to do some of these things.

Code License

To the extent possible under law, Justin Watkins has waived all copyright and related or neighboring rights to this work. This work is published from: United States.

Do whatever you want with it.

Contact Information

Justin Watkins: watkinsjudo@gmail.com