Data Management & Engineering for Sports Betting
Leverage the power of data and code to implement, manage, automate, and execute algorithms for scalable sports betting. Data Management & Engineering for Sports Betting is organized as a two-part program. Part 1 focuses on scraping, parsing, and databases, covering data acquisition, extraction, storage, Python, Excel, SQL, CSV, XML, JSON, and HTML workflows. Part 2 moves into automation, execution, and bots, with GitHub, Selenium, cron jobs, APIs, authentication, scripts, and end-to-end operational case studies. Enrollment is rolling and built for students who want to turn raw data into reliable betting infrastructure.About
What you'll learn
Skills you'll gain
Details to know
Data Management & Engineering for Sports Betting is structured as a two-part curriculum: Scraping, Parsing and Databases, followed by Automation, Execution and Bots. Students begin by learning how to gather, clean, organize, and store betting data across multiple formats and environments, then progress into production-minded engineering topics such as version control, scaling workflows, browser automation, APIs, permissions, and full end-to-end operational systems. The course is designed for serious bettors and builders who want dependable technical infrastructure behind their betting process.Curriculum
A two-part curriculum that teaches you how to collect, structure, transform, and operationalize data so your betting workflows become scalable, automated, and production-ready.Part 1: Data Acquisition
Methods of data acquisition including manual collection, scraping, APIs, and historical datasets. Includes an NFL beat-the-spread example, Google Sheets `IMPORTHTML` and `QUERY`, Microsoft Excel web imports and `XLOOKUP`, and data-validation practices for betting workflows.
Part 1: Scraping
An NBA data example and an introduction to Python, including installation, terminal basics, Jupyter notebooks, modules, variables, lists, and dictionaries, along with HTTP requests and practical spreadsheet shortcuts such as Flash Fill in Excel.
Part 1: Data Extraction
Parsing CSV, XML, JSON, and HTML sources with tools such as BeautifulSoup and regular expressions, while addressing common data-cleansing issues during ingestion.
Part 1: Data Management
File management, cloud versus local storage, files versus databases, Postgres and pgAdmin, database types, schemas, primary keys, indexes, SQL, queries, joins, views, extracting from databases, exporting to CSV, connecting to Excel, SQL libraries for Python, building NBA box-score databases, reparsing, and the challenges of play-by-play database construction.
Part 1: Data Transformation
Populating databases, data visualization, exploratory data analysis, common data-cleaning issues, outlier detection, and indexing across different data sources.
Part 1: Case Study
A golf data case study covering scraping, parsing, database loading, management, transformation, and downstream analysis in a practical end-to-end workflow.
Part 2: Building a Robust, Reliable, Scalable Operation
Production versus staging versus development environments, GitHub workflows, unit tests, docstrings, comments, and the engineering discipline needed for maintainable betting systems.
Part 2: Automation and First Steps Toward Scaling
Batch processing, caching, storage considerations, naming conventions, and cron-based scheduling for repeatable and scalable sports betting workflows.
Part 2: Data Professionalism
JavaScript and password-protected sites, Selenium, and the professional habits needed to automate responsibly while working with brittle or access-controlled data sources.
Part 2: Data Collection Bots I
Building a line-shopping tool and comparison tool as a practical introduction to collection bots that pull, normalize, and compare sportsbook information.
Part 2: Data Collection Bots II
APIs, GET and POST requests, authentication and permissions, tracking systems, big red shutdown buttons, circuit breakers, and the relationship between betting automation and high-frequency equity or crypto trading systems.
Part 2: Guest lecture by Captain Jack Andrews
An end-to-end case study combining APIs and exchange-trading style execution, showing how engineering discipline and automation come together in real-world betting infrastructure.
Instructors
Learn From People Who Actually Beat the Market
Harry Crane
Professor of Statistics · Quantitative Betting Expert · Industry LeaderAssociate Professor and Chancellor's Excellence Scholar in Statistics, Co-Director of the Graduate Program in Statistics, Member, CFTC Innovation Advisory Committee, Board Member, American Bettors' Voice, Advisor, Prediction Markets Research Consortium and Affiliated Faculty in the Graduate Program in Philosophy at Rutgers University.
He is currently Fellow at the London Mathematical Laboratory, and has previously held positions as a Visiting Scholar in Mathematics at UC Berkeley, Research Associate at the RAND Corporation, and Research Fellow at the Foreign Policy Research Institute. He is also a co-founder of Researchers.One, a platform for scholarly publication and initiative for intellectual reform.
Harry received his PhD in Statistics from the University of Chicago and BA in Mathematics, Economics and Actuarial Science from the University of Pennsylvania.
He has profitably applied statistical and other techniques to successful sports betting and other advantage gambling opportunities and has discussed these experiences on the Business of Betting podcast, the Pinnacle podcast, the Political Trade Podcast, the Artful Trader, Old Bull TV, and other media outlets.
He is the author of Probabilistic Foundations of Statistical Network Analysis.
Philip Maymin
Professor of Analytics · Quantitative Researcher · Industry ExecutiveProfessor of analytics and the director of the Master of Science in Business Analytics program at the Fairfield University Dolan School of Business where among other things he teaches both an undergraduate sports analytics course and a graduate sports analytics course. He is the founding managing editor of Algorithmic Finance and the co-founder and co-editor-in-chief of the Journal of Sports Analytics. He is the Chief Technology Officer and Chief Operating Officer for Swipe.bet, an Insight Partner with Essentia Analytics, an advisor to Athletes Unlimited, and an affiliate of the Langer Mindfulness Institute, and has been an analytics consultant with several NBA teams.
He holds a PhD in Finance from the University of Chicago, a Master's in Applied Mathematics from Harvard University, and a Bachelor's in Computer Science from Harvard University. He also holds a J.D. and is an attorney-at-law admitted to practice in California. He has been a portfolio manager at Long-Term Capital Management, Ellington Management Group, and his own hedge fund.
He was awarded a Wolfram Innovator Award in 2015. He has won numerous coding challenges and hackathons. He was named one of the Top 50 Data and Analytics Professionals in the US and Canada in 2018. He is the only person to have won both the Grand Prize for Best Research Paper (2018) and the Hackathon (2020) at the MIT Sloan Sports Analytics Conference.
He is the author of Financial Hacking.
What Our Students Say
Real feedback from bettors who apply analytics in real markets.Frequently Asked Questions
Answers to common questions about course access, learning flow, and how Analytics.Bet training works in practice.What We Teach
Our curriculum spans the full spectrum of sports betting mastery - from foundational bankroll-building techniques that require nothing more than basic arithmetic, to advanced regression, machine learning, closing line value, arbitrage, hedging, backtesting, and automated execution. All courses include code, data, and instructor-built tools, with lifetime access.
What You Get
Members join a professional network of students, alumni, faculty, industry leaders, and legendary sports bettors. It opens up career opportunities, and eligibility to contribute to the Analytics.Bet blog and the Journal of Sports Betting.
Our Standard
We do not sell picks or promises. We teach process - the same process professionals use to maintain an edge over time. If you are ready to bet smarter, you are in the right place.
What makes Analytics.Bet different from other betting education sites?
Analytics.Bet is built around decision-making, modeling, and repeatable process rather than hype. Students learn how to price markets, evaluate risk, track performance, and build evidence-based betting systems instead of following tip sheets.
Will this guarantee profits?
No course can guarantee profits, and any honest sports betting education platform should say that clearly. What we provide is a professional framework for improving your edge, reducing avoidable mistakes, and making better long-term betting decisions.
Are these courses beginner-friendly if I am new to betting analytics?
Yes. The curriculum is designed so motivated beginners can start with foundational concepts like probability, bankroll management, and market mechanics before moving into advanced modeling, automation, and quantitative strategy.
Do I need to know coding or advanced math before enrolling?
No. Some advanced lessons use code, data, and more technical methods, but the learning path starts with practical concepts that do not require programming. As you progress, the platform helps you build the analytical skills needed for more sophisticated work.
Who are the courses best suited for?
These courses are best for serious sports bettors, analysts, aspiring quants, and operators who want to move from intuition to structured decision-making. They are especially valuable for people who care about market efficiency, model validation, and long-term expected value.