Applied Analytics & Sports Betting
1. Develop and learn to implement a process-driven analytics-based approach to sports betting.
2. Learn widely applicable technical, statistical, and practical skills.
3. Network with other alumni on private Slack workspace during and after the course is over.
4. Access to exclusive data and code repository based on course material.
5. Complimentary instant access to Foundations of Sports Betting upon enrollment.
6. Live office hours and group discussions with occasional special guests. Past special guests have included Bill Benter, Gadoon “Spanky” Kyrollos, and Matt “PlusEV Analytics” Buchalter. The February 2022 cohort will have a special guest lecture by “JM” — a renowned sports better and market maker leading a dynamic licensed Maltese syndicate that is set up to specialize in US sports trading.
Data will include historical line data from various sports provided by TXODDS, the official data partner of Analytics.Bet LLC.
Harry Crane is Associate Professor and Chancellor’s Excellence Scholar in Statistics, Co-Director of the Graduate Program in Statistics, 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 is a professor 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.
This course has been attended by seasoned professionals, betting novices, and everywhere in between. Students of all backgrounds found the course valuable to enhancing their approach to profitable betting. Here is what some have said:
Here is a very short excerpt from Lecture 1 of the course on reverse engineering alternative lines for NBA totals.
No technical background required. All necessary skills beyond high school level mathematics will be taught as needed during the course.
Techniques will be demonstrated across different domains (major and minor sports) and using different technical concepts and tools, including Excel, Python and other platforms (based on class interest and prior preparation).
There are many ways to win in sports betting. This course will equip you with the tools, techniques, and thought process you need to create your own approach.
Betting Tactics and Angles
• Scalping and arbitrage: consensus line, sharp vs. square books, stale lines
• Middling: basics, how to evaluate a middle, value of points and half points, converting moneyline to spread and spread to moneyline
• Prop betting: mean vs. median, skewness, Poisson and binomial distribution
• Parlays and teasers: Сorrelation, multiplicative advantage/disadvantage
• In-play betting: Hedging strategies, execution time, practical considerations
• Calculating EV and edge
• Top-down vs. Bottom-up vs. Hybrid: market efficiency, modeling, what info is priced into the line and what isn’t.
• Implied probabilities
• Converting odds (decimal, American) to breakeven probabilities
• Calculating bookmaker hold and synthetic hold
• Computing parlay payouts and multiplicative advantage
• How to develop a backtesting framework
• Caveats of backtesting
• Relationship between backtesting and live betting: when to skip backtesting, when to override backtested recommendations
• Statistics: discrete and continuous distributions (Gaussian, Binomial, Poisson), Bayesian models, parameter uncertainty, modeling dependence, machine learning, deep learning
• Practical: fixing bugs, plugging holes in process, finding flaws in approach, cleaning data, backtesting, live betting (difference between development and production), computational time/efficiency, bankroll management
• Technical: web scraping, data cleaning, databases, and dealing with big data
• Modeling: how to search for edges, how to know if you found an edge, what to do when you think you found an edge, and how to best execute the edge
Evaluation and Execution
• Closing Line Value (CLV)
• Optimal betting, Kelly criterion (simple and generalized)
• How to know when you model is ready for live betting?
• When to bet: betting opening lines at lower limits vs. waiting for higher limits are sharper lines
• Root mean squared error
• Power scores
• Elo ratings
• NBA alternative spreads and totals
• MLB season-long league leaders and team win totals
• NFL props and season-long wins
• NHL alternative totals, player props and season-long points totals
• NBA hybrid models
• Line movements in parimutuel horse race betting
Registration has now closed. The next cohort of this course will be offered in 2022.