Pedro Tabacof
Pedro Tabacof is based in Dublin and is currently a staff Machine Learning scientist at Intercom. Previously, he has worked at Wildlife Studios (mobile gaming), Nubank (fintech), iFood (food delivery app). He has used and deployed machine learning models for anti-fraud, credit risk, lifetime value and marketing attribution, using XGBoost or LightGBM in almost all cases. Academically, he has a master's degree in deep learning and 400+ citations.
Sessions
People have been using machine learning for sports betting for decades. Logistic regression applied to horse racing made someone a multi-millionaire in the 80s. While fun, betting is a losing proposition for most. The house always wins, right?
With a friend, I thought we could beat the house in e-sports by leveraging modern ML tools like LightGBM. E-sports betting is less sophisticated than football or horse racing i.e. the market is less efficient. There is a lot of online data and unknown teams. It was a space ripe for money-making, or so we thought.
First, I will explain the theory behind e-sports betting with ML: what is an edge, financial decision-making, the expected value and decision rule for one bet, multiple bets with the Kelly criterion, probability calibration and the winner's curse.
Then, I will explain how we built a web scraper to extract features, developed a probabilistic classifier using LightGBM, defined betting rules using the Kelly criterion, backtested it with a positive ROI, and then lost actual money, with many priceless lessons coming out of it.