John Kane - Building AI Trading Systems for Sports Prediction Markets

In this episode of Data Day with Greg Michaelson, Greg sits down with John Kane to explore the rapidly growing world of sports prediction markets and the AI systems powering next-generation quantitative trading.John explains how his company, Lazy Edge, applies quantitative finance techniques to live sports trading, using massive real-time data streams, simulation engines, and AI-driven modeling to identify market inefficiencies during games as they unfold.The conversation dives into:How prediction markets like Polymarket and Kalshi actually workWhy live sports trading resembles quantitative stock trading more than gamblingUsing simulations, AI agents, and “digital twins” to model game outcomesReal-time NBA event prediction and market movement analysisHow bots trade sports markets automatically through APIsThe future of prediction markets, regulation, and AI-driven trading systemsWhy chaos modeling matters in live game forecastingJohn also shares how tools like Zerve help accelerate experimentation and backtesting by allowing rapid iteration on complex predictive models and simulations.If you’re interested in AI, quantitative finance, trading systems, simulation modeling, or the intersection of sports and machine learning, this episode is a fascinating look at a space that’s evolving incredibly fast.