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Learn to build an automated NFL prediction market trading bot using Python by examining the complete architecture and implementation of a real-world system that trades on Kalshi during live games. Explore the technical challenges of parsing ESPN's unofficial API for real-time play-by-play data and win probability calculations, while understanding how to structure a bot that monitors NFL games and executes trades based on probability spikes. Discover the integration of websocket clients for real-time odds tracking, environment variable management, and the critical importance of data latency in algorithmic trading. Analyze the economic viability of such systems by examining actual trading scenarios during an NFL playoff game between the Seahawks and 49ers, including how small probability changes, execution speed limitations, and trading fees impact profitability. Understand the data latency hierarchy from official APIs to television broadcasts and evaluate whether prediction market bots can be profitable given current technological constraints and market conditions.
