Building Real-Time Sport Model Insights with Spark Structured Streaming
Description
In the dynamic world of sports betting, precision and adaptability are key. Sports traders must navigate risk management, limitations of data feeds, and much more to prevent small model miscalculations from causing significant losses. To ensure accurate real-time pricing of hundreds of interdependent markets, traders provide key inputs such as player skill-level adjustments, whilst maintaining precise correlations. Black-box models aren’t enough— constant feedback loops drive informed, accurate decisions. Join DraftKings as we showcase how we expose real-time metrics from our simulation engine, to empower traders with deeper insights into how their inputs shape the model. Using Spark Structured Streaming, Kafka, and Databricks dashboards, we transform raw simulation outputs into actionable data. This transparency into our engines enables fine-grained control over pricing― leading to more accurate odds, a more efficient sportsbook, and an elevated customer experience. Talk By: Aaron Hope, Lead Machine Learning Engineer, Draftkings ; Ethan Summers, Lead Data Science Engineer, Draftkings Here’s more to explore: Production ready data pipelines for analytics and AI: https://www.d…
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