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Explore European basketball analytics through this 42-minute conference talk that introduces an open-source Python library for extracting and analyzing Euroleague basketball data. Learn about the current state of sports analytics in European basketball compared to the NBA, and discover the types of data available for analysis. Examine real-world use cases for generating player and team insights, and understand how knowledge graphs can provide new dimensions for extracting insights about teams and players. Discover a simulation framework that models league games and projects team performances throughout the season using Elo ratings alongside defensive and offensive metrics. Gain practical knowledge about applying data science techniques to European basketball analytics, making this session valuable for those interested in sports analytics, Python programming, and data visualization at an introductory technical level.
