2021Unpublished venueOpen access

Big Data in Sports: Predictive Models for Basketball Player's Performance

Dae‐Jin Lee, Garritt L. Page

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Abstract

Aryuna is a platform that allows to perform advanced data analytics of men's professional basketball statistics of the last 16 seasons in more than 25 professional leagues and 71 FIBA tournaments. The complete database consists of more than 37,000 games and upwards of 20,000 players. Based on a historical database, the report aims to: characterize the performance curve, peak and optimal age in professional men's basketball using performance ratings of players in top basketball leagues; determine a rating correction factor for different basketball leagues, which accounts for intra-league and cross-league variability as well as for player characteristics (position, age, player ratings, etc.); determine which are the most important factors for predicting future outcomes of a basketball player.

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Aryuna is a platform that allows to perform advanced data analytics of men's professional basketball statistics of the last 16 seasons in more than 25 professional leagues and 71 FIBA tournaments. The complete database consists of more than 37,000 games and upwards of 20,000 players. Based on a historical database, the report aims to: characterize the performance curve, peak and optimal age in professional men's basketball using performance ratings of players in top basketball leagues; determine a rating correction factor for different basketball leagues, which accounts for intra-league and cross-league variability as well as for player characteristics (position, age, player ratings, etc.); determine which are the most important factors for predicting future outcomes of a basketball player.

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Available abstract

Aryuna is a platform that allows to perform advanced data analytics of men's professional basketball statistics of the last 16 seasons in more than 25 professional leagues and 71 FIBA tournaments. The complete database consists of more than 37,000 games and upwards of 20,000 players. Based on a historical database, the report aims to: characterize the performance curve, peak and optimal age in professional men's basketball using performance ratings of players in top basketball leagues; determine a rating correction factor for different basketball leagues, which accounts for intra-league and cross-league variability as well as for player characteristics (position, age, player ratings, etc.); determine which are the most important factors for predicting future outcomes of a basketball player.

Key concepts: Basketball, League, Analytics, Big data, Position (finance), Psychology, Applied psychology, Computer science

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