Discover how to analyze player statistics using Python. This course will demonstrate a unique project that selects the best players for the season using key basketball metrics.
Analyzing player statistics to build a competitive team is both engaging and relevant for data professionals interested in sports analytics. In this course, Data Show and Tell: Crafting the Ideal Basketball Team with Python, you'll see how to fetch player statistics, clean and filter data, and apply criteria to identify the top performers for each position. You’ll learn the significance of key metrics like points per game (PTS), rebounds per game (TRB), assists per game (AST), steals per game (STL), blocks per game (BLK), field goal percentage (FG%), three-point percentage (3P%), and free-throw percentage (FT%). This hands-on demo aims to equip you with the knowledge to make informed analyses for building a competitive team. The course also includes visualizing team strengths using a pie chart for a comprehensive understanding.
Analyzing player statistics to build a competitive team is both engaging and relevant for data professionals interested in sports analytics. In this course, Data Show and Tell: Crafting the Ideal Basketball Team with Python, you'll see how to fetch player statistics, clean and filter data, and apply criteria to identify the top performers for each position. You’ll learn the significance of key metrics like points per game (PTS), rebounds per game (TRB), assists per game (AST), steals per game (STL), blocks per game (BLK), field goal percentage (FG%), three-point percentage (3P%), and free-throw percentage (FT%). This hands-on demo aims to equip you with the knowledge to make informed analyses for building a competitive team. The course also includes visualizing team strengths using a pie chart for a comprehensive understanding.
