Welcome to the NBA Regular Season 2018-19 Data Challenge project! In this project, we explore various aspects of the NBA regular season using data analysis. Here are the key questions we've answered:
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Average Age of Players:
- We calculated the average age of players in the league based on available data.
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Highest Scorer:
- Identified the player who scored the most points during the season.
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Most Blocks and Post Player Analysis:
- Determined the player with the most blocks and analyzed whether they were a post player (F/C).
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Title Contenders based on Win Percentage:
- Analyzed win percentages to identify teams with the best chance to win a title.
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Best 3-Point Shooter:
- Found the player with the best 3-point percentage.
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Player with Most Minutes Played:
- Identified the player who played the most minutes during the season.
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Clutch Player based on Player Efficiency Rating (PER):
- Determined the player with the highest Player Efficiency Rating, indicating clutch performance.
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Team with Youngest Roster:
- Identified the team with the youngest roster based on player ages.
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Highest Paid Player:
- Determined the highest-paid player during the season.
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Free Throw Line Unreliable Player:
- Explored which player you wouldn't want on the free throw line at the end of a game.
NBA_Exploration.ipynb: Jupyter Notebook containing the data analysis and code.nbastats2018-2019.csv: CSV file with the dataset used for analysis.
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Clone this repository:
git clone https://github.com/your-username/nba-2018-19-data-challenge.git