This project was a term project for STAT250, Applied Statistics, course from METU. The main aim of this project was to analyze and interpret the factors affect the points earned by NBA teams and players. Throughout the project, we conducted Exploratory Data Analysis (EDA), enhancing our proficiency in Python and R programming languages.
Data was taken from the NBA offical website and transferred to an Excel file. The variables we needed were written in the excel file. The data was checked first. Since there was no need for data cleaning the data was used directly. The excel file used in this project has been uploaded.
The dataset consists of 19 variables including 3 categorical and 16 numerical. However, 10 of them are used during this project. Here they are:
PLAYER – players’ name TEAM – team names that players play POSITION – the position of the players AGE – age of the players GP – the number of games played W – the percentage of games played that a player has won L – the number of games lost by a player MIN – the number of minutes played by a player PTS – the number of points scored FGM – the number of field goals that a player has made. This includes both 2 pointers and 3 pointers FGA – the number of field goals that a player has attempted. This includes both 2 pointers and 3 pointers OREB – the number of rebounds a player has collected while they were on offense DREB – the number of rebounds a player has collected while they were on defense REB – the number of total rebounds a player has collected on either offense or defense AST – the number of assists -- passes that lead directly to a made basket -- by a player TOV – a turnover occurs when the player or team on offense loses the ball to the defense STL – number of times a defensive player or team takes the ball from a player on offense, causing a turnover BLK – a block occurs when an offensive player attempts a shot, and the defense player tips the ball, blocking their chance to score FGper – the percentage of field goal attempts that a player makes
In this project, we developed our skills in data visualization and the interpretation of statistical data.