Dataset Used: The NBA games dataset on Kaggle: https://www.kaggle.com/datasets/nathanlauga/nba-games?resource=download
This dataset contains data about NBA (National basketball Association) matches since the season 2004 to season 2022. Also, contains the information of the respective NBA players and their teams, statistics, maximum and minimum points scored by the players/teams, etc. There are five files in total:
• Games contain all games from 2004 season to 2022 season with the date, teams, and some details like number of points, etc.
• Games Details contain the records and details of the game’s dataset, all statistics of NBA players participated in the given seasons and for a given game.
• Players contains names of all the players and in which team they are present in most recent season available in the dataset.
• Ranking provides the list of everyday ranking of all the teams. It is dynamic and trends to change everyday based on the number of games won/lost.
• Teams contains various information with respect to the name of the team, year in which the team was founded, owner, manager, arena capacity, etc.
Project Rationale
Player performance: Analyze the statistics of individual players to identify the top performers in various categories, such as points per game, assists per game, rebounds per game, etc. This could help identify valuable players for fantasy basketball teams or for evaluating potential trades.
Team performance: Analyze team statistics to identify which teams are performing well overall, as well as which teams excel in specific areas such as defense or scoring. This could help you make predictions about which teams will perform well in the playoffs or in future seasons.
Player development: By analyzing player statistics over time, we can identify which players are improving or declining in performance. This could help teams make decisions about which players to invest in, as well as help fans and analysts understand the trajectory of a player's career.
Trends and patterns: Analyze the data to identify trends and patterns in player or team performance over time, such as changes in playing style or the impact of rule changes. This could help you make predictions about future performance or identify areas for further research.
This project demonstrates the team’s skills in the following topics:
• Real-world data fields are arranged into an ER diagram.
• Establishing and populating a SQL database with DDL and DML commands.
• Finding significant trends and patterns from the SQL queries.