With a dataset containing over 65,000 game records per column, we'll be looking further into key variables such as team identifiers, game dates, final scores, and whether the game was played at home or away for each team. By analyzing these factors, we aim to uncover insights into the influence of home court advantage on game outcomes, providing valuable insights for basketball enthusiasts and teams alike.
The model fitting aimed to predict the outcome of NBA games based on the performance of home and away teams. We used logistic regression to classify whether the home team would win or lose, with input variables including team_id_home, pts_home, team_id_away, and pts_away. These variables were chosen because they directly represent the teams involved and their respective points scored in each game, which are critical factors influencing the game's result.
Our analysis confirms that home court advantage significantly influences NBA game outcomes, with home teams often scoring more points and winning more games compared to away teams. Visualizations such as the point distribution and win-loss ratio clearly demonstrate this trend. Therefore, teams should focus on optimizing their strategies and performance for home games to maximize their chances of winning. Additionally, further research could explore specific factors contributing to home court advantage, such as crowd support and travel fatigue. Understanding these factors can help teams better prepare for both home and away games.