https://github.com/harmanveer-2546/nba-homecourt-advantage-data-analysis

In this repo we're diving into NBA game data to explore the phenomenon of home court advantage.
https://github.com/harmanveer-2546/nba-homecourt-advantage-data-analysis

analysis homecourt learning-curve linear-models logisticregression metrics model-selection nba nba-analytics nba-stats numpy pandas preprocessing seaborn sklearn standardscaler

Last synced: 1 day ago
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In this repo we're diving into NBA game data to explore the phenomenon of home court advantage.

• Host: GitHub
• URL: https://github.com/harmanveer-2546/nba-homecourt-advantage-data-analysis
• Owner: harmanveer-2546
• Created: 2024-09-06T17:57:29.000Z (21 days ago)
• Default Branch: main
• Last Pushed: 2024-09-06T18:44:15.000Z (21 days ago)
• Last Synced: 2024-09-26T04:22:15.574Z (1 day ago)
• Topics: analysis, homecourt, learning-curve, linear-models, logisticregression, metrics, model-selection, nba, nba-analytics, nba-stats, numpy, pandas, preprocessing, seaborn, sklearn, standardscaler
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/wyattowalsh/basketball
• Size: 1.44 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NBA HOMECOURT ADVANTAGE 

 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.

 ### Model fitting :

 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.

 ### Conclusion and Recommendation :

 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.