This project, conducted by Jack Rowlands, investigates the homecourt advantage in NBA games, analyzing differences in team performance between home and away games and identifying key factors contributing to this phenomenon. The study spans data from 2004 to 2020, focusing on statistical evidence to support the existence and implications of homecourt advantage.
The analysis leverages a detailed dataset covering games from 2004 to 2020, sourced from reputable platforms like Kaggle, NBA API, and Basketball Reference. This dataset includes comprehensive in-game statistics, attendance records, and foul data to facilitate a multifaceted analysis.
The project's analysis is implemented through a Jupyter Notebook, employing Python for data manipulation and statistical analysis. Here are some highlights of the approach and tools used:
- Libraries: The script uses Pandas for data handling, Matplotlib and Seaborn for data visualization, and SciPy for statistical tests, including chi-square, independent t-tests, and Pearson correlation.
- Data Processing: The script begins with loading the data, followed by calculating win flags for home and away teams to assess win rates.
- Statistical Analysis: Various statistical tests are conducted to examine differences in team performance, with visualizations highlighting key findings such as overall win rates for home and away teams.
- Home teams show statistically significant higher win rates compared to away teams, underlining the impact of homecourt advantage.
- Field goal performance differs with homecourt advantage, unlike free throw percentages, suggesting specific aspects of gameplay are affected.
- The analysis also explores the relationship between attendance and performance, alongside foul call biases against away teams.
The analysis reaffirms the significance of homecourt advantage in the NBA, highlighting its potential impact on playoff strategies and game outcomes. It underscores the need for teams to leverage home games effectively.
Future research could delve into player-specific performance analysis and the psychological aspects of homecourt advantage to further understand its dynamics.
To run this analysis, ensure you have Python installed along with the required libraries: Pandas, Matplotlib, Seaborn, and SciPy. Clone the repository and run the Jupyter Notebook to replicate the findings or explore further.
Gratitude to Kaggle, NBA API, and Basketball Reference for providing the data that enabled this comprehensive analysis.