Basketball Analysis & Visualizations

The Problem

A prepared dataset is useful only when its contents can be interpreted. With hundreds of individual player records and numerous performance metrics, reviewing rows and summary tables alone makes it difficult to recognize meaningful relationships, unusual observations, and broader patterns.

The analysis needed to answer a central question: how did individual player performances compare across the 2023–24 NCAA women’s basketball season, and which statistics made exceptional seasons stand out?

The Approach

I began with the cleaned and feature-engineered dataset produced by the earlier pipeline stages. The analysis used descriptive statistics, correlations, distributions, and exploratory plots to understand the available variables and identify useful comparisons.

Those findings were then translated into a combination of static and interactive visualizations. Each chart was selected to answer a particular question, such as how two statistics relate, how player performance is distributed, or whether an observation appears unusual compared with the rest of the dataset.

The Solution

The resulting workflow combines numerical summaries with visual analysis. pandas and NumPy are used to inspect distributions, select variables, filter player records, and calculate supporting values.

Correlation matrices, heatmaps, and pairwise comparisons help reveal relationships between player statistics. Additional charts compare performance across players, conferences, and statistical categories while making potential outliers easier to recognize.

Static visualizations created with matplotlib and seaborn support detailed analysis and reproducible reporting. Interactive Plotly charts provide another way to explore individual observations and compare players within the larger dataset.

Analysis and Visualizations

The exploratory analysis included statistical summaries, feature comparisons, correlation matrices, heatmaps, and pairwise plots. These steps helped identify variables with useful relationships and provided context for interpreting individual player performance.

The final visualization workflow included several chart types suited to different analytical questions:

• Scatterplots for examining relationships between numerical statistics
• Lines of best fit for showing broader trends within scatterplots
• Heatmaps for presenting correlations between multiple variables
• Histograms and violin plots for reviewing distributions and unusual observations
• Bar charts for comparing players or grouped categories
• Interactive Plotly charts for inspecting individual data points in greater detail

Project Structure

This page focuses on the exploratory analysis and visualization notebooks within the larger basketball data science repository.

ncaa-basketball-stats/
├── data/
│   └── player_data_engineered.xlsx
├── notebooks/
│   ├── 4_data_exploration.ipynb
│   ├── 5_data_visualization.ipynb
│   └── iframe_figures/
├── README.md
├── LICENSE
└── .gitignore

Both notebooks use the feature-engineered dataset produced by the pipeline. The exploration notebook investigates the variables and their relationships, while the visualization notebook turns those findings into focused static and interactive charts.

Tools Used

• Python for analysis and visualization logic
• JupyterLab for developing and documenting the workflow
• pandas for loading, filtering, summarizing, and comparing data
• NumPy for numerical calculations
• matplotlib for foundational static charts
• seaborn for statistical plots and heatmaps
• Plotly for interactive visualizations
• SciPy for supporting statistical calculations such as lines of best fit

Where the Analysis Leads

The analysis provides a clearer understanding of the dataset before more advanced modeling begins. The identified relationships and selected variables can inform feature selection, model design, and later evaluation.

The same workflow can be adapted to business reporting projects that require exploratory analysis, recurring performance comparisons, interactive dashboards, or visual summaries for nontechnical stakeholders.