Diraksa Munajat
Hoanan Peng
Guanqing Li
Onur Tepençelik

• Build
• Data Cleaning
• Plotting and Analysis

Following dependencies are needed to run the code:

• NumPy
• Pandas
• Matplotlib
• Plotly (4.5.4)
• urllib

- pip install -name-
Example:
- pip install plotly==4.5.4

Additional dependencies that we used for data extraction and cleaning

• BeautifulSoup

The main datasets are extracted from basketballreference.com using BeautifulSoup and directly downloaded from Kaggle.

After building all the dependencies, simply run main.py in the Jupyter notebook to get the following output described and shown in the Kobe notebook.

The shot selection dataset from Kaggle requires just a bit of cleaning. The shots that contained missing information in the shot_made_flag column are omitted.

The basketball reference dataset did not require cleaning. Two new columns are added for seasons and seconds remaining until end of quarter, for further analysis.

Data is analyzed and visualized using various techniques. Visualizing the shot attempts on a basketball court is very effective for analyzing shooting trends. Bar, pie, radar and line plots are used to analyze career statistics of Kobe Bryant.

Just click the Jupyter Notebook : Kobe Shot Selection.ipynb