Case Study: Predictive Modeling for Athlete NIL Valuation

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This was an end-to-end data science project that progressed from initial research and data acquisition to a fully deployed predictive application.

• Data Acquisition & Integration: The project began with web scraping to gather publicly available NIL valuation data and detailed athlete biographical information from numerous online sources. This raw data was then cleaned and consolidated into a unified dataset for analysis.
• Advanced Feature Engineering: To prepare the data for modeling, a sophisticated feature engineering process was undertaken. This included developing novel features to handle high-cardinality categorical data (e.g., colleges with over 100 unique values). A rigorous feature selection process, using importance scores from both Decision Tree and XGBoost models, was used to isolate the most predictive variables.
• Predictive Machine Learning Model: A machine learning model was developed and trained on the engineered dataset. The final model was fine-tuned to accurately predict an athlete's potential NIL value, proving effective regardless of their specific sport.
• Interactive Application Deployment: To make the model's insights accessible, a lightweight and interactive web application was built using Python and Streamlit. This allowed the client to input an athlete's details and receive an instant, data-backed NIL value prediction.

The project successfully translated a complex business problem into a powerful, accurate, and usable technology solution.

• High Predictive Accuracy: The final model was able to predict an athlete's NIL valuation with 94% accuracy, providing a highly reliable assessment tool.
• Objective, Data-Driven Insights: The solution replaced subjective evaluation with a quantitative, objective model, allowing for more consistent and defensible assessments of athlete marketability.
• Delivered a Functional Tool: The Streamlit application moved the project from a theoretical model to a practical tool that the incubator network could use and demonstrate to its stakeholders.
• Created Market Advantage: Provided the client with a unique, data-science-driven asset in the rapidly growing and competitive NIL market.