FALL 2025

AUTHOR

Aadi Patangi, Amador Valley High School

ABSTRACT

The NFL draft is an opportunity for teams to draft new players, address positional needs, and strengthen their roster for the upcoming season. Teams often trade players or compensation packages to secure certain prospects, which can be critical for team success. Wide receivers were selected as the focus of this study because the position is heavily influenced by objective data — such as receiving statistics and combine metrics like speed, agility, and explosiveness — making it well-suited for predictive modeling using machine learning.

This study presents a two-stage machine learning approach to first predict whether a wide receiver (WR) invited to the NFL Scouting Combine will be drafted, and if so, at which overall position. Using physical testing data from the NFL combine, college production statistics, and historical draft result training data from 2000 to 2024, we construct a Gradient Boosting Classifier to predict draft likelihood followed by a CatBoost Regressor to estimate draft position for those predicted to be selected. This approach provides NFL teams and scouts with a reliable estimate of whether and when a combine-invited wide receiver will be drafted, helping them make more informed decisions and strategically position themselves to select desired prospects. In validation, our classifier reached 89.2% accuracy (F₁ = 0.936), and our regressor yielded a 49.2-pick MAE (ρ = 0.626), demonstrating robust predictive performance.