Local-first fantasy football draft assistant with custom scoring profiles, projection analytics, multi-source ADP/ECR rankings, and Yahoo sync. FastAPI + React + SQLite.
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Updated
Sep 2, 2026 - TypeScript
Local-first fantasy football draft assistant with custom scoring profiles, projection analytics, multi-source ADP/ECR rankings, and Yahoo sync. FastAPI + React + SQLite.
GCI World 2026 April - NFL Draft Prediction. Binary classification (AUC) solution using a LightGBM/XGBoost/CatBoost stacking ensemble with Optuna tuning. Matsuo-Iwasawa Lab, University of Tokyo.
Predicting NFL Draft selection probability from Combine metrics using a tuned LightGBM + XGBoost ensemble. Features position-normalised z-scores, smoothed school target encoding, and Optuna hyperparameter tuning. Built for GCI World 2026 April In-Class Competition. Public AUC: 0.82983.
NFL Draft Prediction model based in lightgbm, catboost, xgboost with advanced data relations analysis for predictions.
7-round mock draft simulator based on position and prospect grade
NFL Draft Tracker using Python, Flask, and Bootstrap
NFL Draft Simulator For 2026 NFL Draft, Programmed in React/ Google AI Studio
Completed an analysis on the relationship between the combine performance and future league performance of 941 NFL players drafted between 2015 and 2017 using R. Proved that the combine performance of NFL players has little to no correlation on future player performance using linear regression. My goal was to show that the NFL Combine should not…
NFL Fantasy app (by https://brffootball.com.br/) with customized podcast
Scraped data via BeautifulSoup to create a Linear Regression Model to Predict Total Yards Rushing and Receiving of Rookie NFL Running Backs
Analysis of successful NFL players as prospects
Drafting characters from Key & Peele's East/West Bowl in Go
Bot de Telegram (y librería Go) que avisa de selecciones y traspasos del NFL Draft para un equipo, usando el feed público de ESPN
Random forest models using TDA to predict drafted round for WRs
Python analysis using draft position and NFL performance data to identify undervalued and overvalued draft prospects.
NFL draft pick value chart with credible bounds: Bayesian monotone regression on 3,584 players drafted 2012-2025 (Python)
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