Author: Nathan

🔗 GitHub Repo: https://github.com/ndbryant21-eng/fantasy-football-ml📓 Notebooks folder (main): https://github.com/ndbryant21-eng/fantasy-football-ml/tree/main/notebooks📒 This week’s WR notebook (branch): https://github.com/ndbryant21-eng/fantasy-football-ml/blob/wr-week04/notebooks/WR_weekly_V1.ipynb 🚀 TL;DR 🧠 Model (WR) Regressor: tree-based (XGBoost if available, RF fallback) + post-hoc calibration.Features (shifted/rolled): Why this mix? WR scoring is dominated by usage + leverage (targets/air) and game environment. Short rolling windows catch role shifts; opponent allowance and vegas Read more

🔗 GitHub Repo: https://github.com/ndbryant21-eng/fantasy-football-ml📓 Notebooks folder: https://github.com/ndbryant21-eng/fantasy-football-ml/tree/main/notebooks 🚀 TL;DR 🧠 Model (RB) 🧪 Data Flow 🔧 Week-of Fixes (that mattered) 📊 Headline Results (Week 2, 2025 · RBs) Read: baseline model ranks reasonably; raw scale is a bit optimistic for some tiers—light calibration helps without flattening ranks. 📉 Biggest Misses: Why They Happened & What Read more

This is a quick Week 1 post-mortem for the QB model in the Predictive Playbook series. The pipeline ran end-to-end, published projections before kickoff, and now we’ve matched them to actuals to see how we did—and what to fix before Week 2. 🔗 GitHub Repo: https://github.com/ndbryant21-eng/fantasy-football-ml📓 Week 1 Comparison Notebook: https://github.com/ndbryant21-eng/fantasy-football-ml/tree/main/notebooks 📊 Headline Results (2025 Read more

Welcome to the first weekly QB projection model for the Predictive Playbook series! This post covers the data, features, modeling choices, validation results, and some important Week 1 caveats—plus what’s coming next (WR, RB, TE). 🔗 GitHub repo: https://github.com/ndbryant21-eng/fantasy-football-ml 🎯 TL;DR 🧱 Data & Target 🧩 Features (what the model “sees”) Recent form (L3 = Read more

📊 In my last post, I shared the first machine learning predictions for the 2025 fantasy football season. While the overall leaderboard was dominated by quarterbacks (no surprise given how fantasy scoring works), that’s not how most fantasy drafts play out. Since most leagues only allow one starting QB, an overall Top 15 doesn’t give Read more

📊 The data is ready — now it’s time to put it to work.In the last post, I walked through how I scraped and cleaned 15 years of NFL stats into a structured dataset. That gave me a foundation to build on. This time, I’m taking the first step into modeling: training machine learning algorithms Read more

🔗 See the code here: get_fantasy_football_data.ipynb on GitHub 📊 Every prediction starts with data.Before we can talk machine learning models, feature engineering, or forecasts for the 2025 fantasy football season, we need one thing: a reliable dataset. That’s what this post is all about — taking messy, scraped stats from Pro-Football-Reference and turning them into Read more

This is the start of The Predictive Playbook — my personal experiment as a chemical engineer applying machine learning to fantasy football. 🧠 Predicting Fantasy Football with Machine Learning: A New Blog Series Every year, millions of people draft fantasy football teams based on gut instincts, highlight reels, and outdated projections. This year, I’m doing Read more