🧠 Model Card: Walk-Forward AutoGluon Model (By Week)
📘 Overview
This model performs walk-forward training and evaluation for predicting NFL wide receiver (WR) receiving yards on a week-by-week basis using AutoGluon’s TabularPredictor.
It leverages historical player embeddings, pregame contextual features, and weather/game metadata to iteratively train and test within each NFL season (2016–2025).
🧩 Model Details
Model Type: Walk-forward regression (AutoGluon TabularPredictor)
Framework:AutoGluon TabularAuthor: Sebastian Andreu
License: MIT
Primary Use: Predicting receiving_yards for each wide receiver before a game is played.
Key Idea
Instead of training one global model, this script re-trains weekly within each season, always using all prior weeks as training data and the next week as the test set.
This ensures realistic forward-looking performance without data leakage.
⚙️ Data
Source Datasets
The model loads and concatenates season datasets from:
SebastianAndreu/24679_NFL_WR_Dataset_<YEAR>
for 2016 ≤ YEAR ≤ 2025.
Each dataset includes pregame features such as weather, team matchup, and Vegas lines.
Features Used
Pregame input variables:
defteam
posteam
surface
is_dome
is_rain
is_snow
is_clear
temp_f
humidity_pct
wind_mph
home_team
away_team
pregame_spread
pregame_total
passer_player_id
receiver_player_id
The dataset is also merged with player_historical_embeddings.csv, which provides dense numerical representations of player histories.
Target Variable
receiving_yards — the number of receiving yards gained by the WR in the upcoming game.
🧮 Training Procedure
Walk-Forward Logic
For each season:
Extract the total number of weeks in that season.
For each week W starting from 2:
Train on data from weeks < W.
Test on data from week W.
Train a new AutoGluon model from scratch (10-minute time limit).
Time Limit: 600 seconds per week
Preset:medium_quality_faster_trainVerbosity: 0 (minimal logging)
📊 Evaluation
Metric
The model computes Mean Absolute Error (MAE) over all weekly predictions.
Output
After all walk-forward runs:
walkforward_predictions.csv — contains true vs. predicted values per week.
Columns:
season
week
true
pred
error = |true - pred|
Example final output:
✅ Walk-forward complete!
Total predictions: 3,200
Mean Absolute Error: 12.47
📥 Saved: walkforward_predictions.csv
📢 Artifacts
Artifact
Description
player_historical_embeddings.csv
Precomputed player embeddings
autogluon_walkforward/
Directory of trained weekly models
walkforward_predictions.csv
Aggregated results of predictions
SebastianAndreu/24679_NFL_WR_Dataset_<YEAR>
Input datasets (2016–2025)
🧠 Intended Use
Goal: Predict individual WR performance before each NFL game.
Primary Users: Sports analytics researchers, fantasy football data scientists, and betting modelers.
Not intended for: Real-time in-game prediction or commercial wagering advice.
⚠️ Limitations
Training each week from scratch is computationally expensive.
Does not include injury or roster change data.
Embeddings rely on prior model quality (player_historical_embeddings.csv).
Accuracy varies across early vs. late season due to data availability.
🧩 Future Improvements
Incorporate transfer learning between seasons.
Add injury & snap count features.
Experiment with AutoGluon ensemble distillation to reduce retraining cost.
Combine with Model 1 embeddings pipeline for joint optimization.
