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---
title: NFL Receiving Yards Predictor
emoji: 📊
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.49.0
app_file: app.py
pinned: false
license: mit
---
# 🏈 NFL Receiving Yards Predictor
This interactive Gradio app predicts **NFL receiving yards** for any active wide receiver or tight end based on the quarterback, game week, stadium conditions, and real-time weather forecasts.
The model leverages **AutoGluon Tabular** for regression and integrates **player embeddings**, **team data**, and **environmental context** for enhanced accuracy.
---
## 🚀 Demo
Try it live here: [Hugging Face Space](https://huggingface.co/spaces/SebastianAndreu/2025-24679-NFL-Yards-Predictor)
---
## 🧠 Model Overview
* **Model Type:** Tabular Regression
* **Framework:** [AutoGluon Tabular](https://auto.gluon.ai/stable/tutorials/tabular_prediction/index.html)
* **Training Dataset:** Aggregated play-by-play and player stats from NFL seasons
* **Input Features Include:**
* Receiver & quarterback IDs (with learned embeddings)
* Team and opponent IDs
* Stadium type and surface
* Game spread and total line
* Weather conditions (temperature, humidity, wind, precipitation)
* Home/Away team indicators
---
## ⚙️ How It Works
1. The app loads the pre-trained AutoGluon predictor and historical player embeddings from the Hugging Face model repo:
```
SebastianAndreu/2025-24679-NFL-Yards-Predictor
```
2. Users select:
* Receiver
* Quarterback
* Week
* Season
3. The app fetches:
* Game info (from local CSVs)
* Real-time weather forecast (via [Open-Meteo API](https://open-meteo.com/))
4. The combined data is passed into the predictor for inference.
5. The model outputs the **expected receiving yards** for that matchup.
---
## 📁 Repository Structure
```
.
├── app.py # Main Gradio interface and logic
├── players.csv # Active player metadata
├── games.csv # Game schedule and context
├── hf_assets/ # Cached model assets from Hugging Face
├── nfl_model/ # Snapshot of downloaded model files
├── README.md # You are here
```
---
## 🧩 Key Functions
### `download_model_and_embeddings()`
Downloads and prepares the AutoGluon model and the player embedding dataset from Hugging Face.
### `get_game_info()`
Matches the selected receiver’s team, week, and season to the corresponding game, including opponent and stadium metadata.
### `get_weather_forecast()`
Fetches real-time forecast for the stadium using **Open-Meteo**, returning temperature, humidity, wind speed, and rain/snow conditions.
### `predict_yards()`
Prepares the full feature vector and runs inference using the AutoGluon predictor. If missing embeddings are detected, they’re replaced by mean values for stability.
---
## 🧰 Requirements
To run locally:
```bash
pip install gradio autogluon tabular pandas requests huggingface_hub
```
---
## 🌤️ Weather API Integration
The app dynamically queries the [Open-Meteo](https://open-meteo.com/) API based on the selected stadium coordinates.
Example usage:
```python
https://api.open-meteo.com/v1/forecast?latitude=40.4468&longitude=-80.0158&hourly=temperature_2m,relative_humidity_2m,wind_speed_10m,weather_code
```
---
## 🏟️ Stadium Metadata
Each NFL stadium’s coordinates are stored in the `STADIUM_COORDS` dictionary to map weather forecasts accurately.
---
## 🏈 Credits
* **Developer:** [Sebastian Andreu](https://huggingface.co/SebastianAndreu)
* **Model:** AutoGluon Tabular Regression trained on historical NFL data
* **APIs:** Open-Meteo (Weather), ESPN (Game Schedule)
* **Hosted on:** [Hugging Face Spaces](https://huggingface.co/spaces/SebastianAndreu/2025-24679-NFL-Yards-Predictor)
---
## 🧾 License
MIT License © 2025 Sebastian Andreu
---
## 💬 Feedback
If you find a bug or have a feature suggestion, please open an issue on the [model repository](https://huggingface.co/SebastianAndreu/2025-24679-NFL-Yards-Predictor).
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |