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---
license: mit
title: Football Elite Player Predictor
sdk: gradio
emoji: 👀
colorFrom: green
colorTo: blue
short_description: Predicting Elite Receivers
---
title: Football Elite Player Predictor
emoji: 🏈
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: mit
---
# Football: Will this player be Elite? 🏈
Predict whether a football player will be classified as "Elite" based on their performance statistics using an AutoGluon TabularPredictor model.
## Overview
This application uses machine learning to classify football players as either **Elite** or **Not Elite** based on their receiving statistics. The model analyzes 8 key performance metrics to make predictions with confidence probabilities.
## Features
- **Real-time Predictions**: Enter player stats and get instant classification
- **Probability Scores**: See confidence levels for each class
- **Interactive Interface**: Adjust sliders and inputs to explore different scenarios
- **Example Players**: Pre-loaded examples including star players and benchmarks
## Input Features
The model uses the following 8 statistics:
1. **Targets (TGT)**: Number of passes thrown to the player
2. **Receptions (REC)**: Number of catches made
3. **Yards (YDS)**: Total receiving yards
4. **Yards Before Catch per Reception (YBC_R)**: Average yards before catch
5. **Yards After Catch per Reception (YAC_R)**: Average yards after catch
6. **Average Depth of Target (ADOT)**: Average distance from line of scrimmage
7. **Drop Percentage (DROP_PCT)**: Percentage of dropped passes
8. **Rating (RAT)**: Overall passer rating when targeted
## Model
- **Framework**: AutoGluon TabularPredictor
- **Task**: Binary Classification (Elite vs Not Elite)
- **Output**: Class prediction with probability distribution
## How to Use
1. Enter a player name (optional, for tracking)
2. Adjust the statistical inputs using sliders and number fields
3. View the real-time prediction and probability scores
4. Try the example players to see different scenarios
## Examples Included
- **Justin Jefferson**: Elite receiver profile
- **Cooper Kupp**: High-volume elite target
- **Rookie WR**: Developing player profile
- **Tyreek Hill**: Elite deep threat profile
- **Bench Player**: Minimal playing time
## Technical Details
The model is loaded from Hugging Face Hub and makes predictions using ensemble methods via AutoGluon's TabularPredictor.
## Limitations
- Model performance depends on training data quality and representativeness
- Predictions are probabilistic and should not be used as sole decision-making criteria
- Statistics should be from comparable game situations and sample sizes
## Acknowledgments
Built with [AutoGluon](https://auto.gluon.ai/) and [Gradio](https://gradio.app/).
--- |