---
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/).

---