Spaces:
Sleeping
Sleeping
| 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/). | |
| --- |