Update README.md

README.md

@@ -1,14 +1,75 @@
----
-title: Tabular Space
-emoji: 🌖
-colorFrom: purple
+title: Football Elite Player Predictor
+emoji: 🏈
+colorFrom: green
 colorTo: blue
 sdk: gradio
-sdk_version: 5.47.2
+sdk_version: 4.0.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: Predicting Elite WRS
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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/).
+
+---