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README.md

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+---
+title: NBA Performance Predictor
+emoji: 🏀
+colorFrom: orange
+colorTo: red
+sdk: gradio
+sdk_version: 3.40.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+
+# NBA Player Performance Predictor
+
+## Model Description
+
+This model predicts NBA player points per game (PPG) using XGBoost regression with time-series features. The model uses historical player statistics, lag features, and engineered metrics to make predictions.
+
+## Model Details
+
+- **Model Type**: XGBoost Regressor
+- **Task**: Regression (Predicting NBA player points per game)
+- **Framework**: scikit-learn, XGBoost
+- **Performance**: RMSE ~3-5 points per game, R² ~0.6-0.8
+
+## Features
+
+The model uses various features including:
+- Basic stats: Age, Games, Minutes Played, Field Goals, etc.
+- Lag features: Previous season performance metrics
+- Rolling averages: 2-3 year performance averages
+- Efficiency metrics: Points per minute, overall efficiency
+- Categorical encodings: Position, Team, Age category
+
+## Usage
+
+```python
+from huggingface_model import NBAPerformancePredictorHF
+
+# Load the model
+model = NBAPerformancePredictorHF("path/to/model")
+
+# Example prediction
+player_stats = {
+    'Age': 27,
+    'G': 75,
+    'GS': 70,
+    'MP': 35.0,
+    'FG': 8.5,
+    'FGA': 18.0,
+    'FG_1': 0.472,
+    'Pos_encoded': 2,
+    'Team_encoded': 15,
+    'Age_category_encoded': 1,
+    'PTS_lag_1': 22.5,
+    'PTS_lag_2': 21.0,
+    'TRB_lag_1': 7.2,
+    'AST_lag_1': 4.8
+}
+
+predicted_points = model.predict(player_stats)
+print(f"Predicted PPG: {predicted_points:.2f}")
+```
+
+## Training Data
+
+The model was trained on NBA player statistics from multiple seasons, including:
+- Regular season statistics
+- Playoff performance data
+- Historical player performance trends
+
+## Limitations
+
+- Requires historical data (lag features) for accurate predictions
+- Performance may vary for rookie players or players with limited history
+- Model is trained on specific NBA eras and may need retraining for different time periods
+
+## Ethical Considerations
+
+This model is for educational and analytical purposes. It should not be used for:
+- Player salary negotiations
+- Draft decisions without additional context
+- Any form of discrimination or bias
+
+## Citation
+
+```
+@misc{nba_performance_predictor,
+  title={NBA Player Performance Predictor using XGBoost},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/your-username/nba-performance-predictor}}
+}
+```

model_metadata.json

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+{
+  "feature_names": [
+    "Age",
+    "G",
+    "GS",
+    "MP",
+    "FG",
+    "FGA",
+    "FG_1",
+    "3P",
+    "3PA",
+    "3P_1",
+    "2P",
+    "2PA",
+    "2P_1",
+    "eFG",
+    "FT",
+    "FTA",
+    "FT_1",
+    "ORB",
+    "DRB",
+    "TRB",
+    "AST",
+    "STL",
+    "BLK",
+    "TOV",
+    "PF",
+    "Pos_encoded",
+    "Team_encoded",
+    "Age_category_encoded",
+    "Points_per_minute",
+    "Efficiency",
+    "Usage_rate",
+    "PTS_lag_1",
+    "PTS_lag_2",
+    "PTS_lag_3",
+    "TRB_lag_1",
+    "TRB_lag_2",
+    "TRB_lag_3",
+    "AST_lag_1",
+    "AST_lag_2",
+    "AST_lag_3",
+    "FG_1_lag_1",
+    "FG_1_lag_2",
+    "FG_1_lag_3",
+    "FT_1_lag_1",
+    "FT_1_lag_2",
+    "FT_1_lag_3",
+    "MP_lag_1",
+    "MP_lag_2",
+    "MP_lag_3",
+    "STL_lag_1",
+    "STL_lag_2",
+    "STL_lag_3",
+    "BLK_lag_1",
+    "BLK_lag_2",
+    "BLK_lag_3",
+    "Points_per_minute_lag_1",
+    "Points_per_minute_lag_2",
+    "Points_per_minute_lag_3",
+    "Efficiency_lag_1",
+    "Efficiency_lag_2",
+    "Efficiency_lag_3",
+    "Usage_rate_lag_1",
+    "Usage_rate_lag_2",
+    "Usage_rate_lag_3",
+    "PTS_rolling_2",
+    "PTS_rolling_3",
+    "TRB_rolling_2",
+    "TRB_rolling_3",
+    "AST_rolling_2",
+    "AST_rolling_3",
+    "FG_1_rolling_2",
+    "FG_1_rolling_3",
+    "FT_1_rolling_2",
+    "FT_1_rolling_3",
+    "MP_rolling_2",
+    "MP_rolling_3",
+    "STL_rolling_2",
+    "STL_rolling_3",
+    "BLK_rolling_2",
+    "BLK_rolling_3",
+    "Points_per_minute_rolling_2",
+    "Points_per_minute_rolling_3",
+    "Efficiency_rolling_2",
+    "Efficiency_rolling_3",
+    "Usage_rate_rolling_2",
+    "Usage_rate_rolling_3",
+    "PTS_trend",
+    "TRB_trend",
+    "AST_trend",
+    "FG_1_trend",
+    "FT_1_trend",
+    "MP_trend",
+    "STL_trend",
+    "BLK_trend",
+    "Points_per_minute_trend",
+    "Efficiency_trend",
+    "Usage_rate_trend"
+  ],
+  "target_column": "PTS",
+  "model_type": "XGBRegressor",
+  "model_params": {
+    "objective": "reg:squarederror",
+    "base_score": null,
+    "booster": null,
+    "callbacks": null,
+    "colsample_bylevel": null,
+    "colsample_bynode": null,
+    "colsample_bytree": 1.0,
+    "device": null,
+    "early_stopping_rounds": null,
+    "enable_categorical": false,
+    "eval_metric": null,
+    "feature_types": null,
+    "feature_weights": null,
+    "gamma": null,
+    "grow_policy": null,
+    "importance_type": null,
+    "interaction_constraints": null,
+    "learning_rate": 0.1,
+    "max_bin": null,
+    "max_cat_threshold": null,
+    "max_cat_to_onehot": null,
+    "max_delta_step": null,
+    "max_depth": 3,
+    "max_leaves": null,
+    "min_child_weight": null,
+    "missing": NaN,
+    "monotone_constraints": null,
+    "multi_strategy": null,
+    "n_estimators": 300,
+    "n_jobs": -1,
+    "num_parallel_tree": null,
+    "random_state": 42,
+    "reg_alpha": null,
+    "reg_lambda": null,
+    "sampling_method": null,
+    "scale_pos_weight": null,
+    "subsample": 1.0,
+    "tree_method": null,
+    "validate_parameters": null,
+    "verbosity": null
+  }
+}

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