NFL_Game_Prediction_Models

🏈 NFL Predictions 2025 - Machine Learning Model

Welcome to the NFL Game Prediction Models repository! This project uses machine learning, NFL data APIs, and historical game results to predict NFL game outcomes for the 2025 season and beyond.

🚀 Project Overview

This repository contains ensemble machine learning models that predict NFL game results based on team performance metrics, historical data, and advanced statistical analysis. The model leverages:

• nfl_data_py for comprehensive NFL datasets
• Historical game results from 2015-2024
• Real-time team statistics and performance metrics
• Advanced feature engineering for improved predictions
• Ensemble learning combining Random Forest, XGBoost, and Logistic Regression

📊 Data Sources

• nfl_data_py: Official NFL data package for accessing play-by-play data, team stats, and game schedules
• Historical NFL Results: Processed from 2015-2024 seasons for model training
• Team Performance Metrics: Passing/rushing yards, points scored, turnovers, and other key statistics

🏈 How It Works

1. Data Collection: Scripts pull NFL data using nfl_data_py (play-by-play, team stats, schedules)
2. Preprocessing & Feature Engineering: Calculates team metrics, home field advantage, and performance indicators
3. Model Training: Ensemble models trained using historical game results with feature selection
4. Prediction: Models predict game winners with confidence percentages
5. Evaluation: Performance measured using accuracy metrics and backtesting

Dependencies

• nfl_data_py
• xgboost
• scikit-learn
• pandas
• numpy
• matplotlib

File Structure

NFL_Game_Prediction_Models/
├── README.md
└── Week1/
    └── model.ipynb                 # Week 1 predictions system

For every week of the NFL season, predictions will be generated and stored with weekly analysis:

• Week1/model.ipynb: Weekly prediction system with ensemble learning using nfl_data_py
• Future weekly folders: Each week will have its own analysis and predictions

🔧 Usage

Run the prediction models in Jupyter notebooks:

# For weekly predictions
jupyter notebook Week1/model.ipynb

Expected output:

🏈 Predicted Week 1 NFL Games 🏈
Game: Jets vs Steelers
Predicted Winner: Steelers (63.2% confidence)
...
📊 Model Accuracy: 56.5%

📈 Model Performance

The models are evaluated using multiple metrics:

• Overall Accuracy: 55-60% on historical data
• High Confidence Predictions: 62-65% accuracy
• 2023 Season Validation: 56.5% accuracy across 267 games
• Cross-Validation: 59.6% average with ±1.9% standard deviation

Key Features Used:

• Home/away passing yards per game
• Home/away rushing yards per game
• Points scored per game (using fantasy points as proxy)
• Passing touchdowns per game
• Interceptions thrown per game
• Rushing touchdowns per game
• Fumbles lost per game
• Home field advantage adjustments
• Turnover differentials
• Game context (week, season, playoff status)

📌 Future Improvements

• Incorporate injury reports as a feature
• Add betting line analysis for value identification
• Explore deep learning models for improved accuracy
• Enhanced defensive metrics integration
• Player-level impact modeling
• Weather data integration for outdoor games

@sujar.tech on Instagram and TikTok will update with the latest NFL predictions before every week of the 2025 season!

📜 License

This project is licensed under the MIT License.