Overview

Our NFL prediction model is a machine learning-based system designed to predict the outcomes of NFL games throughout the regular season. The model leverages statistical data from previous games, team and player performances, and advanced modeling techniques to make its predictions. Each week, the model generates game forecasts based on current team form, historical data, and player statistics. The predictions are classified into three confidence levels: High, Medium, and Low, helping you make more informed decisions.

Data Used

The model is built using a wide variety of NFL-related data. Key inputs include:

• Team-level statistics (offense, defense, turnovers, etc.)
• Player-level statistics (quarterbacks, running backs, receivers, etc.)
• Advanced statistics like Elo ratings and Glicko-2 ratings
• Game location and weather conditions
• Historical game results
• Betting odds and market sentiment (when available)

The model also accounts for team changes, such as the relocation of the Las Vegas Raiders (previously the Oakland Raiders), ensuring accuracy across seasons. The team names will be changed to their current location, (e.g. for the 2019 Oakland Raiders will be the Las Vegas Raiders for that season)

How the Model Works

Our NFL prediction model is powered by a combination of machine learning techniques. It uses ensemble learning, blending multiple models including decision trees and neural networks, to improve prediction accuracy. The model undergoes continuous training using game data spanning multiple seasons.

Monte Carlo Simulations

For each game, the model runs thousands of Monte Carlo simulations based on team and player statistics, simulating potential game outcomes. These simulations provide insight into the most likely result for each game. The model also applies a statistical consistency constraint to ensure that offensive and defensive stats align in a realistic way.

Prediction Confidence Levels

The model assigns a confidence score to each prediction, indicating how certain it is about the outcome:

• High Confidence (90%+): The model is highly certain about the prediction.
• Medium Confidence (70%-80%): The model has moderate confidence in the prediction.
• Low Confidence (<70%): The model is less certain about the prediction.

Live Results vs. Backtests

All data prior to the 2024 season are backtests only. Going forward from the 2024 season, there will be 2 sets of results; the live predictions ran during the season prior to the games, and continued backtests being ran as the model is being improved. This allows for tracking of the models improvements, while still having the live data accessible.

Feature Roadmap

We are constantly improving our model to ensure the best possible predictions. Below are some of the planned features and improvements we aim to implement in future updates:

• Track Roster Changes: Implement a system to track active and injured players for each team to account for roster changes in predictions.
• Player-Level Statistical Integration: Incorporate individual player statistics (e.g., quarterback performance, wide receiver stats) into the model for more granular predictions.
• Injury Impact Modeling: Add logic to adjust team performance predictions based on the injury status of key players.
• Weather Data: Include game-day weather forecasts to account for the impact of weather conditions on game outcomes.
• Enhanced Betting Data Integration: Improve the accuracy of predictions by integrating more comprehensive betting data from various sources.
• Real-Time Predictions: Move towards real-time, in-game prediction updates based on live performance data.

As we continue to expand our model, we hope to provide more accurate and detailed insights to enhance your game predictions and decision-making.