Can we accurately predict American Football games with Machine Learning?

The integration of machine learning (ML) into American football, particularly the National Football League (NFL), has opened new avenues for predicting game outcomes and enhancing viewer experiences. Researchers and analysts have employed various ML models to forecast game results, with varying degrees of success.​

One notable study utilized data from the 2018 to 2023 NFL seasons, incorporating pre-game player projections, weather conditions, and stadium details to predict total scores and point spreads. By employing techniques such as linear regression, gradient boosting regression, and neural networks, the study aimed to develop a robust betting strategy, providing bettors with data-driven tools to place profitable NFL sports bets. ​

In a practical application, an intern project at IG Labs involved creating a machine learning model to predict NFL game winners. Utilizing open-source data from pro-football-reference.com and up-to-date betting data, the model demonstrated an accuracy rate of 65% over several seasons. The data and models were updated weekly to ensure the most recent games were considered in their predictions. ​

Another approach involved using Python to build a model predicting NFL game outcomes based on in-game metrics and external ratings. By constructing a dataset where each row represented a single game between two teams, and columns were based on various metrics, the model achieved an accuracy score of 85% using logistic regression and XGBoost. ​

Furthermore, a GitHub project aimed to predict NFL games using machine learning by providing a probabilistic forecast for each game. The forecasts could be used to bet on NFL games and make a consistent profit, focusing on the line bet by simply selecting the winners. ​

In summary, machine learning models have shown promise in predicting NFL game outcomes, with accuracy rates varying based on the choice of model, feature selection, and data quality. While some models have achieved accuracy rates as high as 85%, the inherent unpredictability of football games means that predictions are never certain. Nonetheless, ongoing research and practical applications continue to refine these models, aiming to enhance their predictive capabilities in the dynamic realm of American football.

Citations:
Paladino, Andy J. “Mining and Forecasting NFL Drive Statistics via the ESPN API Using Random Forest Classification To...” Medium, Medium, 21 Nov. 2024, medium.com/%40andyjpaladino/mining-and-forecasting-nfl-drive-statistics-via-the-espn-api-using-random-forest-classification-to-f8a3e75bbd81.
Hetzer, Kaidan. “A Fun Dive into Machine Learning: Predicting NFL Game Winners.” IntelliGenesis LLC, 19 Feb. 2025, intelligenesisllc.com/machine-learning-intern-project/.
“Football Power Index.” Wikipedia, Wikimedia Foundation, 18 Apr. 2024, en.wikipedia.org/wiki/Football_Power_Index.
Conrad, Jacob. “Predicting NFL Total Score and Point Spread Bets.” Quinnipiac iQ Career and Experiential Learning Lab, 2024, iq.qu.edu/experiential-learning/course-projects-and-capstones/student-projects/predicting-nfl-total-score-and-point-spread-bets/.