Enhancing Tackle Prediction in NFL Games Through Feature Engineering and Hybrid Machine Learning Models

The ability to accurately predict player performance in football, particularly in the context of tackling, is an asset for teams, coaches, and analysts. This study presents an advanced approach to tackle prediction by considering comprehensive player and game data. This study proposes a hybrid modeling framework that integrates feature engineering with machine learning techniques, including Gradient Boosting Classifiers and Neural Networks, to enhance the accuracy of tackle predictions. Our methodology begins with extensive preprocessing of the dataset, standardizing date formats, handling missing data, and transforming key variables such as player height and weight into more meaningful features like Body Mass Index (BMI). The integration of domain-specific features, including player age, position, and game-related statistics, allows for a more detailed understanding of the factors influencing tackle outcomes. Here, the models are trained and evaluated by using a rigorous train-test split and cross-validation approach, ensuring robustness and generalizability. The results demonstrate that the proposed hybrid model significantly outperforms baseline models, achieving superior accuracy and predictive power. Through the use of confusion matrices, ROC curves, and precision-recall analyses, we provide a comprehensive evaluation of model performance. This research highlights the potential of combining traditional machine learning methods with deep learning techniques to capture the complex dynamics of football. The findings have practical implications for optimizing player training and game strategies, and they pave the way for future explorations into predictive sports analytics.