@thesis{Teklenburg2024, author = {Teklenburg, Linus Paul}, title = {AI-based classification of American football plays combining computer vision and historical play-by-play data}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-47451}, pages = {XII, 58,}, year = {2024}, abstract = {This bachelor's thesis explores the integration of visual and text-based models for predicting American football plays based on National Football League (NFL) games. The study focuses on automatically extracting visual features from pre-snap images of NFL plays and combining them with a text-based model trained on historical play-by-play data. The visual features are extracted using computer vision techniques and YOLOv8 model architecture, while the text-based model utilizes an XGBoost model to analyze historical play-by-play data. The research methodology involves preprocessing and analyzing a dataset of NFL pre-snap images and historical play-by-play data. Computer vision algorithms, such as Optical Character Recognition or Line Extraction are employed to extract relevant visual features, such as player positions, formations, and field dynamics, from the images. Simultaneously, an XGBoost model is trained on historical play-by-play descriptions to capture textual patterns associated with different play outcomes, such as pass or run. The extracted visual features and text-based predictions are then integrated into a unified prediction pipeline. The effectiveness of the integrated model is evaluated through extensive experimentation and performance analysis. Various metrics, including accuracy, precision, recall, and F1 score, are used to assess the predictive capabilities of the model. The results demonstrate the potential of combining visual and text-based information for accurate prediction of NFL plays. The integrated model achieves promising performance in predicting pass or run plays with a test accuracy of 74.13\% and validation accuracy of 73.78\%. The findings of this research contribute to the advancement of predictive analytics in sports and provide valuable insights for coaches, analysts, and the football and data science community.}, language = {en} }