This paper presents four architectures for predicting
tackle probability in an NFL football game. Accu-
racy, precision, recall, loss, and F1 scores are com-
pared to identify the best classification model for
the 2024 NFL Big Data Bowl. The models leverage
NFL tacking data, including player position, speed,
direction, and location relative to key field mark-
ers. Tracking information was processed to extract
meaningful plays, determine which features should
be used in the solution, and identify plays with suc-
cessful and unsuccessful tackle outcomes. A feed-
forward network is presented as a baseline, and
the performance of a convolutional neural network,
transformer, and graph transformer are compared.
The feed-forward network yielded an accuracy of
75%, which establishes the minimum accuracy of
a simple architecture that uses minimal features.
The convolutional network outperformed the base-
line with an accuracy of 85%, but performed worse
than the transformer and graph transformer, which
achieved accuracy results of 90% and 92%, respec-
tively. Ultimately, the graph transformer is found to
be most effective at predicting the tackle probability
for a league-average player.
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