ANALYSIS OF FANTASY FOOTBALL DEFENSIVE OUTPUT IN RELATION TO OFFENSIVE PERFORMANCE

This study leverages machine learning to enhance fantasy football strategies by predicting team defensive season points based on offensive player performance. While fantasy football success often relies on specific player selection, traditional approaches overlook the potential influence of offensive metrics on defensive outcomes. To address this gap, this model utilizes data from NFL.com (2012-2023), aggregating points for key offensive positions such as quarterbacks, wide receivers, running backs, tight ends, and kickers, into team-based position metrics for each season. The data was prepared by summing individual player points for each position to account for team-level fluctuations like injuries or lineup changes. The model uses linear regression to evaluate the relationship between offensive output and defensive scoring potential. Results reveal that offensive metrics can effectively predict team defensive points, suggesting that this approach to team composition may improve fantasy football outcomes by strategically drafting defensive teams based on offensive performance. The findings open new opportunities for drafting and trade strategies in fantasy leagues, indicating that focusing solely on high-scoring individual players may be less effective than considering team-level dynamics. Future work could involve expanding the predictive model to include variables such as player matchup analysis and injury risks, aiming to enhance the model's prediction accuracy and provide fantasy football enthusiasts with even more refined team-building insights. This broader approach could help bridge gaps in current predictive methods and refine strategies for consistent fantasy league success.