Dr. Nic HerndonLucas, Amanda2025-06-052025-06-052025-05May 2025May 2025http://hdl.handle.net/10342/14046Collecting trading cards is a decades-old hobby and fascination. Since statisticians were able to start assigning pricing values to cards, based on rarity of the card and the popularity of the player, the once casual hobby has transformed into a serious one, with some collectors focusing on financial prospecting. Currently, this “prospecting” and literature on the National Football League trading cards focuses on previous purchase prices and a subjective inference of player performance or popularity to “predict” whether a card could be a positive or negative investment. This project works to better understand correlates of average card values from a more comprehensive view of the industry: player’s physical attributes, card condition, NFL performance metrics, and draft performance metrics are considered. This paper shows strong positive relationships between passing touchdowns, passing yards, passing completion, passing attempts, passing interceptions, weighted value for drafting team, approximate weighted career value, draft pick, sacks, and Pro Bowl selection (R2 > 0.60 for all features) with average selling price of a trading card. Overall, ensemble learning produced the best model optimization when selecting the best model from Gradient Boost, XGBoost, Random Forest, and KNN outcomes. Stacking models led to increased explanation in variance and to reduced errors for all positions except Safety. MAE remained optimized for the Defensive End, Defensive Tackle, Linebacker, Offensive Lineman, Quarterback, and Running Back positions with XGBoost models.application/pdfEnglishComputer ScienceMaster's Thesis2025-05-22