The idea of creating a quarterback entirely from randomly generated attributes might remind collectors of the thrill involved when they buy cut coins, never knowing what unique rarity might be revealed. Similarly, this simulation took a quarterback with near-impossible starting stats and gave him elite development to see if a football prodigy—or perhaps a cautionary tale—would emerge over a collegiate career.

Initially, the quarterback's stats were almost comically low: 2 speed, 3 acceleration, 26 throw power, and 27 short throw accuracy. He was essentially immobile, and his throwing power was so weak he couldn’t make most passes effectively. It painted a bleak picture for success in college football, a game that thrives on dynamic play and strong arm talent.

However, the experiment wasn’t about instant glory; it was about growth. With a 6’7” frame and elite development, the player was expected to get better over time despite his lack of initial talent. He wore an unusual QB number, 88, adding to his uniqueness but not influencing on-field performance.

The narrative soon shifted during his games. While initial games were filled with sacks, blown coverage, and interceptions, he surprisingly threw an 86-yard touchdown—a rare highlight in an otherwise tough freshman year. Despite limited mobility, slow processing, and the inability to throw deep accurately early on, the player began to show signs of competency as his stats gradually improved.

As the seasons progressed, the elite development led to increases in his speed and agility, throwing accuracy, and awareness. By his final years, the quarterback wasn’t a star but a competent starter with an 80+ overall rating. This level of improvement showed how developmental incentives in simulation games can transform raw potential into usability, regardless of a painfully slow start.

This story of growth is analogous to collectors who buy cut coins, hoping for a valuable piece hidden among common finds. Both require patience and a keen eye for potential. This QB's rise is proof that even the most unlikely candidates can improve with the right conditions and persistence.

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