Probability-driven NASCAR DFS projections using 20,000 simulations are superior to basic, single-outcome projections because they account for the inherent variability and unpredictability of racing. By simulating the race thousands of times, these projections capture a range of possible outcomes for each driver, such as crashes, pit strategy impacts, and performance variability. This method provides a more comprehensive view of a driver’s potential, including upside, downside, and likelihood of finishing in key positions. In contrast, single-outcome projections oversimplify the race by assuming one deterministic result, ignoring the volatility that makes NASCAR unique and can create value in DFS contests.