Collegiate cross country teams often build their season schedules on intuition rather than evidence, partly because large-scale performance datasets were not publicly accessible prior to the National Running Club Database (NRCD). We analyze the comprehensive-era Cross Country subset of NRCD, 23,360 results from 7,056 athletes (2023-2025; >99% course/weather coverage). Under leakage control and temporal validation, race-result features do not support out-of-year forecasting of individual improvement (best men's R^2 = 0.044; women's -0.018), capturing only a small fraction of the outcome's reliability ceiling (approximately 0.23-0.28). Against this null, team race frequency associates with nationals placement (pooled RR = 2.09; GEE OR = 2.56/SD). Program-wide opportunity (roster depth; Effective Racing Opportunity) outranks a single workhorse's max race count cross-sectionally, but overall team depth for race count is controlled. Converted Only times (not adjusted for weather and elevation) overstate mean first-to-last gains by 15-21 s relative to Standardized. These results challenge coaching practices that treat schedule design as purely anecdotal and show how NRCD enables evidence-based decision-making in collegiate cross country.

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