Collegiate Booster Collective Roster Allocator
Simulate booster fund allocation across football and basketball, compute market-clearing retention wages, and project regular season wins and playoff odds.
Football Positional Unit Spend vs. P4 Benchmark
Adjust sliders in table to redistribute available payroll| Unit Position | Share of FB Pool | Allocated NIL | Market Min | Portal Flight Risk | Unit WAR Impact |
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Basketball Unit Spend vs. Top 25 Benchmark
Lead playmakers and rim protection command top collective bids| Position Unit | Share of Hoops Pool | Allocated NIL | Market Min | Portal Flight Risk | Win Contribution |
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How Booster Collectives Optimize Win Equity
Since the NCAA interim NIL policy and subsequent judicial settlements, university boosters—like University of Michigan's Champions Circle and prominent donor groups nationwide—function as de-facto general managers. Capital is allocated to prevent tampering from the transfer portal while acquiring high-impact game-changers.
Quarterback & Offensive Line Premium in Football
Empirical analytics prove that quarterback efficiency and offensive tackle pass-protection metrics generate over 48% of team EPA (Expected Points Added). Under-investing in line play degrades even five-star perimeter skill recruits.
Basketball's High Return on Capital
With only five starters on the hardwood and an 8-man primary rotation, every $1.0M invested in basketball generates roughly 2.8x more marginal wins than football, making elite guards the single most cost-effective NIL asset.
Market Dynamics & Flight Risk Modeling
Every position group features an empirical market-clearing wage based on national transfer portal transactions. When a collective offers below 90% of the P4 median, player retention plummets, triggering spring portal departures.
Portal War Chest Reserve Buffer
Holding 15% to 25% of annual collective revenue in reserve prevents emergency booster solicitation when key players face out-of-market poaching during the 15-day transfer windows.
Methodology & Game Simulation
Win projections utilize a Pythagorean win-expectation curve adjusted for conference strength of schedule and diminishing marginal returns past 95th percentile roster talent thresholds.