This event group covers a men's college basketball game between Florida A&M Rattlers and Grambling State Tigers scheduled for February 28, 2026. Markets include moneyline (winner), spread (-5.5 for Grambling), and two over/under totals (139.5 and 140.5 points).
Kalshi market structure is logically tautological (Yes if either team wins), whereas Polymarket markets are outcome-specific (moneyline, spread, totals). This represents a scope/design divergence rather than a data integrity failure, as both can technically resolve, but they measure fundamentally different propositions.
Hero Tip:
Use Polymarket moneyline and spread as primary outcome markets. Treat Kalshi as a binary game-completion market only. Do not assume Kalshi Yes and Polymarket moneyline outcomes are equivalent—Kalshi lacks outcome discrimination.
Critical Divergence Points:
Polymarket: Outcome-specific markets: Moneyline resolves to winner name (FAMU or Grambling); Spread resolves Yes if Grambling wins by 6+, otherwise No (FAMU); Totals resolve Over/Under based on combined points (140+ or 141+). All remain open if postponed; resolve 50-50 if canceled with no makeup. Source: NCAA.org.
Kalshi: Binary market resolves Yes if Grambling wins OR if Florida A&M wins. This is logically equivalent to 'the game is played' and does not discriminate between outcomes. Quote: 'If Grambling St. wins...then the market resolves to Yes. If Florida A&M wins...then the market resolves to Yes.'
Our PredictionHero Resolution Divergence Alerts (RDA) are there to help users identify potential differences across platforms. They do not replace or supersede the official rules and description of any prediction market. Users are solely responsible for reviewing and understanding the applicable rules and resolution criteria before placing any trade or bet. If you notice a potential inconsistency, discrepancy, or error in an alert, please report it to our team so we can review and improve the accuracy of our data.
Follow the signals, not the noise
Get insights on market conviction, notable shifts, and what the data is quietly signaling.