Kalshi and the resolution controversy: from UFC to the 2025 Oscars
April 17, 2026 · 1 min read
Prediction markets are growing rapidly, but they are also exposing their limitations. A recent case involving Kalshi has reignited debate over how contracts are resolved, following controversial decisions in both sports and entertainment markets.
🥊 The UFC case: when results change after the fact
During a UFC 327 fight, one fighter was initially declared the winner, and Kalshi resolved the market accordingly, paying out based on that result.
However, the decision was later corrected to a draw. Despite this, Kalshi did not revise the market outcome, as its rules state that only the initial official result is considered.
This sparked criticism, since the final real-world outcome differed from the market payout.
🎬 The precedent: the 2025 Oscars
A similar controversy occurred with the Academy Awards 2025.
Initial reports indicated 18 million viewers, leading certain contracts to resolve negatively. Later revised data showed 19.7 million viewers—enough to change the outcome.
Kalshi did not update the resolution, sticking to its rule of using only initial reported data.
This frustrated traders who expected markets to reflect final, corrected figures.
⚖️ Clear rules vs messy reality
Kalshi’s approach has advantages:
Fast and decisive settlements
Reduced ambiguity
Consistent rule enforcement
But it also reveals a key issue:
👉 markets may not reflect the final reality of events.
This raises questions about whether prediction markets are reliable information tools or rule-based systems detached from real outcomes.
📉 The limits of prediction markets
These cases highlight that:
Market rules can outweigh real-world outcomes
Traders may be paid based on incomplete data
“Market truth” doesn’t always match reality
In a system built to predict the future, defining the past becomes equally critical.
🔮 Structural flaw or necessary trade-off?
Kalshi has remained consistent, but these cases raise bigger questions:
Should markets update with revised data?
Or is operational certainty more important than absolute accuracy?
The answer may shape the long-term credibility of prediction markets.
