TOTAL VOLUME:
$134b
24H VOL:
$103,397,351
24H TRANSACTIONS:
2,388,728,490
OPEN INTEREST:
$1,410,176,180
399,592
Markets across
30,097
events
MATCHED EVENTS:
2,622
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: Sep 14, 9:02 PM EST
Kalshi
This event group predicts the winner of the Outstanding Supporting Actress in a Comedy Series award at the 78th Primetime Emmy Awards, scheduled for September 14, 2026. Both Kalshi and Polymarket offer markets on this category, with overlapping nominee lists across platforms.
The 78th Primetime Emmy Awards, given by the Academy of Television Arts & Sciences, honor the best in American prime time television programming aired between June 1, 2025, until May 31, 2026. The awards ceremony is scheduled for September 14, 2026, at the Peacock Theater in Downtown Los Angeles, California This market will resolve according to the person who wins the 2026 Emmy award for “Outstanding supporting actress in a comedy series”. If for any reason no winner is declared by October 31, 2026, 11:59 PM ET, or in case of overall cancellation or a tie for winner, this market will resolve in favor of the nominee which comes first in alphabetical order. The primary resolution source for this market will be official information from the Academy of Television Arts & Sciences and the Emmys, including data from their websites (e.g. https://theemmys.tv/, https://www.televisionacademy.com/) and the live broadcast of the award ceremony, however a consensus of credible reporting may also be used.
The market resolves to Yes for whichever actress wins the Outstanding Supporting Actress in a Comedy Series award at the 78th Emmy Awards, or if a tie is declared in that category.
Prediction markets like those tracked here often diverge from traditional analyst picks because they aggregate real-money bets from a broad base of traders rather than relying on a small group of expert opinions. Market prices reflect collective intelligence and incentivize accuracy, since traders profit when their predictions prove correct. Analysts may emphasize narrative, past wins, or industry relationships, while this market responds dynamically to voting patterns, campaign momentum, and public sentiment. Over time, prediction markets have demonstrated competitive or superior accuracy compared to expert consensus on entertainment awards. Comparing the two approaches can reveal where conventional wisdom and market expectations align or diverge most sharply.
Polymarket and Kalshi can show different implied probabilities for the same outcome because of liquidity, fee structure, participant mix, and how each venue defines the contract. Each platform attracts different trader demographics, liquidity pools, and regulatory frameworks, which can lead to price variations on the same outcome. Polymarket and Kalshi may have different fee structures, user interfaces, or promotional incentives that influence who trades where and how aggressively they bet. Market depth and order-book dynamics also vary; one venue might see larger volume on a particular nominee, pushing odds higher or lower relative to the other. These differences typically narrow as traders arbitrage between platforms, but temporary spreads can persist. Monitoring both sites helps you identify mispriced outcomes and understand where the broader prediction community leans most heavily.
Several catalysts can shift odds significantly before the ceremony. New episodes or season finales airing between now and the Emmys may boost or diminish a nominee's perceived chances, especially if performances are widely praised or criticized. Industry awards earlier in the season—such as guild awards or critics' prizes—often serve as leading indicators and can realign market expectations. Social media momentum, fan campaigns, and trade publication coverage all influence trader sentiment. Major cast changes, show cancellations, or unexpected critical acclaim can also trigger repricing. Additionally, if competing nominees withdraw or face controversy, their odds may collapse while others benefit from reduced competition.