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$130,522,377

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2,388,728,490

OPEN INTEREST:

$1,438,389,636

404,028

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2,681

PLATFORM COVERAGE:

5

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Kalshi:

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Entertainment
Tony Award for Best Performance by an Actress in a Featured Role in a play?
kalshi

Tony Award for Best Performance by an Actress in a Featured Role in a play?

Volume:
$18,438

Laurie Metcalf

 - Kalshi

Laurie Metcalf - Kalshi

1W

News

Positive

Negative

Neutral

Hover marker for details

Vol.

·

Resolved Jun 8, 2026

Closed: Jun 8, 10:05 AM EST

kalshi

Kalshi

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Join Kalshi and score $25 for your first trade.
Outcome
Trade
Chance %
Price
Spread
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
kalshi

Laurie Metcalf

View
100%
Yes 100¢No 0¢
100¢
N/A
$8,476
N/A
N/A
$5,306
3mo 2d
Yes
kalshi

June Squibb

0%
Yes 0¢No 100¢
100¢
N/A
$4,809
N/A
N/A
$3,414
3mo 2d
No
kalshi

Betsy Aidem

0%
Yes 0¢No 100¢
100¢
N/A
$2,063
N/A
N/A
$1,579
3mo 2d
No
kalshi

Marylouise Burke

0%
Yes 0¢No 100¢
100¢
N/A
$1,205
N/A
N/A
$1,078
3mo 2d
No
kalshi

Aya Cash

0%
Yes 0¢No 100¢
100¢
N/A
$1,000
N/A
N/A
$757
3mo 2d
No
kalshi

Tie

0%
Yes 0¢No 100¢
100¢
N/A
$885
N/A
N/A
$634
3mo 2d
No
Total markets: 6

Intro

This market tracks whether Laurie Metcalf will win the Tony Award for Best Performance by an Actress in a Featured Role in a Play at the 79th Tony Awards. On Kalshi, Metcalf holds 87.0% probability, with June Squibb at 10.0%. The market resolves according to the official outcome announced at the 79th Tony Awards ceremony. Watch the ceremony announcement to see which nominee is selected for this featured acting category.

Frequently asked questions

The Tony Award for Best Performance by an Actress in a Featured Role in a play dashboard on Kalshi tracks real-time odds and trading activity for this prestigious theater award. It displays the current probability of each nominated actress winning, updated as traders buy and sell shares. The dashboard shows $18,438 in total trading volume and $5,299 in 24-hour activity, reflecting market confidence in different outcomes. You can monitor price movements, historical trends, and trading depth to understand how market sentiment evolves as the Tony Awards approach.

Prediction market odds on Kalshi often diverge from traditional analyst forecasts and award-show punditry. While critics and industry analysts rely on voting patterns and historical precedent, prediction markets incorporate real-money incentives that reward accuracy. Traders on Kalshi continuously update odds based on new information, industry buzz, and voting bloc signals. This dynamic pricing typically reflects a broader consensus than any single analyst opinion, though markets can occasionally overweight recent momentum or underweight established frontrunners. Comparing market odds to published predictions reveals where professional opinion and trader conviction align or diverge.

The Tony Award for Best Performance by an Actress in a Featured Role in a play market on Kalshi resolves at Dec 31, 2026. Resolution is determined by the official winner announced at the Tony Awards ceremony. The market will settle based on the actual award recipient as confirmed by the Tony Awards organization. All contracts for the winning actress will resolve to 100 cents, while all other contracts resolve to zero. Traders should monitor official Tony Awards communications and ceremony coverage to track the announcement in real time.

Several key events and signals can shift odds for Best Performance by an Actress in a Featured Role. Precursor awards—such as wins at the Drama Desk Awards, Outer Critics Circle Awards, or other theater honors—often signal voting momentum. Critical reviews and media coverage of nominated performances can sway trader sentiment. Industry interviews, award-show predictions from prominent critics, and social media campaigns may influence market perception. Changes in voter eligibility or voting bloc composition can also impact odds. Additionally, any breaking news about nominees or unexpected industry developments will trigger repricing on Kalshi as traders reassess probabilities and adjust positions accordingly.