SimpleFunctions
KalshiNov 3, 2027543 days left

Will the total vote count for all participants in Michigan Governor General Election be above 5300000?

This contract is priced at 10¢ on Kalshi. Current book: 12¢ bid, 13¢ ask, 1¢ spread.

Implied probability

10¢
$1 volume
$1 liquidity
42% of event volume

Event outcomes

5

Family volume

$2

Best sibling

Above 4.6M 39¢

Ticker

KXMIDTERMVOTETURN-MIGOV-5300000

Market snapshot

Above 5.3M in market context.

This page tracks the Kalshi contract for Will the total vote count for all participants in Michigan Governor General Election be above 5300000?. The displayed quote is 10¢ from the latest venue quote. The cached market record reports reported volume of $1. In the Will the total vote count for all participants in Michigan Governor General Election be above family, this outcome ranks #5 of 5 by current quote across 5 sibling outcomes. The indicator bundle was refreshed May 9, 2026, 9:53 AM UTC.

Outcome

Above 5.3M

Family rank

#5 of 5

Venue

Kalshi

Current quote

10¢

Quote source

Latest venue quote

Timing

Listed until Nov 3, 2027

Reported volume

$1

Family context

5 outcomes · Will the total vote count for all participants in Michigan Governor General Election be above

Quote range

12¢-47¢

Family leader

Above 4.4M 47¢

Last updated

May 9, 2026, 9:53 AM UTC · 3m ago

Venue identifier: KXMIDTERMVOTETURN-MIGOV-5300000. Family volume: $2.

Price history

10¢ current

+5¢
25¢50¢75¢100¢
May 6, 2026May 7, 2026

Orderbook snapshot

12 / 13¢

Kalshi
1¢ spread
BidSize
12¢2.2K
11¢10
6¢1.1K
5¢200
3¢200
AskSize
13¢2.0K
14¢100
16¢200
30¢1
86¢200

Contract terms

Resolution, venue, and identifiers.

Resolution rules

If the total vote count for all participants in Michigan Governor General Election is above 5300000, then the market resolves to Yes.

Venue

Kalshi

Closes

Nov 3, 2027

Identifier

KXMIDTERMVOTETURN-MIGOV-5300000

Event family

Will the total vote count for all participants in Michigan Governor General Election be above.

This view keeps the individual contract next to its sibling outcomes. For long-tail search traffic, this is the useful context: where the current price sits inside the event, how much volume exists around the family, and which outcomes have actual depth.

Total volume

$2

Outcomes

5

Highest price

Above 4.4M 47¢

Current share

0%

Indicators

Yield, cliff risk, volatility, and regime.

IY (Yes)

492.7%

IY (No)

9.2%

Adj IY

246%

CRI

7

Overround

0.5%

Regime

neutral

Score

0.5

Full indicator table

492.7%
9.2%
Adj IY
246%
7
Overround
0.5%

Related readings

Matched from SimpleFunctions blog, opinions, technical guides, concepts, and learn pages.

Browse library
Opinionanalysis

Liquidity Availability Is the Real Edge in Prediction Markets

Implied yield, cliff risk, and overround all describe what to trade. Liquidity Availability Score describes whether the orderbook can absorb the trade. Why LAS is the indicator that decides who actually books P&L.

Blogmarkets

Kalshi vs Polymarket: Which Prediction Market Should You Trade?

In-depth comparison of Kalshi and Polymarket for prediction market traders. Regulatory structure, liquidity, fees, API tooling, and cross-venue trading with SimpleFunctions.

Blogmarkets

Prediction Market Orderbook Analysis: Reading Depth, Spread, and Liquidity

How to read prediction market orderbooks. Binary settlement, spread-as-percentage, depth asymmetry, executable edge calculation, and cross-venue arbitrage analysis.

Technicalguide

Kalshi vs Polymarket: A Developer's Comparison of APIs, Orderbooks, and Liquidity

Data-driven comparison of Kalshi and Polymarket APIs, orderbooks, rate limits, and liquidity. Code examples for building on both prediction markets.

Technicalrisk

Reading Prediction Market Orderbooks: Liquidity, Spread, and When to Enter

How to read prediction market orderbooks on Kalshi. Covers bid-ask spread analysis, liquidity scoring, executable edge calculation, and when thin markets are opportunities vs traps.

Opinionanalysis

Implied Yield vs Raw Probability: Why Bond-Adjacent Prediction Markets Need a Different Lens

Why fixed-income-adjacent prediction-market contracts need to be priced in implied yield, not raw probability, with two real Kalshi Fed-decision contracts as a case study.

SimpleFunctions context

Index, screen, query, and monitor.

Open index