66.5 points scored · Dallas vs Las Vegas: Over 1
66.5 points scored is priced at 71¢ on Kalshi. Current book: 73¢ bid, 77¢ ask, 4¢ spread. This outcome ranks #3 of 9 inside Dallas vs Las Vegas: Over 1.
Price history
71¢ current
+44¢Contract brief
If the teams in the Dallas vs Las Vegas women's professional basketball game originally scheduled for Jun 25, 2026 collectively score more than 166.5 points, then the market resolves to Yes.
Outcome
66.5 points scored
Rank
#3 of 9
Leader
60.5 points scored 82¢
Range
29¢-82¢
Family volume
$16K
Identifier
KXWNBATOTAL-26JUN25DALLV-167
Jun 24, 2026, 7:08 PM UTC · 20m ago
Implied probability
Bid
73¢
Ask
77¢
Spread
4¢
24h volume
$33
Family rank
#3 of 9
9 outcomes · Dallas vs Las Vegas: Over 1
Closes
Jul 10, 2026
Family volume
$16K
Orderbook snapshot
73 / 77¢
Contract terms
What resolves this market.
YES condition
If the teams in the Dallas vs Las Vegas women's professional basketball game originally scheduled for Jun 25, 2026 collectively score more than 166.5 points, then the market resolves to Yes.
Venue
Kalshi
Closes
Jul 10, 2026
Identifier
KXWNBATOTAL-26JUN25DALLV-167
Event family
Dallas vs Las Vegas: Over 1.
The same race as a probability stack: rank, volume, and where this contract sits against the other outcomes.
Total volume
$16K
Outcomes
9
Highest price
60.5 points scored 82¢
Current share
0%
60.5 points scored
kalshi · KXWNBATOTAL-26JUN25DALLV-161
63.5 points scored
kalshi · KXWNBATOTAL-26JUN25DALLV-164
66.5 points scored
kalshi · KXWNBATOTAL-26JUN25DALLV-167
69.5 points scored
kalshi · KXWNBATOTAL-26JUN25DALLV-170
72.5 points scored
kalshi · KXWNBATOTAL-26JUN25DALLV-173
75.5 points scored
kalshi · KXWNBATOTAL-26JUN25DALLV-176
78.5 points scored
kalshi · KXWNBATOTAL-26JUN25DALLV-179
81.5 points scored
kalshi · KXWNBATOTAL-26JUN25DALLV-182
84.5 points scored
kalshi · KXWNBATOTAL-26JUN25DALLV-185
Indicators
Yield, cliff risk, volatility, and regime.
Regime
neutral
Score
0.5
Full indicator table
Odds pages
Related prediction questions
Related readings
Matched from SimpleFunctions blog, opinions, technical guides, concepts, and learn pages.
Prediction market liquidity: why depth matters more than volume for serious traders
Why orderbook depth matters more than volume for prediction market traders. Real Kalshi examples, liquidity scoring framework, and how to avoid slippage.
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.
Liquidity Availability Score (LAS)
LAS measures whether a prediction-market orderbook is thick enough to trade against. Formula, the warm-cron null caveat, and why null is itself a signal.
Computing Liquidity Availability Score from the Orderbook
Step-by-step guide to computing the Liquidity Availability Score in TypeScript and Python, with edge cases for thin orderbooks, missing data, and the warm-cron coverage limitation.
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.
Kalshi vs Polymarket: Mechanics, Fees, Regulation, Liquidity (2026)
Side-by-side comparison of Kalshi and Polymarket in 2026. Fee math, calibration data, withdrawal speed, and a decision tree for picking the right venue.
SimpleFunctions context
Index, screen, query, and monitor.
Prediction Market Index
Market-wide volatility, geo risk, breadth, and activity around this contract.
Market Screener
Filter adjacent contracts by volume, expiry, IY, CRI, venue, and theme.
Event Probability API
Read 71% as a structured event probability object for agents and apps.
Realtime Data API
Prices, orderbooks, movement, heat, and liquidity indicators across venues.
World State API
Compact market-aware context packets for agent sessions and scheduled refresh.
Hedging Workflows
Map a thesis or exposure to candidate event markets and monitoring paths.
How we compute these odds
SimpleFunctions aggregates live prediction-market contracts from Kalshi and Polymarket. Each slug groups contracts that resolve on the same underlying event, identified by venue event_id.
For binary slugs, the headline probability is the liquidity-weighted mid-price across all bound contracts. For multi-outcome slugs (e.g. elections with 3+ candidates), the headline is the leader’s price; we never arithmetically average disjoint outcomes — that would produce a number with no real-world meaning.
Snapshots refresh every 5 minutes during market hours; daily aggregates are computed at 04:00 UTC. The 30-day sparkline is drawn from per-ticker daily means stored in market_indicator_daily; 24h delta and movement events are derived from the same source.