| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| NIP | 99% | 99¢ | 100¢ | — | $23K | Trade → |
| M80 | 1% | 0¢ | 1¢ | — | $22K | Trade → |
This market asks which team will win Map 2 of the ESL Pro League 2026 match between M80 and NIP; map outcomes affect the match result and reveal map-specific strengths that bettors and analysts track. It matters because map-by-map performance is often more predictive than overall reputation for a single match.
The ESL Pro League is a recurring international league featuring many of the top Counter-Strike teams; event formats commonly use a combination of best-of-one and best-of-three series, with Map 2 often pivotal in best-of-three ties. M80 and NIP are professional squads participating at this level—historical form, roster continuity, and recent patch changes all shape expectations for any given map.
Market prices on Kalshi reflect the collective information available at the time (lineups, map picks, injuries, recent form) and update in real time; treat them as a snapshot of market sentiment rather than a fixed truth. Use them alongside independent analysis of map vetoes, player roles, and match context.
Kalshi will settle the market based on the official Map 2 result as recorded by the tournament organizer (ESL); if an official scoreboard shows a winner for Map 2, that result governs settlement.
If Map 2 is not played or the match format means no second map occurs, Kalshi will follow its stated settlement policy for unplayed maps—check the market page and Kalshi rules for whether the market is voided or settled using an alternate rule.
Map 2 is determined by the match’s veto/pick sequence (which varies by format); in best-of-three series teams typically ban and pick in an order that produces Map 2 explicitly, so follow the official matchroom or broadcast to see the actual Map 2 selection.
Prioritize map-role matchups: the performance of primary AWPers, entry fraggers, the in-game leader’s tactical calls, and utility usage patterns on the chosen map—those roles most often swing individual map outcomes.
Map-specific head-to-head data is useful if it’s recent and involves the same or similar rosters and patch conditions; give greater weight to recent matches on the same map and treat older or roster-different results with caution.