| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Over 149.5 points scored | 47% | 47¢ | 48¢ | — | $846 | Trade → |
| Over 140.5 points scored | 72% | 66¢ | 72¢ | — | $10 | Trade → |
| Over 155.5 points scored | 0% | 31¢ | 36¢ | — | $0 | Trade → |
| Over 143.5 points scored | 0% | 59¢ | 64¢ | — | $0 | Trade → |
| Over 137.5 points scored | 0% | 71¢ | 78¢ | — | $0 | Trade → |
| Over 164.5 points scored | 0% | 14¢ | 21¢ | — | $0 | Trade → |
| Over 152.5 points scored | 0% | 38¢ | 43¢ | — | $0 | Trade → |
| Over 134.5 points scored | 0% | 76¢ | 82¢ | — | $0 | Trade → |
| Over 161.5 points scored | 0% | 19¢ | 25¢ | — | $0 | Trade → |
| Over 146.5 points scored | 0% | 54¢ | 56¢ | — | $0 | Trade → |
| Over 158.5 points scored | 0% | 25¢ | 30¢ | — | $0 | Trade → |
This market asks how many total points will be scored in the Little Rock at Lindenwood game; it matters because the market aggregates real-money trader views about whether the game will be high- or low-scoring.
The event pits two collegiate programs against one another; totals markets are a common way to trade expectations about game pace and scoring rather than which team wins. This market currently offers 11 distinct outcomes, has had $856 in reported volume, and its official close time is listed as TBD on the platform.
Market prices reflect the aggregate expectations of traders about which total-points range will occur; use them as a real-time summary of market sentiment while remembering to review the contract text for exact settlement rules.
The event page currently lists the close time as TBD; check the market on the platform for final timing and any updates before placing trades.
Each outcome corresponds to a specific point-total bucket or threshold defined by the market contract; view the market description on the platform to see the exact ranges and labels used for settlement.
Settlement rules vary by contract—some totals count only regulation, others include overtime—so consult the event's settlement terms on the platform to know which applies here.
Adjust expectations for scoring when key shooters or playmakers are out or limited; think about both offensive output and how a changed lineup affects pace and defensive matchups, and update positions as confirmed news arrives.
Head-to-head data can help but is often a small sample and may not reflect current rosters or styles; combine it with recent season scoring trends, opponent-adjusted metrics, and contextual factors like venue and rest for a more robust view.