| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Over 149.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 122.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 119.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 131.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 137.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 128.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 140.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 143.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 125.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 146.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 134.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This market asks how many combined points will be scored in the college basketball game between UT Arlington and Utah Valley; it matters to traders and fans who want to express views on scoring outcomes and game tempo.
UT Arlington and Utah Valley are NCAA Division I programs with differing styles that can influence total scoring — one team may emphasize halfcourt sets while the other prefers a faster pace. Home-court factors, recent team form, and roster availability are all part of the context that shapes expectations for this specific matchup.
Market prices aggregate participant views about the likely combined score; each outcome represents a range or threshold for total points, so prices indicate how the market ranks those ranges relative to one another rather than a single deterministic forecast.
The event page lists the close as TBD; check the platform for real-time updates because markets typically set a definitive close time before tipoff and may suspend trading shortly before the game starts.
The 11 outcomes represent discrete total-score ranges or thresholds that partition possible combined scores; consult the market interface for the exact bins or boundary points used in this listing.
Relevant data include recent head-to-head meetings (if any), each team’s seasonal scoring and allowed points trends, last several games’ totals, and any shifts in lineup or style that occurred during the season; small sample sizes can limit reliability.
Look for changes to each team’s primary scorers, ball handlers, three-point specialists, and key defenders — a bench player stepping into a larger role or a starter being ruled out can materially alter offensive output and pace.
Treat late news as potentially market-moving: verify sources (team reports, official injury lists), reassess expected pace and scoring based on the change, and be aware that markets often update quickly as participants react to confirmed roster information.