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UCLA at USC: Spread

📊 $2K traded 🏦 Source: Kalshi
Total Volume
$2K
Open Interest
1,384
Active Markets
11
Markets
11

Trade This Market

Yes Bid
Yes Ask
Last Price
Prev Close
Buy YES → Buy NO

Prices in cents (1¢ = 1%). Trade on Kalshi.

All Outcomes (11)
Outcome Probability Yes Bid Yes Ask 24h Change Volume
UCLA wins by over 4.5 Points 58%
56¢ 58¢ $1K Trade →
UCLA wins by over 7.5 Points 46%
43¢ 47¢ $479 Trade →
USC wins by over 17.5 Points 3%
$50 Trade →
USC wins by over 20.5 Points 3%
$50 Trade →
UCLA wins by over 10.5 Points 31%
31¢ 36¢ $40 Trade →
USC wins by over 5.5 Points 23%
13¢ 22¢ $1 Trade →
UCLA wins by over 1.5 Points 68%
64¢ 70¢ $1 Trade →
USC wins by over 8.5 Points 0%
11¢ 15¢ $0 Trade →
USC wins by over 2.5 Points 0%
22¢ 29¢ $0 Trade →
USC wins by over 11.5 Points 0%
12¢ $0 Trade →
USC wins by over 14.5 Points 0%
$0 Trade →

About This Market

This market asks which spread range will best describe the final margin in the UCLA at USC game; it matters because spreads summarize expected margins and drive how traders express views on competitive balance. The market currently lists 11 discrete spread outcomes and has traded $1,738 in volume; closing time is TBD.

UCLA and USC are long-standing rivals with frequently close games; recent seasons, coaching changes, and roster turnover have influenced matchup dynamics. Historical head-to-head trends, each program's recruiting cycles, and the specific game context (home stadium, season timing, injuries) shape how bookmakers and markets set spreads. Because this is a rivalry with emotional and tactical intensity, in-game adjustments and situational factors often matter more than long-term metrics.

Prediction market prices here reflect the collective market view on which spread-range outcome is most likely, and they move as new information arrives. Use prices as a snapshot of market expectations rather than a fixed forecast — they update with injuries, lineup news, weather, and betting flow.

Key Factors

Frequently Asked Questions

How does a late injury to UCLA's starting quarterback affect the UCLA at USC: Spread market?

A late injury to a starting quarterback is high-impact news that typically increases uncertainty and shifts market pricing toward outcomes favoring the team less affected. Because this market has modest volume, such news can produce large price moves quickly as traders repriced expectations for the final margin.

What pieces of pregame information are most likely to move the UCLA at USC: Spread outcomes?

Key movers include confirmed starting lineups, official injury reports, kicker/returner availability, and notable weather or field-condition updates. Publicized coaching decisions (e.g., a starter rested or opt-out announcements) and large directional bets reported in the market can also change prices.

How should I interpret the market if USC is listed as the home team for the UCLA at USC: Spread?

Home status means USC will play in its stadium, which typically contributes to a small-to-moderate advantage due to crowd, travel, and familiarity with venue. The market incorporates that edge into the listed spread outcomes, but other factors like matchup-specific advantages and injuries can outweigh home-field effects.

Does historical UCLA vs USC rivalry data matter for predicting the UCLA at USC: Spread outcome?

Historical rivalry context provides qualitative insight—coaches’ familiarity, rivalry intensity, and typical game scripts—but each game is also driven by current rosters, form, and situational factors. Use historical trends as background, not a determinative rule.

When will this UCLA at USC: Spread market close and how will that affect trading?

The market currently lists its close time as TBD; organizers typically set a definitive close before kickoff. As closing approaches, prices can become more sensitive to last-minute news and can move faster because participants have less time to react to new information.

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