WNCAAB Desk Strategy Spec

Extracted from Sports Desk Merged Spec (2026-03-15)

Full merged reference: spec-final.md

OVERVIEW

The WNCAAB (Women's College Basketball) desk detects mispricings between Kalshi alt line prices and Pinnacle sharp book fair value for women's college basketball games. Same core thesis as all desks: Pinnacle is the anchor, prediction markets lag.

WNCAAB has less data coverage, wider lines, and thinner liquidity compared to NCAAB. This creates both opportunity (larger mispricings when they exist) and risk (fewer data sources to validate edges, harder to execute).

CORE EDGE DETECTION

How It Works

1. Get Pinnacle's odds for a WNCAAB game (moneyline, spread, total)
2. Remove the vig to get the "fair" probability
3. Compare that fair probability to Kalshi's price for the same market
4. If Kalshi's price is off by more than 4 cents after fees, that's a tradeable edge

De-Vig Method

2-Way Markets (Spreads, Totals, Moneylines) — Multiplicative method:

total_implied = implied_home + implied_away
novig_home = implied_home / total_implied
novig_away = implied_away / total_implied

Kalshi Fee Impact

Kalshi charges 7% fee on profit.

Minimum net edge: 4 cents after fees. Consider raising to 5 cents for WNCAAB given thinner data — model confidence is lower.

Sweet spot: implied probabilities between 35% and 65%.

Position Sizing (Kelly Criterion)

Use 1/4 Kelly for WNCAAB bets:

bet_size = min(bankroll * full_kelly * 0.25, bankroll * 0.05)

Hard Caps:

• Maximum single bet: 5% of bankroll
• Maximum per game: 10% of bankroll
• Maximum daily WNCAAB exposure: 20% of bankroll (lower than men's due to less data confidence)

WNCAAB-SPECIFIC CONSIDERATIONS

Data Scarcity

WNCAAB has significantly less public analytics coverage than NCAAB:

• No KenPom equivalent with the same depth
• BartTorvik covers WNCAAB but with less granularity
• Fewer rating models available
• Less historical data for backtesting

Implication: Model confidence is inherently lower. Compensate by:

• Requiring larger minimum edges (consider 5 cents vs 4 for men's)
• Reducing position sizes
• Being more conservative on deep tail bets

Efficiency Ratings

Talent Parity

WNCAAB has even larger talent gaps than NCAAB:

• Dominant programs (UConn, South Carolina, etc.) create 30-40 point spreads
• Model is most reliable for competitive matchups
• For spreads > 20 points, do not bet — model accuracy drops significantly

Market Characteristics

• Thinner liquidity: Fewer traders, wider bid/ask spreads
• Larger mispricings: When edges exist, they're often bigger than NCAAB
• Slower market adjustment: Kalshi prices update more slowly for WNCAAB
• Less Pinnacle coverage: Pinnacle may not offer lines for all WNCAAB games — only bet when Pinnacle anchor exists

Tournament Play (March Madness)

• Women's tournament has expanded to 68 teams (same as men's)
• Public interest has grown significantly — Kalshi volume increasing
• Same tournament considerations as NCAAB: neutral sites, motivation asymmetry, single-elimination variance

WNCAAB DATA SOURCES

Rating Models

Average available models. Exclude NULL silently.

WHEN NOT TO BET (Hard Rules)

COLLECTION SCHEDULE

[11:00 PM ET prior night]
  - Fetch tomorrow's WNCAAB schedule from ESPN API
  - Check if Pinnacle has lines (only proceed if anchor exists)

[9:00 AM ET — Early Snapshot]
  - Odds API: Pinnacle WNCAAB lines (if available)
  - Kalshi: Pre-game alt line snapshot
  - Calculate no-vig probabilities, run edge scan

[11:00 AM ET — Model Collection]
  - HerHoopStats
  - BartTorvik WNCAAB

[2:00 PM ET — Mid Snapshot]
  - Odds API: Pinnacle update
  - Kalshi: Mid-day snapshot

[30 min before game]
  - ESPN injury check
  - Pre-close polling

[10 min before game — Closing Snapshot]
  - Final closing lines + CLV baseline

[After game → Grade edges, update Brier scores]

SETTLEMENT AND LEARNING

Same framework as NCAAB:

• Track every edge with full metadata
• Brier scores per market type
• Monthly review with performance by spread size bucket

Additional WNCAAB tracking:

• Compare model accuracy vs NCAAB (is the thinner data actually hurting?)
• Track which games had Pinnacle lines vs which didn't
• Monitor if mispricings are systematically larger than NCAAB (expected due to thinner markets)

RISK MANAGEMENT

QUICK REFERENCE: KEY CONSTANTS

TODO: Upgrade Kalshi REST to WebSocket Feed

Status: NOT BUILT — add when this desk goes live for execution

Current state: All Kalshi data (prices, order books, trades) is fetched via REST API polling on cron schedules. This is fine for edge detection and monitoring, but NOT sufficient for live trade execution.

Why WebSocket matters:

• REST polling introduces lag — by the time you see a price, it may already be gone
• On tight edges (2-5 cents), even 1-2 seconds of lag can eat the entire edge
• WebSocket gives real-time order book deltas and trade notifications
• Essential for hitting specific fill prices and monitoring execution quality

What to build when ready:

1. Connect to Kalshi WebSocket feed (wss://trading-api.kalshi.com/trade-api/ws/v2)
2. Subscribe to order book channels for active tickers
3. Stream trade events for real-time freshness (replaces REST trades polling)
4. Feed live prices into execution engine for precise entry/exit
5. Update Fill Quality Monitoring to measure slippage vs quoted price

Note: Sports markets use RFQ (Request for Quote) so the visible order book is usually empty — but the trade feed still matters for freshness signals and the WebSocket is required for order submission. Weather markets DO have real visible order books where this upgrade is even more critical.

Added 2026-03-29 — upgrade REST to WS when desk moves to live execution