48h Player Prop Consistency Trader

This is a template. The default signal is cross-stat consistency analysis for NBA player props -- remix it with additional sports, stat types, or team-level hierarchy checks. The skill handles all the plumbing (market discovery, player grouping, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Polymarket lists multiple stat lines for the same NBA player:

• "Jayson Tatum: Points O/U 23.5" = 51.5%
• "Jayson Tatum: Rebounds O/U 8.5" = 51.5%
• "Jayson Tatum: Assists O/U 4.5" = 51.0%

Retail trades each stat in isolation. But these stats share the same underlying game context -- minutes played, matchup difficulty, pace, injury status. When all stats for a player deviate in the same direction, it is a confirmed signal. When one stat diverges, it is likely mispriced.

The Edge: Cross-Stat Consistency

Signal Type 1: Confirmation (All Stats Agree)

When all of a player's stats deviate from 50% in the same direction:

Derrick White: Points O/U 16.5 = 22%   (below 50%)
Derrick White: Rebounds O/U 4.5 = 20.5% (below 50%)
Derrick White: Assists O/U 4.5 = 20.9%  (below 50%)

All three stats say "under is likely." This multi-stat confirmation boosts conviction on each individual market.

Signal Type 2: Outlier Divergence (One Stat Disagrees)

When most stats agree but one diverges:

Player X: Points O/U 20.5 = 35%   (below 50% -- consensus: under)
Player X: Rebounds O/U 6.5 = 33%   (below 50% -- consensus: under)
Player X: Assists O/U 5.5 = 65%    (above 50% -- OUTLIER!)

The assists line disagrees with the points and rebounds consensus. This outlier is likely mispriced -- trade it toward the consensus direction.

Why This Works

1. Retail trades in silos -- most users evaluate each stat line independently without cross-referencing other stats for the same player
2. Shared game context -- a player's minutes, matchup, and pace affect all stat categories simultaneously
3. No cross-stat enforcement -- unlike sportsbooks, Polymarket has no mechanism to maintain consistency across a player's prop lines
4. Statistical confirmation -- when 2-3 stats agree, the probability of the consensus being correct is significantly higher than any single stat alone

Signal Logic

1. Discover all NBA player prop markets via keyword search (Points O/U, Rebounds O/U, Assists O/U)
2. Parse each question: extract player name, stat type, O/U line value
3. Group markets by player
4. For each player with 2+ stat lines:
   • Check if all stats deviate from 50% in the same direction (confirmation)
   • If not, identify the outlier stat that disagrees with the consensus
5. Trade outliers toward the consensus direction; boost conviction on confirmed signals
6. Size by conviction (divergence magnitude + cross-stat boost), not flat amount

Safety & Execution Mode

The skill defaults to paper trading (venue="sim"). Real trades only with --live flag.

autostart: false and cron: null mean nothing runs automatically until configured in Simmer UI.

Required Credentials

Tunables (Risk Parameters)

All declared as tunables in clawhub.json and adjustable from the Simmer UI.

Edge Thesis

Traditional sportsbooks use correlated models that price a player's points, rebounds, and assists as a coherent package. Polymarket has no such mechanism -- each stat line is priced by its own order book with its own liquidity pool. This creates systematic inconsistencies when:

• A sharp bettor pushes one stat line without the others following
• New information (injury report, lineup change) propagates unevenly across stat types
• Low-liquidity stat lines (assists, rebounds) lag behind high-liquidity lines (points)
• Market makers leave cross-stat gaps during off-hours

This skill treats a player's stat lines as a correlated bundle and trades the repair when they diverge.

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

• PyPI: https://pypi.org/project/simmer-sdk/
• GitHub: https://github.com/SpartanLabsXyz/simmer-sdk