Bundle -- Dota 2 BO3 Structure Trader

This is a template. The default signal is structural inconsistency detection between BO3 winner, game winner, and handicap markets -- remix it with Elo ratings, draft data, or live match feeds. The skill handles all the plumbing (market discovery, match grouping, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Dota 2 BO3 matches spawn multiple related markets on Polymarket: the overall BO3 winner, individual Game 1/2/3 winners, and game handicap lines (-1.5 / +1.5). These markets must be structurally consistent -- the BO3 win probability is a mathematical function of the individual game probabilities. When they diverge, it is pure structural arbitrage.

Example: If P(Team A wins Game 1) = 60% and P(Team A wins Game 2) = 55%, then P(Team A wins BO3) should be approximately 70%. If the BO3 market prices Team A at 55%, it is underpriced by 15%.

Edge

Each market in a Dota 2 BO3 bundle is priced by its own independent order book. Retail participants trade each market in isolation -- they bet on "Team A wins Game 1" without considering how that price constrains the BO3 winner or handicap markets. Professional sportsbooks enforce consistency across related lines; Polymarket does not. The result is persistent structural inconsistencies between mathematically linked markets.

Signal Logic

1. Discover active Dota 2 markets via keyword search + get_markets(limit=200) as primary fallback
2. Parse each question to extract:
   • "Dota 2: Team A vs Team B - Game N Winner" -> (match_key, game_number)
   • "Dota 2: Team A vs Team B (BO3)" -> (match_key, bo3_winner)
   • "Game Handicap: Team A (-1.5) vs Team B (+1.5)" -> (match_key, handicap_team, handicap_value)
3. Group by match_key
4. For each match with BO3 + game winners:
   • Calculate implied BO3 from game probabilities: P(BO3) = P(G1)*P(G2) + P(G1)*(1-P(G2))*P(G3) + (1-P(G1))*P(G2)*P(G3)
   • Compare implied BO3 to market BO3 price
   • Check handicap consistency: P(handicap -1.5 covers) ~ P(G1) * P(G2) (must win both games for 2-0)
5. Trade inconsistencies exceeding SIMMER_MIN_INCONSISTENCY (default 8%)
6. Size by conviction (inconsistency magnitude), not flat amount

Remix Signal Ideas

• OpenDota API: Pull team Elo ratings and recent match history -- compare Elo-implied game win probabilities to market prices for an additional edge layer
• Draft-phase data: Dota 2 drafts dramatically affect game-level win probability -- if a team drafts a late-game composition, their Game 1 probability drops but Game 3 rises, creating systematic mispricing in early-game markets
• PandaScore live feed: Stream real-time match data during a BO3 -- after Game 1 result is known, Game 2/3 and BO3 markets should adjust instantly but often lag by minutes
• Hero win rate data: Cross-reference the drafted heroes against Dotabuff/OpenDota hero win rates for the current patch to estimate game-level probabilities more accurately than market prices reflect

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

Professional sportsbooks employ line-setters who enforce mathematical consistency across BO3 winner, game winner, and handicap lines for the same match. Polymarket has no such mechanism -- each market is an independent order book. Dota 2 BO3 markets are particularly vulnerable because:

• Three game winner markets plus BO3 winner plus handicap lines create 5+ related markets per match
• Each market attracts different participants (BO3 bettors vs game-level bettors vs handicap traders)
• Directional flow on one market (e.g., big bet on Game 1 winner) does not propagate to the BO3 or handicap markets
• Draft-phase information affects game-level probabilities asymmetrically but BO3 markets don't adjust
• Tournament context (elimination vs group stage) changes how teams approach individual games vs the series
• After Game 1 result, the conditional probabilities shift dramatically but market repricing is slow

This skill enforces the structural constraint P(BO3) = f(P(G1), P(G2), P(G3)) and trades the repair.

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

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