Sports Prediction Market Trading — FIFA World Cup 2026 Win Simulation

How It Works This strategy applies a sports analytics simulation model to FIFA World Cup 2026 markets on Polymarket. The model likely uses team ELO ratings, historical head-to-head data, and/or current squad statistics to run Monte Carlo or bracket simulations, generating win probability distributions for each team. These are compared against Polymarket's implied probabilities to identify underpriced and overpriced teams.

Entry Conditions Buy YES on teams where the simulation assigns a meaningfully higher win probability than the market price implies. Conversely, sell (or buy NO) on overpriced favorites. The tournament bracket structure creates compounding probability paths — model edge is amplified for teams with favorable draw brackets that the market may not fully price.

Exit Conditions Positions can be held to resolution (tournament end) or traded out as teams advance/are eliminated and prices reprice. The ticket resale system component mentioned in the repo description is unrelated to the prediction market strategy.

Verified Results & Implementation Notes `polydex4/2026-worldcup-prediction-market` (10705KB, 1 star) is the primary repo. The large repo size suggests substantial code, possibly including the simulation engine. Sports prediction markets on Polymarket tend to attract less sophisticated liquidity than political markets, potentially offering more persistent mispricings. The World Cup 2026 is an active, timely market as of the report date (June 20, 2026).

Risks Sports simulation models have well-known limitations — upsets are structurally undermodeled by ELO-based systems. Player injuries, weather conditions, and tournament momentum are difficult to quantify. World Cup markets attract global attention and may be efficiently priced by professional sports bettors who migrate arbitrage from regulated sportsbooks. The repo description is heavily SEO-oriented, raising questions about implementation depth.