Methodology

The 6 key model inputs

• Recent form: rolling performance and matchup trend signals.
• Odds movement: market drift/steam and relative price efficiency.
• xG (football): expected goals profile instead of raw scoreline noise.
• Serve/return stats (tennis): first serve %, break points, return points won.
• Weather: conditions that materially impact totals and scoring pace.
• Injury/availability data: lineup impact and player-out adjustments.

Confidence scoring

Each market gets a model probability and confidence score. Tips only qualify at 65%+, then are tiered VALUE (65–74%), STRONG (75–84%), and BANKER (85%+). This keeps staking consistent and avoids forcing low-edge plays.

Why XGBoost

XGBoost handles mixed feature types, non-linear interactions, and noisy sports data better than simple linear models. It performs well when combining structured stats, contextual match factors, and market signals in one stack.

Backtesting approach

Models are tested on historical out-of-sample periods, with threshold checks by sport and market type. We compare strike rate, units P&L and ROI under fixed staking rules, then keep only configurations that remain stable across multiple windows.

Limitations

Models can be wrong. Markets adapt. Short-term variance is unavoidable. Past performance is not indicative of future results. Tips are informational only and not financial advice.