Lina Gjorcheska vs Noemi Basiletti

Highlights

• • Current market overprices the heavy favorite (Lina) relative to a simple win-rate-based model
• • At 3.25, Basiletti's implied probability (30.8%) is well below our 38.1% estimate

Pros

• + Clear numerical gap between market-implied and model-implied probabilities
• + Underdog volatility (small sample) increases chance of profitable surprises

Cons

• - Model is simple and driven by career win rates; lacks head-to-head or up-to-date injury/form detail
• - Baselitti's poor raw win rate and limited match experience mean actual upset probability could be lower

Details

We estimate value on Noemi Basiletti because the market strongly favors Lina Gjorcheska at 1.285 (implied ~77.8%) while a simple, transparent strength comparison using the available career win rates implies a materially higher chance for Basiletti than the market gives her. Lina Gjorcheska's career record (559-507, ~52.5% win rate) versus Noemi Basiletti's (10-21, ~32.3% win rate) produces a relative-strength-derived probability for Basiletti of about 38.1%. That converts to a fair decimal price ≈2.63; the current offer of 3.25 therefore represents positive expected value. We recognize Basiletti's small sample size and form noise increase variance, but the odds on offer (~3.25) pay more than we judge Basiletti's true chance, producing an EV of roughly +0.238 per unit at the quoted price. We use the posted 3.25 decimal price for the EV calculation and present a conservative relative-strength method rather than overfitting to sparse recent-match lines.

Key factors

• • Career win-rate gap (Gjorcheska ~52.5% vs Basiletti ~32.3%) used to derive relative win probabilities
• • Market-implied probability for favorite is steep (1.285 => ~77.8%), creating room for underdog value
• • Baselitti's small sample size increases variance — higher upset potential than raw form suggests