Highlights

• • Market's implied probability for Tagger is very high (~82.6%) and appears overstated versus career-WR-normalized estimate
• • Underdog Malygina's price of 4.03 yields a large positive EV under our conservative normalization method

Pros

• + Significant positive EV at current home price using the available data
• + Simple, transparent calculation based on provided career win rates

Cons

• - Model omits head-to-head, rankings, live fitness, and match-specific context (increasing uncertainty)
• - Elena's small sample size (31 matches) makes her win-rate noisy and higher variance

Details

We compare the two players using the available career win-rate data. Elena Malygina is 10-21 (≈32.3% career win rate) across 31 matches while Lilli Tagger is 559-507 (≈52.4% career win rate) across 1,066 matches. Normalizing those win rates to generate a head-to-head-style probability (Elena_winrate / (Elena_winrate + Tagger_winrate)) gives an estimated true probability for Elena of ≈0.381 (38.1%). The market price for Elena is 4.03 (implied probability ≈24.8%), so the market is underestimating Elena relative to our estimate. Using the current home decimal price 4.03, EV = 0.381 * 4.03 - 1 ≈ +0.535 (53.5% ROI on a 1-unit stake), which indicates clear value at the quoted price. We recognize this model is simplistic (it relies only on the provided aggregate win rates and does not have H2H, ranking, or up-to-the-minute fitness info), so we flag variance risk, but on the data provided the underdog home price offers positive expected value versus the favorite price of 1.211 (which we estimate as overconfident by the market).

Key factors

• • Normalized career win rates (Elena 32.3% vs Tagger 52.4%) produce a 38.1% estimate for Elena
• • Market implies ~24.8% for Elena (4.03) and ~82.6% for Tagger (1.211) — market appears to over-favor Tagger
• • Small sample size for Elena and lack of detailed recent-form/injury data increases variance