CÓMO IMPORTA CADA FACTOR
Level (Elo/ranking)▸ Grabher●●●
Grabher #113 vs Montgomery #200: 87 ranking places gap favors opponent. Montgomery trending worse (284 vs 8). Model assigns 50%, market 74% to favorite—model skepticism justified.
Surface▸ Montgomery●●●
Hard court: Montgomery 67% baseline vs 56%, Grabher 23% vs 25%. Montgomery gains 11 points on surface; Grabher loses 2. Significant edge to favorite on hard.
Serve/return▸ Montgomery●●
Montgomery serves 65% vs Grabher's 57% (+8 pts). Both return equally at 45%. Montgomery's serve advantage on hard court compounds her structural edge.
Form▸ Grabher●●
Grabher: 5–5 in last 10, streak −1 (recent loss). No quality wins listed. Poor form undercuts opponent's ranking advantage, but weakness is real and recent.
Rest= Igualado●
Grabher: 12 days since last match, 2 matches in 14 days. Adequate recovery; no fatigue signal. Montgomery rest unknown—cannot assess comparative advantage.
Weather▸ Montgomery●
30°C, 60% humidity, 14 km/h wind. Heat and humidity slow rally tempo; 14 km/h wind affects precision. Favors Montgomery's higher serve/baseline consistency marginally.
Ranking Paradox
Grabher holds #113, Montgomery #200—an 87-place gap that conventionally signals clear opponent advantage. Yet the model assigns them equal 50% probability each, while the market pushes Montgomery to 74%. This disconnect reveals the crux: Montgomery's hard-court credentials (67% baseline) and serve power (65%) compress the ranking gap significantly on this surface, and Grabher's recent form—5 wins, 5 losses with a current losing streak and zero quality victories—erodes the reliability of her ranking position.
Montgomery's ranking trend (284) is deteriorating faster than Grabher's (8), a warning sign. However, ranking is a lagging indicator; Grabher's form decline is immediate and on the record. The model is correct to treat them as near-equals: ranking matters, but surface fit and serve quality matter as much here.
Surface Edge Decisive
Hard court is Montgomery's terrain. She wins 67% of points on hard vs her 56% baseline—an 11-point premium that translates to a structural advantage in first-serve win rate and baseline rally conversion. Grabher drops to 23% on hard, 2 points below her 25% baseline: the surface dulls her game slightly, but her true weakness is that 23% is poor in absolute terms.
Combined with Montgomery's 65% serve (8 points above Grabher's 57%), the favorite gains traction. On hard, with heat and moderate wind, Montgomery's flatter, more aggressive serve style should dominate Grabher's return game (both are 45%). The surface is not neutral; it tilts distinctly toward Montgomery.
Momentum vs. Tier
Grabher arrives with recent losses (streak −1) and no quality wins to anchor confidence. She is a ranked player facing a lower-ranked opponent, yet her form is fragile. Montgomery's data omits recent match detail, but her hard-court profile (67%) suggests consistency. Grabher's 12-day rest is adequate, but it follows two matches in 14 days—a mild workload that does not excuse the current losing streak.
This is not a case where rest or fatigue swings the match. It is about Grabher carrying form doubt into a surface where she underperforms (23%), against a serve she struggles to break (Montgomery 65% on serve, Grabher returning at 45%).
Value & Model Honesty
The model estimates Montgomery at 50%; the market prices her at 74% (odds 1.35). Expected value for backing Montgomery is −31.8%—a substantial negative edge. The market is overpricing the favorite. This is not because Montgomery is unlikely to win; she is favored by ranking position, surface fit, and serve quality. It is because the 74% probability overweights those advantages and ignores Grabher's ranking (113 is still credible) and the inherent variance in tennis.
For a player at 50% true probability, 1.35 odds (74%) represent poor value. The favorite is the favorite, but at these odds, she is not a sound bet. If you lean toward Montgomery, the logic is sound; the price is not. Grabher, despite form weakness, retains ~26% true probability and faces a market implying 26%—approximately fair.
Impacto y análisis a partir de datos reales del partido (Elo, forma, cara a cara, descanso, superficie vs base, clima, altitud). El modelo ≈ el mercado de media; la cuota ya captura casi toda la ventaja. +18 · juega con responsabilidad.