CÓMO IMPORTA CADA FACTOR
Level (Elo/ranking)▸ Montgomery●●●
Montgomery (Elo 1663, #199) outranks Cross (1490, #163) by 173 Elo points. Model gives Montgomery 59%, anchored in this skill gap.
Serve/Return▸ Montgomery●●
Montgomery serves 64% vs Cross's 55%, a 9-point edge. Return is near parity (Montgomery 43%, Cross 41%). Serve advantage favors Montgomery on hard court.
Surface▸ Montgomery●●
Montgomery shoots 67% on hard, 11 points above her 56% baseline. Cross data absent, but Montgomery's hard-court strength is clear and material.
Rest/Fatigue▸ Cross●●
Cross rested 19 days; Montgomery returns after 55 days with only 2 matches in 14 days. Long layoff risks rustiness and match sharpness loss for Montgomery.
Form= Igualado●
Both 5–10 in last ten matches, both on losing streaks (−1). No quality wins recorded for either. Forms are equivalent and weak.
Weather= Igualado●
25°C, 54% humidity, 18 km/h wind. Warm and moderately windy; no extreme altitude or humidity to heavily penalize a particular style.
SKILL ADVANTAGE
Montgomery holds a clear and substantial edge in underlying level. Her Elo rating of 1663 sits 173 points above Cross's 1490, a gap that typically translates to roughly a 59% win probability—exactly what the model calculates. Her ranking of #199, while higher, reflects a smaller market signal than Elo; Cross at #163 is the higher-ranked player, but Elo better captures recent form and consistency.
On the hard court, Montgomery's serve becomes a material weapon: 64% versus Cross's 55% is a meaningful 9-point differential. Montgomery's 67% hard-court conversion (11 points above her 56% baseline) shows she is genuinely more effective on this surface, likely through aggressive serving and volley pressure. Cross's return metrics (41%) are not strong enough to neutralize that serve advantage.
REST AND RUST
Cross arrives well-rested at 19 days since her last match, with zero competitive matches in the last 14 days. Montgomery, conversely, is returning from a 55-day layoff—one of the longest absences in professional tennis—though she has played only 2 matches in the past 14 days. This inversion of rest presents a genuine trade-off: Montgomery may feel fresher in the legs after a long break, but she also risks match rustiness, particularly in pressure moments.
In the context of a Grand Slam first or early round, Cross's recent competitive rhythm is an advantage that can offset some of Montgomery's skill edge. However, the model's 59% probability already reflects this; it is not an edge large enough to flip the favorite.
FORM AND SURFACE CONTEXT
Both players are in equivalent and unimpressive recent form: 5–10 records in their last ten matches, both on losing streaks, and neither has recorded quality wins to suggest momentum. On hard court, where both have been tested, Montgomery's documented 67% conversion provides real evidence of comfort; Cross's hard-court percentage is unmeasured in the data. The US Open hard court—fast, consistent, and favoring big servers—aligns with Montgomery's strengths more than Cross's documented profile.
Weather (25°C, 54% humidity, 18 km/h wind) is warm and moderately windy but not extreme. The wind may slightly inhibit precision, but neither player is flagged as wind-sensitive, and the conditions do not strongly favor one over the other.
VALUE AND ODDS ANALYSIS
The market prices Montgomery at 1.17 (85% implied probability), a sharp overestimate relative to the model's 59%. The expected value of backing Montgomery at these odds is −30.4%, meaning a long-term gambler laying 1.17 would expect to lose 30 cents per dollar wagered. Montgomery is the deserving favorite, but she is significantly overpriced.
The model reflects genuine structural advantages for Montgomery (serve, hard-court skill, Elo), but does not grant her the 85% certainty the market does. Rest and form noise are real. At 1.17, this is not a recommended play despite Montgomery being the stronger player. A sharper assessment of true probability (around 59%) would require odds closer to 1.70 to represent positive value.
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.