F. Cerundolo vs A. Gea — prediction
Consistent bounce, medium-fast: neutral conditions, no style favored.
Strong heat: warm air speeds the ball up and physical wear tells in long matches.
Dry air: the ball travels normally.
Some wind: makes baseline control harder.
Surface feeds the model (surface specialization is one of its factors). Weather and altitude are context we publish for you — they do NOT move the probability.
›Ranking: #21 vs #135 (better ranked)
›Model 79% vs market 67% → the model sees it as MORE likely than the odds
The clearest signal in this match is the gap in level: Cerundolo sits at #21 against Gea's #135, and the baseline model already assigns him 65% before adjustments. Once form, rest and other factors are layered in, the model lands at 79%, well above the market's 67% implied probability, which is where the model's edge comes from.
This is a case where the ranking gap is large enough to dominate the read, even though the underlying serve/return snapshot doesn't tell the same story. The model is treating Cerundolo's overall level and consistency at tour level as more predictive than the specific point-by-point splits.
Looking purely at the serve and return percentages, Gea is not overmatched: he holds 64% of service points against Cerundolo's 61%, and returns slightly better too (40% vs 39%). If this were the only data point, it would suggest a near-even contest at the point level.
That gap between the point-level numbers and the lopsided 79-21 model output is worth flagging honestly: the model's confidence is being driven mainly by ranking and contextual factors like rest, not by the serve/return metrics, which are the market's typical lifeblood in-match.
Gea arrives in good form: 7 wins in his last 10 matches, a 2-match win streak, and a notable win over S. Kwon (Elo 1910), all of which cut against a simple ranking-based read.
But that form comes at a cost — Gea has played 5 matches in the last 14 days and has just 1 day of rest before this one, a real fatigue liability in a tour-level match. Cerundolo, by contrast, carries a 30-day layoff risk instead, so both players enter with some question marks, just different ones.
The 33°C heat and dry air in Los Cabos can quicken the ball and reward the better server, but with serve numbers this close (61% vs 64%) there's no clear directional edge to assign here. The 23 km/h wind could disrupt precision play, but no data isolates either player's sensitivity to it, so this factor stays neutral.
The model's 79% is notably higher than the market's 67%, producing a stated EV of +19.1% at 1.50 odds. That gap is real but should be read with some caution: it is driven heavily by the ranking disparity and Gea's rest deficit, while the actual serve/return numbers show a much tighter contest than the headline probability implies.
Favorite does not equal a safe bet — Cerundolo being favored is consistent with both the model and the market, just to different degrees. The value here is plausible given the schedule congestion working against Gea, but it should be treated as a moderate edge rather than a lock, since the point-level data doesn't fully back the size of the gap.
Impact and analysis from real match data (Elo, form, head-to-head, rest, surface vs baseline, weather, altitude). The model ≈ the market on average; the odds already capture almost all the edge. 18+ · gamble responsibly.