L. Darderi vs D. Svrcina — 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 #114 (better ranked)
›Recent form: 6/10 in recent matches
›Match-sharp: 7 matches in the last 2 weeks
›Model 76% vs market 62% → the model sees it as MORE likely than the odds
Darderi's Elo rating of 1963 sits 129 points above Svrcina's 1834, a gap that mirrors the ranking difference between #21 and #114. The model's baseline probability reflects this cleanly: 57% for Darderi against 33% for Svrcina, a 24-point separation before any other factor is layered in. This is the single largest driver of the overall 76% figure.
Darderi's raw serve number (64%) is stronger than Svrcina's (58%), but the cross-matchup is more balanced than it first appears. Darderi's serve beats Svrcina's return (45%) by 19 points, yet Svrcina's own serve (58%) beats Darderi's return (35%) by an even larger 23 points — meaning Svrcina is not simply outclassed on the exchanges most relevant to his own service games.
The 34°C heat and dry conditions tend to speed up the ball and reward the more effective server, which numerically is Darderi. The 27 km/h wind adds a layer of unpredictability that could disrupt precision for either player, but it isn't tied to a specific number for either man here.
Darderi's recent form (6/10, including wins over Hanfmann and Faria, both Elo 1900+) is clearly stronger than Svrcina's 5/10 with no notable scalps. That said, Darderi has played 7 matches in the last 14 days and comes in on just 4 days of rest after a semifinal run at Estoril, while Svrcina has been idle for 15 days with zero matches in that span. This sets up a real trade-off between match sharpness and physical freshness that the win-loss record alone doesn't capture.
The model's 76% for Darderi is notably higher than the market-implied 62% from the 1.61 odds, generating a +22.2% expected value figure. That edge is largely explained by the ranking/Elo gap and recent form rather than surface or altitude specifics, both of which are unavailable in this data set, so the signal is less multi-dimensional than in matches with fuller inputs.
Being the favorite is not the same as holding a durable market edge, and this model is not infallible. Darderi's heavier recent workload and the deep-run fatigue flag are real considerations working against him even if the model doesn't convert them into a specific probability penalty. Treat the positive EV as one data point, not a certainty.
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.