V. Vacherot vs R. Collignon — prediction
Slow court, high bounce: longer points, rewards whoever holds up from the baseline.
Mild: neutral conditions.
Humid air: the ball loses some speed.
Light wind: no noticeable effect.
High altitude: thin air, faster ball and longer bounce.
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: #19 vs #43 (better ranked)
›Model 64% vs market 43% → the model sees it as MORE likely than the odds
›Recent form: 7/10 in recent matches
›On a streak: 2 wins in a row
Vacherot holds the clearer profile on paper: a #19 ranking against Collignon's #43, and a baseline model score of 64% versus 47%. That gap is reinforced by ranking trend — Vacherot has improved by 2 spots while Collignon has slipped 21 — suggesting diverging trajectories right now.
The one wrinkle is Elo, where Collignon (2015) actually rates fractionally above Vacherot (2002). That keeps this from being a one-sided class gap; it is a case where surface-level indicators (ranking, form) point one way while a single rating system points the other.
Vacherot's recent form is the strongest single differentiator in this data set. His 7-3 record over the last ten matches includes wins over De Minaur (Elo 2044) and Musetti (Elo 2042) — results well above his own level. Collignon's 6-4 record over the same span carries no listed quality wins.
This form gap matters more than a single Elo number: beating top-50-caliber players signals Vacherot is playing above his ranking right now, while Collignon's wins have not been tested against similar opposition.
Collignon's game leans heavily on his serve (69% of service points won) but his return is comparatively weak (31%), a split that suggests he needs to hold consistently to stay competitive. The 1,050 m altitude in Gstaad thins the air and speeds the ball, which should amplify his serve advantage somewhat.
Working against that, the warm and humid conditions (23°C, 61% humidity, 13 km/h wind) tend to slow the ball and lengthen rallies — a dynamic that historically penalizes serve-dependent players more than all-around ones. With no serve/return numbers available for Vacherot, this cuts as a mild headwind for Collignon rather than a hard edge for either player.
The model's rest factor favors Vacherot, citing 52 days since his last match against Collignon's 17 — extended recovery time that should help physically over a long tournament run. But the data's own risk flag notes this same layoff could bring rustiness, a fair caution given how much time off is unusual heading into a tour-level match.
This is a factor that cuts both ways: fresher legs are an asset, but form sharpness from repetition is not guaranteed after over seven weeks away.
The model rates Vacherot at 64% to win, well above the market's implied 43% (odds of 2.3), producing a stated expected value of +46.7%. That is a large gap, and it is worth treating with some caution rather than at face value — a difference of this size between model and market usually reflects either a genuine mispricing or a blind spot in the model's inputs (surface data, for instance, is null here).
Being the favorite is not the same as holding value, and this model's method is calibrated to roughly 65% out-of-sample accuracy — solid but not infallible. The head-to-head split (1-1, dated results) and the Elo discrepancy noted above are reasons for some humility. If backing Vacherot, it should be understood as a data-supported lean, not a guaranteed outcome.
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.