A. Rublev vs A. Tabilo — prediction
›Ranking: #13 vs #33 (better ranked)
›Recent form: 6/10 in recent matches
›Head-to-head: 1-1 even
›Model 65% vs market 71% → the model sees it as less likely than the odds
!Coming off 3 losses in a row
Rublev holds a clear structural edge: a 51-point Elo gap (1984 vs 1933) and a 20-spot ranking difference (#13 vs #33) both point to him as the stronger player overall. The model's own baseline splits 58% to 47% in his favor before any match-specific adjustments, confirming that on paper this is a mismatch in quality, not just reputation.
That gap is the foundation of the model's 65% favorite probability, and it is the single largest driver of the pick. Nothing in the rest of the data reverses it, though several factors trim its size.
The service numbers tell a coherent story: Rublev wins 69% of his service points against Tabilo's 59%, a 10-point gap that should translate into more comfortable holds. Return numbers narrow this slightly — Tabilo returns at 32% versus Rublev's 28% — meaning Tabilo generates a few more break chances than Rublev does, but not enough to offset the serve gap.
Net hold margins favor Rublev: his serve-minus-opponent-return gap is 37 points, versus Tabilo's 31. In practical terms, Rublev should protect his own service games slightly more easily than Tabilo protects his, which is a real, quantifiable edge in a match expected to be close on paper.
Recent form is essentially even: both players are 6-10 in their last ten matches, with comparable quality wins (Rublev over I. Buse, Elo 1913; Tabilo over K. Majchrzak, Elo 1929). Rublev's 2-match winning streak, following an earlier stretch of three straight losses, gives him a modest current-form edge over Tabilo's single-match streak. The head-to-head is split 1-1, with each player winning the most recent meeting in his own respective year, so history offers no tiebreaker.
Schedule context cuts against Rublev: he reached the Bastad quarter-finals just one day ago and has played two matches in the last 14 days, while Tabilo has had two days of rest and only one match in the same span. This deep-run fatigue flag doesn't change the probability, but it is a tangible physical variable working against the favorite in a tightly contested match.
Conditions are warm and humid (22°C, 68% humidity) with a moderate 15 km/h wind. Humid air tends to slow the ball and lengthen rallies, which generally reduces the impact of a big serve — a mechanism that works somewhat against Rublev, whose 69% service number is his largest advantage in this match. The wind adds a layer of unpredictability that can affect both players' precision, though there's no surface data to quantify how either handles these specific conditions.
The model rates Rublev's win probability at 65%, while the market prices him at 71% (odds of 1.41). That gap produces a negative expected value of -8.9%, meaning the market is more confident in Rublev than the model's factor-based analysis supports. Being the favorite here is not the same as being the value play.
On balance, Rublev's level, serve edge, and recent streak justify favoring him to win more often than not, but the price already reflects — and exceeds — that edge. Backing him at these odds is a bet against the model's own numbers, not a bet supported by them.
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.