S. Baez vs M. Dahlin — prediction
Slow court, high bounce: longer points, rewards whoever holds up from the baseline.
Warm: the ball flies a little more and fitness counts.
Humid air: the ball loses some speed.
Light wind: no noticeable effect.
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.
›Tour Elo: 1878 vs 1550 — favorite by rating
›ATP qualifying / early round · 304 matches in the favorite's track record
›Elo estimate (not the ATP factor model): qualifying draws have no clean main-tour history
!Qualifying/soft context: Elo estimate only — read the round context (already-through, lucky loser, dead rubber) from the dossier; it is not a proven edge.
The core driver of this match is the rating gap: Baez's 1878 Elo sits 328 points above Dahlin's 1550, and his No. 57 ATP ranking with a positive trend (+5) reinforces that he is playing at a materially higher level. This gap alone explains most of the model's 87% favorite probability — it's a structural class difference, not a marginal edge.
Baez arrives with a 6-4 record over his last 10 matches, including a notable win over A. Molcan (Elo 1926), a result above his own rating band. That said, he is on a 1-match losing streak, a minor caution flag heading into Bastad.
His game numbers back up the level gap: 65% of service points won and 38% of return points won reflect a balanced, high-functioning game on both sides of the ball. No equivalent data exists for Dahlin, so this comparison rests on Baez's standalone quality rather than a head-to-head statistical edge.
Baez has had 15 days of rest with zero matches in the last 14 days. This cuts both ways: he's fully recovered physically, but lacks recent competitive rhythm, which can matter more in early rounds where reading a new opponent's rhythm takes a set or two.
Weather is warm and dry (25°C, 54% humidity, 11 km/h wind) — not extreme enough to clearly favor a particular serve style or rally pattern based on the data available. With no surface or altitude figures provided, conditions here are best read as a neutral backdrop rather than a deciding factor.
Being the favorite is not the same as being a value bet. The model gives Baez an 87% win probability, but the market prices him at 97% implied (odds of 1.03), producing a negative expected value of -10.6%. That means the market is already pricing in — and slightly overpricing relative to the model — Baez's level advantage.
This is worth stating plainly: Baez is very likely to win this match, but at these odds there is no statistical edge to exploit. The Elo method here also runs on a softer Challenger/ITF-style market, so treat the 87% as an estimate rather than a proven line, not as backing for value at 1.03.
Impact and analysis from real match data (Elo, form, head-to-head, rest, surface vs baseline, weather, altitude). Soft-market estimate: the value is unproven live. 18+ · gamble responsibly.