M. Kostyuk vs J. Paolini — prediction
Fast court, low bounce: rewards the serve and short points.
Strong heat: warm air speeds the ball up and physical wear tells in long matches.
Very dry air: the ball travels faster.
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.
›Ranking: #15 vs #14
›Recent form: 9/10 in recent matches
›Head-to-head: 0-2 against
›More rested: 34d vs opponent's 15d
›Model 64% vs market 71% → the model sees it as less likely than the odds
!Returning from a long layoff (34d) — possible rustiness
!The opponent is ranked higher (#14)
!Unfavorable head-to-head record (0-2)
The 148-point Elo difference (1938 vs 1790) is the clearest signal in this match, and it lines up with the surface-independent numbers: Kostyuk's 67% baseline win rate sits 6 points above Paolini's 61%. Her ranking trend (+8) also points upward while Paolini's (-6) points down, suggesting the gap in current form is wider than the static rankings (#15 vs #14) imply.
This shows up directly in styles: Kostyuk holds a serve-point edge (64% vs 58%) and a return-point edge (48% vs 44%) over Paolini. Winning more points in both phases of play is a rare combination and is the mechanical basis for her higher standalone probability of winning.
Kostyuk's 9-1 run in her last 10 matches includes wins over Swiatek (Elo 1922) and Svitolina (Elo 1917), meaning her recent success has come against elite-level opposition, not soft schedules. Paolini's 7-3 stretch, by contrast, includes no listed quality wins, a meaningful gap in the caliber of recent results.
Working against Kostyuk is the head-to-head: Paolini has won 2 of the 3 previous meetings, including the most recent one in 2023. Head-to-head history carries real weight in tennis matchups, so this is a genuine offsetting factor even against her stronger current form and level.
The hot (31°C), dry (34% humidity), essentially windless (3 km/h) conditions favor cleaner, faster serving. Since Kostyuk already serves at a higher clip (64% vs 58%), these conditions should marginally reinforce her existing advantage rather than neutralize it — heat and dry air speed up the ball and reward the stronger server.
Rest is a non-factor here: both players come in on 2 days off with 5 matches played in the last 14 days, an identical and fairly heavy recent workload. Neither side gets a fatigue-based edge from the schedule.
The model gives Kostyuk a 64% win probability, while the market prices her closer to 71% (implied by odds of 1.41). That gap produces a negative expected value of -10.2%, meaning the price is asking you to pay more confidence than the model's own calibration supports.
Kostyuk is the more likely winner on the numbers — better Elo, better serve/return splits, better recent form against quality opposition — but 'more likely to win' is not the same as 'good value.' At the current odds, backing her does not clear the model's own bar for a worthwhile bet, and the 0-2 head-to-head deficit adds a note of caution rather than reassurance.
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.