HOW EACH FACTOR MATTERS
Level (Elo/ranking)▸ Svajda●●●
Svajda's 1908 Elo and #66 ranking clearly outrank Kozlov's 1705 Elo, and the model gives him 69% win probability.
Serve/return▸ Svajda●●
Svajda wins 65% of service points to Kozlov's 59%, but Kozlov's 42% return topping Svajda's 36% partly offsets that edge.
Rest▸ Svajda●●
Kozlov has played 4 matches in 14 days (4 days rest) risking fatigue, while Svajda's 21-day layoff brings rustiness risk instead.
Head-to-head▸ Svajda●
Svajda has won both prior ATP meetings (2023, 2025), a small psychological edge in a short sample.
Form▸ Svajda●
Both are 6/10 in their last 10, but only Svajda has quality wins (Cerundolo Elo 2020, Majchrzak 1929) on his resume.
LEVEL AND HISTORY
Svajda's Elo advantage (1908 vs 1705) and his #66 ranking put him clearly ahead of Kozlov on the model's baseline metrics, and the calibrated model reflects this with a 69% win probability. The two past meetings, both won by Svajda in ATP singles, add a modest layer of confidence though the sample is small.
Recent form is close on paper — both players sit at 6/10 over their last ten matches — but Svajda's résumé includes wins over higher-Elo opponents (Cerundolo at 2020, Majchrzak at 1929), something Kozlov's log does not show. This tilts the qualitative form picture toward Svajda even though both are on short losing streaks.
SERVE VS RETURN DYNAMICS
Svajda's service game (65% of points won) is stronger than Kozlov's (59%), suggesting he can hold more comfortably. However, Kozlov's return numbers (42%) outperform Svajda's (36%), meaning Kozlov is more likely to generate break chances when he returns than Svajda is.
Net effect: Svajda's serving edge is the primary advantage, but Kozlov's superior return could keep games tighter than the overall probability gap implies, especially if rallies extend.
FATIGUE VS RUST
Kozlov arrives with a heavy recent workload — four matches in the last 14 days and only 4 days of rest — which can sap legs and focus in a tight ATP match. Svajda, by contrast, has had 21 days off with zero matches in the last two weeks, which cuts fatigue risk but introduces the opposite concern: match rustiness after a long layoff, as flagged in the data.
These two risks partially offset each other, but the model still leans toward Svajda benefiting more from Kozlov's congestion than suffering from his own inactivity.
VALUE READ
The math here is the central point: the model prices Svajda at 69% to win, while the market — via 1.13 odds — implies roughly 88%. That gap produces a negative expected value of -21.6%, meaning the odds are asking bettors to overpay relative to the model's assessment.
Favorite status does not equal betting value. Even with Svajda holding real edges in level, form quality, and Kozlov's fatigue, the current price already bakes in more certainty than the model can support. This is a case where being the likely winner and being a good bet are two different things.
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.