HOW EACH FACTOR MATTERS
Level (Elo/ranking)▸ Bejlek●●●
Bejlek's higher Elo (1670 vs 1573) and ranking (#42 vs #84) drive the model's 59% baseline probability for her.
Serve/return▸ Tagger●●●
Tagger holds at 65% on serve, 11 points above Bejlek's 54%, a bigger gap than Bejlek's 4-point return edge (46% vs 42%).
Head-to-head▸ Bejlek●
Bejlek won the only prior meeting (2026, WTA Singles), a small but real edge given the limited sample.
Form= Even●
Both arrive with identical 6-4 records over their last 10 and a 2-match win streak — no separation here.
Rest▸ Bejlek●
Tagger has played 7 matches in the last 14 days versus Bejlek's 6, a slightly heavier recent workload.
RANKING AND FORM BASE
Bejlek's edge in the model starts with the numbers that measure sustained level: an Elo rating of 1670 against Tagger's 1573, and a ranking of #42 versus #84. Together these push the calibrated baseline to 59% for Bejlek, reflecting a real quality gap built over a larger sample of matches than any single-week form read.
That edge is not reinforced by recent form, though — both players are 6-4 over their last 10 matches with identical 2-match win streaks. The one head-to-head meeting, a 2026 WTA Singles win for Bejlek, adds a small tiebreaker but comes from just one data point and should not be overweighted.
SERVE VS RETURN CLASH
The clearest tactical wrinkle in this match is the serve/return split. Tagger wins 65% of points on her own serve, a full 11 points above Bejlek's 54% hold rate — a meaningful gap suggesting Tagger's service games will be harder to break into than Bejlek's. On the other side, Bejlek's return rate (46%) tops Tagger's (42%) by 4 points, giving her a modest edge in return games.
Because the serve gap (11 points) is larger than the return gap (4 points), the net mechanical picture leans toward Tagger generating more free points on her own delivery than Bejlek can claw back on return. This is a factor working against the favorite that the ranking and Elo numbers do not capture.
SCHEDULE AND SHARPNESS
Both players are working on one day of rest, so neither holds a scheduling advantage there. The difference shows up in recent workload: Tagger has played 7 matches in the last 14 days against Bejlek's 6, a marginally heavier load that could matter in physical matches but is not a decisive signal on its own.
VALUE READ
The model gives Bejlek a 59% chance to win, but the market prices her considerably higher at an implied 68% (odds of 1.48). That gap produces a projected expected value of -12.4%, meaning the price is asking bettors to pay more certainty than the model's factors — ranking, Elo, serve/return splits, form and rest — actually support.
Being the favorite here does not equate to being a value pick. The model sees a real but modest edge for Bejlek, undercut by Tagger's clearly superior service numbers; at the current price, backing the favorite is not supported by a rigorous read of these inputs.
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.