S. Bejlek vs L. Tagger — prediction
›Ranking: #45 vs #82 (better ranked)
›Recent form: 3/10 in recent matches
Bejlek's Elo advantage (1655 vs 1559) and higher ranking (#45 vs #82) are the clearest structural edge in this match, and they anchor the model's 55% probability for her. Yet the picture isn't one-sided: her ranking trend is -10 (declining) while Tagger's is +10 (rising), and the baseline win-rate split (48% Bejlek, 50% Tagger) suggests the underlying model doesn't see a dominant favorite once other factors are folded in.
This tension — Elo/ranking favoring Bejlek, baseline and trend favoring Tagger — is a key reason the calibrated probability sits close to a coin flip rather than showing a large gap.
The service numbers are the sharpest differentiator in the data: Tagger holds at 69% on serve, 15 points clear of Bejlek's 54%. That's a substantial gap, indicating Tagger's service games should be considerably harder for Bejlek to break than vice versa.
Bejlek does hold a slim return edge (44% vs 41%), but it's nowhere near enough to offset Tagger's serving strength. On paper, this factor tilts the match mechanics toward the opponent.
Recent form favors Tagger, who is 4-for-4 in her last four matches (WLLLLLWWWW) and riding real momentum. Bejlek's form is choppier — LLWLLLWLWW — with only a 2-match winning streak and a heavier recent-loss pattern.
Combined with Tagger's positive ranking trend, this points to an opponent playing with more confidence entering the match, even though her overall ranking remains lower.
Both players had 2 days of rest, but workload differs: Tagger played 4 matches in the last 14 days versus Bejlek's 2. That heavier recent schedule could mean added physical load on Tagger, a modest factor working in Bejlek's favor even as other indicators lean the other way.
The model gives Bejlek 55%, close to the market's implied 57% at odds of 1.75 — essentially in line with the market rather than identifying a gap. The resulting expected value is -3%, meaning the price does not compensate for the model's assessed risk.
Bejlek being favored does not equate to being the safer bet here: her serving disadvantage (54% vs 69%) and Tagger's superior recent form are real counterweights to her ranking and Elo edge. On the numbers, this is a close match with no clear value on either side.
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.