Season script report

Most Predictable WTA Players

Every match, our model publishes a win probability — and prices its own uncertainty. This board compares each player's actual results with that script: straight-sets players land where the model expected, wild cards keep tearing the script up. A season-level read with sample sizes and confidence intervals, never a career label.

The metronome · 2026
Naomi Osaka
3 upsets where the model expected 7.3 · 19–7 in 26 matches · model called it right 88.5% of the time
The wild card · 2026
Petra Marcinko
15 upsets where the model expected only 8.3 · 13–12 in 25 matches · model called it right 40.0% of the time

Script vs chaos — the two ends of 2026

Bars show extra upsets: how many more (or fewer) times the model's favorite fell in this player's matches than the model itself expected. Verdicts in the table are assigned only when the difference is statistically meaningful for that sample. Everyone else stays "On serve". How to read this →

Full board · 77 players with ≥20 matches (1626 matches, source: as-published daily record)

# Player Verdict Upsets Expected Extra Model hit Confidence W–L M
1 Naomi Osaka Straight sets 3 7.3 -4.3 ±4.3 88.5% 71.9% 19–7 26
2 Marie Bouzkova Straight sets 7 12.2 -5.2 ±5.5 81.6% 67.9% 24–14 38
3 Karolina Muchova Straight sets 8 13.4 -5.4 ±5.8 81.8% 69.5% 35–9 44
4 Madison Keys Straight sets 7 11.3 -4.3 ±5.3 80.0% 67.6% 25–10 35
5 Sorana Cirstea On serve 10 14.5 -4.5 ±5.9 76.7% 66.3% 32–11 43
6 McCartney Kessler On serve 6 9.5 -3.5 ±4.8 77.8% 64.7% 13–14 27
7 Anna Bondar On serve 8 11.9 -3.9 ±5.3 75.8% 64.0% 16–17 33
8 Iva Jovic On serve 10 14.2 -4.2 ±5.9 76.7% 67.0% 29–14 43
9 Barbora Krejcikova On serve 4 6.2 -2.2 ±4.0 81.0% 70.6% 15–6 21
10 Belinda Bencic On serve 6 8.2 -2.2 ±4.6 77.8% 69.7% 18–9 27
11 Linda Noskova On serve 10 12.6 -2.6 ±5.6 73.7% 66.8% 27–11 38
12 Tereza Valentova On serve 7 8.9 -1.9 ±4.5 70.8% 62.8% 11–13 24
13 Mirra Andreeva On serve 10 12.2 -2.2 ±5.7 79.2% 74.6% 37–11 48
14 Diana Shnaider On serve 9 10.9 -1.9 ±5.2 73.5% 67.9% 19–15 34
15 Jessica Pegula On serve 10 12.0 -2.0 ±5.6 77.3% 72.7% 35–9 44
16 Caty McNally On serve 9 10.4 -1.4 ±5.1 72.7% 68.6% 17–16 33
17 Laura Siegemund On serve 6 7.1 -1.1 ±4.1 70.0% 64.5% 8–12 20
18 Katerina Siniakova On serve 8 9.2 -1.2 ±4.6 68.0% 63.3% 12–13 25
19 Anastasia Zakharova On serve 6 7.0 -1.0 ±4.1 71.4% 66.5% 8–13 21
20 Elise Mertens On serve 9 10.2 -1.2 ±5.1 72.7% 69.0% 20–13 33
21 Alexandra Eala On serve 12 13.3 -1.3 ±5.7 70.0% 66.6% 23–17 40
22 Emiliana Arango On serve 7 8.0 -1.0 ±4.3 68.2% 63.7% 7–15 22
23 Nikola Bartunkova On serve 6 6.7 -0.7 ±4.0 70.0% 66.5% 12–8 20
24 Shuai Zhang On serve 7 7.7 -0.7 ±4.3 69.6% 66.4% 8–15 23
25 Zeynep Sonmez On serve 10 10.6 -0.6 ±5.0 66.7% 64.7% 15–15 30
26 Katie Volynets On serve 7 7.4 -0.4 ±4.1 65.0% 63.1% 7–13 20
27 Jasmine Paolini On serve 8 8.4 -0.4 ±4.6 69.2% 67.7% 14–12 26
28 Emma Navarro On serve 11 11.4 -0.4 ±5.2 65.6% 64.3% 18–14 32
29 Anna Kalinskaya On serve 12 12.3 -0.3 ±5.4 64.7% 63.7% 20–14 34
30 Jessica Bouzas Maneiro On serve 9 9.1 -0.1 ±4.8 69.0% 68.8% 11–18 29
31 Panna Udvardy On serve 10 9.9 +0.1 ±4.8 64.3% 64.5% 15–13 28
32 Janice Tjen On serve 10 9.8 +0.2 ±4.8 61.5% 62.4% 8–18 26
33 Camila Osorio On serve 9 8.7 +0.3 ±4.6 64.0% 65.1% 13–12 25
34 Elina Svitolina On serve 14 13.6 +0.4 ±5.9 68.9% 69.8% 35–10 45
35 Magdalena Frech On serve 10 9.6 +0.4 ±4.7 60.0% 61.6% 10–15 25
36 Victoria Mboko On serve 10 9.6 +0.4 ±5.0 68.8% 70.0% 23–9 32
37 Alycia Parks On serve 11 10.5 +0.5 ±5.0 63.3% 64.9% 14–16 30
38 Coco Gauff On serve 10 9.5 +0.5 ±5.1 74.4% 75.6% 28–11 39
39 Aryna Sabalenka On serve 6 5.6 +0.4 ±4.2 85.7% 86.8% 36–6 42
40 Ann Li On serve 14 13.3 +0.7 ±5.5 60.0% 62.1% 17–18 35
41 Yulia Putintseva On serve 10 9.3 +0.7 ±4.6 60.0% 62.8% 13–12 25
42 Jelena Ostapenko On serve 13 11.9 +1.1 ±5.4 63.9% 66.9% 21–15 36
43 Oleksandra Oliynykova On serve 8 7.1 +0.9 ±4.2 65.2% 69.2% 13–10 23
44 Xinyu Wang On serve 12 10.7 +1.3 ±5.1 62.5% 66.5% 16–16 32
45 Maria Sakkari On serve 11 9.8 +1.2 ±4.9 62.1% 66.4% 15–14 29
46 Qinwen Zheng On serve 8 6.8 +1.2 ±4.1 63.6% 69.1% 11–11 22
47 Peyton Stearns On serve 12 10.5 +1.5 ±5.0 58.6% 63.9% 16–13 29
48 Hailey Baptiste On serve 11 9.5 +1.5 ±4.8 62.1% 67.4% 17–12 29
49 Elena Rybakina On serve 13 11.2 +1.8 ±5.5 71.1% 75.2% 34–11 45
50 Ajla Tomljanovic On serve 11 9.3 +1.7 ±4.7 57.7% 64.0% 12–14 26
51 Marta Kostyuk On serve 12 10.2 +1.8 ±5.1 63.6% 69.1% 27–6 33
52 Karolina Pliskova On serve 12 10.2 +1.8 ±4.9 57.1% 63.6% 19–9 28
53 Kimberly Birrell On serve 11 9.0 +2.0 ±4.5 54.2% 62.6% 11–13 24
54 Emma Raducanu On serve 8 6.2 +1.8 ±4.0 60.0% 69.0% 11–9 20
55 Anastasia Potapova On serve 14 11.5 +2.5 ±5.3 58.8% 66.1% 21–13 34
56 Jaqueline Cristian On serve 13 10.5 +2.5 ±5.0 58.1% 66.0% 15–16 31
57 Tatjana Maria On serve 14 11.2 +2.8 ±5.3 60.0% 68.0% 15–20 35
58 Katie Boulter On serve 13 10.3 +2.7 ±4.9 55.2% 64.5% 17–12 29
59 Ashlyn Krueger On serve 10 7.6 +2.4 ±4.2 50.0% 61.8% 10–10 20
60 Magda Linette On serve 14 10.9 +3.1 ±5.1 56.2% 65.9% 18–14 32
61 Dayana Yastremska On serve 13 10.0 +3.0 ±4.9 53.6% 64.3% 11–17 28
62 Sara Bejlek On serve 12 9.1 +2.9 ±4.6 50.0% 62.0% 13–11 24
63 Elena-Gabriela Ruse On serve 12 8.9 +3.1 ±4.7 55.6% 66.9% 12–15 27
64 Amanda Anisimova On serve 9 6.3 +2.7 ±4.1 64.0% 74.7% 16–9 25
65 Leylah Fernandez On serve 15 11.5 +3.5 ±5.1 50.0% 61.6% 12–18 30
66 Daria Kasatkina On serve 11 7.8 +3.2 ±4.4 52.2% 66.0% 10–13 23
67 Solana Sierra On serve 11 7.8 +3.2 ±4.3 52.2% 66.2% 11–12 23
68 Paula Badosa On serve 12 8.6 +3.4 ±4.5 53.8% 67.1% 12–14 26
69 Antonia Ruzic Wild card 12 8.4 +3.6 ±4.5 53.8% 67.9% 11–15 26
70 Liudmila Samsonova Wild card 14 9.7 +4.3 ±4.8 48.1% 64.1% 10–17 27
71 Talia Gibson Wild card 12 7.9 +4.1 ±4.4 47.8% 65.6% 11–12 23
72 Clara Tauson Wild card 14 9.4 +4.6 ±4.8 51.7% 67.4% 13–16 29
73 Elisabetta Cocciaretto Wild card 13 8.2 +4.8 ±4.5 48.0% 67.0% 14–11 25
74 Yuliia Starodubtseva Wild card 12 7.2 +4.8 ±4.3 47.8% 68.6% 12–11 23
75 Iga Swiatek Wild card 11 6.3 +4.7 ±4.2 63.3% 79.1% 20–10 30
76 Ekaterina Alexandrova Wild card 15 8.8 +6.2 ±4.7 46.4% 68.6% 11–17 28
77 Petra Marcinko Wild card 15 8.3 +6.7 ±4.5 40.0% 66.7% 13–12 25

How this is measured, and what it does not claim

An upset is a match where our model's favorite loses. The model does not expect zero upsets: a 55/45 call is priced to go wrong 45 times out of 100. Adding those probabilities across a player's schedule gives their expected upsets, tailored to the exact opponents they faced. The board compares that number with the upsets that actually happened: fewer than expected earns Straight sets, more than expected earns Wild card, and anything within the statistical noise for that sample stays On serve, which with 60-plus matches is where most players genuinely belong.

Two independent model vintages agree on who broke script within a season, but a wild-card season does not predict a wild-card next season. That is why this is a season report, not a career trait, and why sample sizes and 95% intervals are always shown. The full reasoning, including the proper-scoring-rule version of this metric (the Brier delta) that backs the verdicts, is in the explainer.

Recent seasons use our as-published daily record (the same reconciled predictions behind the performance page); earlier seasons use a strict out-of-sample backtest of the current model. One source per season, never mixed. Probabilities and calibration are public on the model transparency page.