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
4 upsets where the model expected 10.3 · 27-9 in 36 matches · model called it right 88.9% of the time
The wild card · 2026
Yuliia Starodubtseva
18 upsets where the model expected only 10.3 · 16-16 in 32 matches · model called it right 43.8% 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 · 91 players with ≥20 matches (2040 matches, source: as-published daily record)

# Player Verdict Upsets Expected Extra Model hit Confidence W-L M
1 Naomi Osaka Straight sets 4 10.3 -6.3 ±5.1 88.9% 71.4% 27-9 36
2 Marie Bouzkova Straight sets 9 14.4 -5.4 ±6.0 80.9% 69.4% 29-18 47
3 Karolina Muchova Straight sets 9 14.1 -5.1 ±6.0 80.9% 69.9% 37-10 47
4 McCartney Kessler Straight sets 7 11.3 -4.3 ±5.2 78.1% 64.7% 14-18 32
5 Iva Jovic Straight sets 11 16.3 -5.3 ±6.3 78.0% 67.4% 34-16 50
6 Sorana Cirstea Straight sets 12 16.9 -4.9 ±6.4 76.0% 66.2% 37-13 50
7 Anna Bondar On serve 11 15.4 -4.4 ±6.0 74.4% 64.2% 21-22 43
8 Madison Keys On serve 11 14.9 -3.9 ±6.0 75.6% 66.9% 31-14 45
9 Jessica Pegula On serve 12 15.9 -3.9 ±6.4 79.7% 73.1% 48-11 59
10 Kamilla Rakhimova On serve 7 9.2 -2.2 ±4.7 74.1% 65.8% 12-15 27
11 Mirra Andreeva On serve 11 13.9 -2.9 ±6.1 80.4% 75.2% 43-13 56
12 Diana Shnaider On serve 12 14.8 -2.8 ±6.1 74.5% 68.6% 28-19 47
13 Linda Noskova On serve 12 14.7 -2.7 ±6.0 73.3% 67.4% 32-13 45
14 Alexandra Eala On serve 15 17.9 -2.9 ±6.6 71.2% 65.5% 32-20 52
15 Emiliana Arango On serve 7 8.8 -1.8 ±4.6 72.0% 64.7% 8-17 25
16 Coco Gauff On serve 11 12.7 -1.7 ±5.9 78.8% 75.5% 40-12 52
17 Zeynep Sonmez On serve 11 12.6 -1.6 ±5.5 70.3% 65.9% 18-19 37
18 Janice Tjen On serve 11 12.5 -1.5 ±5.4 67.6% 63.2% 11-23 34
19 Belinda Bencic On serve 8 9.4 -1.4 ±4.9 75.0% 70.7% 21-11 32
20 Elise Mertens On serve 12 13.6 -1.6 ±5.8 72.1% 68.4% 26-17 43
21 Laura Siegemund On serve 6 7.1 -1.1 ±4.1 70.0% 64.5% 8-12 20
22 Jasmine Paolini On serve 8 9.1 -1.1 ±4.8 72.4% 68.6% 16-13 29
23 Elena Rybakina On serve 14 15.4 -1.4 ±6.4 75.9% 73.4% 45-13 58
24 Maya Joint On serve 8 9.0 -1.0 ±4.7 69.2% 65.3% 8-18 26
25 Emma Navarro On serve 13 14.3 -1.3 ±5.8 67.5% 64.4% 23-17 40
26 Katerina Siniakova On serve 10 10.9 -0.9 ±5.0 66.7% 63.6% 14-16 30
27 Tereza Valentova On serve 10 10.9 -0.9 ±5.1 66.7% 63.6% 14-16 30
28 Marta Kostyuk On serve 12 13.0 -1.0 ±5.7 72.1% 69.8% 35-8 43
29 Elina Svitolina On serve 16 17.0 -1.0 ±6.6 71.4% 69.7% 43-13 56
30 Nikola Bartunkova On serve 10 10.5 -0.5 ±5.1 69.7% 68.1% 20-13 33
31 Sofia Kenin On serve 7 7.4 -0.4 ±4.3 69.6% 67.7% 6-17 23
32 Caty McNally On serve 13 13.6 -0.6 ±5.8 69.8% 68.4% 23-20 43
33 Anastasia Zakharova On serve 8 8.3 -0.3 ±4.5 68.0% 66.6% 10-15 25
34 Magdalena Frech On serve 12 12.4 -0.4 ±5.4 63.6% 62.5% 14-19 33
35 Panna Udvardy On serve 12 12.3 -0.3 ±5.4 65.7% 65.0% 17-18 35
36 Renata Zarazua On serve 8 8.2 -0.2 ±4.4 65.2% 64.4% 8-15 23
37 Katie Volynets On serve 9 9.1 -0.1 ±4.6 64.0% 63.4% 9-16 25
38 Jessica Bouzas Maneiro On serve 11 11.1 -0.1 ±5.3 68.6% 68.3% 14-21 35
39 Camila Osorio On serve 11 11.0 +0.0 ±5.1 65.6% 65.7% 15-17 32
40 Daria Snigur On serve 8 7.9 +0.1 ±4.3 65.2% 65.6% 14-9 23
41 Barbora Krejcikova On serve 8 7.9 +0.1 ±4.5 70.4% 70.8% 17-10 27
42 Anna Kalinskaya On serve 16 15.7 +0.3 ±6.1 63.6% 64.2% 27-17 44
43 Victoria Mboko On serve 10 9.6 +0.4 ±5.0 68.8% 70.0% 23-9 32
44 Maria Sakkari On serve 12 11.5 +0.5 ±5.3 65.7% 67.0% 18-17 35
45 Xinyu Wang On serve 14 13.5 +0.5 ±5.7 65.0% 66.4% 20-20 40
46 Ann Li On serve 18 17.3 +0.7 ±6.3 60.9% 62.3% 23-23 46
47 Qinwen Zheng On serve 9 8.2 +0.8 ±4.5 65.4% 68.4% 14-12 26
48 Oleksandra Oliynykova On serve 11 9.8 +1.2 ±4.9 64.5% 68.3% 17-14 31
49 Viktorija Golubic On serve 9 7.9 +1.1 ±4.4 62.5% 67.0% 12-12 24
50 Karolina Pliskova On serve 13 11.7 +1.3 ±5.3 60.6% 64.6% 21-12 33
51 Cristina Bucsa On serve 11 9.7 +1.3 ±4.7 59.3% 64.0% 11-16 27
52 Alycia Parks On serve 15 13.4 +1.6 ±5.6 59.5% 63.7% 17-20 37
53 Tamara Korpatsch On serve 9 7.8 +1.2 ±4.3 60.9% 66.2% 13-10 23
54 Hailey Baptiste On serve 11 9.5 +1.5 ±4.8 62.1% 67.4% 17-12 29
55 Diane Parry On serve 12 10.3 +1.7 ±5.0 60.0% 65.5% 19-11 30
56 Aryna Sabalenka On serve 8 6.4 +1.6 ±4.6 84.3% 87.4% 43-8 51
57 Tatjana Maria On serve 16 14.0 +2.0 ±5.8 61.9% 66.7% 18-24 42
58 Ajla Tomljanovic On serve 11 9.3 +1.7 ±4.7 57.7% 64.0% 12-14 26
59 Taylor Townsend On serve 10 8.4 +1.6 ±4.5 58.3% 64.9% 14-10 24
60 Leylah Fernandez On serve 17 14.7 +2.3 ±5.8 56.4% 62.2% 17-22 39
61 Peyton Stearns On serve 15 12.8 +2.2 ±5.5 58.3% 64.4% 19-17 36
62 Amanda Anisimova On serve 11 9.0 +2.0 ±4.8 67.6% 73.4% 22-12 34
63 Yulia Putintseva On serve 13 10.9 +2.1 ±5.0 56.7% 63.7% 14-16 30
64 Shuai Zhang On serve 12 9.9 +2.1 ±4.9 60.0% 67.1% 11-19 30
65 Emma Raducanu On serve 8 6.2 +1.8 ±4.0 60.0% 69.0% 11-9 20
66 Magda Linette On serve 15 12.5 +2.5 ±5.5 59.5% 66.2% 19-18 37
67 Ashlyn Krueger On serve 11 8.8 +2.2 ±4.6 54.2% 63.1% 11-13 24
68 Daria Kasatkina On serve 12 9.7 +2.3 ±4.8 57.1% 65.3% 11-17 28
69 Iga Swiatek On serve 12 9.5 +2.5 ±5.1 72.7% 78.3% 33-11 44
70 Jaqueline Cristian On serve 13 10.5 +2.5 ±5.0 58.1% 66.0% 15-16 31
71 Anna Blinkova On serve 10 7.8 +2.2 ±4.3 56.5% 65.9% 5-18 23
72 Jelena Ostapenko On serve 16 13.1 +2.9 ±5.6 59.0% 66.5% 21-18 39
73 Anastasia Potapova On serve 17 13.9 +3.1 ±5.8 59.5% 66.9% 26-16 42
74 Dayana Yastremska On serve 14 11.2 +2.8 ±5.2 54.8% 63.9% 11-20 31
75 Katie Boulter On serve 15 12.0 +3.0 ±5.4 57.1% 65.7% 19-16 35
76 Antonia Ruzic On serve 13 10.2 +2.8 ±5.0 58.1% 67.0% 12-19 31
77 Kimberly Birrell On serve 14 11.1 +2.9 ±5.0 53.3% 62.9% 12-18 30
78 Paula Badosa On serve 13 10.1 +2.9 ±5.0 58.1% 67.5% 15-16 31
79 Liudmila Samsonova On serve 17 13.6 +3.4 ±5.7 54.1% 63.2% 16-21 37
80 Elena-Gabriela Ruse On serve 14 10.8 +3.2 ±5.1 56.2% 66.1% 14-18 32
81 Donna Vekic On serve 11 8.2 +2.8 ±4.4 54.2% 65.9% 11-13 24
82 Lilli Tagger On serve 10 6.9 +3.1 ±4.2 54.5% 68.4% 11-11 22
83 Sara Bejlek Wild card 18 13.0 +5.0 ±5.5 48.6% 62.9% 20-15 35
84 Solana Sierra Wild card 13 8.5 +4.5 ±4.5 50.0% 67.1% 11-15 26
85 Talia Gibson Wild card 16 10.5 +5.5 ±5.0 46.7% 65.0% 14-16 30
86 Ekaterina Alexandrova Wild card 19 12.5 +6.5 ±5.5 51.3% 67.9% 17-22 39
87 Clara Tauson Wild card 19 12.2 +6.8 ±5.5 48.6% 67.1% 16-21 37
88 Anhelina Kalinina Wild card 15 8.9 +6.1 ±4.5 34.8% 61.1% 11-12 23
89 Elisabetta Cocciaretto Wild card 17 10.2 +6.8 ±5.0 45.2% 67.0% 16-15 31
90 Petra Marcinko Wild card 17 9.8 +7.2 ±4.8 39.3% 65.0% 13-15 28
91 Yuliia Starodubtseva Wild card 18 10.3 +7.7 ±5.1 43.8% 67.7% 16-16 32

Serve projections: who we read best, and worst

Before every match we project how many aces each player will serve. The bar is how much closer that projection lands than the naive forecast anyone can build without a model, which is the average of the player's own last 5 matches. Positive means we add something on that player. Ranking by raw error would just rank the tour by serve volume, so the comparison is always against each player's own baseline.

We read it our projection beats the player's own last-5 average, and the whole 95% interval agrees Level the gap is inside the noise for this sample, where most players belong Baseline wins the last-5 average reads this player better than we do

Full board below, 110 players with at least 12 matches in 2025 where both our projection and the last-5 baseline could be scored (source: out-of-sample backtest). A verdict is printed only when the 95% interval of the per-match comparison stays on one side of zero. Everyone else is level with the baseline for this sample, which is where most players belong. The serve record does not cover 2026, so this board shows 2025, the most recent season whose serve projections we can score.

# Player Verdict Edge Our miss Baseline miss Actual avg Projected avg M

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 expected 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.

The serve board answers a different question with a different metric, and the two never mix. Upsets are about who wins, and are scored against the model's own expectation. Aces and double faults are counts, and are scored against the forecast anyone could make without a model, the average of that player's last five matches. A player can be perfectly on script and still be the one whose serve we read worst.

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.