Market beaters
WTA market beaters: win rate against closing odds
PickFinder Team•Updated:
Maja Chwalinska has beaten the closing tennis odds by the most on the WTA tour over the last 52 weeks: 70.3% wins against 51.8% implied, over 37 matches. Sofia Kenin has lagged them the most, 22.9% against 42.2%. Most of this looks like noise: 5 of 104 players sit outside the noise band where chance alone would put about 4.7, and a player's gap in the first 26 weeks correlates 0.13 with the last 26.
- Players with 30+ priced matches
- 104
- Beyond noise. Chance alone gives about 4.7
- 5
- First 26 weeks vs last 26 (correlation of the gap)
- 0.13
- Priced matches counted
- 3,649
WTA win rate against closing odds, biggest gap first
| 1 |  Maja ChwalinskaWorld No. 12 | 70.3% | 51.8% | +18.4 | ±14.8 | Beyond noise | 26-11 | 13-4 | 13-7 |
| 2 |  Sloane StephensWorld No. 113 | 54.8% | 38.1% | +16.7 | ±16.2 | Beyond noise | 17-14 | 7-1 | 10-13 |
| 3 |  Kaitlin QuevedoWorld No. 71 | 64.5% | 48.8% | +15.7 | ±16.7 | Within noise | 20-11 | 14-2 | 6-9 |
| 4 |  Karolina PliskovaWorld No. 51 | 62.5% | 47.0% | +15.5 | ±16.2 | Within noise | 20-12 | 12-4 | 8-8 |
| 5 |  Victoria MbokoWorld No. 20 | 74.2% | 59.6% | +14.6 | ±15.9 | Within noise | 23-8 | 19-3 | 4-5 |
| 6 |  Harriet DartWorld No. 84 | 64.7% | 50.5% | +14.2 | ±14.8 | Within noise | 22-12 | 13-2 | 9-10 |
| 7 |  Karolina MuchovaWorld No. 7 | 76.5% | 65.9% | +10.6 | ±11.9 | Within noise | 39-12 | 33-5 | 6-7 |
| 8 |  Nikola BartunkovaWorld No. 29 | 62.3% | 51.6% | +10.6 | ±12.4 | Within noise | 33-20 | 23-8 | 10-12 |
| 9 |  Marta KostyukWorld No. 10 | 76.1% | 66.3% | +9.7 | ±12.4 | Within noise | 35-11 | 28-6 | 7-5 |
| 10 |  Polina KudermetovaWorld No. 93 | 61.8% | 52.1% | +9.6 | ±15.5 | Within noise | 21-13 | 14-1 | 7-12 |
| 11 |  Talia GibsonWorld No. 36 | 53.2% | 43.8% | +9.4 | ±13.4 | Within noise | 25-22 | 14-6 | 11-16 |
| 12 |  Sara BejlekWorld No. 33 | 57.8% | 49.0% | +8.8 | ±13.3 | Within noise | 26-19 | 16-6 | 10-13 |
| 13 |  Anna KalinskayaWorld No. 26 | 62.8% | 54.0% | +8.8 | ±14.3 | Within noise | 27-16 | 19-5 | 8-11 |
| 14 |  Naomi OsakaWorld No. 16 | 72.5% | 63.8% | +8.7 | ±13.7 | Within noise | 29-11 | 25-5 | 4-6 |
| 15 |  Taylor TownsendWorld No. 88 | 61.8% | 53.4% | +8.4 | ±15.6 | Within noise | 21-13 | 17-4 | 4-9 |
| 16 |  Jaqueline CristianWorld No. 79 | 50.0% | 41.7% | +8.3 | ±15.1 | Within noise | 16-16 | 7-4 | 9-12 |
| 17 |  Tamara KorpatschWorld No. 61 | 51.6% | 43.3% | +8.3 | ±16.0 | Within noise | 16-15 | 10-2 | 6-13 |
| 18 |  Jelena OstapenkoWorld No. 25 | 60.0% | 51.8% | +8.2 | ±14.0 | Within noise | 27-18 | 19-6 | 8-12 |
| 19 |  Elena-Gabriela RuseWorld No. 60 | 57.8% | 50.2% | +7.6 | ±13.6 | Within noise | 26-19 | 16-7 | 10-12 |
| 20 |  Elisabetta CocciarettoWorld No. 72 | 58.8% | 51.5% | +7.4 | ±15.2 | Within noise | 20-14 | 13-5 | 7-9 |
| 21 |  Daria SnigurWorld No. 42 | 64.3% | 56.9% | +7.4 | ±13.7 | Within noise | 27-15 | 18-8 | 9-7 |
| 22 |  Elina SvitolinaWorld No. 6 | 75.0% | 67.8% | +7.2 | ±10.9 | Within noise | 45-15 | 38-10 | 7-5 |
| 23 |  Elena RybakinaWorld No. 1 | 82.4% | 75.4% | +7.0 | ±9.6 | Within noise | 56-12 | 49-9 | 7-3 |
| 24 |  Alexandra EalaWorld No. 17 | 57.9% | 51.0% | +6.9 | ±12.1 | Within noise | 33-24 | 22-5 | 11-19 |
| 25 |  Diana ShnaiderWorld No. 11 | 63.9% | 57.3% | +6.6 | ±11.8 | Within noise | 39-22 | 34-9 | 5-13 |
| 26 |  Anastasia PotapovaWorld No. 27 | 61.7% | 55.2% | +6.5 | ±12.9 | Within noise | 29-18 | 19-8 | 10-10 |
| 27 |  Linda NoskovaWorld No. 8 | 70.0% | 63.7% | +6.3 | ±12.6 | Within noise | 35-15 | 28-8 | 7-7 |
| 28 |  Oleksandra OliynykovaWorld No. 62 | 51.4% | 45.1% | +6.3 | ±14.6 | Within noise | 19-18 | 12-4 | 7-14 |
| 29 |  Sorana CirsteaWorld No. 15 | 68.5% | 62.4% | +6.1 | ±11.9 | Within noise | 37-17 | 33-7 | 4-10 |
| 30 |  Jessica PegulaWorld No. 5 | 75.8% | 69.7% | +6.0 | ±10.3 | Within noise | 50-16 | 47-8 | 3-8 |
| 31 |  Ashlyn KruegerWorld No. 41 | 58.1% | 52.7% | +5.4 | ±14.3 | Within noise | 25-18 | 18-7 | 7-11 |
| 32 |  Diane ParryWorld No. 34 | 53.3% | 48.4% | +4.9 | ±13.8 | Within noise | 24-21 | 10-10 | 14-11 |
| 33 |  Sinja KrausWorld No. 92 | 53.5% | 48.7% | +4.8 | ±14.2 | Within noise | 23-20 | 13-6 | 10-14 |
| 34 |  Magda LinetteWorld No. 68 | 47.7% | 43.1% | +4.6 | ±13.5 | Within noise | 21-23 | 8-7 | 13-16 |
| 35 |  Solana SierraWorld No. 104 | 45.5% | 41.3% | +4.1 | ±15.5 | Within noise | 15-18 | 8-4 | 7-14 |
| 36 |  Katie BoulterWorld No. 49 | 53.5% | 49.7% | +3.8 | ±13.9 | Within noise | 23-20 | 19-7 | 4-13 |
| 37 |  Kimberly BirrellWorld No. 46 | 51.0% | 47.5% | +3.5 | ±12.8 | Within noise | 26-25 | 16-8 | 10-17 |
| 38 |  Coco GauffWorld No. 4 | 76.3% | 72.9% | +3.4 | ±10.7 | Within noise | 45-14 | 41-11 | 4-3 |
| 39 |  Viktorija GolubicWorld No. 89 | 50.0% | 46.8% | +3.2 | ±14.9 | Within noise | 19-19 | 11-4 | 8-15 |
| 40 |  Iva JovićWorld No. 13 | 67.7% | 64.8% | +2.9 | ±11.2 | Within noise | 42-20 | 39-11 | 3-9 |
| 41 |  Alina CharaevaWorld No. 82 | 51.5% | 48.7% | +2.8 | ±15.8 | Within noise | 17-16 | 9-4 | 8-12 |
| 42 |  Alycia ParksWorld No. 59 | 48.4% | 45.6% | +2.8 | ±11.7 | Within noise | 30-32 | 17-12 | 13-20 |
| 43 |  Zeynep SonmezWorld No. 67 | 56.1% | 53.6% | +2.5 | ±12.1 | Within noise | 32-25 | 25-9 | 7-16 |
| 44 |  Kamilla RakhimovaWorld No. 44 | 48.2% | 45.8% | +2.4 | ±11.9 | Within noise | 27-29 | 17-9 | 10-20 |
| 45 |  Panna UdvardyWorld No. 86 | 41.9% | 39.4% | +2.4 | ±13.8 | Within noise | 18-25 | 10-3 | 8-22 |
| 46 |  Catherine McNallyWorld No. 65 | 56.0% | 53.6% | +2.4 | ±12.7 | Within noise | 28-22 | 21-10 | 7-12 |
| 47 |  Qinwen ZhengWorld No. 21 | 63.2% | 61.0% | +2.2 | ±14.1 | Within noise | 24-14 | 19-8 | 5-6 |
| 48 |  Tatjana MariaWorld No. 66 | 44.4% | 42.4% | +2.0 | ±12.7 | Within noise | 20-25 | 11-2 | 9-23 |
| 49 |  Maria SakkariWorld No. 30 | 54.3% | 52.4% | +2.0 | ±13.5 | Within noise | 25-21 | 20-8 | 5-13 |
| 50 |  Claire LiuWorld No. 102 | 60.0% | 58.0% | +2.0 | ±16.9 | Within noise | 18-12 | 14-7 | 4-5 |
| 51 |  Aliaksandra SasnovichWorld No. 101 | 59.3% | 57.7% | +1.6 | ±11.7 | Within noise | 35-24 | 28-11 | 7-13 |
| 52 |  Anna BondarWorld No. 74 | 50.9% | 49.5% | +1.4 | ±12.3 | Within noise | 29-28 | 21-8 | 8-20 |
| 53 |  Elvina KalievaWorld No. 120 | 52.9% | 51.5% | +1.4 | ±15.4 | Within noise | 18-16 | 11-5 | 7-11 |
| 54 |  Ann LiWorld No. 31 | 52.7% | 51.4% | +1.3 | ±12.6 | Within noise | 29-26 | 21-12 | 8-14 |
| 55 |  Marie BouzkovaWorld No. 23 | 60.8% | 59.7% | +1.0 | ±12.4 | Within noise | 31-20 | 28-9 | 3-11 |
| 56 |  Katerina SiniakovaWorld No. 47 | 55.3% | 54.3% | +1.0 | ±15.0 | Within noise | 21-17 | 18-9 | 3-8 |
| 57 |  Emma NavarroWorld No. 28 | 56.1% | 55.2% | +0.9 | ±14.5 | Within noise | 23-18 | 16-8 | 7-10 |
| 58 |  Mary StoianaWorld No. 114 | 58.1% | 57.1% | +0.9 | ±16.3 | Within noise | 18-13 | 16-3 | 2-10 |
| 59 |  Anastasia ZakharovaWorld No. 107 | 46.0% | 45.2% | +0.8 | ±12.8 | Within noise | 23-27 | 14-7 | 9-20 |
| 60 |  Maria TimofeevaWorld No. 70 | 54.5% | 54.2% | +0.4 | ±15.9 | Within noise | 18-15 | 13-5 | 5-10 |
| 61 |  Mirra AndreevaWorld No. 3 | 75.8% | 75.6% | +0.2 | ±9.9 | Within noise | 50-16 | 48-12 | 2-4 |
| 62 |  Lilli TaggerWorld No. 53 | 55.8% | 56.4% | -0.6 | ±14.0 | Within noise | 24-19 | 17-10 | 7-9 |
| 63 |  Lanlana TararudeeWorld No. 63 | 50.0% | 50.8% | -0.8 | ±16.5 | Within noise | 16-16 | 12-6 | 4-10 |
| 64 |  Yulia PutintsevaWorld No. 64 | 52.1% | 53.1% | -1.0 | ±12.6 | Within noise | 25-23 | 18-8 | 7-15 |
| 65 |  Leylah FernandezWorld No. 18 | 53.6% | 54.7% | -1.1 | ±12.3 | Within noise | 30-26 | 20-14 | 10-12 |
| 66 |  Peyton StearnsWorld No. 37 | 53.2% | 54.5% | -1.3 | ±13.7 | Within noise | 25-22 | 19-11 | 6-11 |
| 67 |  Cristina BucsaWorld No. 43 | 42.9% | 44.5% | -1.6 | ±15.7 | Within noise | 15-20 | 8-7 | 7-13 |
| 68 |  Madison KeysWorld No. 24 | 66.7% | 68.4% | -1.7 | ±12.8 | Within noise | 30-15 | 28-10 | 2-5 |
| 69 |  Antonia RuzicWorld No. 81 | 39.0% | 40.9% | -1.9 | ±14.3 | Within noise | 16-25 | 10-5 | 6-20 |
| 70 |  Anna BlinkovaWorld No. 105 | 43.6% | 45.5% | -1.9 | ±14.5 | Within noise | 17-22 | 10-5 | 7-17 |
| 71 |  Yulia StarodubtsevaWorld No. 54 | 49.0% | 51.3% | -2.4 | ±13.1 | Within noise | 24-25 | 15-12 | 9-13 |
| 72 |  Elise MertensWorld No. 14 | 58.5% | 61.1% | -2.6 | ±12.1 | Within noise | 31-22 | 27-12 | 4-10 |
| 73 |  Oksana SelekhmetevaWorld No. 122 | 43.3% | 46.1% | -2.7 | ±17.0 | Within noise | 13-17 | 7-6 | 6-11 |
| 74 |  Renata ZarazuaWorld No. 97 | 39.5% | 42.5% | -3.0 | ±14.7 | Within noise | 15-23 | 8-5 | 7-18 |
| 75 |  Katie VolynetsWorld No. 78 | 55.6% | 59.0% | -3.4 | ±12.5 | Within noise | 30-24 | 25-13 | 5-11 |
| 76 |  Magdalena FrechWorld No. 50 | 43.5% | 46.9% | -3.4 | ±13.3 | Within noise | 20-26 | 11-9 | 9-17 |
| 77 |  Aryna SabalenkaWorld No. 2 | 80.6% | 84.1% | -3.5 | ±8.8 | Within noise | 50-12 | 50-12 | 0-0 |
| 78 |  Xinyu WangWorld No. 38 | 50.0% | 53.5% | -3.5 | ±13.4 | Within noise | 23-23 | 19-9 | 4-14 |
| 79 |  Shuai ZhangWorld No. 95 | 42.2% | 46.3% | -4.1 | ±13.4 | Within noise | 19-26 | 10-7 | 9-19 |
| 80 |  McCartney KesslerWorld No. 110 | 47.1% | 51.2% | -4.2 | ±15.7 | Within noise | 16-18 | 13-5 | 3-13 |
| 81 |  Amanda AnisimovaWorld No. 22 | 65.7% | 70.0% | -4.3 | ±14.1 | Within noise | 23-12 | 21-7 | 2-5 |
| 82 |  Linda FruhvirtovaWorld No. 167 | 47.2% | 51.8% | -4.6 | ±15.4 | Within noise | 17-19 | 11-5 | 6-14 |
| 83 |  Emiliana ArangoWorld No. 171 | 36.4% | 41.4% | -5.0 | ±15.9 | Within noise | 12-21 | 6-6 | 6-15 |
| 84 |  Maya JointWorld No. 90 | 45.2% | 50.2% | -5.0 | ±13.9 | Within noise | 19-23 | 12-8 | 7-15 |
| 85 |  Camila OsorioWorld No. 69 | 47.4% | 52.7% | -5.3 | ±14.8 | Within noise | 18-20 | 14-6 | 4-14 |
| 86 |  Janice TjenWorld No. 75 | 36.4% | 43.0% | -6.6 | ±14.0 | Within noise | 16-28 | 10-7 | 6-21 |
| 87 |  Jasmine PaoliniWorld No. 35 | 50.0% | 56.9% | -6.9 | ±15.2 | Within noise | 19-19 | 15-12 | 4-7 |
| 88 |  Belinda BencicWorld No. 19 | 62.9% | 69.9% | -7.0 | ±14.6 | Within noise | 22-13 | 21-10 | 1-3 |
| 89 |  Anhelina KalininaWorld No. 55 | 52.6% | 59.8% | -7.2 | ±15.1 | Within noise | 20-18 | 18-9 | 2-9 |
| 90 |  Liudmila SamsonovaWorld No. 40 | 44.7% | 52.2% | -7.6 | ±13.4 | Within noise | 21-26 | 15-11 | 6-15 |
| 91 |  Rebecca SramkovaWorld No. 146 | 48.5% | 56.3% | -7.8 | ±16.4 | Within noise | 16-17 | 12-9 | 4-8 |
| 92 |  Yue YuanWorld No. 168 | 45.5% | 54.0% | -8.5 | ±15.5 | Within noise | 15-18 | 11-6 | 4-12 |
| 93 |  Tereza ValentovaWorld No. 85 | 51.4% | 60.1% | -8.7 | ±14.5 | Within noise | 19-18 | 17-10 | 2-8 |
| 94 |  Daria KasatkinaWorld No. 73 | 45.2% | 54.2% | -8.9 | ±13.8 | Within noise | 19-23 | 13-12 | 6-11 |
| 95 |  Donna VekicWorld No. 39 | 44.4% | 53.7% | -9.3 | ±14.9 | Within noise | 16-20 | 10-9 | 6-11 |
| 96 |  Dayana YastremskaWorld No. 99 | 41.5% | 51.8% | -10.3 | ±14.9 | Within noise | 17-24 | 11-12 | 6-12 |
| 97 |  Petra MarcinkoWorld No. 87 | 43.8% | 54.1% | -10.4 | ±16.6 | Within noise | 14-18 | 11-9 | 3-9 |
| 98 |  Jessica Bouzas ManeiroWorld No. 100 | 37.1% | 48.0% | -10.9 | ±14.9 | Within noise | 13-22 | 12-7 | 1-15 |
| 99 |  Iga SwiatekWorld No. 9 | 71.7% | 82.8% | -11.2 | ±9.7 | Beyond noise | 38-15 | 38-14 | 0-1 |
| 100 |  Paula BadosaWorld No. 57 | 51.3% | 62.7% | -11.4 | ±14.2 | Within noise | 20-19 | 18-13 | 2-6 |
| 101 |  Ekaterina AlexandrovaWorld No. 32 | 44.7% | 56.9% | -12.2 | ±13.3 | Within noise | 21-26 | 16-15 | 5-11 |
| 102 |  Clara TausonWorld No. 58 | 45.7% | 59.0% | -13.3 | ±15.7 | Within noise | 16-19 | 14-12 | 2-7 |
| 103 |  Elsa JacquemotWorld No. 130 | 25.0% | 42.6% | -17.6 | ±15.6 | Beyond noise | 8-24 | 6-4 | 2-20 |
| 104 |  Sofia KeninWorld No. 187 | 22.9% | 42.2% | -19.4 | ±15.6 | Beyond noise | 8-27 | 3-6 | 5-21 |
How good is the closing price?
Each band is the favorite's no-vig chance at the close. Power de-vig tracks the real win rate. Proportional de-vig, the common shortcut, understates favorites, which would make every heavy favorite look like a market beater. This page uses power.
Using market beaters on a prop
Read the gap
Maja Chwalinska won 70.3% against 51.8% implied, a gap of +18.4 points over 37 matches. The noise band for that sample is ±14.8, so a gap has to be that big before it stands out at all.
Split favorites and underdogs
Maja Chwalinska is 13-4 as a favorite and 13-7 as an underdog. Sofia Kenin is 3-6 and 5-21. It shows where a record came from.
Treat it as context for the price
This explains what has happened, not what comes next. A player's gap correlates 0.13 from one half of the window to the next, across 94 players. Don't read a big gap as a reason to bet.
Start from the close
Favorites priced at 80% to 90% won 86.5% of 505 matches against 84.6% implied by the power de-vig. The closing price is hard to beat, so treat it as your starting point. Today's line and the hit rate against it are in PickFinder.
How this is counted
Counts finished singles matches with a closing moneyline on both players, Nov 2, 2025 to Nov 2, 2026. 96% of prices are Pinnacle's, and the rest come from another major book. Implied % removes the vig with a power de-vig, because it matches real win rates. The proportional shortcut understates favorites: at 80% to 90% it gives 82.9%, power gives 84.6%, and they actually won 86.5%. Gap is win % minus average implied %. The noise band is two standard deviations of what those prices would produce by chance over that many matches. Beyond noise means the gap is outside it, and with this many players some will be by luck, which is why the page shows the count. Retired matches and walkovers are left out. Tour level means WTA events, the Grand Slams and the Finals, qualifying included. Challenger and WTA 125 matches are left out and have their own view, because they would flatter anyone who plays down a level. Team cups are left out too. A player needs 30+ priced matches.
Market beaters FAQ
Who has beaten the market the most on the WTA tour?
Maja Chwalinska: 70.3% wins against 51.8% implied over 37 matches, a gap of +18.4 points with a noise band of ±14.8.
Who has been most overrated on the WTA tour?
Sofia Kenin: 22.9% wins against 42.2% implied over 35 matches, a gap of -19.4 points.
Does beating the market carry over?
Not much. A player's gap in the first 26 weeks correlates 0.13 with the last 26 across 94 players. 5 of 104 players sit outside the noise band, and chance alone would put about 4.7 there.
Are closing tennis odds accurate?
Close to it, after a power de-vig. Favorites priced at 80% to 90% won 86.5% of 505 matches against 84.6% implied.
Why not use the simple proportional de-vig?
It understates favorites. At 80% to 90% it gives 82.9% against a real win rate of 86.5%, which would make every heavy favorite look like a market beater.
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