2026
Live Ratings
Updated 7 Jul 2026
WTA Tennis Power Rankings
WTA players ranked across six metrics that go beyond official rankings. Each metric captures a different dimension of player strength — from overall skill to surface dominance to recent form.
Rating Systems
ELOOverall strength, updated every match
Glicko-2Rating + confidence interval
SurfaceHard / Clay / Grass specific
FormPerformance vs expectations, last 15 matches
714
Players Ranked
2169
Top ELO (Aryna Sabalenka)
2470
Top Glicko-2 (Aryna Sabalenka)
Player Search
ELO Rating measures overall player strength. It updates after every match based on the result, opponent strength, tournament importance, match round, and set dominance. A player who beats a higher-rated opponent in a Grand Slam final gains more points than winning a first-round match at a 250.
Full methodology →
Glicko-2 extends ELO with two extra numbers: RD (Rating Deviation) shows confidence — lower RD means the rating is more reliable. Volatility tracks consistency — a player with low volatility performs at a steady level.
ELO Top 10
| # | Player | ELO | MP | Win% |
|---|---|---|---|---|
| 1 | Aryna Sabalenka | 2169 | 42 | 85.7% |
| 2 | Marta Kostyuk | 2109 | 32 | 81.2% |
| 3 | Mirra Andreeva | 2089 | 48 | 77.1% |
| 4 | Elina Svitolina | 2069 | 45 | 77.8% |
| 5 | Jessica Pegula | 2035 | 44 | 79.5% |
| 6 | Karolina Muchova | 2035 | 42 | 78.6% |
| 7 | Coco Gauff | 2020 | 38 | 71.1% |
| 8 | Naomi Osaka | 2014 | 26 | 73.1% |
| 9 | Iga Swiatek | 2004 | 30 | 66.7% |
| 10 | Linda Noskova | 1984 | 36 | 66.7% |
Glicko-2 Top 10
| # | Player | Rating | RD | Vol |
|---|---|---|---|---|
| 1 | Aryna Sabalenka | 2470 | 61.3 | 0.020 |
| 2 | Iga Swiatek | 2204 | 56.9 | 0.020 |
| 3 | Jessica Pegula | 2126 | 61.6 | 0.020 |
| 4 | Elena Rybakina | 2120 | 62.3 | 0.020 |
| 5 | Marta Kostyuk | 2114 | 76.4 | 0.020 |
| 6 | Coco Gauff | 2082 | 58.8 | 0.020 |
| 7 | Karolina Muchova | 2071 | 65.4 | 0.020 |
| 8 | Elina Svitolina | 2067 | 62.6 | 0.020 |
| 9 | Mirra Andreeva | 2035 | 58.7 | 0.021 |
| 10 | Amanda Anisimova | 2033 | 62.0 | 0.020 |
Surface ratings track each player's strength separately on hard courts, clay, and grass. A player's overall ELO may be high, but their clay rating could be much lower if they rarely win on that surface. These use the same ELO algorithm but only update when a match is played on that specific surface.
Full methodology →
Hard Court
Australian Open, US Open, most WTA events| # | Player | Rating |
|---|---|---|
| 1 | Aryna Sabalenka | 100 |
| 2 | Elena Rybakina | 83 |
| 3 | Jessica Pegula | 77 |
| 4 | Karolina Muchova | 69 |
| 5 | Coco Gauff | 69 |
| 6 | Victoria Mboko | 68 |
| 7 | Elina Svitolina | 64 |
| 8 | Iga Swiatek | 60 |
| 9 | Amanda Anisimova | 59 |
| 10 | Naomi Osaka | 55 |
Clay Court
Roland Garros, Madrid, Rome, Charleston| # | Player | Rating |
|---|---|---|
| 1 | Mirra Andreeva | 70 |
| 2 | Elina Svitolina | 63 |
| 3 | Marta Kostyuk | 62 |
| 4 | Aryna Sabalenka | 61 |
| 5 | Coco Gauff | 61 |
| 6 | Iga Swiatek | 57 |
| 7 | Elena Rybakina | 50 |
| 8 | Anastasia Potapova | 47 |
| 9 | Sorana Cirstea | 47 |
| 10 | Amanda Anisimova | 47 |
Grass Court
Wimbledon, Eastbourne, Berlin| # | Player | Rating |
|---|---|---|
| 1 | Aryna Sabalenka | 81 |
| 2 | Iga Swiatek | 76 |
| 3 | Jessica Pegula | 65 |
| 4 | Amanda Anisimova | 63 |
| 5 | Coco Gauff | 62 |
| 6 | Linda Noskova | 57 |
| 7 | Naomi Osaka | 54 |
| 8 | Belinda Bencic | 53 |
| 9 | Elise Mertens | 52 |
| 10 | Madison Keys | 51 |
Form Index measures who is outperforming expectations right now. For each of a player's last 15 matches, we compare the actual result against the win probability from our surface ELO model. Beating someone you had a 20% chance against earns far more than beating someone you were 95% to beat. Losses work the same way in reverse: losing a match you should have won is heavily penalized, while losing to a genuine threat barely dents your form. Results are weighted by tournament importance (Grand Slam > WTA 1000 > 500 > 250) and round (final > early rounds), with recent matches counting more. Inactive players see their form decay rapidly.
Full methodology →
How to read this table
ELO #
Shows overall strength rank. A player ranked #4 ELO and #1 Form is a top player on a hot streak. A player ranked #80 ELO and #3 Form is a lower-ranked player punching well above their weight.
Rising
Players ranked outside the ELO top 30 who have broken into the Form top 10 — names to watch in upcoming draws.
Example
A player with surface ELO 1700 faces an opponent rated 1900 — our model gives her roughly a 25% chance. If she wins, the surplus is +0.75 (1.0 − 0.25). At a WTA 1000 semifinal, that's multiplied by the tournament weight (1.8) and round weight (1.3), giving +1.76 form points for that match. Meanwhile, the world #1 beating a qualifier she was 97% to beat earns only +0.05 form points — the win is expected, so the form reward is minimal.
Form Index Top 10
| # | Player | Form | ELO # | MP |
|---|---|---|---|---|
| 1 | Marta Kostyuk | 87.66 | #2 | 32 |
| 2 | Naomi Osaka | 77.12 | #8 | 26 |
| 3 | Karolina Muchova | 74.14 | #6 | 42 |
| 4 | Maja Chwalinska | 68.58 | #18 | 15 |
| 5 | Iva Jovic | 63.76 | #17 | 43 |
| 6 | Elise Mertens | 63.26 | #20 | 33 |
| 7 | Linda Noskova | 60.65 | #10 | 36 |
| 8 | Sorana Cirstea | 60.17 | #13 | 45 |
| 9 | Marie Bouzkova | 60.05 | #25 | 39 |
| 10 | Alexandra Eala | 58.43 | #26 | 40 |
Understanding the Ratings
ELO — Overall strength, updated after every match.
Learn more
Glicko-2 — Like ELO but includes a confidence interval (RD) and consistency measure (volatility).
Surface Ratings — Separate ELO for hard, clay, grass.
Learn more
Form Index — Who is outperforming expectations over their last 15 matches.
Learn more
Get Tennis Predictions Daily
Power Rankings updated after every tournament. Join our Telegram channel for daily AI predictions for tennis, NBA, and football.
Join Telegram Channel