WTA · Right-handed · 10 charted matches · 2013–2023
Kaia Kanepi
Archetype: First-strike aggressor · Short-point player
Against an average opponent
Serve and return points won, refitted against every opponent at once so a record built on weak or strong opposition is put on the same scale. Career, all surfaces, with a 90% margin. Raw is the plain share of points won.
Value per 100 shots
Points gained per 100 shots compared with an average tour player in the same position, adjusted for the strength of the opponents faced, with a 90% margin (shots clustered by match). Raw is before the opponent adjustment. Built on 3,253 shots.
Shot expected value
The share of points Kaia Kanepi goes on to win after each option in the positions they face most often, shrunk toward tour average when the sample is small. Showing the 8 most-used options; teal marks the best one with at least 30 shots.
Rally, shots 5–8: drive to your middle
position worth 50% to the average player · 220 shots
| Option | Used | Win % | Tour |
|---|---|---|---|
| FH crosscourt | 24% | 41.8%±9.5 | 52.7% |
| FH through the middle | 24% | 32.2%±9.1 | 45.8% |
| FH down the line | 20% | 43.8%±10.1 | 52.2% |
| BH crosscourt | 13% | 52.5%±11.9 | 50.9% |
| BH through the middle | 10% | 40.1%±12.3 | 46.2% |
| BH down the line | 9% | 48.7%±13.2 | 50.0% |
Rally, shots 5–8: drive to your forehand side
position worth 43% to the average player · 150 shots
| Option | Used | Win % | Tour |
|---|---|---|---|
| FH crosscourt | 40% | 45.4%±9.2 | 46.7% |
| FH through the middle | 35% | 36.5%±9.3 | 41.3% |
| FH down the line | 20% | 40.0%±11.4 | 44.9% |
Rally, shots 5–8: drive to your backhand side
position worth 45% to the average player · 131 shots
| Option | Used | Win % | Tour |
|---|---|---|---|
| BH crosscourt | 31% | 45.9%±10.6 | 47.6% |
| BH through the middle | 28% | 36.2%±10.5 | 43.3% |
| BH down the line | 24% | 43.0%±11.3 | 46.8% |
| BH slice through the middle | 8% | 26.3%±13.2 | 34.4% |
Serve +1: mid-depth return to your middle
position worth 51% to the average player · 116 shots
| Option | Used | Win % | Tour |
|---|---|---|---|
| FH through the middle | 22% | 50.2%±12.1 | 45.5% |
| BH through the middle | 21% | 41.5%±12.2 | 46.3% |
| FH down the line | 18% | 60.1%±12.6 | 53.3% |
| FH crosscourt | 17% | 49.5%±13.0 | 54.0% |
| BH crosscourt | 16% | 47.4%±13.2 | 52.5% |
Serve under pressure
Pressure predictability index +8 How much less varied Kaia Kanepi's first-serve direction gets on break points. Positive means easier to read. Based on 80 break-point first serves.
Deuce court
| 1st serve | Usage | Break pt | Won when in |
|---|---|---|---|
| Wide | 55% | 54% | 70% / 66% |
| Body | 10% | 8% | 53% / 57% |
| T | 35% | 38% | 68% / 68% |
428 normal · 13 break-point 1st serves
Ad court
| 1st serve | Usage | Break pt | Won when in |
|---|---|---|---|
| Wide | 50% | 55% | 67% / 66% |
| Body | 10% | 6% | 64% / 56% |
| T | 41% | 39% | 69% / 64% |
343 normal · 67 break-point 1st serves
Is the serve mix in equilibrium?
Game theory says a well-mixed server wins equally often with every direction they use. If one direction wins more, it's underused and points are being left behind. This is the minimax test Walker and Wooders ran on Wimbledon finals, applied to every charted first serve. Win rates include faults. "Optimal" allows for returners reading a habit. A direction loses 0.19 points per 100 serves for every 10 points of habitual usage, measured from WTA servers whose mix drifted between matches. Shifts stay within the range servers' habits actually vary, the only range that response was measured over.
Deuce court
| 1st serve | Usage | Points won | Optimal |
|---|---|---|---|
| Wide | 55% | 57.6%±4.9 n=243 | 52% ▼ |
| Body | 10% | 49.8%±9.6 n=44 | 0% ▼ |
| T | 35% | 58.8%±6.0 n=154 | 48% ▲ |
Consistent with an optimal mix (p = 0.22).
Optimal mix: +0.5 per 100 first serves.
Ad court
| 1st serve | Usage | Points won | Optimal |
|---|---|---|---|
| Wide | 50% | 58.2%±5.3 n=207 | 49% ▼ |
| Body | 9% | 64.2%±9.6 n=38 | 0% ▼ |
| T | 40% | 58.8%±5.8 n=165 | 51% ▲ |
Consistent with an optimal mix (p = 0.46).
Optimal mix: +0.2 per 100 first serves.
Exploitability 0.35 points per 100 first serves What the optimal mix would win over the current one, both courts. More exploitable than 100% of WTA servers. Tested on matches they weren't fitted on, WTA mixes picked this way win 0.42 per 100 first serves on average.
Repeating the previous direction to the same court: +3.3±6.6 points per 100 against switching. Negative means returners read repeats. Tour-wide, repeating costs women about 0.4 points per 100 and costs men nothing, so men's returners don't measurably anticipate direction. (304 repeats, 527 switches.)
Return by serve direction
Return points won against each serve direction, compared with the tour average.
| Serve | Court | Direction | Points | Won | vs tour | |
|---|---|---|---|---|---|---|
| 1st | Ad court | Body | 57 | 36% | −8.1±8.5 | |
| 1st | Ad court | T | 71 | 39% | +3.7±8.0 | |
| 1st | Ad court | Wide | 92 | 23% | −11.2±6.3 | |
| 1st | Deuce court | Body | 75 | 32% | −10.4±7.5 | |
| 1st | Deuce court | T | 94 | 30% | −1.8±6.8 | |
| 1st | Deuce court | Wide | 82 | 23% | −11.5±6.5 | |
| 2nd | Ad court | Body | 93 | 53% | −1.8±7.4 | |
| 2nd | Ad court | T | 25 | 55% | +0.4±11.0 | |
| 2nd | Ad court | Wide | 59 | 53% | −0.7±8.7 | |
| 2nd | Deuce court | Body | 95 | 56% | +1.8±7.3 | |
| 2nd | Deuce court | T | 56 | 52% | −3.9±8.9 | |
| 2nd | Deuce court | Wide | 27 | 60% | +6.1±10.7 |
Signature patterns
Recurring sequences that win more than Kaia Kanepi's own baseline, ranked by edge weighted by how often they're used.
Serve → +1
- T serve (ad court) → FH down the line used 3.2% · won 65% · −0.1±11.2 vs own baseline
- Body serve (deuce court) → BH crosscourt used 3.2% · won 59% · −6.1±11.5 vs own baseline
- Wide serve (ad court) → BH crosscourt used 3.4% · won 56% · −9.2±11.5 vs own baseline
- Body serve (deuce court) → FH through the middle used 4.1% · won 52% · −13.3±11.1 vs own baseline
- Wide serve (ad court) → BH through the middle used 4.4% · won 48% · −16.8±10.9 vs own baseline
Return
- Not enough data
Rally, consecutive own shots
- FH direction unknown → FH direction unknown used 8.3% · won 43% · +3.3±10.0 vs own baseline
- BH through the middle → FH crosscourt used 5.0% · won 41% · +1.3±11.4 vs own baseline
- FH through the middle → BH through the middle used 5.2% · won 40% · +0.5±11.3 vs own baseline
- FH crosscourt → FH through the middle used 6.1% · won 37% · −2.3±10.7 vs own baseline
- BH through the middle → FH through the middle used 5.9% · won 36% · −3.5±10.7 vs own baseline
Discovered sequences
Mined from every me → opponent → me run of three shots, with no templates. Ranked by how much more often Kaia Kanepi wins the point once the sequence happens, weighted by how often it happens. Think of them as chess openings.
- FH crosscourt → FH crosscourt → FH down the line used 1.2% · won 35% · −5.5±12.0 vs own baseline · −19.9 vs tour on the same sequence Disrupted by Kiki Bertens (3/7)
- FH crosscourt → FH crosscourt → FH crosscourt used 1.4% · won 35% · −5.7±11.6 vs own baseline · −19.7 vs tour on the same sequence Disrupted by Kiki Bertens (3/9)
- BH through the middle → FH crosscourt → FH through the middle used 1.1% · won 32% · −8.7±12.1 vs own baseline · −20.3 vs tour on the same sequence Disrupted by Iga Swiatek (1/10)
Strengths and vulnerabilities
Value per 100 shots compared with the average player hitting (strengths) or facing (vulnerabilities) the same shot. Only shot types seen at least 120 times.
Hurts opponents most with
| FH to the middle · rally | +0.7 | 133 |
| Wide 1st serve · ad court | +0.5 | 177 |
| T 1st serve · deuce court | +0.1 | 129 |
| Wide 1st serve · deuce court | −0.9 | 206 |
| T 1st serve · ad court | −0.9 | 146 |
Most exposed to
| FH to the middle · rally | −1.2 | 123 |
| FH to their forehand · rally | −0.5 | 170 |
| Wide 1st serve · ad court | −0.2 | 136 |
| T 1st serve · deuce court | +0.2 | 149 |
| BH to their backhand · return | +0.6 | 123 |
Active players who are best at the shot in the top weakness: Sara Sorribes Tormo, Caroline Wozniacki, Linda Fruhvirtova, Daria Kasatkina, Magdalena Frech
Tactical fingerprint
Each bar shows how far a style trait is from the WTA average, in standard deviations.
| Wide serves · deuce | 55% | |
| Unforced errors / shot | 13.9% | |
| Wide serves · ad | 50% | |
| BH down the line | 26% | |
| Through the middle | 33% | |
| Forehand share | 57% | |
| Point-ending shots | 27.9% | |
| FH down the line | 33% | |
| T serves · ad | 40% | |
| Chipped returns | 13% | |
| Backhand slice | 14% | |
| Serve & volley | 0% | |
| Run-around forehands | 6% | |
| T serves · deuce | 35% | |
| Deep returns | 31% | |
| Drop shots / shot | 0.8% | |
| Points at net | 4% | |
| 1st serve in | 58% | |
| Avg rally length | 3.5 |
Plays most like
- Shelby Rogers 2014–2024 plan v
- Madison Keys 2014–2026 plan v
- Karolina Pliskova 2013–2026 plan v
- Ekaterina Alexandrova 2017–2026 plan v
- Anastasia Pavlyuchenkova 2014–2026 plan v
- Victoria Mboko 2022–2026 plan v
- Irina Camelia Begu 2015–2024 plan v
- Viktoria Hruncakova 2018–2026 plan v
Closest from another era
- Lindsay Davenport 1995–2006
- Mary Pierce 1994–2005
- Monica Seles 1990–2003
Charted matches
- Barbora Strycova v Kaia Kanepi US Open R128 · Hard · 30 Aug 2023
- Camila Giorgi v Kaia Kanepi L Miami R128 · Hard · 21 Mar 2023
- Kaia Kanepi v Liudmila Samsonova L Guadalajara R64 · Hard · 17 Oct 2022
- Iga Swiatek v Kaia Kanepi L Australian Open QF · Hard · 26 Jan 2022
- Leylah Fernandez v Kaia Kanepi US Open R64 · Hard · 1 Sep 2021
- Kiki Bertens v Kaia Kanepi L Moscow R16 · Hard · 17 Oct 2019
- Kaia Kanepi v Donna Vekic L US Open R64 · Hard · 29 Aug 2019
- Monica Puig v Kaia Kanepi W Hobart International R32 · Hard · 11 Jan 2015
- Kaia Kanepi v Sabine Lisicki L Wimbledon QF · Grass · 2 Jul 2013
- Kaia Kanepi v Laura Robson W Wimbledon R16 · Grass · 1 Jul 2013