RallyIQ

WTA · Right-handed · 80 charted matches · 2013–2026

Karolina Pliskova

Archetype: First-strike aggressor · Short-point player

Against an average opponent

Serve points won 61.2% ±2.4 raw 59.9% · tour 56.3% · 6,035 points
Return points won 43.7% ±2.5 raw 41.1% · tour 43.7% · 5,849 points

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

Direction choice +0.15 ±0.05 better than 80% of WTA · raw +0.15
Shot selection −0.12 ±0.08 better than 34% of WTA · raw −0.12
Execution −0.01 ±0.35 better than 60% of WTA · raw −0.04
Tactical adaptability +0.01 first serves toward what's working, set to set · 76 matches
Adaptation speed +0.04 same, every two to three service games · per 100 first serves
Points left on the table 2.45 per 100 shots vs best direction · lower than 70% of WTA

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 27,457 shots.

Shot expected value

The share of points Karolina Pliskova 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 backhand side

position worth 45% to the average player · 1,526 shots

OptionUsedWin %Tour
BH crosscourt 33% 44.9%±3.6 47.6%
BH through the middle 29% 38.8%±3.7 43.3%
BH down the line 16% 45.7%±5.0 46.8%
BH slice crosscourt 10% 39.8%±6.2 40.4%
BH slice through the middle 6% 26.7%±6.9 34.4%
BH slice down the line 1% 30.7%±12.5 31.7%
BH drop shot crosscourt 1% 48.6%±13.7 47.5%
FH inside-out 1% 52.9%±13.9 52.5%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 1,515 shots

OptionUsedWin %Tour
FH crosscourt 22% 50.2%±4.4 52.7%
FH down the line 19% 54.9%±4.7 52.2%
FH through the middle 18% 47.1%±4.8 45.8%
BH through the middle 16% 48.8%±5.0 46.2%
BH crosscourt 12% 50.8%±5.8 50.9%
BH down the line 9% 51.0%±6.7 50.0%
FH down the line + approach 1% 63.4%±12.7 68.6%
FH crosscourt + approach 1% 66.5%±13.5 69.7%

Rally, shots 5–8: drive to your forehand side

position worth 43% to the average player · 1,267 shots

OptionUsedWin %Tour
FH crosscourt 44% 44.4%±3.4 46.7%
FH through the middle 21% 38.1%±4.8 41.3%
FH down the line 19% 43.2%±5.1 44.9%
FH slice crosscourt 9% 26.7%±6.4 31.9%
FH slice through the middle 7% 26.7%±7.0 29.2%
FH slice down the line 1% 15.2%±10.4 24.3%

Return +1: drive to your middle

position worth 50% to the average player · 946 shots

OptionUsedWin %Tour
FH crosscourt 21% 51.8%±5.6 52.3%
FH down the line 19% 51.3%±5.9 53.0%
BH through the middle 18% 50.9%±6.0 46.2%
FH through the middle 17% 48.2%±6.1 46.5%
BH crosscourt 12% 53.4%±7.0 50.8%
BH down the line 9% 47.7%±8.1 50.6%
FH down the line + approach 1% 54.0%±14.3 69.2%

Serve under pressure

Pressure predictability index +8 How much less varied Karolina Pliskova's first-serve direction gets on break points. Positive means easier to read. Based on 606 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 45% 64% ▲ 67% / 66%
Body 15% 6% ▼ 63% / 57%
T 39% 30% ▼ 75% / 68%

2,990 normal · 145 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 44% 45% 74% / 66%
Body 11% 8% 55% / 56%
T 45% 47% 70% / 64%

2,425 normal · 461 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 serveUsagePoints wonOptimal
Wide46% 58.8%±2.1 n=1,448 46%
Body15% 58.8%±3.6 n=468 0% ▼
T39% 61.8%±2.3 n=1,219 54% ▲

Consistent with an optimal mix (p = 0.20).
Optimal mix: +0.6 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide44% 59.7%±2.2 n=1,279 39% ▼
Body10% 53.8%±4.5 n=296 0% ▼
T45% 61.6%±2.2 n=1,311 61% ▲

Off equilibrium (p = 0.019): serve T more. Gap 1.6 points per 100 first serves.
Optimal mix: +0.8 per 100 first serves.

Exploitability 0.69 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: +2.4±2.2 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. (2,319 repeats, 3,542 switches.)

Return by serve direction

Return points won against each serve direction, compared with the tour average.

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 374 45% +1.4±4.1
1stAd courtT 659 31% −4.5±2.9
1stAd courtWide 681 32% −2.6±2.9
1stDeuce courtBody 424 44% +1.0±3.8
1stDeuce courtT 725 31% −1.6±2.8
1stDeuce courtWide 667 30% −3.7±2.9
2ndAd courtBody 500 51% −4.4±3.6
2ndAd courtT 146 61% +6.1±6.0
2ndAd courtWide 433 53% −0.6±3.8
2ndDeuce courtBody 637 52% −2.7±3.2
2ndDeuce courtT 381 51% −5.2±4.1
2ndDeuce courtWide 208 52% −2.0±5.3

Signature patterns

Recurring sequences that win more than Karolina Pliskova's own baseline, ranked by edge weighted by how often they're used.

Serve → +1

  1. T serve (ad court) → FH down the line used 2.3% · won 61% · −3.6±6.5 vs own baseline
  2. T serve (ad court) → FH crosscourt used 3.1% · won 59% · −5.9±5.7 vs own baseline
  3. Wide serve (deuce court) → FH crosscourt used 2.6% · won 57% · −7.7±6.2 vs own baseline
  4. Body serve (deuce court) → BH through the middle used 2.3% · won 55% · −9.9±6.6 vs own baseline
  5. Wide serve (deuce court) → FH down the line used 3.0% · won 55% · −9.5±5.9 vs own baseline

Return

  1. vs T serve (ad court) → FH through the middle, mid used 2.5% · won 52% · +10.8±6.7 vs own baseline
  2. vs wide serve (deuce court) → FH through the middle, deep used 2.3% · won 52% · +11.3±6.9 vs own baseline
  3. vs T serve (ad court) → FH through the middle, deep used 2.2% · won 52% · +10.6±7.0 vs own baseline
  4. vs T serve (deuce court) → BH through the middle, mid used 3.8% · won 48% · +6.5±5.6 vs own baseline
  5. vs wide serve (ad court) → BH through the middle, deep used 2.6% · won 49% · +7.5±6.6 vs own baseline

Rally, consecutive own shots

  1. FH down the line → FH down the line used 1.2% · won 60% · +15.7±8.9 vs own baseline
  2. FH down the line → FH crosscourt used 1.4% · won 56% · +11.3±8.5 vs own baseline
  3. FH crosscourt → BH crosscourt used 2.3% · won 52% · +7.7±7.1 vs own baseline
  4. FH through the middle → FH crosscourt used 2.8% · won 51% · +6.4±6.6 vs own baseline
  5. FH crosscourt → FH crosscourt used 5.0% · won 49% · +4.1±5.0 vs own baseline

Discovered sequences

Mined from every me → opponent → me run of three shots, with no templates. Ranked by how much more often Karolina Pliskova wins the point once the sequence happens, weighted by how often it happens. Think of them as chess openings.

  1. FH down the line → BH slice through the middle → FH down the line used 0.2% · won 61% · +14.6±11.2 vs own baseline · +12.6 vs tour on the same sequence
  2. FH down the line → BH through the middle → FH crosscourt used 0.4% · won 57% · +10.7±9.4 vs own baseline · +5.0 vs tour on the same sequence
  3. FH crosscourt → FH through the middle → FH down the line used 0.8% · won 53% · +7.4±7.4 vs own baseline · +1.6 vs tour on the same sequence Disrupted by Elina Svitolina (8/19)
  4. T serve → FH through the middle return, mid → FH down the line used 0.3% · won 56% · +10.2±10.1 vs own baseline · +10.6 vs tour on the same sequence
  5. FH through the middle → BH through the middle → BH through the middle used 0.2% · won 57% · +11.6±11.2 vs own baseline · +21.4 vs tour on the same sequence
  6. FH down the line → BH slice through the middle → FH crosscourt used 0.1% · won 59% · +13.3±12.3 vs own baseline · +13.2 vs tour on the same sequence

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

BH to their forehand · serve +1+2.5432
T 1st serve · deuce court+2.41,219
T 1st serve · ad court+2.21,311
Wide 1st serve · deuce court+1.61,448
Wide 1st serve · ad court+1.41,279

Most exposed to

FH to their forehand · return +1−4.0497
BH to their forehand · return +1−3.4224
FH to their forehand · serve +1−2.5665
BH to their forehand · rally−1.8646
T 1st serve · ad court−1.21,089

Best-equipped opponents

Active players whose shot mix lines up best against these weaknesses, by structural compatibility per 100 rally shots: Caroline Wozniacki +2.46, Sara Sorribes Tormo +2.46, Angelique Kerber +1.90, Daria Kasatkina +1.80, Sara Errani +1.66

Favourable matchups

Sara Errani +1.66, Angelique Kerber +1.21, Marie Bouzkova +1.13, Elina Avanesyan +1.09, Katie Volynets +0.79

Active players who are best at the shot in the top weakness: Sara Errani, Paula Badosa, Victoria Azarenka, Linda Noskova, Sara Sorribes Tormo

Tactical fingerprint

Each bar shows how far a style trait is from the WTA average, in standard deviations.

T serves · ad45%
Wide serves · deuce46%
Deep returns36%
BH down the line24%
Wide serves · ad44%
Point-ending shots25.8%
FH down the line31%
T serves · deuce39%
Through the middle30%
Unforced errors / shot10.8%
Backhand slice15%
1st serve in62%
Serve & volley0%
Run-around forehands6%
Chipped returns8%
Points at net6%
Forehand share52%
Drop shots / shot0.9%
Avg rally length3.6

Plays most like

  1. Sorana Cirstea 2014–2026 plan v
  2. Ekaterina Alexandrova 2017–2026 plan v
  3. Shuai Zhang 2009–2026 plan v
  4. Belinda Bencic 2014–2026 plan v
  5. Veronika Kudermetova 2018–2025 plan v
  6. Shelby Rogers 2014–2024 plan v
  7. Anastasia Potapova 2017–2026 plan v
  8. Anett Kontaveit 2015–2023 plan v

Closest from another era

  1. Elena Dementieva 1999–2010
  2. Lindsay Davenport 1995–2006
  3. Jelena Dokic 2000–2009

Charted matches