RallyIQ

WTA · Right-handed · 74 charted matches · 2019–2026

Karolina Muchova

Archetype: Runs around the backhand · Forehand-dominant

Against an average opponent

Serve points won 61.9% ±2.5 raw 61.6% · tour 56.3% · 5,249 points
Return points won 45.5% ±2.6 raw 42.9% · tour 43.7% · 5,466 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 −0.01 ±0.05 better than 52% of WTA · raw −0.01
Shot selection −0.17 ±0.09 better than 30% of WTA · raw −0.18
Execution +0.22 ±0.28 better than 70% of WTA · raw +0.18
Tactical adaptability +0.12 first serves toward what's working, set to set · 71 matches
Adaptation speed +0.01 same, every two to three service games · per 100 first serves
Points left on the table 2.88 per 100 shots vs best direction · lower than 18% 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 26,785 shots.

Shot expected value

The share of points Karolina Muchova 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 · 1,650 shots

OptionUsedWin %Tour
BH crosscourt 23% 52.3%±4.1 47.6%
BH through the middle 21% 43.4%±4.2 43.3%
BH slice through the middle 14% 43.1%±5.1 34.4%
BH slice crosscourt 13% 47.3%±5.3 40.4%
BH down the line 12% 49.3%±5.5 46.8%
BH slice down the line 6% 29.9%±6.7 31.7%
FH inside-out 2% 51.8%±10.9 52.5%
FH inside-in 2% 62.5%±10.9 55.6%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 1,384 shots

OptionUsedWin %Tour
FH crosscourt 26% 55.7%±4.2 52.7%
FH down the line 16% 51.7%±5.4 52.2%
FH through the middle 15% 51.8%±5.4 45.8%
BH through the middle 12% 46.2%±6.0 46.2%
BH down the line 7% 51.8%±7.8 50.0%
BH crosscourt 6% 48.3%±8.0 50.9%
FH down the line + approach 4% 63.9%±9.4 68.6%
BH slice through the middle 3% 43.2%±10.0 44.8%

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

position worth 43% to the average player · 1,210 shots

OptionUsedWin %Tour
FH crosscourt 33% 43.8%±4.0 46.7%
FH down the line 25% 40.7%±4.5 44.9%
FH through the middle 23% 42.1%±4.7 41.3%
FH slice through the middle 7% 35.1%±7.7 29.2%
FH slice crosscourt 3% 41.6%±10.4 31.9%
FH down the line + approach 2% 68.3%±11.2 65.4%
FH slice down the line 1% 21.9%±11.3 24.3%
BH through the middle 1% 44.8%±14.5 36.6%

Return +1: drive to your middle

position worth 50% to the average player · 849 shots

OptionUsedWin %Tour
FH crosscourt 21% 51.0%±5.9 52.3%
FH through the middle 17% 42.2%±6.4 46.5%
FH down the line 14% 54.1%±7.0 53.0%
BH through the middle 12% 36.5%±7.1 46.2%
BH down the line 8% 53.1%±8.9 50.6%
BH crosscourt 7% 39.4%±9.0 50.8%
BH slice through the middle 5% 48.8%±10.1 45.9%
BH slice down the line 4% 41.0%±11.0 40.6%

Serve under pressure

Pressure predictability index +4 How much less varied Karolina Muchova's first-serve direction gets on break points. Positive means easier to read. Based on 493 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 40% 37% 68% / 66%
Body 22% 17% 58% / 57%
T 38% 46% ▲ 74% / 68%

2,609 normal · 115 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 47% 53% 68% / 66%
Body 14% 12% 57% / 56%
T 39% 35% 67% / 64%

2,131 normal · 378 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
Wide40% 62.2%±2.4 n=1,083 40%
Body22% 57.8%±3.2 n=601 7% ▼
T38% 67.1%±2.4 n=1,040 53% ▲

Off equilibrium (p < 0.001): serve T more. Gap 4.0 points per 100 first serves.
Optimal mix: +1.3 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide48% 60.3%±2.3 n=1,195 63% ▲
Body14% 57.9%±4.2 n=344 0% ▼
T39% 60.5%±2.5 n=970 37% ▼

Consistent with an optimal mix (p = 0.61).
Optimal mix: +0.6 per 100 first serves.

Exploitability 1.00 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: −1.4±2.4 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. (2,066 repeats, 3,019 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 304 46% +2.6±4.5
1stAd courtT 584 36% +0.7±3.2
1stAd courtWide 737 34% −0.5±2.8
1stDeuce courtBody 402 42% −0.5±3.9
1stDeuce courtT 630 31% −0.7±3.0
1stDeuce courtWide 780 35% +0.8±2.8
2ndAd courtBody 435 52% −2.9±3.8
2ndAd courtT 141 49% −6.2±6.3
2ndAd courtWide 418 54% +0.9±3.9
2ndDeuce courtBody 529 57% +2.9±3.4
2ndDeuce courtT 299 55% −1.1±4.5
2ndDeuce courtWide 199 55% +1.3±5.4

Signature patterns

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

Serve → +1

  1. Body serve (deuce court) → FH crosscourt used 2.4% · won 59% · −5.4±6.8 vs own baseline
  2. Wide serve (ad court) → FH crosscourt used 2.4% · won 58% · −5.9±6.8 vs own baseline
  3. Wide serve (deuce court) → FH down the line used 3.2% · won 58% · −6.6±6.1 vs own baseline
  4. Body serve (deuce court) → BH through the middle used 2.5% · won 55% · −9.2±6.7 vs own baseline
  5. Body serve (ad court) → BH through the middle used 2.0% · won 51% · −13.6±7.4 vs own baseline

Return

  1. vs wide serve (deuce court) → FH through the middle, deep used 2.8% · won 56% · +13.8±6.5 vs own baseline
  2. vs body serve (deuce court) → BH through the middle, mid used 2.3% · won 54% · +11.8±7.2 vs own baseline
  3. vs body serve (deuce court) → BH through the middle, deep used 2.6% · won 53% · +10.7±6.8 vs own baseline
  4. vs wide serve (ad court) → BH through the middle, mid used 3.0% · won 50% · +7.9±6.4 vs own baseline
  5. vs body serve (ad court) → BH through the middle, deep used 2.1% · won 51% · +9.2±7.5 vs own baseline

Rally, consecutive own shots

  1. FH crosscourt → FH crosscourt used 3.6% · won 56% · +8.4±6.0 vs own baseline
  2. FH through the middle → FH down the line used 1.4% · won 59% · +11.9±8.6 vs own baseline
  3. FH crosscourt → BH crosscourt used 1.8% · won 57% · +9.8±7.8 vs own baseline
  4. BH crosscourt → BH down the line used 1.8% · won 54% · +6.9±7.8 vs own baseline
  5. FH down the line → FH crosscourt used 1.4% · won 54% · +6.2±8.6 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 Muchova wins the point once the sequence happens, weighted by how often it happens. Think of them as chess openings.

  1. FH crosscourt → FH through the middle → FH crosscourt used 0.6% · won 57% · +8.2±8.3 vs own baseline · +4.4 vs tour on the same sequence Disrupted by Naomi Osaka (2/6), Sara Sorribes Tormo (11/19)
  2. Body serve → BH through the middle return, mid → FH crosscourt used 0.4% · won 59% · +10.4±9.8 vs own baseline · +12.1 vs tour on the same sequence
  3. FH crosscourt → FH crosscourt → FH down the line + approach used 0.2% · won 63% · +14.2±11.5 vs own baseline · +10.4 vs tour on the same sequence
  4. BH through the middle → FH through the middle → FH crosscourt used 0.5% · won 58% · +9.3±9.2 vs own baseline · +13.3 vs tour on the same sequence
  5. BH crosscourt → BH through the middle → FH crosscourt used 0.5% · won 58% · +8.9±9.0 vs own baseline · +7.5 vs tour on the same sequence Disrupted by Coco Gauff (3/6), Paula Badosa (4/6)
  6. FH through the middle → BH through the middle → FH down the line used 0.2% · won 61% · +12.5±11.2 vs own baseline · +22.5 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

Wide 2nd serve · deuce court+4.3319
Wide 2nd serve · ad court+3.6363
T 2nd serve · ad court+3.0218
FH slice to their forehand · return+2.8127
FH to their backhand · serve +1+2.7710

Most exposed to

BH to the middle · serve +1−1.2503
BH to the middle · return +1−0.4372
Body 1st serve · ad court−0.4403
Wide 1st serve · deuce court−0.41,205
Body 2nd serve · ad court−0.2435

Best-equipped opponents

Active players whose shot mix lines up best against these weaknesses, by structural compatibility per 100 rally shots: Sara Sorribes Tormo +1.61, Caroline Wozniacki +1.33, Daria Kasatkina +0.74, Sara Errani +0.62, Angelique Kerber +0.60

Favourable matchups

Sara Errani +1.83, Angelique Kerber +1.79, Marie Bouzkova +1.58, Elina Avanesyan +1.46, Linda Fruhvirtova +1.21

Active players who are best at the shot in the top weakness: Sara Sorribes Tormo, Marie Bouzkova, Tamara Zidansek, Magda Linette, Su Wei Hsieh

Tactical fingerprint

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

Points at net13%
Backhand slice33%
Drop shots / shot2.6%
Run-around forehands13%
Serve & volley5%
Wide serves · ad48%
Deep returns36%
Through the middle32%
BH down the line23%
Chipped returns16%
Point-ending shots26.5%
FH down the line31%
1st serve in63%
T serves · deuce38%
T serves · ad39%
Unforced errors / shot10.7%
Forehand share53%
Wide serves · deuce40%
Avg rally length4.0

Plays most like

  1. Bianca Andreescu 2017–2026 plan v
  2. Leylah Fernandez 2020–2026 plan v
  3. Petra Martic 2010–2024 plan v
  4. Marta Kostyuk 2018–2026 plan v
  5. Kiki Bertens 2012–2021 plan v
  6. R – plan v
  7. Alison Van Uytvanck 2015–2022 plan v
  8. Karolina Pliskova 2013–2026 plan v

Closest from another era

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

Charted matches