RallyIQ

WTA · Right-handed · 8 charted matches · 2013–2019

Magdalena Rybarikova

Archetype: Rarely serves the T · Patient builder

Against an average opponent

Serve points won 53.7% ±3.9 raw 47.9% · tour 56.3% · 422 points
Return points won 44.0% ±3.9 raw 39.3% · tour 43.7% · 415 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 −0.24 ±0.20 better than 12% of WTA · raw −0.26
Shot selection −0.43 ±0.35 better than 14% of WTA · raw −0.47
Execution +0.07 ±0.86 better than 64% of WTA · raw −0.23

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 2,323 shots.

Shot expected value

The share of points Magdalena Rybarikova 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 · 156 shots

OptionUsedWin %Tour
BH through the middle 20% 26.8%±10.2 43.3%
BH slice crosscourt 18% 41.8%±11.7 40.4%
BH slice through the middle 16% 28.6%±11.1 34.4%
BH crosscourt 15% 42.1%±12.2 47.6%
BH down the line 15% 40.4%±12.3 46.8%
BH slice down the line 10% 35.3%±13.3 31.7%

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

position worth 43% to the average player · 111 shots

OptionUsedWin %Tour
FH through the middle 36% 28.8%±9.6 41.3%
FH down the line 32% 37.5%±10.6 44.9%
FH crosscourt 27% 36.7%±11.2 46.7%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 111 shots

OptionUsedWin %Tour
FH through the middle 23% 41.6%±12.0 45.8%
FH down the line 19% 42.6%±12.7 52.2%
BH through the middle 16% 34.8%±12.7 46.2%
FH crosscourt 14% 34.8%±13.1 52.7%
BH slice through the middle 12% 39.3%±14.0 44.8%

Return +1: drive to your backhand side

position worth 44% to the average player · 99 shots

OptionUsedWin %Tour
BH slice through the middle 24% 33.3%±11.7 33.2%
BH through the middle 22% 46.7%±12.7 43.0%
BH slice crosscourt 20% 42.3%±12.8 39.6%
BH down the line 14% 50.7%±14.1 46.2%
BH crosscourt 13% 47.1%±14.3 47.8%

Serve under pressure

Pressure predictability index −2 How much less varied Magdalena Rybarikova's first-serve direction gets on break points. Positive means easier to read. Based on 67 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 54% 44% ▼ 58% / 66%
Body 20% 13% 57% / 57%
T 26% 44% ▲ 67% / 68%

202 normal · 16 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 36% 41% 67% / 66%
Body 15% 18% 58% / 56%
T 49% 41% 54% / 64%

151 normal · 51 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
Wide53% 48.6%±6.8 n=116 53%
Body20% 47.7%±9.6 n=43 5% ▼
T27% 46.3%±8.7 n=59 42% ▲

Consistent with an optimal mix (p = 0.91).
Optimal mix: +0.4 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide38% 47.8%±8.0 n=76 53% ▲
Body15% 46.2%±10.5 n=31 0% ▼
T47% 49.1%±7.4 n=95 47%

Consistent with an optimal mix (p = 0.88).
Optimal mix: +0.5 per 100 first serves.

Exploitability 0.41 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: −3.0±7.2 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. (127 repeats, 277 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 40 47% +3.5±9.8
1stAd courtT 49 30% −5.6±8.5
1stAd courtWide 49 37% +2.7±8.9
1stDeuce courtBody 35 34% −9.1±9.6
1stDeuce courtT 44 37% +5.2±9.3
1stDeuce courtWide 56 26% −8.2±7.8
2ndAd courtBody 27 48% −6.8±10.9
2ndAd courtT 16 49% −6.1±12.1
2ndAd courtWide 19 51% −2.4±11.7
2ndDeuce courtBody 33 55% ±0.0±10.3
2ndDeuce courtT 35 61% +5.2±9.9
2ndDeuce courtWide 10 53% −0.9±13.0

Discovered sequences

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

  1. FH crosscourt → FH crosscourt → FH down the line used 1.3% · won 27% · −8.3±11.7 vs own baseline · −34.4 vs tour on the same sequence Disrupted by Garbine Muguruza (1/6)

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

Most exposed to

BH to their backhand · rally−1.7179
FH to their forehand · rally+0.1143

Active players who are best at the shot in the top weakness: Maja Chwalinska, Sara Sorribes Tormo, Yulia Putintseva, Elsa Jacquemot, Caroline Wozniacki

Tactical fingerprint

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

FH down the line38%
Wide serves · deuce53%
Backhand slice41%
Through the middle36%
Points at net13%
Drop shots / shot2.8%
Serve & volley6%
T serves · ad47%
Avg rally length4.5
BH down the line24%
Chipped returns18%
Unforced errors / shot11.6%
Run-around forehands7%
Deep returns32%
Wide serves · ad38%
Point-ending shots20.3%
1st serve in59%
Forehand share49%
T serves · deuce27%

Plays most like

  1. Saisai Zheng 2014–2020 plan v
  2. Elise Mertens 2018–2026 plan v
  3. Karolina Muchova 2019–2026 plan v
  4. Marie Bouzkova 2018–2026 plan v
  5. Alison Van Uytvanck 2015–2022 plan v
  6. Mirra Andreeva 2022–2026 plan v
  7. Clara Burel 2018–2024 plan v
  8. Anna Bondar 2017–2026 plan v

Closest from another era

  1. Jennifer Capriati 1990–2002
  2. Lindsay Davenport 1995–2006
  3. Mary Pierce 1994–2005

Charted matches