RallyIQ

WTA · Right-handed · 9 charted matches · 2015–2022

Tereza Martincova

Archetype: Rarely serves the T · Patient builder

Against an average opponent

Serve points won 55.6% ±3.7 raw 52.2% · tour 56.3% · 565 points
Return points won 42.6% ±3.7 raw 38.7% · tour 43.7% · 568 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 −0.28 ±0.15 better than 9% of WTA · raw −0.29
Shot selection −0.26 ±0.20 better than 24% of WTA · raw −0.27
Execution −0.64 ±1.13 better than 29% of WTA · raw −0.74
Points left on the table 2.83 per 100 shots vs best direction · lower than 21% 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 2,955 shots.

Shot expected value

The share of points Tereza Martincova 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 · 186 shots

OptionUsedWin %Tour
FH crosscourt 25% 46.3%±10.1 52.7%
FH down the line 24% 46.8%±10.2 52.2%
FH through the middle 19% 44.9%±10.9 45.8%
BH crosscourt 13% 48.1%±12.4 50.9%
BH through the middle 12% 53.0%±12.7 46.2%

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

position worth 43% to the average player · 164 shots

OptionUsedWin %Tour
FH crosscourt 35% 39.4%±9.2 46.7%
FH through the middle 30% 42.4%±9.8 41.3%
FH down the line 25% 52.4%±10.5 44.9%
FH slice through the middle 7% 25.3%±12.8 29.2%

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

position worth 45% to the average player · 143 shots

OptionUsedWin %Tour
BH crosscourt 49% 40.6%±8.5 47.6%
BH through the middle 32% 35.9%±9.7 43.3%
BH slice through the middle 9% 26.9%±12.7 34.4%

Serve +1: mid-depth return to your middle

position worth 51% to the average player · 110 shots

OptionUsedWin %Tour
FH through the middle 29% 48.3%±11.4 45.5%
FH crosscourt 20% 59.1%±12.5 54.0%
FH down the line 18% 51.6%±13.0 53.3%
BH through the middle 15% 57.5%±13.4 46.3%
BH crosscourt 13% 48.5%±14.1 52.5%

Serve under pressure

Pressure predictability index +12 How much less varied Tereza Martincova's first-serve direction gets on break points. Positive means easier to read. Based on 66 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 48% 35% ▼ 60% / 66%
Body 28% 12% ▼ 58% / 57%
T 24% 53% ▲ 58% / 68%

278 normal · 17 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 34% 59% ▲ 61% / 66%
Body 29% 18% ▼ 59% / 56%
T 37% 22% ▼ 58% / 64%

221 normal · 49 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
Wide47% 53.6%±6.3 n=140 60% ▲
Body27% 53.7%±7.9 n=79 12% ▼
T26% 49.8%±8.0 n=76 28% ▲

Consistent with an optimal mix (p = 0.71).
Optimal mix: +0.3 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide39% 48.7%±7.1 n=104 38%
Body27% 54.3%±8.1 n=73 12% ▼
T34% 53.2%±7.4 n=93 50% ▲

Consistent with an optimal mix (p = 0.53).
Optimal mix: +0.4 per 100 first serves.

Exploitability 0.34 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: −6.9±10.0 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. (191 repeats, 356 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 36 41% −2.7±10.0
1stAd courtT 55 33% −3.0±8.4
1stAd courtWide 76 29% −5.8±7.2
1stDeuce courtBody 36 36% −6.6±9.7
1stDeuce courtT 62 29% −3.2±7.8
1stDeuce courtWide 81 22% −12.2±6.4
2ndAd courtBody 51 56% +1.1±9.1
2ndAd courtT 19 52% −3.0±11.7
2ndAd courtWide 37 60% +6.3±9.9
2ndDeuce courtBody 45 59% +4.7±9.3
2ndDeuce courtT 26 55% −1.0±10.9
2ndDeuce courtWide 43 54% −0.2±9.6

Signature patterns

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

Serve → +1

  1. Body serve (deuce court) → FH through the middle used 6.4% · won 54% · −0.2±11.2 vs own baseline

Return

  1. vs T serve (deuce court) → BH through the middle, mid used 12.5% · won 44% · +2.2±11.6 vs own baseline

Rally, consecutive own shots

  1. FH crosscourt → FH down the line used 7.7% · won 50% · +6.9±10.2 vs own baseline
  2. FH through the middle → FH down the line used 4.8% · won 51% · +7.9±11.6 vs own baseline
  3. FH down the line → BH crosscourt used 5.7% · won 45% · +2.4±11.1 vs own baseline
  4. FH through the middle → FH crosscourt used 5.2% · won 43% · +0.3±11.3 vs own baseline
  5. FH through the middle → BH through the middle used 4.6% · won 42% · −1.1±11.6 vs own baseline

Discovered sequences

Mined from every me → opponent → me run of three shots, with no templates. Ranked by how much more often Tereza Martincova 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 1.5% · won 51% · +8.8±11.9 vs own baseline · +13.2 vs tour on the same sequence
  2. FH through the middle → FH crosscourt → FH through the middle used 1.4% · won 47% · +4.5±12.1 vs own baseline · +11.2 vs tour on the same sequence Disrupted by Kiki Bertens (4/7)
  3. FH crosscourt → FH through the middle → FH down the line used 1.0% · won 45% · +2.9±12.8 vs own baseline · −3.6 vs tour on the same sequence Disrupted by Paula Badosa (4/6)
  4. FH down the line → BH crosscourt → BH crosscourt used 1.4% · won 42% · −0.7±11.8 vs own baseline · −7.5 vs tour on the same sequence Disrupted by Anett Kontaveit (4/7)
  5. BH crosscourt → BH crosscourt → BH crosscourt used 1.5% · won 41% · −1.6±11.7 vs own baseline · −9.5 vs tour on the same sequence Disrupted by Anett Kontaveit (4/8)
  6. FH crosscourt → FH crosscourt → FH through the middle used 1.4% · won 36% · −6.3±11.7 vs own baseline · −14.6 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

FH to the middle · rally+1.9139
Wide 1st serve · deuce court+1.2140
BH to their backhand · rally+0.7163
FH to their forehand · rally−1.9175
FH to their backhand · rally−3.8180

Most exposed to

BH to the middle · return−2.4121
FH to the middle · return−0.7151
FH to their backhand · rally−0.4139
Wide 1st serve · deuce court−0.3128
Wide 1st serve · ad court+0.1129

Active players who are best at the shot in the top weakness: Sara Sorribes Tormo, Maja Chwalinska, Daria Saville, Daria Kasatkina, Caroline Wozniacki

Tactical fingerprint

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

1st serve in70%
Forehand share59%
Through the middle35%
FH down the line34%
Wide serves · deuce47%
Avg rally length4.2
Backhand slice16%
Serve & volley0%
Chipped returns9%
Points at net6%
Unforced errors / shot10.0%
Wide serves · ad39%
T serves · ad34%
Run-around forehands5%
Deep returns30%
Drop shots / shot0.6%
Point-ending shots18.6%
T serves · deuce26%
BH down the line12%

Plays most like

  1. Emma Navarro 2019–2026 plan v
  2. Coco Gauff 2019–2026 plan v
  3. Emma Raducanu 2018–2026 plan v
  4. Nao Hibino 2016–2025 plan v
  5. Yafan Wang 2019–2025 plan v
  6. Carla Suarez Navarro 2009–2021 plan v
  7. Andrea Petkovic 2010–2022 plan v
  8. Rebecca Peterson 2020–2023 plan v

Closest from another era

  1. Jennifer Capriati 1990–2002
  2. Martina Hingis 1996–2007
  3. Lindsay Davenport 1995–2006

Charted matches