RallyIQ

WTA · Right-handed · 12 charted matches · 2012–2024

Qiang Wang

Archetype: Ad-court T server · Rallies through the middle

Against an average opponent

Serve points won 54.9% ±3.7 raw 50.5% · tour 56.3% · 826 points
Return points won 43.5% ±3.7 raw 40.4% · tour 43.7% · 824 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 +0.04 ±0.17 better than 61% of WTA · raw +0.03
Shot selection +0.37 ±0.16 better than 87% of WTA · raw +0.36
Execution +0.03 ±0.80 better than 62% of WTA · raw −0.06
Tactical adaptability −0.01 first serves toward what's working, set to set · 12 matches
Adaptation speed +0.02 same, every two to three service games · per 100 first serves
Points left on the table 2.24 per 100 shots vs best direction · lower than 90% 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 4,913 shots.

Shot expected value

The share of points Qiang Wang 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 · 378 shots

OptionUsedWin %Tour
BH crosscourt 47% 45.0%±5.8 47.6%
BH through the middle 29% 38.5%±7.0 43.3%
BH down the line 17% 39.9%±8.7 46.8%
BH slice crosscourt 3% 33.6%±14.2 40.4%

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

position worth 43% to the average player · 317 shots

OptionUsedWin %Tour
FH crosscourt 45% 40.8%±6.4 46.7%
FH through the middle 31% 41.6%±7.5 41.3%
FH down the line 22% 42.0%±8.7 44.9%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 272 shots

OptionUsedWin %Tour
FH down the line 28% 51.5%±8.4 52.2%
FH crosscourt 21% 50.7%±9.3 52.7%
FH through the middle 18% 52.4%±9.9 45.8%
BH crosscourt 14% 45.1%±10.7 50.9%
BH through the middle 12% 47.6%±11.3 46.2%
BH down the line 7% 39.5%±13.0 50.0%

Long rally, 9+: drive to your forehand side

position worth 44% to the average player · 214 shots

OptionUsedWin %Tour
FH crosscourt 53% 48.8%±7.1 47.0%
FH down the line 25% 49.0%±9.6 46.4%
FH through the middle 21% 48.9%±10.1 41.5%

Serve under pressure

Pressure predictability index +1 How much less varied Qiang Wang's first-serve direction gets on break points. Positive means easier to read. Based on 111 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 37% 21% ▼ 61% / 66%
Body 23% 39% ▲ 59% / 57%
T 41% 39% 65% / 68%

397 normal · 28 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 30% 25% 60% / 66%
Body 19% 20% 50% / 56%
T 51% 54% 52% / 64%

315 normal · 83 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
Wide36% 54.1%±6.1 n=151 49% ▲
Body24% 57.9%±7.1 n=102 9% ▼
T40% 53.5%±5.8 n=172 42% ▲

Consistent with an optimal mix (p = 0.61).
Optimal mix: +0.3 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide29% 50.8%±6.9 n=114 44% ▲
Body20% 42.3%±7.8 n=78 4% ▼
T52% 44.2%±5.3 n=206 52%

Consistent with an optimal mix (p = 0.20).
Optimal mix: +0.8 per 100 first serves.

Exploitability 0.56 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: +0.6±5.1 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. (245 repeats, 554 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 52 37% −7.1±8.8
1stAd courtT 103 31% −5.0±6.6
1stAd courtWide 104 36% +1.6±6.8
1stDeuce courtBody 60 45% +2.7±8.6
1stDeuce courtT 91 29% −3.5±6.8
1stDeuce courtWide 125 39% +4.8±6.4
2ndAd courtBody 58 59% +3.5±8.6
2ndAd courtT 17 52% −2.9±12.0
2ndAd courtWide 59 48% −5.2±8.7
2ndDeuce courtBody 63 49% −5.7±8.5
2ndDeuce courtT 68 52% −4.2±8.3
2ndDeuce courtWide 23 53% −0.7±11.3

Signature patterns

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

Serve → +1

  1. Wide serve (deuce court) → FH down the line used 3.9% · won 51% · +0.6±10.9 vs own baseline
  2. T serve (ad court) → FH through the middle used 4.1% · won 50% · −0.2±10.8 vs own baseline
  3. Body serve (deuce court) → BH crosscourt used 3.9% · won 49% · −1.1±10.9 vs own baseline
  4. T serve (deuce court) → FH crosscourt used 4.2% · won 49% · −1.1±10.7 vs own baseline
  5. Wide serve (deuce court) → BH crosscourt used 3.2% · won 46% · −4.1±11.4 vs own baseline

Return

  1. vs wide serve (deuce court) → FH through the middle, deep used 6.8% · won 49% · +3.9±10.8 vs own baseline
  2. vs wide serve (ad court) → BH crosscourt, mid used 9.2% · won 48% · +2.6±9.8 vs own baseline
  3. vs wide serve (deuce court) → FH through the middle, mid used 7.7% · won 46% · +0.8±10.4 vs own baseline
  4. vs T serve (deuce court) → BH through the middle, mid used 8.0% · won 45% · ±0.0±10.3 vs own baseline
  5. vs wide serve (ad court) → BH crosscourt, short used 4.8% · won 45% · −0.2±11.7 vs own baseline

Rally, consecutive own shots

  1. BH down the line → FH crosscourt used 4.2% · won 58% · +8.9±9.1 vs own baseline
  2. FH crosscourt → FH crosscourt used 5.7% · won 53% · +4.4±8.2 vs own baseline
  3. BH crosscourt → FH down the line used 3.0% · won 55% · +6.0±10.1 vs own baseline
  4. FH down the line → BH crosscourt used 4.9% · won 53% · +4.2±8.6 vs own baseline
  5. FH through the middle → FH crosscourt used 3.6% · won 54% · +4.8±9.6 vs own baseline

Discovered sequences

Mined from every me → opponent → me run of three shots, with no templates. Ranked by how much more often Qiang Wang 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 crosscourt → BH crosscourt used 2.4% · won 54% · +9.1±8.5 vs own baseline · +9.2 vs tour on the same sequence Disrupted by Coco Gauff (3/8), Garbine Muguruza (4/8)
  2. BH down the line → FH crosscourt → FH crosscourt used 1.1% · won 55% · +9.8±10.8 vs own baseline · +14.6 vs tour on the same sequence Disrupted by Daria Kasatkina (6/12), Mandy Minella (6/10)
  3. BH through the middle → BH down the line → FH crosscourt used 0.6% · won 54% · +9.3±12.8 vs own baseline · +23.9 vs tour on the same sequence
  4. BH crosscourt → BH crosscourt → BH down the line used 1.6% · won 50% · +4.6±9.9 vs own baseline · +4.2 vs tour on the same sequence Disrupted by Daria Kasatkina (5/9), Garbine Muguruza (5/8)
  5. BH through the middle → FH crosscourt → FH through the middle used 0.7% · won 52% · +6.6±12.3 vs own baseline · +21.0 vs tour on the same sequence
  6. FH through the middle → FH crosscourt → FH crosscourt used 1.0% · won 50% · +5.5±11.4 vs own baseline · +8.9 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 · return+3.4185
FH to the middle · serve +1+1.8125
BH to their backhand · return+1.7158
FH to the middle · rally+0.8238
BH to their backhand · rally+0.5383

Most exposed to

FH to their backhand · serve +1−3.0122
FH to their backhand · rally−2.6268
BH to their backhand · rally−2.3345
BH to their forehand · rally−1.2190
FH to the middle · rally−0.9170

Active players who are best at the shot in the top weakness: Katie Boulter, Linda Fruhvirtova, Sara Errani, Leylah Fernandez, Karolina Muchova

Tactical fingerprint

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

T serves · ad52%
Avg rally length4.9
T serves · deuce40%
Forehand share55%
BH down the line21%
1st serve in63%
Through the middle29%
Unforced errors / shot10.5%
Serve & volley0%
FH down the line28%
Run-around forehands5%
Deep returns31%
Chipped returns4%
Backhand slice5%
Wide serves · deuce36%
Points at net4%
Drop shots / shot0.7%
Point-ending shots19.7%
Wide serves · ad29%

Plays most like

  1. Jaqueline Cristian 2021–2026 plan v
  2. Simona Halep 2013–2022 plan v
  3. Linda Fruhvirtova 2022–2025 plan v
  4. Elisabetta Cocciaretto 2019–2026 plan v
  5. Heather Watson 2014–2024 plan v
  6. Anna Karolina Schmiedlova 2014–2024 plan v
  7. Katerina Siniakova 2015–2026 plan v
  8. Dominika Cibulkova 2009–2019 plan v

Closest from another era

  1. Monica Seles 1990–2003
  2. Lindsay Davenport 1995–2006
  3. Jennifer Capriati 1990–2002

Charted matches