RallyIQ

WTA · Left-handed · 12 charted matches · 2019–2025

Xiyu Wang

Archetype: Ad-court slider · Avoids the wide serve

Against an average opponent

Serve points won 59.2% ±3.3 raw 58.7% · tour 56.3% · 882 points
Return points won 44.8% ±3.4 raw 43.5% · tour 43.7% · 896 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 −0.20 ±0.07 better than 16% of WTA · raw −0.18
Shot selection +0.20 ±0.25 better than 65% of WTA · raw +0.23
Execution −0.96 ±0.91 better than 19% of WTA · raw −0.72
Tactical adaptability −0.01 first serves toward what's working, set to set · 12 matches
Adaptation speed −0.10 same, every two to three service games · per 100 first serves
Points left on the table 3.04 per 100 shots vs best direction · lower than 9% 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 4,335 shots.

Shot expected value

The share of points Xiyu 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 middle

position worth 50% to the average player · 325 shots

OptionUsedWin %Tour
FH crosscourt 32% 52.5%±7.4 52.7%
FH through the middle 20% 46.7%±8.8 45.8%
BH crosscourt 18% 50.9%±9.4 50.9%
FH down the line 14% 57.4%±9.9 52.2%
BH through the middle 10% 45.7%±11.3 46.2%
BH down the line 4% 61.8%±13.7 50.0%

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

position worth 45% to the average player · 216 shots

OptionUsedWin %Tour
BH crosscourt 41% 45.9%±7.9 47.6%
BH through the middle 29% 55.0%±9.0 43.3%
BH down the line 22% 49.8%±10.0 46.8%
BH slice through the middle 5% 29.6%±13.7 34.4%

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

position worth 43% to the average player · 179 shots

OptionUsedWin %Tour
FH crosscourt 44% 53.9%±8.2 46.7%
FH down the line 30% 45.9%±9.5 44.9%
FH through the middle 23% 41.4%±10.4 41.3%

Return +1: drive to your middle

position worth 50% to the average player · 169 shots

OptionUsedWin %Tour
FH crosscourt 35% 43.6%±9.2 52.3%
BH crosscourt 18% 56.3%±11.5 50.8%
FH through the middle 17% 40.2%±11.6 46.5%
FH down the line 15% 56.9%±12.1 53.0%
BH through the middle 11% 44.2%±13.1 46.2%

Serve under pressure

Pressure predictability index ±0 How much less varied Xiyu Wang's first-serve direction gets on break points. Positive means easier to read. Based on 88 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 31% 35% 64% / 66%
Body 32% 30% 61% / 57%
T 37% 35% 65% / 68%

435 normal · 23 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 48% 48% 66% / 66%
Body 24% 25% 59% / 56%
T 28% 28% 68% / 64%

359 normal · 65 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
Wide31% 56.4%±6.2 n=141 40% ▲
Body32% 57.6%±6.1 n=146 17% ▼
T37% 58.1%±5.7 n=171 43% ▲

Consistent with an optimal mix (p = 0.93).
Optimal mix: +0.4 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide48% 59.7%±5.3 n=203 58% ▲
Body24% 61.1%±7.0 n=102 9% ▼
T28% 59.9%±6.6 n=119 33% ▲

Consistent with an optimal mix (p = 0.95).
Optimal mix: +0.5 per 100 first serves.

Exploitability 0.46 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: +4.4±4.3 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. (294 repeats, 564 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 86 46% +1.9±7.6
1stAd courtT 107 37% +1.4±6.8
1stAd courtWide 98 30% −4.5±6.7
1stDeuce courtBody 110 41% −1.3±6.8
1stDeuce courtT 109 33% +0.7±6.6
1stDeuce courtWide 111 38% +3.7±6.7
2ndAd courtBody 69 62% +7.1±8.0
2ndAd courtT 38 57% +1.6±9.9
2ndAd courtWide 33 59% +5.3±10.2
2ndDeuce courtBody 76 56% +1.5±7.9
2ndDeuce courtT 28 53% −2.9±10.8
2ndDeuce courtWide 31 48% −6.0±10.5

Signature patterns

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

Serve → +1

  1. Wide serve (ad court) → FH down the line used 7.0% · won 65% · +0.9±8.9 vs own baseline
  2. Body serve (ad court) → FH crosscourt used 7.1% · won 60% · −3.8±9.0 vs own baseline
  3. T serve (deuce court) → FH through the middle used 3.3% · won 58% · −5.8±11.3 vs own baseline
  4. T serve (deuce court) → FH crosscourt used 4.6% · won 59% · −5.2±10.4 vs own baseline
  5. Wide serve (deuce court) → FH through the middle used 3.0% · won 56% · −7.5±11.6 vs own baseline

Return

  1. vs T serve (ad court) → BH through the middle, deep used 4.6% · won 52% · +5.5±11.2 vs own baseline
  2. vs wide serve (deuce court) → BH through the middle, mid used 3.9% · won 52% · +5.6±11.7 vs own baseline
  3. vs T serve (deuce court) → FH through the middle, mid used 4.1% · won 51% · +4.6±11.6 vs own baseline
  4. vs wide serve (deuce court) → BH through the middle, deep used 4.3% · won 50% · +3.6±11.5 vs own baseline
  5. vs wide serve (ad court) → FH through the middle, mid used 5.2% · won 49% · +2.8±10.9 vs own baseline

Rally, consecutive own shots

  1. FH crosscourt → BH crosscourt used 4.0% · won 63% · +10.7±11.1 vs own baseline
  2. BH crosscourt → FH crosscourt used 5.1% · won 56% · +4.3±10.7 vs own baseline
  3. BH crosscourt → BH crosscourt used 3.8% · won 56% · +4.1±11.5 vs own baseline
  4. BH crosscourt → BH down the line used 4.7% · won 53% · +0.9±11.0 vs own baseline
  5. BH through the middle → FH crosscourt used 6.1% · won 51% · −1.0±10.2 vs own baseline

Discovered sequences

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

  1. FH crosscourt → BH through the middle → BH crosscourt used 1.0% · won 60% · +10.3±12.2 vs own baseline · +22.5 vs tour on the same sequence Disrupted by Qiang Wang (5/6)
  2. FH crosscourt → BH crosscourt → FH crosscourt used 1.9% · won 57% · +7.3±10.3 vs own baseline · +13.9 vs tour on the same sequence Disrupted by Laura Siegemund (6/8), Monica Puig (6/6)
  3. BH crosscourt → FH through the middle → FH crosscourt used 0.9% · won 61% · +10.8±12.4 vs own baseline · +21.9 vs tour on the same sequence
  4. FH crosscourt → BH through the middle → FH down the line used 0.9% · won 58% · +8.4±12.5 vs own baseline · +16.3 vs tour on the same sequence Disrupted by Laura Siegemund (7/7)
  5. FH crosscourt → BH through the middle → FH through the middle used 1.0% · won 56% · +5.7±12.3 vs own baseline · +17.5 vs tour on the same sequence
  6. BH crosscourt → FH crosscourt → BH crosscourt used 0.8% · won 55% · +4.9±12.8 vs own baseline · +14.3 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 · return+1.4122
Wide 1st serve · ad court+1.4203
BH to the middle · return+1.2205
T 1st serve · deuce court+0.5171
BH to their forehand · rally+0.1174

Most exposed to

FH to their forehand · serve +1−4.7140
T 1st serve · deuce court−1.2166
Body 1st serve · deuce court−1.1132
BH to their forehand · return−0.7120
BH to their forehand · rally−0.7152

Active players who are best at the shot in the top weakness: Barbora Krejcikova, Shelby Rogers, Sara Sorribes Tormo, Jil Teichmann, Maria Sakkari

Tactical fingerprint

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

Forehand share60%
Point-ending shots30.9%
Unforced errors / shot13.5%
1st serve in66%
Wide serves · ad48%
Deep returns35%
T serves · deuce37%
Points at net7%
Through the middle29%
Serve & volley0%
FH down the line28%
Backhand slice9%
Run-around forehands5%
Chipped returns5%
Avg rally length3.8
Drop shots / shot0.9%
BH down the line17%
T serves · ad28%
Wide serves · deuce31%

Plays most like

  1. Olga Danilovic 2019–2026 plan v
  2. Bernarda Pera 2017–2025 plan v
  3. Robin Montgomery 2022–2026 plan v
  4. Diana Shnaider 2022–2026 plan v
  5. Jasmine Paolini 2016–2026 plan v
  6. Eugenie Bouchard 2013–2023 plan v
  7. Olivia Gadecki 2023–2026 plan v
  8. Marta Kostyuk 2018–2026 plan v

Closest from another era

  1. Jelena Dokic 2000–2009
  2. Lindsay Davenport 1995–2006
  3. Monica Seles 1990–2003

Charted matches