RallyIQ

WTA · Left-handed · 12 charted matches · 2022–2026

Maja Chwalinska

Archetype: Ad-court slider · Avoids the wide serve

Against an average opponent

Serve points won 59.0% ±3.5 raw 58.7% · tour 56.3% · 777 points
Return points won 47.6% ±3.6 raw 49.1% · tour 43.7% · 799 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 −0.08 ±0.06 better than 40% of WTA · raw −0.07
Shot selection −0.52 ±0.28 better than 11% of WTA · raw −0.50
Execution +2.12 ±0.53 better than 99% of WTA · raw +2.25
Tactical adaptability −0.05 first serves toward what's working, set to set · 12 matches
Adaptation speed +0.06 same, every two to three service games · per 100 first serves
Points left on the table 2.93 per 100 shots vs best direction · lower than 14% 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 5,002 shots.

Shot expected value

The share of points Maja Chwalinska 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 · 413 shots

OptionUsedWin %Tour
BH crosscourt 28% 53.4%±7.1 47.6%
BH through the middle 17% 41.4%±8.5 43.3%
BH slice through the middle 13% 50.5%±9.6 34.4%
BH down the line 11% 40.5%±10.0 46.8%
BH slice down the line 10% 49.7%±10.4 31.7%
BH slice crosscourt 6% 51.3%±12.3 40.4%
BH drop shot down the line 4% 63.4%±13.2 49.1%
BH lob through the middle 3% 43.7%±14.2 27.1%

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

position worth 43% to the average player · 341 shots

OptionUsedWin %Tour
FH crosscourt 38% 48.6%±6.7 46.7%
FH down the line 25% 47.6%±8.0 44.9%
FH through the middle 21% 44.7%±8.6 41.3%
FH slice crosscourt 6% 38.5%±12.7 31.9%
FH slice through the middle 6% 27.8%±11.8 29.2%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 290 shots

OptionUsedWin %Tour
FH crosscourt 27% 47.5%±8.3 52.7%
FH down the line 18% 58.4%±9.6 52.2%
FH through the middle 16% 57.2%±10.1 45.8%
BH crosscourt 14% 56.7%±10.4 50.9%
BH through the middle 7% 51.9%±13.2 46.2%
BH slice down the line 5% 45.1%±13.8 43.9%
BH slice through the middle 4% 40.5%±14.3 44.8%
BH drop shot down the line 4% 48.2%±14.5 47.1%

Long rally, 9+: drive to your backhand side

position worth 44% to the average player · 180 shots

OptionUsedWin %Tour
BH crosscourt 34% 46.4%±9.1 47.9%
BH slice through the middle 18% 40.9%±11.1 33.4%
BH through the middle 14% 49.0%±12.1 42.7%
BH slice down the line 11% 43.1%±13.0 34.0%
BH slice crosscourt 8% 42.1%±13.7 38.7%
BH drop shot down the line 7% 52.3%±14.5 48.7%

Serve under pressure

Pressure predictability index +1 How much less varied Maja Chwalinska's first-serve direction gets on break points. Positive means easier to read. Based on 78 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 26% 20% 62% / 66%
Body 29% 20% ▼ 55% / 57%
T 45% 60% ▲ 65% / 68%

383 normal · 20 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 53% 48% 60% / 66%
Body 22% 21% 53% / 56%
T 25% 31% 67% / 64%

316 normal · 58 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
Wide26% 55.0%±7.0 n=105 29% ▲
Body28% 57.5%±6.8 n=114 13% ▼
T46% 58.7%±5.5 n=184 58% ▲

Consistent with an optimal mix (p = 0.73).
Optimal mix: +0.5 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide53% 59.3%±5.4 n=197 52%
Body22% 57.6%±7.7 n=81 7% ▼
T26% 63.9%±7.0 n=96 41% ▲

Consistent with an optimal mix (p = 0.47).
Optimal mix: +0.7 per 100 first serves.

Exploitability 0.60 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: +3.1±6.3 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. (250 repeats, 503 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 70 47% +3.3±8.2
1stAd courtT 120 42% +6.2±6.6
1stAd courtWide 65 41% +7.0±8.3
1stDeuce courtBody 69 43% +0.6±8.2
1stDeuce courtT 76 38% +6.2±7.8
1stDeuce courtWide 139 42% +8.1±6.2
2ndAd courtBody 66 63% +8.0±8.1
2ndAd courtT 45 50% −5.0±9.5
2ndAd courtWide 13 63% +9.4±12.1
2ndDeuce courtBody 45 56% +2.0±9.4
2ndDeuce courtT 9 61% +5.0±12.8
2ndDeuce courtWide 81 56% +2.2±7.8

Signature patterns

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

Serve → +1

  1. T serve (deuce court) → FH through the middle used 4.3% · won 62% · +1.4±11.2 vs own baseline
  2. Wide serve (ad court) → FH through the middle used 6.8% · won 60% · −0.1±9.9 vs own baseline
  3. T serve (deuce court) → BH crosscourt used 5.0% · won 59% · −1.2±10.8 vs own baseline
  4. Wide serve (ad court) → FH down the line used 5.4% · won 57% · −3.2±10.7 vs own baseline
  5. T serve (deuce court) → FH crosscourt used 7.2% · won 57% · −3.4±9.9 vs own baseline

Return

  1. vs wide serve (deuce court) → BH through the middle, deep used 6.5% · won 61% · +10.9±11.0 vs own baseline
  2. vs body serve (ad court) → FH through the middle, mid used 7.0% · won 56% · +5.1±11.0 vs own baseline
  3. vs body serve (ad court) → FH crosscourt, mid used 7.8% · won 53% · +2.3±10.7 vs own baseline
  4. vs wide serve (deuce court) → BH crosscourt, deep used 7.8% · won 51% · +0.6±10.7 vs own baseline
  5. vs T serve (ad court) → BH through the middle, deep used 5.6% · won 47% · −3.2±11.6 vs own baseline

Rally, consecutive own shots

  1. FH down the line → FH crosscourt used 2.5% · won 62% · +9.6±11.2 vs own baseline
  2. FH crosscourt → BH crosscourt used 4.4% · won 59% · +6.6±9.8 vs own baseline
  3. BH crosscourt → FH crosscourt used 3.0% · won 60% · +6.9±10.9 vs own baseline
  4. FH crosscourt → FH down the line used 6.7% · won 57% · +3.9±8.6 vs own baseline
  5. FH crosscourt → FH through the middle used 4.0% · won 57% · +4.0±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 Maja Chwalinska wins the point once the sequence happens, weighted by how often it happens. Think of them as chess openings.

  1. FH down the line → FH crosscourt → BH slice through the middle used 0.8% · won 60% · +6.6±12.2 vs own baseline · +28.2 vs tour on the same sequence Disrupted by Katerina Siniakova (6/9)
  2. FH crosscourt → BH crosscourt → FH crosscourt used 1.8% · won 57% · +4.0±9.9 vs own baseline · +11.3 vs tour on the same sequence Disrupted by Suzan Lamens (6/14), Tara Wurth (4/7)
  3. FH crosscourt → BH through the middle → FH down the line used 0.7% · won 58% · +4.7±12.6 vs own baseline · +10.4 vs tour on the same sequence Disrupted by Katerina Siniakova (4/7)
  4. FH down the line → FH crosscourt → BH crosscourt used 0.7% · won 56% · +2.6±12.9 vs own baseline · +11.6 vs tour on the same sequence
  5. FH crosscourt → BH down the line → BH crosscourt used 0.7% · won 55% · +2.3±12.6 vs own baseline · +9.4 vs tour on the same sequence
  6. FH crosscourt → BH crosscourt → FH down the line used 1.5% · won 54% · +0.7±10.6 vs own baseline · +6.0 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 backhand · rally+3.6161
FH to their forehand · rally+3.6281
BH to the middle · return+3.3163
BH to their forehand · rally+2.9189
BH to the middle · rally+2.0134

Most exposed to

BH to the middle · return−3.0229
BH to their forehand · return−1.1128
T 1st serve · deuce court−0.9130
FH to their backhand · serve +1−0.7181
BH to the middle · rally−0.4200

Best-equipped opponents

Active players whose shot mix lines up best against these weaknesses, by structural compatibility per 100 rally shots: Sara Sorribes Tormo +2.48, Caroline Wozniacki +2.31, Angelique Kerber +1.88, Daria Kasatkina +1.77, Sara Errani +1.65

Favourable matchups

Angelique Kerber +3.62, Sara Errani +3.40, Marie Bouzkova +3.22, Elina Avanesyan +3.00, Beatriz Haddad Maia +2.82

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

Tactical fingerprint

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

Drop shots / shot4.5%
Avg rally length5.5
1st serve in72%
Wide serves · ad53%
T serves · deuce46%
Backhand slice33%
Forehand share56%
Points at net9%
Run-around forehands9%
FH down the line30%
Chipped returns10%
Serve & volley0%
Through the middle27%
BH down the line17%
Deep returns29%
T serves · ad26%
Point-ending shots16.4%
Unforced errors / shot6.8%
Wide serves · deuce26%

Plays most like

  1. Brenda Fruhvirtova 2022–2024 plan v
  2. Angelique Kerber 2011–2024 plan v
  3. Magdalena Frech 2021–2026 plan v
  4. Kateryna Baindl 2017–2023 plan v
  5. Tiantsoa Sarah Rakotomanga Rajaonah 2025–2025 plan v
  6. Sara Bejlek 2022–2026 plan v
  7. Kaja Juvan 2018–2026 plan v
  8. Martina Trevisan 2019–2023 plan v

Closest from another era

  1. Arantxa Sanchez Vicario 1988–2001
  2. Dinara Safina 2007–2011
  3. Monica Seles 1990–2003

Charted matches