RallyIQ

WTA · Right-handed · 132 charted matches · 2022–2026

Mirra Andreeva

Archetype: Forehand line-changer · Backhand line-changer

Against an average opponent

Serve points won 61.0% ±2.3 raw 60.1% · tour 56.3% · 9,099 points
Return points won 47.9% ±2.4 raw 46.2% · tour 43.7% · 9,066 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 +0.12 ±0.04 better than 75% of WTA · raw +0.12
Shot selection −0.43 ±0.08 better than 15% of WTA · raw −0.42
Execution +1.22 ±0.22 better than 95% of WTA · raw +1.29
Tactical adaptability +0.03 first serves toward what's working, set to set · 130 matches
Adaptation speed +0.01 same, every two to three service games · per 100 first serves
Points left on the table 2.42 per 100 shots vs best direction · lower than 73% 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 49,894 shots.

Shot expected value

The share of points Mirra Andreeva 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 · 3,258 shots

OptionUsedWin %Tour
BH crosscourt 35% 51.3%±2.4 47.6%
BH through the middle 24% 48.1%±2.9 43.3%
BH down the line 23% 51.9%±3.0 46.8%
BH slice through the middle 5% 40.1%±5.7 34.4%
BH slice crosscourt 5% 46.4%±6.0 40.4%
FH inside-out 1% 52.4%±10.3 52.5%
BH slice down the line 1% 25.2%±9.5 31.7%
BH drop shot down the line 1% 52.3%±10.9 49.1%

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

position worth 43% to the average player · 2,973 shots

OptionUsedWin %Tour
FH crosscourt 30% 53.1%±2.7 46.7%
FH down the line 25% 48.4%±3.0 44.9%
FH through the middle 18% 46.6%±3.5 41.3%
FH slice through the middle 12% 38.7%±4.1 29.2%
FH slice crosscourt 9% 37.3%±4.6 31.9%
FH slice down the line 2% 26.3%±7.8 24.3%
FH drop shot crosscourt 1% 55.1%±11.1 48.6%
FH lob through the middle 1% 26.8%±10.5 29.4%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 2,806 shots

OptionUsedWin %Tour
FH down the line 25% 55.1%±3.1 52.2%
FH crosscourt 17% 54.0%±3.6 52.7%
BH crosscourt 14% 60.5%±3.9 50.9%
FH through the middle 13% 46.3%±4.2 45.8%
BH through the middle 13% 48.7%±4.3 46.2%
BH down the line 10% 58.7%±4.6 50.0%
FH slice through the middle 2% 44.3%±10.1 41.0%
FH down the line + approach 1% 73.4%±10.3 68.6%

Long rally, 9+: drive to your forehand side

position worth 44% to the average player · 1,606 shots

OptionUsedWin %Tour
FH crosscourt 26% 47.3%±4.0 47.0%
FH down the line 24% 53.4%±4.1 46.4%
FH through the middle 15% 48.1%±5.2 41.5%
FH slice through the middle 14% 39.4%±5.2 29.3%
FH slice crosscourt 11% 43.9%±5.8 31.6%
FH slice down the line 4% 34.5%±8.4 25.3%
FH drop shot crosscourt 3% 50.4%±9.8 46.4%
FH lob through the middle 1% 38.2%±13.1 30.6%

Serve under pressure

Pressure predictability index −1 How much less varied Mirra Andreeva's first-serve direction gets on break points. Positive means easier to read. Based on 897 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 51% 44% 69% / 66%
Body 13% 15% 59% / 57%
T 36% 41% 73% / 68%

4,523 normal · 196 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 32% 39% 67% / 66%
Body 22% 21% 61% / 56%
T 46% 40% 65% / 64%

3,674 normal · 701 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
Wide51% 61.5%±1.6 n=2,386 51%
Body13% 57.6%±3.2 n=625 0% ▼
T36% 61.6%±1.9 n=1,708 49% ▲

Consistent with an optimal mix (p = 0.12).
Optimal mix: +0.8 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide33% 59.3%±2.1 n=1,455 47% ▲
Body21% 58.4%±2.6 n=938 6% ▼
T45% 59.6%±1.8 n=1,982 47% ▲

Consistent with an optimal mix (p = 0.78).
Optimal mix: +0.3 per 100 first serves.

Exploitability 0.53 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: +1.0±1.9 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. (3,018 repeats, 5,812 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 362 53% +9.5±4.1
1stAd courtT 1,282 38% +2.6±2.2
1stAd courtWide 1,061 36% +1.9±2.4
1stDeuce courtBody 456 44% +1.6±3.7
1stDeuce courtT 1,119 39% +6.8±2.4
1stDeuce courtWide 1,492 40% +6.3±2.1
2ndAd courtBody 637 56% +1.4±3.2
2ndAd courtT 456 56% +1.4±3.7
2ndAd courtWide 538 55% +1.4±3.4
2ndDeuce courtBody 516 58% +3.9±3.5
2ndDeuce courtT 690 59% +2.8±3.0
2ndDeuce courtWide 451 56% +1.8±3.7

Signature patterns

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

Serve → +1

  1. T serve (ad court) → BH down the line used 2.2% · won 63% · −1.2±5.5 vs own baseline
  2. Wide serve (deuce court) → BH crosscourt used 3.2% · won 62% · −1.9±4.7 vs own baseline
  3. T serve (deuce court) → BH through the middle used 2.4% · won 60% · −3.5±5.3 vs own baseline
  4. T serve (deuce court) → FH down the line used 2.8% · won 59% · −4.8±5.0 vs own baseline
  5. Wide serve (ad court) → BH crosscourt used 2.8% · won 57% · −6.6±5.0 vs own baseline

Return

  1. vs T serve (deuce court) → BH through the middle, deep used 2.9% · won 59% · +13.1±4.9 vs own baseline
  2. vs wide serve (deuce court) → FH through the middle, deep used 2.1% · won 61% · +14.9±5.6 vs own baseline
  3. vs T serve (ad court) → FH down the line, mid used 2.9% · won 58% · +11.7±4.9 vs own baseline
  4. vs T serve (deuce court) → BH through the middle, mid used 4.0% · won 55% · +9.3±4.3 vs own baseline
  5. vs wide serve (deuce court) → FH through the middle, mid used 2.8% · won 56% · +9.7±5.1 vs own baseline

Rally, consecutive own shots

  1. BH crosscourt → BH crosscourt used 3.7% · won 58% · +6.9±3.7 vs own baseline
  2. BH crosscourt → FH crosscourt used 2.7% · won 58% · +7.0±4.3 vs own baseline
  3. FH down the line → BH down the line used 3.1% · won 58% · +6.5±4.0 vs own baseline
  4. FH down the line → FH down the line used 1.7% · won 59% · +7.8±5.3 vs own baseline
  5. FH down the line → BH crosscourt used 3.3% · won 57% · +5.3±3.9 vs own baseline

Discovered sequences

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

  1. Wide serve → FH through the middle return, mid → BH crosscourt used 0.3% · won 64% · +12.1±7.8 vs own baseline · +11.7 vs tour on the same sequence Disrupted by Coco Gauff (4/9), Victoria Mboko (5/7)
  2. BH crosscourt → BH through the middle → BH crosscourt used 0.4% · won 63% · +10.8±7.2 vs own baseline · +14.2 vs tour on the same sequence Disrupted by Marie Bouzkova (6/8)
  3. FH down the line → BH crosscourt → BH down the line used 1.2% · won 57% · +5.4±4.5 vs own baseline · +7.9 vs tour on the same sequence Disrupted by Coco Gauff (8/25), Elena Rybakina (2/6)
  4. BH down the line → FH through the middle → BH down the line used 0.1% · won 68% · +15.9±10.3 vs own baseline · +25.2 vs tour on the same sequence
  5. BH crosscourt → BH through the middle → BH down the line used 0.3% · won 61% · +9.5±8.2 vs own baseline · +14.6 vs tour on the same sequence
  6. BH crosscourt → BH slice crosscourt → BH crosscourt used 0.3% · won 61% · +8.7±7.8 vs own baseline · +10.7 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 drop shot to their forehand · rally+5.5180
BH drop shot to their forehand · rally+4.7126
BH to their forehand · rally+3.41,853
FH volley to their forehand · rally+3.1159
BH to their backhand · return +1+2.91,010

Most exposed to

BH to their backhand · return +1−1.91,073
T 2nd serve · deuce court−1.6688
BH to the middle · return +1−1.5708
Body 2nd serve · ad court−1.3630
BH volley to their backhand · rally−1.3138

Best-equipped opponents

Active players whose shot mix lines up best against these weaknesses, by structural compatibility per 100 rally shots: Sara Sorribes Tormo +2.30, Caroline Wozniacki +1.97, Daria Kasatkina +1.34, Sara Errani +1.21, Angelique Kerber +1.15

Favourable matchups

Sara Errani +3.20, Marie Bouzkova +2.95, Angelique Kerber +2.89, Elina Avanesyan +2.72, Magdalena Frech +2.41

Active players who are best at the shot in the top weakness: Caroline Wozniacki, Sara Errani, Jessica Pegula, Danielle Collins, Emma Navarro

Tactical fingerprint

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

FH down the line41%
BH down the line27%
Wide serves · deuce51%
Drop shots / shot2.4%
T serves · ad45%
Avg rally length4.5
Chipped returns15%
1st serve in63%
T serves · deuce36%
Points at net7%
Serve & volley0%
Backhand slice10%
Run-around forehands4%
Point-ending shots21.0%
Through the middle26%
Unforced errors / shot9.0%
Deep returns29%
Wide serves · ad33%
Forehand share50%

Plays most like

  1. Elise Mertens 2018–2026 plan v
  2. Timea Bacsinszky 2015–2019 plan v
  3. Agnieszka Radwanska 2007–2018 plan v
  4. Simona Halep 2013–2022 plan v
  5. Victoria Azarenka 2009–2025 plan v
  6. Christina Mchale 2012–2021 plan v
  7. Elena Dementieva 1999–2010 plan v
  8. Belinda Bencic 2014–2026 plan v

Closest from another era

  1. Elena Dementieva 1999–2010
  2. Martina Hingis 1996–2007
  3. Anastasia Myskina 2002–2006

Charted matches