RallyIQ

WTA · Right-handed · 28 charted matches · 2011–2025

Sara Errani

Archetype: Rarely serves the T · Patient builder

Against an average opponent

Serve points won 51.7% ±3.0 raw 47.4% · tour 56.3% · 1,888 points
Return points won 46.4% ±3.0 raw 44.8% · tour 43.7% · 1,857 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 −0.23 ±0.11 better than 14% of WTA · raw −0.23
Shot selection −0.34 ±0.17 better than 19% of WTA · raw −0.34
Execution +1.67 ±0.48 better than 97% of WTA · raw +1.63
Tactical adaptability −0.06 first serves toward what's working, set to set · 26 matches
Adaptation speed −0.12 same, every two to three service games · per 100 first serves
Points left on the table 2.62 per 100 shots vs best direction · lower than 48% 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 10,907 shots.

Shot expected value

The share of points Sara Errani 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 · 868 shots

OptionUsedWin %Tour
BH crosscourt 38% 51.7%±4.4 47.6%
BH through the middle 27% 47.2%±5.1 43.3%
BH down the line 7% 46.8%±9.0 46.8%
BH slice through the middle 7% 40.9%±9.2 34.4%
BH slice crosscourt 6% 50.1%±9.6 40.4%
BH slice down the line 4% 32.0%±10.7 31.7%
FH inside-out 3% 53.0%±11.6 52.5%
FH inside-in 3% 56.2%±11.5 55.6%

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

position worth 43% to the average player · 720 shots

OptionUsedWin %Tour
FH crosscourt 31% 48.5%±5.3 46.7%
FH down the line 25% 47.4%±5.9 44.9%
FH through the middle 19% 45.0%±6.6 41.3%
FH slice through the middle 7% 29.6%±9.2 29.2%
FH slice down the line 6% 30.6%±9.4 24.3%
FH slice crosscourt 4% 37.0%±11.6 31.9%
FH lob down the line 3% 18.6%±10.1 27.3%
FH drop shot down the line 2% 44.9%±13.6 50.9%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 668 shots

OptionUsedWin %Tour
FH crosscourt 23% 60.1%±6.1 52.7%
FH down the line 23% 48.0%±6.2 52.2%
FH through the middle 16% 47.0%±7.3 45.8%
BH crosscourt 15% 46.8%±7.5 50.9%
BH through the middle 15% 49.4%±7.6 46.2%
FH down the line + approach 3% 72.5%±11.5 68.6%
BH down the line 2% 44.1%±14.0 50.0%

Long rally, 9+: drive to your backhand side

position worth 44% to the average player · 504 shots

OptionUsedWin %Tour
BH crosscourt 41% 52.0%±5.4 47.9%
BH through the middle 24% 41.5%±6.8 42.7%
BH down the line 10% 55.6%±9.8 46.7%
BH slice crosscourt 8% 41.9%±10.6 38.7%
BH slice through the middle 7% 33.4%±10.4 33.4%
BH slice down the line 3% 30.0%±12.6 34.0%
FH inside-out 3% 57.0%±14.2 54.1%
BH drop shot down the line 2% 47.6%±14.8 48.7%

Serve under pressure

Pressure predictability index −1 How much less varied Sara Errani's first-serve direction gets on break points. Positive means easier to read. Based on 296 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 34% 39% 54% / 66%
Body 46% 36% ▼ 50% / 57%
T 20% 24% 53% / 68%

886 normal · 74 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 37% 38% 54% / 66%
Body 46% 45% 53% / 56%
T 17% 17% 53% / 64%

684 normal · 222 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
Wide35% 48.0%±4.3 n=333 35%
Body45% 45.9%±3.8 n=432 30% ▼
T20% 45.3%±5.5 n=195 35% ▲

Consistent with an optimal mix (p = 0.71).
Optimal mix: +0.7 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide37% 47.9%±4.3 n=337 37%
Body46% 50.9%±3.9 n=417 31% ▼
T17% 45.5%±6.1 n=152 32% ▲

Consistent with an optimal mix (p = 0.34).
Optimal mix: +0.3 per 100 first serves.

Exploitability 0.47 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: −1.4±4.0 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. (832 repeats, 978 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 120 46% +2.2±6.7
1stAd courtT 221 31% −4.6±4.8
1stAd courtWide 199 42% +7.6±5.4
1stDeuce courtBody 121 34% −8.3±6.4
1stDeuce courtT 203 36% +4.2±5.2
1stDeuce courtWide 264 40% +5.5±4.7
2ndAd courtBody 165 59% +4.2±5.8
2ndAd courtT 70 56% +0.5±8.2
2ndAd courtWide 114 54% ±0.0±6.8
2ndDeuce courtBody 198 52% −2.5±5.4
2ndDeuce courtT 94 58% +1.9±7.3
2ndDeuce courtWide 76 55% +1.1±8.0

Signature patterns

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

Serve → +1

  1. Body serve (ad court) → BH through the middle used 5.8% · won 57% · +4.0±7.4 vs own baseline
  2. Wide serve (deuce court) → FH down the line used 3.0% · won 58% · +5.0±9.3 vs own baseline
  3. Body serve (deuce court) → FH down the line used 4.9% · won 55% · +2.4±7.9 vs own baseline
  4. Wide serve (ad court) → BH crosscourt used 3.9% · won 54% · +1.6±8.6 vs own baseline
  5. Wide serve (deuce court) → FH crosscourt used 2.8% · won 51% · −1.5±9.6 vs own baseline

Return

  1. vs wide serve (deuce court) → FH crosscourt, mid used 2.3% · won 62% · +12.8±10.7 vs own baseline
  2. vs wide serve (deuce court) → FH down the line, mid used 2.3% · won 57% · +7.5±10.9 vs own baseline
  3. vs T serve (deuce court) → BH through the middle, mid used 3.8% · won 53% · +3.8±9.5 vs own baseline
  4. vs body serve (ad court) → FH down the line, mid used 2.0% · won 54% · +5.0±11.3 vs own baseline
  5. vs body serve (deuce court) → FH down the line, mid used 2.5% · won 53% · +3.8±10.8 vs own baseline

Rally, consecutive own shots

  1. BH crosscourt → FH through the middle used 1.9% · won 59% · +8.9±9.1 vs own baseline
  2. BH crosscourt → BH slice crosscourt used 1.4% · won 55% · +5.3±10.1 vs own baseline
  3. BH crosscourt → BH crosscourt used 5.0% · won 53% · +2.7±6.4 vs own baseline
  4. FH crosscourt → BH crosscourt used 3.0% · won 53% · +3.1±7.9 vs own baseline
  5. FH through the middle → FH through the middle used 1.5% · won 54% · +3.6±10.0 vs own baseline

Discovered sequences

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

  1. BH crosscourt → BH through the middle → FH through the middle used 0.6% · won 59% · +10.4±10.3 vs own baseline · +21.0 vs tour on the same sequence Disrupted by Caroline Wozniacki (4/7)
  2. BH crosscourt → BH through the middle → FH down the line used 0.8% · won 57% · +8.3±9.6 vs own baseline · +8.3 vs tour on the same sequence Disrupted by Sara Sorribes Tormo (6/9), Alize Cornet (5/7)
  3. FH down the line → BH through the middle → FH crosscourt used 0.7% · won 57% · +8.3±10.1 vs own baseline · +5.5 vs tour on the same sequence Disrupted by Kiki Bertens (3/6), Sara Sorribes Tormo (9/14)
  4. BH crosscourt → BH slice through the middle → FH down the line used 0.4% · won 59% · +9.5±11.5 vs own baseline · +10.9 vs tour on the same sequence Disrupted by Sara Sorribes Tormo (12/16)
  5. BH crosscourt → BH down the line → FH through the middle used 0.4% · won 59% · +9.8±11.7 vs own baseline · +28.9 vs tour on the same sequence
  6. Wide serve → FH through the middle return, mid → FH down the line used 0.2% · won 61% · +11.6±12.7 vs own baseline · +24.9 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 their forehand · return +1+4.1174
BH to their forehand · rally+3.7217
BH to their backhand · return +1+3.2222
FH to their forehand · serve +1+3.0186
FH to their backhand · serve +1+3.0240

Most exposed to

BH to their forehand · return−7.7160
FH to their backhand · return−7.5273
FH to their forehand · return−6.6285
BH to their forehand · rally−5.2462
BH to their backhand · return−4.8393

Best-equipped opponents

Active players whose shot mix lines up best against these weaknesses, by structural compatibility per 100 rally shots: Caroline Wozniacki +4.52, Angelique Kerber +4.23, Sara Sorribes Tormo +4.23, Daria Kasatkina +3.82, Linda Fruhvirtova +3.70

Favourable matchups

Angelique Kerber +3.37, Marie Bouzkova +3.26, Magdalena Frech +2.95, Elina Avanesyan +2.94, Linda Fruhvirtova +2.87

Active players who are best at the shot in the top weakness: Alexandra Eala, Magdalena Frech, Belinda Bencic, Angelique Kerber, Clara Tauson

Tactical fingerprint

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

1st serve in77%
Avg rally length5.3
Drop shots / shot2.6%
FH down the line33%
Run-around forehands12%
Through the middle30%
Backhand slice17%
Points at net7%
Chipped returns11%
Serve & volley0%
Wide serves · ad37%
Forehand share51%
Wide serves · deuce35%
Deep returns26%
BH down the line12%
Point-ending shots14.0%
Unforced errors / shot5.8%
T serves · ad17%
T serves · deuce20%

Plays most like

  1. Madison Brengle 2014–2023 plan v
  2. Lesia Tsurenko 2013–2024 plan v
  3. Alize Cornet 2013–2024 plan v
  4. Daria Kasatkina 2015–2026 plan v
  5. Kateryna Baindl 2017–2023 plan v
  6. Sara Sorribes Tormo 2019–2025 plan v
  7. Viktorija Golubic 2017–2026 plan v
  8. Tamara Zidansek 2019–2026 plan v

Closest from another era

  1. Arantxa Sanchez Vicario 1988–2001
  2. Jennifer Capriati 1990–2002
  3. Chris Evert 1979–1989

Charted matches