RallyIQ

WTA · Left-handed · 9 charted matches · 2022–2026

Sara Bejlek

Archetype: Ad-court slider · Avoids the wide serve

Against an average opponent

Serve points won 50.8% ±3.7 raw 45.0% · tour 56.3% · 502 points
Return points won 44.5% ±3.7 raw 41.0% · tour 43.7% · 503 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 −0.14 ±0.15 better than 27% of WTA · raw −0.15
Shot selection +0.24 ±0.28 better than 72% of WTA · raw +0.23
Execution −0.48 ±0.87 better than 34% of WTA · raw −0.57

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 2,715 shots.

Shot expected value

The share of points Sara Bejlek 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 · 188 shots

OptionUsedWin %Tour
BH crosscourt 39% 46.3%±8.5 47.6%
BH through the middle 32% 45.8%±9.2 43.3%
BH down the line 14% 48.6%±12.1 46.8%
BH slice crosscourt 6% 37.8%±14.1 40.4%
BH slice through the middle 5% 26.3%±13.2 34.4%

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

position worth 43% to the average player · 178 shots

OptionUsedWin %Tour
FH crosscourt 46% 43.5%±8.1 46.7%
FH through the middle 25% 41.9%±10.1 41.3%
FH down the line 22% 45.0%±10.6 44.9%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 125 shots

OptionUsedWin %Tour
FH crosscourt 25% 46.2%±11.5 52.7%
BH crosscourt 20% 44.9%±12.2 50.9%
FH down the line 18% 46.3%±12.7 52.2%
FH through the middle 15% 49.1%±13.2 45.8%
BH through the middle 14% 43.9%±13.4 46.2%
BH down the line 9% 54.8%±14.7 50.0%

Serve +1: deep return to your middle

position worth 45% to the average player · 85 shots

OptionUsedWin %Tour
FH crosscourt 31% 53.7%±12.1 48.6%
FH through the middle 26% 41.7%±12.5 42.5%
FH down the line 13% 39.1%±14.4 45.6%
BH through the middle 13% 40.5%±14.5 42.8%
BH crosscourt 12% 51.3%±15.0 46.9%

Serve under pressure

Pressure predictability index −5 How much less varied Sara Bejlek's first-serve direction gets on break points. Positive means easier to read. Based on 76 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 29% 20% ▼ 59% / 66%
Body 36% 40% 51% / 57%
T 34% 40% 56% / 68%

235 normal · 20 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 51% 41% ▼ 65% / 66%
Body 33% 30% 57% / 56%
T 16% 29% ▲ 53% / 64%

178 normal · 56 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
Wide29% 42.8%±8.0 n=73 30% ▲
Body36% 41.5%±7.3 n=93 21% ▼
T35% 46.3%±7.5 n=89 49% ▲

Consistent with an optimal mix (p = 0.66).
Optimal mix: +0.7 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide49% 50.9%±6.9 n=114 64% ▲
Body32% 48.0%±8.0 n=75 17% ▼
T19% 39.9%±9.3 n=45 19%

Consistent with an optimal mix (p = 0.11).
Optimal mix: +0.5 per 100 first serves.

Exploitability 0.62 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: +4.1±8.2 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. (142 repeats, 329 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 28 43% −0.5±10.7
1stAd courtT 60 35% −0.4±8.3
1stAd courtWide 56 32% −2.7±8.3
1stDeuce courtBody 35 47% +4.7±10.2
1stDeuce courtT 41 26% −5.9±8.6
1stDeuce courtWide 96 33% −0.5±6.9
2ndAd courtBody 38 52% −2.8±10.0
2ndAd courtT 35 56% +1.1±10.1
2ndAd courtWide 17 49% −4.5±12.0
2ndDeuce courtBody 26 54% −0.3±11.0
2ndDeuce courtT 11 53% −2.8±12.8
2ndDeuce courtWide 47 59% +4.8±9.2

Signature patterns

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

Serve → +1

  1. Wide serve (ad court) → FH crosscourt used 10.1% · won 61% · +7.5±10.8 vs own baseline
  2. T serve (deuce court) → FH crosscourt used 8.1% · won 49% · −4.2±11.7 vs own baseline
  3. Body serve (deuce court) → FH through the middle used 7.8% · won 46% · −7.2±11.8 vs own baseline
  4. Body serve (ad court) → FH crosscourt used 9.7% · won 45% · −7.8±11.1 vs own baseline

Return

  1. vs wide serve (deuce court) → BH crosscourt, mid used 14.2% · won 46% · −0.4±11.8 vs own baseline

Rally, consecutive own shots

  1. FH through the middle → BH crosscourt used 6.3% · won 53% · +4.8±11.5 vs own baseline
  2. BH crosscourt → FH crosscourt used 5.7% · won 51% · +2.9±11.8 vs own baseline
  3. FH crosscourt → FH down the line used 10.1% · won 50% · +2.0±10.1 vs own baseline
  4. BH crosscourt → BH crosscourt used 6.9% · won 47% · −0.8±11.2 vs own baseline
  5. BH crosscourt → BH through the middle used 6.0% · won 46% · −2.0±11.6 vs own baseline

Discovered sequences

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

  1. FH crosscourt → BH crosscourt → FH down the line used 1.8% · won 50% · +5.8±11.9 vs own baseline · +8.2 vs tour on the same sequence Disrupted by Liudmila Samsonova (4/6)
  2. BH crosscourt → FH crosscourt → BH crosscourt used 1.3% · won 47% · +2.2±12.9 vs own baseline · +1.8 vs tour on the same sequence
  3. FH crosscourt → BH crosscourt → FH crosscourt used 2.3% · won 45% · +0.2±11.1 vs own baseline · −3.4 vs tour on the same sequence Disrupted by Iga Swiatek (3/6), Sahaja Yamalapalli (4/7)
  4. BH crosscourt → FH crosscourt → BH through the middle used 1.6% · won 45% · +0.3±12.2 vs own baseline · −0.4 vs tour on the same sequence
  5. FH crosscourt → BH crosscourt → FH through the middle used 1.3% · won 42% · −2.7±12.7 vs own baseline · −4.9 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 backhand · rally+1.7181
FH to their forehand · rally−1.0137

Most exposed to

BH to their forehand · rally−3.8137
FH to their forehand · rally−1.7153
BH to the middle · return−1.6139
FH to their backhand · rally−0.9231
Wide 1st serve · deuce court+0.1139

Active players who are best at the shot in the top weakness: Sara Sorribes Tormo, Alexandra Eala, Kayla Day, Caroline Wozniacki, Alize Cornet

Tactical fingerprint

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

Wide serves · ad49%
Deep returns37%
Forehand share56%
Drop shots / shot2.1%
Avg rally length4.4
BH down the line22%
Run-around forehands8%
Points at net7%
Serve & volley0%
Backhand slice13%
1st serve in62%
T serves · deuce35%
Through the middle27%
Unforced errors / shot9.7%
Chipped returns5%
FH down the line25%
Point-ending shots19.1%
Wide serves · deuce29%
T serves · ad19%

Plays most like

  1. Victoria Jimenez Kasintseva 2020–2025 plan v
  2. Martina Trevisan 2019–2023 plan v
  3. Anhelina Kalinina 2019–2025 plan v
  4. Alina Korneeva 2023–2026 plan v
  5. Jil Teichmann 2017–2026 plan v
  6. Marta Kostyuk 2018–2026 plan v
  7. Olga Danilovic 2019–2026 plan v
  8. Beatriz Haddad Maia 2019–2025 plan v

Closest from another era

  1. Dinara Safina 2007–2011
  2. Elena Dementieva 1999–2010
  3. Monica Seles 1990–2003

Charted matches