RallyIQ

WTA · Right-handed · 8 charted matches · 2013–2019

Johanna Larsson

Archetype: Ad-court slider · Avoids the wide serve

Against an average opponent

Serve points won 54.0% ±3.8 raw 49.2% · tour 56.3% · 449 points
Return points won 40.1% ±3.7 raw 35.7% · tour 43.7% · 460 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 ±0.22 better than 16% of WTA · raw −0.20
Shot selection −0.07 ±0.27 better than 38% of WTA · raw −0.06
Execution −0.86 ±0.93 better than 22% of WTA · raw −0.79

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 2,626 shots.

Shot expected value

The share of points Johanna Larsson 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 · 184 shots

OptionUsedWin %Tour
BH crosscourt 34% 38.0%±8.8 47.6%
BH through the middle 30% 32.9%±8.9 43.3%
BH slice through the middle 10% 25.3%±11.5 34.4%
BH slice crosscourt 10% 42.3%±13.2 40.4%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 175 shots

OptionUsedWin %Tour
FH crosscourt 29% 45.1%±9.8 52.7%
FH through the middle 26% 33.6%±9.6 45.8%
BH through the middle 21% 42.5%±10.8 46.2%
FH down the line 13% 46.3%±12.7 52.2%
BH crosscourt 8% 35.8%±13.5 50.9%

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

position worth 43% to the average player · 151 shots

OptionUsedWin %Tour
FH crosscourt 54% 34.7%±7.8 46.7%
FH through the middle 30% 32.2%±9.5 41.3%
FH down the line 10% 48.5%±13.9 44.9%

Long rally, 9+: drive to your backhand side

position worth 44% to the average player · 120 shots

OptionUsedWin %Tour
BH crosscourt 31% 36.1%±10.5 47.9%
BH through the middle 26% 28.5%±10.4 42.7%
BH down the line 13% 45.4%±13.7 46.7%
BH slice through the middle 13% 29.7%±12.5 33.4%
BH slice crosscourt 13% 36.4%±13.4 38.7%

Serve under pressure

Pressure predictability index −4 How much less varied Johanna Larsson's first-serve direction gets on break points. Positive means easier to read. Based on 73 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 46% 25% ▼ 59% / 66%
Body 17% 19% 54% / 57%
T 37% 56% ▲ 60% / 68%

215 normal · 16 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 53% 47% 54% / 66%
Body 15% 21% 53% / 56%
T 33% 32% 60% / 64%

160 normal · 57 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
Wide44% 53.9%±7.1 n=102 59% ▲
Body17% 47.5%±9.8 n=40 2% ▼
T39% 46.6%±7.5 n=89 39%

Consistent with an optimal mix (p = 0.34).
Optimal mix: +0.6 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide51% 49.0%±6.9 n=111 61% ▲
Body17% 44.8%±10.1 n=36 1% ▼
T32% 49.8%±8.2 n=70 38% ▲

Consistent with an optimal mix (p = 0.66).
Optimal mix: +0.5 per 100 first serves.

Exploitability 0.54 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: +0.8±8.7 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. (166 repeats, 266 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 30 42% −1.9±10.5
1stAd courtT 55 30% −5.3±8.2
1stAd courtWide 60 35% +0.4±8.3
1stDeuce courtBody 22 32% −10.3±10.7
1stDeuce courtT 60 31% −1.4±8.0
1stDeuce courtWide 66 28% −5.7±7.6
2ndAd courtBody 29 47% −8.4±10.7
2ndAd courtT 14 53% −1.6±12.4
2ndAd courtWide 32 57% +3.0±10.4
2ndDeuce courtBody 39 47% −7.6±9.9
2ndDeuce courtT 28 46% −9.8±10.8
2ndDeuce courtWide 24 54% +0.2±11.2

Signature patterns

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

Serve → +1

  1. Not enough data

Return

  1. Not enough data

Rally, consecutive own shots

  1. FH crosscourt → FH crosscourt used 8.4% · won 33% · +3.3±9.4 vs own baseline
  2. BH through the middle → FH crosscourt used 8.2% · won 32% · +2.2±9.4 vs own baseline
  3. FH through the middle → BH through the middle used 4.7% · won 32% · +1.9±10.9 vs own baseline
  4. FH crosscourt → BH through the middle used 6.5% · won 31% · +1.0±10.0 vs own baseline
  5. BH through the middle → FH through the middle used 6.1% · won 29% · −1.5±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 Johanna Larsson wins the point once the sequence happens, weighted by how often it happens. Think of them as chess openings.

  1. FH crosscourt → FH crosscourt → FH down the line used 1.3% · won 44% · +8.6±12.5 vs own baseline · +7.9 vs tour on the same sequence
  2. FH crosscourt → FH through the middle → BH through the middle used 1.2% · won 42% · +7.3±12.6 vs own baseline · +6.1 vs tour on the same sequence Disrupted by Mona Barthel (3/6), Kurumi Nara (3/6)
  3. BH crosscourt → BH through the middle → FH crosscourt used 1.1% · won 41% · +5.9±12.7 vs own baseline · −3.9 vs tour on the same sequence Disrupted by Kurumi Nara (2/6)
  4. BH crosscourt → BH crosscourt → BH through the middle used 1.3% · won 38% · +3.1±12.1 vs own baseline · −1.8 vs tour on the same sequence Disrupted by Kurumi Nara (2/8)
  5. FH crosscourt → FH crosscourt → FH crosscourt used 2.4% · won 37% · +1.9±10.3 vs own baseline · −10.0 vs tour on the same sequence Disrupted by Kurumi Nara (2/6), Agnieszka Radwanska (3/6)
  6. FH crosscourt → FH through the middle → FH through the middle used 1.2% · won 37% · +2.5±12.3 vs own baseline · −4.6 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 the middle · rally−0.3143
FH to their forehand · rally−0.9220
BH to their backhand · rally−1.4143
FH to the middle · rally−1.8153

Most exposed to

FH to their forehand · rally−2.9203
FH to their backhand · rally+0.1157
FH to the middle · rally+0.6130
BH to their backhand · rally+0.8185

Active players who are best at the shot in the top weakness: Linda Fruhvirtova, Maja Chwalinska, Sara Sorribes Tormo, Linda Klimovicova, Nadia Podoroska

Tactical fingerprint

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

Through the middle38%
Avg rally length4.8
Wide serves · ad51%
Run-around forehands14%
Wide serves · deuce44%
Backhand slice22%
1st serve in63%
T serves · deuce39%
Points at net8%
Chipped returns12%
Forehand share54%
Serve & volley1%
Unforced errors / shot9.8%
Drop shots / shot1.1%
T serves · ad32%
Deep returns29%
BH down the line15%
Point-ending shots17.0%
FH down the line19%

Plays most like

  1. Elina Svitolina 2013–2026 plan v
  2. Jil Teichmann 2017–2026 plan v
  3. Suzan Lamens 2021–2026 plan v
  4. Ana Bogdan 2019–2024 plan v
  5. Anna Lena Friedsam 2014–2023 plan v
  6. Arantxa Rus 2019–2026 plan v
  7. Andrea Petkovic 2010–2022 plan v
  8. Lucia Bronzetti 2021–2025 plan v

Closest from another era

  1. Jennifer Capriati 1990–2002
  2. Arantxa Sanchez Vicario 1988–2001
  3. Monica Seles 1990–2003

Charted matches