RallyIQ

WTA · Right-handed · 17 charted matches · 2023–2026

Olivia Gadecki

Archetype: First-strike aggressor · Short-point player

Against an average opponent

Serve points won 55.3% ±3.3 raw 54.1% · tour 56.3% · 1,022 points
Return points won 42.2% ±3.3 raw 38.8% · tour 43.7% · 916 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 +0.01 ±0.09 better than 55% of WTA · raw +0.03
Shot selection −0.05 ±0.19 better than 41% of WTA · raw −0.01
Execution −2.60 ±0.67 better than 3% of WTA · raw −2.38
Tactical adaptability −0.02 first serves toward what's working, set to set · 16 matches
Adaptation speed ±0.00 same, every two to three service games · per 100 first serves
Points left on the table 2.54 per 100 shots vs best direction · lower than 60% 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 4,504 shots.

Shot expected value

The share of points Olivia Gadecki 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 · 229 shots

OptionUsedWin %Tour
BH crosscourt 41% 38.7%±7.5 47.6%
BH through the middle 27% 30.4%±8.4 43.3%
BH down the line 14% 35.3%±10.9 46.8%
BH slice crosscourt 7% 46.2%±13.5 40.4%
BH slice through the middle 5% 25.4%±12.9 34.4%

Rally, shots 5–8: drive to your middle

position worth 50% to the average player · 211 shots

OptionUsedWin %Tour
FH crosscourt 37% 54.1%±8.2 52.7%
FH down the line 19% 40.1%±10.3 52.2%
BH through the middle 17% 52.2%±11.0 46.2%
FH through the middle 11% 35.2%±12.0 45.8%
BH crosscourt 10% 52.8%±12.7 50.9%
BH down the line 5% 46.7%±15.0 50.0%

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

position worth 43% to the average player · 181 shots

OptionUsedWin %Tour
FH crosscourt 46% 47.9%±8.1 46.7%
FH through the middle 23% 34.3%±9.9 41.3%
FH down the line 18% 32.0%±10.5 44.9%
FH slice through the middle 6% 29.5%±13.7 29.2%

Serve +1: mid-depth return to your middle

position worth 51% to the average player · 148 shots

OptionUsedWin %Tour
FH crosscourt 32% 43.0%±9.9 54.0%
FH down the line 17% 39.2%±12.0 53.3%
BH through the middle 16% 52.9%±12.4 46.3%
BH crosscourt 12% 61.8%±13.0 52.5%
FH through the middle 11% 43.5%±13.4 45.5%

Serve under pressure

Pressure predictability index +5 How much less varied Olivia Gadecki's first-serve direction gets on break points. Positive means easier to read. Based on 127 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 38% 41% 65% / 66%
Body 30% 11% ▼ 64% / 57%
T 32% 48% ▲ 64% / 68%

498 normal · 27 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 49% 44% 67% / 66%
Body 21% 15% 55% / 56%
T 30% 41% ▲ 58% / 64%

391 normal · 100 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
Wide38% 54.3%±5.4 n=199 53% ▲
Body29% 57.6%±6.0 n=152 29%
T33% 50.2%±5.8 n=174 18% ▼

Consistent with an optimal mix (p = 0.26).
Optimal mix: +0.3 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide48% 55.5%±5.0 n=234 63% ▲
Body20% 53.6%±7.3 n=97 5% ▼
T33% 52.1%±6.0 n=160 32%

Consistent with an optimal mix (p = 0.72).
Optimal mix: +0.4 per 100 first serves.

Exploitability 0.36 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: −2.9±3.8 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. (322 repeats, 660 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 64 38% −5.4±8.3
1stAd courtT 101 36% +0.1±6.9
1stAd courtWide 102 24% −10.0±6.2
1stDeuce courtBody 86 38% −4.9±7.4
1stDeuce courtT 85 27% −5.5±6.8
1stDeuce courtWide 139 32% −1.9±5.9
2ndAd courtBody 76 55% +0.2±7.9
2ndAd courtT 37 52% −3.5±10.0
2ndAd courtWide 49 52% −1.6±9.2
2ndDeuce courtBody 74 53% −1.2±8.0
2ndDeuce courtT 37 56% +0.4±10.0
2ndDeuce courtWide 56 50% −3.6±8.9

Signature patterns

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

Serve → +1

  1. T serve (deuce court) → FH crosscourt used 4.3% · won 57% · −1.1±10.3 vs own baseline
  2. Wide serve (ad court) → FH crosscourt used 3.9% · won 57% · −1.6±10.6 vs own baseline
  3. Body serve (deuce court) → BH crosscourt used 3.2% · won 56% · −1.9±11.1 vs own baseline
  4. Body serve (deuce court) → FH crosscourt used 5.5% · won 54% · −4.1±9.6 vs own baseline
  5. Body serve (deuce court) → BH through the middle used 3.5% · won 53% · −5.7±11.0 vs own baseline

Return

  1. vs T serve (ad court) → FH through the middle, mid used 6.5% · won 49% · +9.4±11.6 vs own baseline
  2. vs wide serve (deuce court) → FH through the middle, deep used 6.5% · won 45% · +5.4±11.5 vs own baseline
  3. vs wide serve (deuce court) → FH through the middle, mid used 8.0% · won 42% · +3.2±10.9 vs own baseline
  4. vs body serve (deuce court) → BH through the middle, mid used 6.5% · won 39% · −0.5±11.3 vs own baseline
  5. vs wide serve (ad court) → BH crosscourt, mid used 6.5% · won 37% · −2.4±11.2 vs own baseline

Rally, consecutive own shots

  1. BH crosscourt → BH crosscourt used 7.6% · won 49% · +7.3±10.4 vs own baseline
  2. FH crosscourt → BH through the middle used 4.7% · won 45% · +3.2±11.7 vs own baseline
  3. FH crosscourt → FH crosscourt used 8.7% · won 43% · +0.6±9.8 vs own baseline
  4. BH crosscourt → FH crosscourt used 6.6% · won 42% · +0.4±10.7 vs own baseline
  5. BH crosscourt → BH down the line used 5.0% · won 42% · +0.3±11.5 vs own baseline

Discovered sequences

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

  1. FH crosscourt → FH through the middle → BH through the middle used 0.8% · won 52% · +8.8±13.0 vs own baseline · +19.8 vs tour on the same sequence
  2. BH crosscourt → BH crosscourt → BH crosscourt used 1.5% · won 48% · +4.6±11.2 vs own baseline · +3.1 vs tour on the same sequence Disrupted by Magdalena Frech (3/8), Kaitlin Quevedo (5/10)
  3. Body serve → BH through the middle return, mid → FH crosscourt used 0.8% · won 47% · +3.8±13.0 vs own baseline · +0.3 vs tour on the same sequence
  4. FH crosscourt → FH through the middle → FH crosscourt used 1.0% · won 46% · +3.0±12.2 vs own baseline · −5.3 vs tour on the same sequence
  5. BH through the middle → FH through the middle → FH crosscourt used 0.8% · won 46% · +2.7±12.8 vs own baseline · +1.0 vs tour on the same sequence
  6. BH through the middle → FH crosscourt → FH crosscourt used 0.8% · won 44% · +1.3±13.0 vs own baseline · +2.1 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

T 1st serve · ad court+0.4160
Wide 1st serve · deuce court+0.2199
FH to their forehand · serve +1±0.0205
Wide 1st serve · ad court±0.0234
Body 1st serve · deuce court−0.3152

Most exposed to

Wide 1st serve · deuce court−1.5192
T 1st serve · ad court−0.5161
FH to the middle · rally−0.4137
FH to their forehand · rally±0.0206
BH to the middle · return+0.2235

Active players who are best at the shot in the top weakness: Caroline Garcia, Elena Rybakina, Linda Noskova, Katie Volynets, Serena Williams

Tactical fingerprint

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

Point-ending shots29.0%
Chipped returns20%
Unforced errors / shot12.6%
Wide serves · ad48%
Points at net10%
Serve & volley2%
Forehand share55%
Run-around forehands9%
Backhand slice17%
Drop shots / shot1.5%
1st serve in62%
Deep returns31%
Wide serves · deuce38%
Through the middle26%
T serves · deuce33%
T serves · ad33%
BH down the line17%
FH down the line25%
Avg rally length3.5

Plays most like

  1. Barbora Krejcikova 2017–2026 plan v
  2. Diana Shnaider 2022–2026 plan v
  3. Anett Kontaveit 2015–2023 plan v
  4. Eugenie Bouchard 2013–2023 plan v
  5. Anastasia Pavlyuchenkova 2014–2026 plan v
  6. Johanna Konta 2013–2020 plan v
  7. Marta Kostyuk 2018–2026 plan v
  8. Elena Vesnina 2007–2016 plan v

Closest from another era

  1. Daniela Hantuchova 2002–2015
  2. Jelena Dokic 2000–2009
  3. Dinara Safina 2007–2011

Charted matches