RallyIQ

WTA · Right-handed · 7 charted matches · 1984–1993

Helena Sukova

Against an average opponent

Serve points won 56.8% ±3.7 raw 55.3% · tour 56.3% · 557 points
Return points won 42.6% ±3.7 raw 36.8% · tour 43.7% · 571 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 ±0.30 better than 76% of WTA · raw +0.11
Shot selection +0.20 ±0.33 better than 66% of WTA · raw +0.18
Execution −1.46 ±0.74 better than 11% of WTA · raw −1.60

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 2,327 shots.

Shot expected value

The share of points Helena Sukova 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 forehand side

position worth 43% to the average player · 39 shots

OptionUsedWin %Tour
FH through the middle 36% 42.0%±13.9 41.3%
FH down the line 33% 33.3%±13.5 44.9%
FH crosscourt 31% 44.8%±14.5 46.7%

Serve under pressure

Pressure predictability index +2 How much less varied Helena Sukova's first-serve direction gets on break points. Positive means easier to read. Based on 72 break-point first serves.

Deuce court

1st serveUsageBreak ptWon when in
Wide 45% 38% 68% / 66%
Body 9% 19% ▲ 57% / 57%
T 46% 43% 61% / 68%

261 normal · 21 break-point 1st serves

Ad court

1st serveUsageBreak ptWon when in
Wide 44% 49% 61% / 66%
Body 10% 6% 66% / 56%
T 46% 45% 71% / 64%

210 normal · 51 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% 57.4%±6.5 n=125 59% ▲
Body10% 60.4%±10.6 n=28 0% ▼
T46% 55.6%±6.5 n=129 41% ▼

Consistent with an optimal mix (p = 0.70).
Optimal mix: +0.2 per 100 first serves.

Ad court

1st serveUsagePoints wonOptimal
Wide45% 48.0%±6.8 n=118 38% ▼
Body9% 55.0%±11.2 n=23 1% ▼
T46% 56.0%±6.7 n=120 61% ▲

Consistent with an optimal mix (p = 0.15).
Optimal mix: +0.5 per 100 first serves.

Exploitability 0.35 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: −5.0±7.8 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. (249 repeats, 280 switches.)

Return by serve direction

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

ServeCourtDirectionPointsWonvs tour
1stAd courtBody 17 45% +1.1±11.9
1stAd courtT 45 32% −4.0±8.8
1stAd courtWide 111 35% +0.5±6.6
1stDeuce courtBody 36 39% −3.5±9.9
1stDeuce courtT 80 25% −7.0±6.8
1stDeuce courtWide 74 35% +0.8±7.7
2ndAd courtBody 16 51% −3.9±12.1
2ndAd courtT 15 50% −5.0±12.3
2ndAd courtWide 65 48% −5.1±8.4
2ndDeuce courtBody 30 51% −3.9±10.6
2ndDeuce courtT 33 57% +0.8±10.3
2ndDeuce courtWide 38 47% −6.5±10.0

Signature patterns

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

Serve → +1

  1. Not enough data

Return

  1. vs wide serve (ad court) → BH slice through the middle, mid used 16.5% · won 40% · −1.2±11.5 vs own baseline

Rally, consecutive own shots

  1. Not enough data

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

Wide 1st serve · deuce court−0.6125
T 1st serve · deuce court−1.1129
T 1st serve · ad court−1.2120

Most exposed to

T 1st serve · deuce court+0.2127
Wide 1st serve · ad court+1.0175

Active players who are best at the shot in the top weakness: Serena Williams, Madison Keys, Ashlyn Krueger, Karolina Pliskova, Victoria Mboko

Charted matches