RallyIQ

Game plan

Linda Fruhvirtova v Qiang Wang

Every number combines what Linda Fruhvirtova does well with what Qiang Wang allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Linda Fruhvirtova wins, best of 3 71%90%: 28%–95% · best of 5: 76%
Serve points won 56.7% / 52.5% Linda / Qiang · tour 55.0%
Strengths only, no similarity priors 71%serve 56.7% / 52.5%

Each player's serve and return strength is fitted against every opponent they were charted against, so a record built on weak opponents counts for less. At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers. The result is then nudged by Linda Fruhvirtova's record against Qiang Wang's tactical lookalikes and in their charted head-to-heads. A game-by-game Markov chain turns point odds into match odds; the 90% range covers the uncertainty in the two strengths, not the nudges. Charted matches lean toward big events, so treat this as a scouting estimate, not a betting line.

Head to head, per 100 shots

CareerLindaQiang
Direction choice+0.10 ±0.09
better than 72%
+0.04 ±0.17
better than 61%
Shot selection−0.11 ±0.11
better than 35%
+0.37 ±0.16
better than 87%
Execution+1.26 ±0.49
better than 95%
+0.03 ±0.80
better than 62%
Points left on the table2.35 ±0.12
lower than 81%
2.24 ±0.25
lower than 90%

Each player's career against an average tour player in the same position, adjusted for opponent strength, with a 90% margin (shots clustered by match). Points left on the table is the gap to the best-value direction for the same stroke, so lower is better. Percentiles are within each player's own tour. A side is highlighted only when the gap is larger than the margin on the difference.

Serve plan

The share of points the server wins when a first serve lands in that direction (hover a rate for its 90% interval; ± is the 90% margin). "Matchup" combines the server's rate with how this returner handles that serve. "Optimal" is the mix that wins most against this returner once they start reading a habit, at the response measured across the tour, and only within the range servers' habits actually vary. The gain over the current mix is how exploitable that mix is.

Linda Fruhvirtova serving

Deuce court

1st serveNowLinda winsv QiangMatchupOptimal
Wide39%67%61%62.2%±8.137% ▼
Body14%63%55%60.2%±11.30% ▼
T47%71%71%74.7%±7.763% ▲

Optimal v Qiang Wang: +0.5±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLinda winsv QiangMatchupOptimal
Wide39%68%64%66.6%±8.839%
Body16%52%63%59.2%±11.61% ▼
T45%64%69%69.1%±8.160% ▲

Optimal v Qiang Wang: +0.9±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.6 per 100 first serves in before the returner adjusts.

Qiang Wang serving

Deuce court

1st serveNowQiang winsv LindaMatchupOptimal
Wide36%61%68%62.9%±8.836%
Body24%59%52%53.5%±10.39% ▼
T40%65%72%69.0%±8.755% ▲

Optimal v Linda Fruhvirtova: +0.4±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowQiang winsv LindaMatchupOptimal
Wide29%60%63%57.1%±10.344% ▲
Body20%50%58%52.1%±11.74% ▼
T52%52%65%52.5%±8.652%

Optimal v Linda Fruhvirtova: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.4 per 100 first serves in before the returner adjusts.

Return plan

Value of each return, in points per 100 returns against an average return of the same serve (direction, court, surface): the tour's result with that return, the returner's own edge with it, and what this server gives up when it comes back to that side. Returns with no charted direction are left out, so values compare with each other rather than with zero. Depth isn't a choice here: missed returns have no depth. Serve quality isn't charted, so a block through the middle partly reflects the serve that forced it.

Linda Fruhvirtova returning

1st serve to the forehand

ReturnNowTourOwnv QiangValue
FH through the middle54%+4.2+3.1+0.1+7.4±2.8
FH crosscourt23%+5.3+2.7+4.1+12.1±4.3
FH down the line19%+1.5+1.3+1.6+4.4±4.7
FH slice through the middle2%−6.7+0.5−0.3−6.5±2.0
FH slice crosscourt2%−6.6+0.6−0.7−6.7±1.7

Lean FH crosscourt: +4.7±3.8 per 100 returns v the current mix (331 returns charted)

1st serve to the backhand

ReturnNowTourOwnv QiangValue
BH crosscourt43%+7.7+1.5+3.4+12.6±3.5
BH through the middle42%+6.0+1.6−1.0+6.6±2.7
BH down the line8%+2.2−2.2−0.3−0.2±4.4
BH slice through the middle4%−6.2+0.2+0.3−5.8±1.9
BH slice crosscourt2%−4.2+0.1−0.4−4.4±1.8

Lean BH crosscourt: +4.8±2.4 per 100 returns v the current mix (272 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv QiangValue
FH through the middle44%−3.2+2.1−0.3−1.3±3.1
FH crosscourt35%+0.5±0.0+2.3+2.8±4.4
FH down the line21%−0.6+2.1+2.7+4.1±4.9

Lean FH down the line: +2.9±4.4 per 100 returns v the current mix (95 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv QiangValue
BH through the middle44%−2.6+0.9+1.0−0.7±2.8
BH crosscourt41%+1.5−0.3+1.9+3.2±3.7
BH down the line15%−0.5−0.2+6.5+5.7±5.0

Lean BH down the line: +3.9±4.6 per 100 returns v the current mix (119 returns charted, inside the 90% margin)

Qiang Wang returning

1st serve to the forehand

ReturnNowTourOwnv LindaValue
FH through the middle56%+4.2+2.6−1.1+5.7±2.8
FH down the line25%+1.5+2.7±0.0+4.2±4.7
FH crosscourt18%+5.3−0.1+2.3+7.6±4.2

Lean FH crosscourt: +1.9±4.0 per 100 returns v the current mix (208 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv LindaValue
BH crosscourt41%+7.7+1.0−2.9+5.7±3.6
BH through the middle39%+6.0−0.9+0.7+5.8±2.6
BH down the line9%+2.2−1.3+3.4+4.2±4.3
BH slice crosscourt6%−4.2+0.3−0.7−4.5±2.3
BH slice through the middle6%−6.2±0.0−0.1−6.3±2.0

Lean BH through the middle: +1.4±2.2 per 100 returns v the current mix (161 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LindaValue
BH crosscourt45%+1.5+0.6−0.8+1.3±3.7
BH through the middle38%−2.6+1.6+1.5+0.5±2.7
BH down the line16%−0.5+1.2+1.9+2.6±4.9

Lean BH down the line: +1.4±4.6 per 100 returns v the current mix (104 returns charted, inside the 90% margin)

Rally plan

Edge, in points per 100 shots: the hitter's skill with the shot (own) plus how much the receiver usually gives up against it (theirs), both measured against the tour average on clay. Each player's clay record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Linda Fruhvirtova

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+8.4±4.6+5.4+3.0
BH to their backhand · serve +1+7.4±4.7+1.9+5.5
FH to their backhand · rally+7.1±4.4+2.7+4.4
FH to their backhand · return+6.0±5.1+3.1+2.9
FH to their forehand · return+5.9±5.1+1.4+4.4
BH to their backhand · return+5.6±3.8+2.0+3.6

Avoid

ShotEdgeOwnTheirs
BH to the middle · serve +1−2.7±3.1−4.1+1.4
BH to their backhand · return +1+0.2±4.0−1.0+1.3
BH to the middle · rally+1.8±2.9+0.6+1.2
FH to the middle · return+1.8±3.4+0.9+0.9
BH to the middle · return+2.1±3.3+1.6+0.5

Qiang Wang

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+4.2±4.0+0.7+3.5
FH to their forehand · serve +1+4.1±4.7−1.3+5.3
FH to the middle · serve +1+3.9±3.5+2.2+1.6
FH to their forehand · return+3.6±5.1−1.5+5.1
BH to their backhand · serve +1+2.8±4.7+0.8+2.0
FH to their forehand · rally+2.8±4.0+2.2+0.6

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−7.7±5.2−6.0−1.7
FH to the middle · rally−1.4±3.2−0.3−1.1
FH to their forehand · return +1−1.2±4.7−0.5−0.7
BH to the middle · rally−1.1±2.8−1.8+0.7
BH to their backhand · return−1.1±4.0+1.7−2.8

Against Qiang Wang-like opponents

Linda Fruhvirtova vMatchesServe pts wonReturn pts won
All charted opponents–56.2%42.6%

Similar by tactical fingerprint: Jessica Pegula, Rebecca Sramkova, Jaqueline Cristian, Katerina Siniakova, Elisabetta Cocciaretto, Heather Watson, Anna Karolina Schmiedlova, Simona Halep, Dominika Cibulkova. When two players have rarely met, their records against these lookalikes fill the gap.