RallyIQ

Game plan

Leylah Fernandez v Qiang Wang

Every number combines what Leylah Fernandez 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

Leylah Fernandez wins, best of 3 79%90%: 39%–97% · best of 5: 85%
Serve points won 58.2% / 52.0% Leylah / Qiang · tour 55.0%
Strengths only, no similarity priors 79%serve 58.2% / 52.0%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Leylah Fernandez'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

CareerLeylahQiang
Direction choice−0.17 ±0.06
better than 23%
+0.04 ±0.17
better than 61%
Shot selection+0.22 ±0.11
better than 69%
+0.37 ±0.16
better than 87%
Execution+0.34 ±0.42
better than 74%
+0.03 ±0.80
better than 62%
Points left on the table3.33 ±0.10
lower than 3%
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.

Leylah Fernandez serving

Deuce court

1st serveNowLeylah winsv QiangMatchupOptimal
Wide35%69%61%64.5%±7.635%
Body19%57%55%53.9%±10.14% ▼
T45%64%71%68.0%±7.961% ▲

Optimal v Qiang Wang: +1.1±1.1 per 100 first serves (faults included) over the current mix. Serving T every time would read +3.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLeylah winsv QiangMatchupOptimal
Wide54%69%64%67.0%±7.356% ▲
Body16%55%63%62.1%±10.41% ▼
T30%71%69%75.6%±7.043% ▲

Optimal v Qiang Wang: +0.8±1.0 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.

Qiang Wang serving

Deuce court

1st serveNowQiang winsv LeylahMatchupOptimal
Wide36%61%67%62.4%±7.845% ▲
Body24%59%58%59.7%±9.39% ▼
T40%65%69%65.8%±8.246% ▲

Optimal v Leylah Fernandez: +0.4±0.9 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.

Ad court

1st serveNowQiang winsv LeylahMatchupOptimal
Wide29%60%69%63.7%±9.044% ▲
Body20%50%53%46.2%±10.64% ▼
T52%52%66%53.9%±7.552%

Optimal v Leylah Fernandez: +1.3±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +8.5 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.

Leylah Fernandez returning

1st serve to the forehand

ReturnNowTourOwnv QiangValue
FH through the middle48%+4.2+2.6+0.1+6.9±2.6
FH crosscourt29%+5.3+1.3+1.6+8.2±4.0
FH down the line20%+1.5−3.0+4.1+2.6±4.5
FH slice through the middle2%−6.7−0.4−0.3−7.4±2.1
FH slice down the line1%−10.5−1.0−0.7−12.2±2.1

Lean FH crosscourt: +2.2±3.2 per 100 returns v the current mix (626 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv QiangValue
BH through the middle45%+6.0−1.6−1.0+3.5±2.4
BH crosscourt24%+7.7+0.7−0.3+8.2±3.2
BH down the line11%+2.2−1.0+3.4+4.5±4.6
BH slice through the middle10%−6.2−0.3+0.3−6.2±2.2
BH slice crosscourt6%−4.2−1.9+0.1−6.0±2.5

Lean BH crosscourt: +6.0±2.7 per 100 returns v the current mix (845 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv QiangValue
FH crosscourt44%+0.5±0.0+2.7+3.2±4.3
FH through the middle39%−3.2+3.1−0.3−0.4±3.1
FH down the line17%−0.6+0.6+2.3+2.3±5.2

Lean FH crosscourt: +1.5±2.9 per 100 returns v the current mix (186 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv QiangValue
BH through the middle45%−2.6+1.0+1.0−0.6±2.6
BH crosscourt34%+1.5±0.0+6.5+8.0±3.5
BH down the line15%−0.5+1.5+1.9+2.9±5.3
FH through the middle3%−2.7+0.1−0.3−2.8±2.4
BH slice through the middle1%−11.7−0.9±0.0−12.6±1.1

Lean BH crosscourt: +5.3±2.7 per 100 returns v the current mix (406 returns charted)

Qiang Wang returning

1st serve to the forehand

ReturnNowTourOwnv LeylahValue
FH through the middle56%+4.2+2.6±0.0+6.7±2.5
FH down the line25%+1.5+2.7−1.5+2.7±4.5
FH crosscourt18%+5.3−0.1+0.9+6.1±4.1

Lean FH through the middle: +1.1±1.8 per 100 returns v the current mix (208 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv LeylahValue
BH crosscourt41%+7.7+1.0+2.6+11.3±3.2
BH through the middle39%+6.0−0.9+1.2+6.3±2.4
BH down the line9%+2.2−1.3−3.0−2.1±3.9
BH slice crosscourt6%−4.2+0.3−1.9−5.7±2.7
BH slice through the middle6%−6.2±0.0−1.7−7.9±2.2

Lean BH crosscourt: +5.2±2.2 per 100 returns v the current mix (161 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv LeylahValue
BH crosscourt45%+1.5+0.6−2.1−0.1±3.5
BH through the middle38%−2.6+1.6+1.7+0.7±2.4
BH down the line16%−0.5+1.2−2.0−1.4±4.8

Lean BH through the middle: +0.7±2.3 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.

Leylah Fernandez

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+8.5±5.1+4.5+4.0
FH to their backhand · serve +1+6.4±4.2+3.4+3.0
FH to their backhand · return+5.5±4.5+2.7+2.9
BH to their forehand · return+5.0±4.7+0.3+4.7
BH to their backhand · serve +1+4.9±4.7−0.6+5.5
FH to their backhand · rally+4.6±4.3+0.2+4.4

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−2.8±2.9−4.0+1.2
BH to their backhand · return +1−2.2±4.0−3.5+1.3
BH to the middle · return +1−1.7±2.9−1.8+0.1
FH to their forehand · rally−0.8±4.4−0.6−0.3
BH to the middle · return−0.5±3.2−1.0+0.5

Qiang Wang

Favour

ShotEdgeOwnTheirs
FH to their forehand · rally+5.9±4.1+2.2+3.7
FH to their backhand · return+4.4±4.8+2.9+1.6
FH to the middle · return+4.2±3.3+3.4+0.8
BH to the middle · serve +1+2.1±2.9+0.2+2.0
FH to the middle · rally+1.8±3.1−0.3+2.1
FH to the middle · serve +1+1.4±3.2+2.2−0.9

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−4.8±5.1−6.0+1.2
BH to the middle · rally−3.2±2.8−1.8−1.4
FH to their forehand · return−2.6±5.0−1.5−1.1
BH to their backhand · return−2.3±3.8+1.7−4.0
BH to the middle · return−0.7±2.6−0.3−0.4

Against Qiang Wang-like opponents

Leylah Fernandez vMatchesServe pts wonReturn pts won
All charted opponents–58.9%41.8%

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