RallyIQ

Game plan

Sloane Stephens v Qiang Wang

Every number combines what Sloane Stephens 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

Sloane Stephens wins, best of 3 78%90%: 47%–95% · best of 5: 83%
Serve points won 58.2% / 52.4% Sloane / Qiang · tour 56.3%
Strengths only, no similarity priors 80%serve 58.6% / 52.1%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Sloane Stephens's record against Qiang Wang's tactical lookalikes and in their charted head-to-heads (lookalikes: −4.3 on serve, −2.4 on return vs expectation (322 points)). 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

CareerSloaneQiang
Direction choice−0.12 ±0.11
better than 32%
+0.04 ±0.17
better than 61%
Shot selection−0.16 ±0.14
better than 31%
+0.37 ±0.16
better than 87%
Execution−0.13 ±0.58
better than 56%
+0.03 ±0.80
better than 62%
Points left on the table2.62 ±0.13
lower than 48%
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.

Sloane Stephens serving

Deuce court

1st serveNowSloane winsv QiangMatchupOptimal
Wide50%66%61%60.9%±7.550%
Body30%56%55%53.1%±9.815% ▼
T20%63%71%66.7%±9.135% ▲

Optimal v Qiang Wang: +1.1±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowSloane winsv QiangMatchupOptimal
Wide32%60%64%57.8%±8.630% ▼
Body13%53%63%60.0%±11.20% ▼
T55%61%69%66.4%±7.870% ▲

Optimal v Qiang Wang: +1.0±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.6 per 100 first serves in before the returner adjusts.

Qiang Wang serving

Deuce court

1st serveNowQiang winsv SloaneMatchupOptimal
Wide36%61%66%61.1%±8.535%
Body24%59%58%59.5%±9.59% ▼
T40%65%71%67.4%±8.256% ▲

Optimal v Sloane Stephens: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +4.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowQiang winsv SloaneMatchupOptimal
Wide29%60%64%57.6%±9.744% ▲
Body20%50%59%52.8%±11.116% ▼
T52%52%67%55.1%±8.140% ▼

Optimal v Sloane Stephens: +0.4±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.3 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.

Sloane Stephens returning

1st serve to the forehand

ReturnNowTourOwnv QiangValue
FH through the middle37%+4.2+2.0+0.1+6.3±2.8
FH slice through the middle20%−6.7−0.4−0.3−7.4±2.3
FH crosscourt14%+5.3−1.5+4.1+7.9±4.3
FH down the line13%+1.5−0.6+1.6+2.5±4.7
FH slice crosscourt12%−6.6+0.3−0.7−7.1±2.3

Lean FH crosscourt: +6.9±4.0 per 100 returns v the current mix (466 returns charted)

1st serve to the backhand

ReturnNowTourOwnv QiangValue
BH through the middle43%+6.0−0.1−1.0+4.9±2.5
BH crosscourt32%+7.7+0.3+3.4+11.5±3.4
BH down the line10%+2.2−3.1−0.3−1.2±4.6
BH slice through the middle8%−6.2−2.7+0.3−8.6±2.2
BH slice crosscourt4%−4.2−1.3−0.4−5.9±2.3

Lean BH crosscourt: +7.2±2.6 per 100 returns v the current mix (509 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv QiangValue
FH through the middle58%−3.2+0.2−0.3−3.3±3.1
FH crosscourt26%+0.5−1.5+2.3+1.3±4.3
FH down the line10%−0.6−0.5+2.7+1.6±4.5
FH slice through the middle6%−15.2−0.8±0.0−16.0±1.1

Lean FH crosscourt: +3.6±3.7 per 100 returns v the current mix (88 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv QiangValue
BH through the middle45%−2.6−0.9+1.0−2.5±2.7
BH crosscourt41%+1.5−1.0+1.9+2.5±3.5
BH down the line6%−0.5−0.1+6.5+5.9±5.0
FH through the middle5%−2.7±0.0−0.3−2.9±2.5
FH inside-out3%+1.4+2.3+2.7+6.3±3.6

Lean BH down the line: +5.6±5.0 per 100 returns v the current mix (299 returns charted)

Qiang Wang returning

1st serve to the forehand

ReturnNowTourOwnv SloaneValue
FH through the middle56%+4.2+2.6+1.2+7.9±2.5
FH down the line25%+1.5+2.7+0.6+4.8±4.4
FH crosscourt18%+5.3−0.1+0.8+6.0±4.0

Lean FH through the middle: +1.1±1.7 per 100 returns v the current mix (208 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv SloaneValue
BH crosscourt41%+7.7+1.0+2.2+10.9±3.3
BH through the middle39%+6.0−0.9−0.4+4.7±2.5
BH down the line9%+2.2−1.3−1.0−0.1±4.1
BH slice crosscourt6%−4.2+0.3±0.0−3.9±2.2
BH slice through the middle6%−6.2±0.0−0.4−6.6±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 SloaneValue
BH crosscourt45%+1.5+0.6−0.5+1.6±3.4
BH through the middle38%−2.6+1.6−2.3−3.3±2.6
BH down the line16%−0.5+1.2−0.4+0.3±4.9

Lean BH crosscourt: +2.1±2.2 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.

Sloane Stephens

Favour

ShotEdgeOwnTheirs
BH to their backhand · serve +1+4.7±3.2+0.6+4.1
FH to their backhand · serve +1+4.2±3.6+1.2+3.0
BH to their backhand · return+3.7±3.1+0.1+3.6
FH to their backhand · return+3.7±4.2+0.8+2.9
FH to their backhand · rally+3.3±2.9+0.7+2.6
BH to their forehand · return+2.4±4.5−2.3+4.7

Avoid

ShotEdgeOwnTheirs
BH to the middle · return +1−2.7±2.3−2.8+0.1
FH to their forehand · return +1−1.7±3.6−2.2+0.5
FH to the middle · rally−1.5±2.0−2.5+0.9
FH to the middle · serve +1−0.4±2.5−0.1−0.3
FH to their forehand · serve +1−0.4±3.7−1.2+0.8

Qiang Wang

Favour

ShotEdgeOwnTheirs
FH to the middle · return+5.5±2.2+3.4+2.1
FH to their backhand · return+3.4±4.0+2.9+0.5
FH to the middle · serve +1+3.1±2.4+1.8+1.3
BH to their backhand · return+2.6±2.8+1.7+1.0
FH to their forehand · return+0.3±4.0−1.5+1.8
FH to the middle · rally−0.2±1.9+0.8−1.1

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−5.7±3.9−4.2−1.5
FH to their backhand · rally−4.6±2.7−1.8−2.8
FH to their forehand · rally−2.3±2.2+0.3−2.6
FH to their backhand · serve +1−2.0±3.6+0.9−2.9
BH to their backhand · return +1−1.8±3.0−1.4−0.4

Against Qiang Wang-like opponents

Sloane Stephens vMatchesServe pts wonReturn pts won
All charted opponents–56.4%42.8%
Players most similar to Qiang Wang3 54.1%43.6%

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.