RallyIQ

Game plan

Xin Yu Wang v Shelby Rogers

Every number combines what Xin Yu Wang does well with what Shelby Rogers allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Xin Yu Wang wins, best of 3 22%90%: 6%–51% · best of 5: 17%
Serve points won 56.4% / 62.3% Xin / Shelby · tour 56.3%
Strengths only, no similarity priors 22%serve 56.4% / 62.3%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Xin Yu Wang's record against Shelby Rogers'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

CareerXinShelby
Direction choice+0.04 ±0.10
better than 62%
−0.15 ±0.09
better than 26%
Shot selection+0.30 ±0.16
better than 78%
−0.18 ±0.13
better than 29%
Execution−0.76 ±0.77
better than 25%
−0.20 ±0.44
better than 49%
Points left on the table2.56 ±0.19
lower than 58%
2.78 ±0.16
lower than 25%

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.

Structural compatibility

Expected edge per 100 rally shots from style alone: Xin Yu Wang −0.59, Shelby Rogers −0.21. Each player's shot mix weighted by their own skill with each shot and by how much the other gives up against it. This is why some rankings gaps don't hold in a given matchup.

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.

Xin Yu Wang serving

Deuce court

1st serveNowXin winsv ShelbyMatchupOptimal
Wide48%64%73%71.5%±6.956% ▲
Body16%62%57%62.4%±10.11% ▼
T35%70%69%70.5%±7.743% ▲

Optimal v Shelby Rogers: +0.7±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +1.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowXin winsv ShelbyMatchupOptimal
Wide46%66%70%71.0%±7.545%
Body16%48%55%47.0%±10.71% ▼
T38%65%69%69.3%±7.954% ▲

Optimal v Shelby Rogers: +1.7±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +4.4 per 100 first serves in before the returner adjusts.

Shelby Rogers serving

Deuce court

1st serveNowShelby winsv XinMatchupOptimal
Wide50%72%66%71.5%±6.665% ▲
Body18%59%58%60.2%±10.42% ▼
T32%65%69%66.0%±8.233%

Optimal v Xin Yu Wang: +1.0±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowShelby winsv XinMatchupOptimal
Wide44%66%72%73.0%±6.842% ▼
Body13%62%62%67.5%±10.60% ▼
T43%65%68%68.4%±7.658% ▲

Optimal v Xin Yu Wang: +0.5±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.7 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.

Xin Yu Wang returning

1st serve to the forehand

ReturnNowTourOwnv ShelbyValue
FH through the middle52%+4.2−1.4+0.9+3.7±2.7
FH crosscourt22%+5.3−1.2+2.6+6.7±4.4
FH down the line13%+1.5−3.3−2.7−4.4±4.6
FH slice through the middle7%−6.7−0.6+0.9−6.5±2.5
BH through the middle4%+5.2+0.5+1.3+7.1±2.4

Lean FH crosscourt: +4.2±3.7 per 100 returns v the current mix (293 returns charted)

1st serve to the backhand

ReturnNowTourOwnv ShelbyValue
BH through the middle47%+6.0−2.0+1.3+5.4±2.6
BH crosscourt22%+7.7−0.7+0.9+7.9±3.6
BH down the line11%+2.2+1.7−2.6+1.2±4.6
BH slice through the middle8%−6.2−0.4+1.5−5.2±2.4
BH slice crosscourt4%−4.2−2.9−0.7−7.8±2.4

Lean BH crosscourt: +4.4±3.1 per 100 returns v the current mix (295 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv ShelbyValue
FH through the middle44%−3.2−1.5−0.5−5.2±3.0
FH down the line31%−0.6−1.0+0.7−0.8±5.1
FH crosscourt25%+0.5−0.6+0.3+0.3±4.3

Lean FH crosscourt: +2.8±3.8 per 100 returns v the current mix (84 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv ShelbyValue
BH crosscourt37%+1.5−3.7−2.4−4.6±3.6
BH through the middle29%−2.6−2.0−1.1−5.7±2.7
BH down the line20%−0.5+0.2+1.8+1.5±5.1
FH through the middle5%−2.7+0.4−0.5−2.8±2.2
FH inside-in5%+0.7+1.2+0.3+2.2±4.0

Lean BH down the line: +4.6±4.3 per 100 returns v the current mix (132 returns charted)

Shelby Rogers returning

1st serve to the forehand

ReturnNowTourOwnv XinValue
FH through the middle42%+4.2±0.0+2.5+6.6±2.8
FH crosscourt21%+5.3−0.1−0.1+5.1±4.3
FH down the line15%+1.5−0.2−1.8−0.4±4.7
FH slice through the middle12%−6.7±0.0+0.7−6.0±2.5
FH slice crosscourt6%−6.6−1.0+1.3−6.3±2.3

Lean FH through the middle: +4.1±2.0 per 100 returns v the current mix (264 returns charted)

1st serve to the backhand

ReturnNowTourOwnv XinValue
BH through the middle46%+6.0−2.3−0.2+3.5±2.6
BH crosscourt19%+7.7+1.8+0.4+9.9±3.6
BH down the line13%+2.2−0.3−0.4+1.4±4.6
BH slice through the middle10%−6.2−0.7+0.6−6.3±2.5
BH slice crosscourt7%−4.2−2.9−1.3−8.4±2.4

Lean BH crosscourt: +8.0±3.2 per 100 returns v the current mix (237 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv XinValue
FH through the middle71%−3.2+1.0+2.6+0.4±3.0
FH crosscourt29%+0.5−1.4+3.9+3.0±4.0

Lean FH through the middle: −0.8±1.5 per 100 returns v the current mix (41 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv XinValue
BH through the middle52%−2.6−0.5−1.1−4.2±2.7
BH crosscourt26%+1.5−1.0−3.3−2.7±3.6
BH down the line19%−0.5+0.8−3.2−3.0±5.1
BH slice down the line3%−10.6−1.0±0.0−11.6±1.8

Lean BH crosscourt: +1.1±3.2 per 100 returns v the current mix (153 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.

Xin Yu Wang

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+4.0±3.7−0.2+4.2
FH to their backhand · rally+2.7±3.1+1.1+1.6
BH to their backhand · serve +1+1.7±3.7+0.4+1.3
FH to their forehand · rally+1.3±2.7+0.9+0.4
FH to their forehand · return+0.4±4.2−1.2+1.6
BH to their forehand · serve +1+0.1±4.9−0.5+0.6

Avoid

ShotEdgeOwnTheirs
BH to their backhand · return−4.3±3.3−3.1−1.2
BH to the middle · return +1−4.1±2.6−2.8−1.4
FH to their backhand · return−3.4±4.1−1.9−1.5
FH to the middle · serve +1−2.8±2.4−0.2−2.7
FH to their forehand · return +1−2.7±3.7−0.3−2.3

Shelby Rogers

Favour

ShotEdgeOwnTheirs
FH to their forehand · rally+5.8±3.0+0.8+5.0
FH to their forehand · return +1+5.7±3.8+4.0+1.7
FH to their forehand · serve +1+3.5±3.7+3.4+0.2
FH to the middle · return+2.7±2.4+0.1+2.6
FH to their forehand · return+2.6±4.2+0.4+2.2
BH to the middle · serve +1+2.5±2.4+1.9+0.6

Avoid

ShotEdgeOwnTheirs
BH to their forehand · serve +1−6.6±4.9−1.9−4.6
BH to their forehand · return−4.5±4.3−1.8−2.7
BH to their backhand · rally−3.7±2.9−1.9−1.8
BH to their backhand · return−3.2±3.2−1.5−1.8
BH to their backhand · serve +1−2.9±3.7−1.9−1.0

Against Shelby Rogers-like opponents

Xin Yu Wang vMatchesServe pts wonReturn pts won
All charted opponents–56.8%39.6%

Similar by tactical fingerprint: Coco Gauff, Iva Jovic, Anastasia Potapova, Karolina Pliskova, Belinda Bencic, Sorana Cirstea, Victoria Mboko, Nao Hibino, Irina Camelia Begu, Kaia Kanepi. When two players have rarely met, their records against these lookalikes fill the gap.