RallyIQ

Game plan

Shuai Zhang v Paula Badosa

Every number combines what Shuai Zhang does well with what Paula Badosa allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Shuai Zhang wins, best of 3 14%90%: 5%–29% · best of 5: 9%
Serve points won 52.1% / 60.3% Shuai / Paula · tour 56.4%
Strengths only, no similarity priors 15%serve 52.5% / 60.5%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Shuai Zhang's record against Paula Badosa's tactical lookalikes and in their charted head-to-heads (lookalikes: −4.3 on serve, +2.6 on return vs expectation (289 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

CareerShuaiPaula
Direction choice+0.05 ±0.16
better than 64%
−0.24 ±0.05
better than 13%
Shot selection+0.37 ±0.17
better than 87%
+0.29 ±0.09
better than 77%
Execution−0.41 ±0.83
better than 38%
+0.75 ±0.35
better than 86%
Points left on the table2.51 ±0.19
lower than 65%
2.83 ±0.08
lower than 22%

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: Shuai Zhang −0.39, Paula Badosa +2.25. 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.

Shuai Zhang serving

Deuce court

1st serveNowShuai winsv PaulaMatchupOptimal
Wide42%64%67%65.0%±6.557% ▲
Body19%58%54%55.1%±8.84% ▼
T39%61%65%57.1%±6.939%

Optimal v Paula Badosa: +0.6±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.0 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowShuai winsv PaulaMatchupOptimal
Wide39%63%62%59.2%±7.141% ▲
Body16%55%55%53.7%±9.828% ▲
T46%56%62%52.7%±7.031% ▼

Optimal v Paula Badosa: +0.3±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.9 per 100 first serves in before the returner adjusts.

Paula Badosa serving

Deuce court

1st serveNowPaula winsv ShuaiMatchupOptimal
Wide47%67%69%69.9%±6.048%
Body19%62%56%59.9%±8.24% ▼
T33%73%75%79.4%±5.948% ▲

Optimal v Shuai Zhang: +1.1±1.0 per 100 first serves (faults included) over the current mix. Serving T every time would read +8.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowPaula winsv ShuaiMatchupOptimal
Wide26%67%71%72.6%±6.830% ▲
Body16%53%56%52.5%±9.41% ▼
T58%72%66%72.7%±6.069% ▲

Optimal v Shuai Zhang: +1.3±0.9 per 100 first serves (faults included) over the current mix. Serving T every time would read +3.2 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.

Shuai Zhang returning

1st serve to the forehand

ReturnNowTourOwnv PaulaValue
FH through the middle47%+4.2−0.2+1.2+5.2±2.4
FH crosscourt33%+5.3−1.5−1.4+2.4±3.9
FH down the line17%+1.5+1.7−0.5+2.8±4.3
FH slice through the middle3%−6.7−1.0+0.5−7.2±2.1

Lean FH through the middle: +1.7±2.0 per 100 returns v the current mix (273 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv PaulaValue
BH through the middle43%+6.0−3.0−0.9+2.2±2.4
BH crosscourt31%+7.7−2.4−0.1+5.2±3.4
BH down the line14%+2.2+0.2−2.7−0.4±4.4
BH slice through the middle8%−6.2−1.6−0.9−8.7±2.4
BH slice down the line3%−12.5−0.8−2.4−15.7±2.9

Lean BH crosscourt: +4.0±2.6 per 100 returns v the current mix (201 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv PaulaValue
FH through the middle51%−3.2−2.0+1.4−3.8±2.8
FH crosscourt36%+0.5−0.8+0.7+0.4±4.1
FH down the line13%−0.6−1.2+0.9−0.8±4.5

Lean FH crosscourt: +2.3±3.0 per 100 returns v the current mix (70 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv PaulaValue
BH crosscourt41%+1.5+1.9−0.3+3.1±3.3
BH through the middle40%−2.6−1.2+2.0−1.8±2.4
BH down the line19%−0.5−0.7−1.7−2.9±4.7

Lean BH crosscourt: +3.1±2.4 per 100 returns v the current mix (98 returns charted)

Paula Badosa returning

1st serve to the forehand

ReturnNowTourOwnv ShuaiValue
FH through the middle45%+4.2+0.5+1.4+6.0±2.4
FH down the line21%+1.5+1.4−0.9+2.1±4.3
FH crosscourt15%+5.3+0.1±0.0+5.4±4.1
FH slice through the middle12%−6.7−0.7+0.7−6.7±2.5
FH slice crosscourt4%−6.6+1.1+1.1−4.5±2.3

Lean FH through the middle: +3.3±1.7 per 100 returns v the current mix (726 returns charted)

1st serve to the backhand

ReturnNowTourOwnv ShuaiValue
BH through the middle49%+6.0+1.3+2.3+9.6±2.2
BH crosscourt24%+7.7+1.9+0.5+10.1±3.2
BH down the line16%+2.2+4.0+1.2+7.4±4.5
BH slice through the middle7%−6.2−0.4−0.5−7.2±2.6
BH slice crosscourt2%−4.2+0.5−0.1−3.7±2.6

Lean BH crosscourt: +2.6±2.8 per 100 returns v the current mix (767 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv ShuaiValue
FH through the middle54%−3.2+1.0+0.6−1.5±2.9
FH crosscourt30%+0.5−0.8+4.7+4.4±4.5
FH down the line13%−0.6−2.2−6.7−9.4±5.2
FH slice through the middle3%−15.2+0.6−0.2−14.8±1.7

Lean FH crosscourt: +5.6±3.6 per 100 returns v the current mix (217 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv ShuaiValue
BH through the middle45%−2.6+2.0+0.4−0.2±2.5
BH crosscourt38%+1.5+3.3−0.1+4.7±3.4
BH down the line8%−0.5+0.7+3.3+3.4±5.2
FH through the middle4%−2.7−1.1+0.6−3.1±2.5
FH inside-out3%+1.4+0.8−6.7−4.5±4.0

Lean BH crosscourt: +2.9±2.4 per 100 returns v the current mix (450 returns charted)

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 hard. Each player's hard record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Shuai Zhang

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+4.8±4.8+3.0+1.8
FH to their backhand · serve +1+4.7±4.5+1.8+2.8
BH to their forehand · serve +1+2.7±5.8+1.2+1.5
FH to their backhand · return +1+2.0±5.5−0.7+2.7
FH to their backhand · return+0.5±5.5+1.7−1.2
BH to the middle · serve +1+0.2±2.8−1.0+1.2

Avoid

ShotEdgeOwnTheirs
BH to their forehand · return−4.0±5.4−1.8−2.2
BH to the middle · return−3.5±2.3−3.4−0.1
BH to their backhand · return +1−3.3±4.1−2.3−1.1
BH to their backhand · serve +1−3.2±3.7−2.6−0.6
FH to their forehand · return−3.0±4.5−0.3−2.7

Paula Badosa

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+9.4±5.7+3.9+5.5
FH to their backhand · serve +1+7.6±4.4+3.2+4.4
FH to their forehand · return +1+7.3±4.4+3.7+3.6
BH to their forehand · rally+6.8±5.0+0.8+6.1
FH to their forehand · serve +1+4.8±4.1+2.0+2.8
FH to the middle · rally+3.7±2.1+1.9+1.8

Avoid

ShotEdgeOwnTheirs
FH to their backhand · return−5.8±4.9−0.5−5.3
BH to their forehand · serve +1−5.7±6.2−2.5−3.2
FH to their backhand · return +1±0.0±5.3+0.1−0.1
FH to the middle · return +1+0.5±3.0+1.1−0.6
FH to the middle · serve +1+0.6±3.0+0.6±0.0

Against Paula Badosa-like opponents

Shuai Zhang vMatchesServe pts wonReturn pts won
All charted opponents–53.5%40.3%
Players most similar to Paula Badosa2 49.3%43.4%

Similar by tactical fingerprint: Coco Gauff, Marie Bouzkova, Kimberly Birrell, Jaqueline Cristian, Anna Bondar, Lin Zhu, Qiang Wang, Jennifer Brady, Carla Suarez Navarro, Louisa Chirico. When two players have rarely met, their records against these lookalikes fill the gap.