RallyIQ

Game plan

Qiang Wang v Maya Joint

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

Forecast

Qiang Wang wins, best of 3 34%90%: 5%–80% · best of 5: 30%
Serve points won 53.9% / 57.0% Qiang / Maya · tour 55.0%
Strengths only, no similarity priors 32%serve 53.4% / 57.0%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Qiang Wang's record against Maya Joint's tactical lookalikes and in their charted head-to-heads (lookalikes: +4.9 on serve, +0.1 on return vs expectation (312 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

CareerQiangMaya
Direction choice+0.04 ±0.17
better than 61%
−0.21 ±0.12
better than 16%
Shot selection+0.37 ±0.16
better than 87%
+0.13 ±0.15
better than 59%
Execution+0.03 ±0.80
better than 62%
−0.78 ±0.58
better than 24%
Points left on the table2.24 ±0.25
lower than 90%
2.76 ±0.15
lower than 28%

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.

Qiang Wang serving

Deuce court

1st serveNowQiang winsv MayaMatchupOptimal
Wide36%61%69%63.9%±9.042% ▲
Body24%59%55%56.9%±11.09% ▼
T40%65%70%66.7%±9.149% ▲

Optimal v Maya Joint: +0.4±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.4 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowQiang winsv MayaMatchupOptimal
Wide29%60%72%66.6%±9.844% ▲
Body20%50%54%47.8%±12.04% ▼
T52%52%68%56.2%±9.652%

Optimal v Maya Joint: +1.1±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +9.1 per 100 first serves in before the returner adjusts.

Maya Joint serving

Deuce court

1st serveNowMaya winsv QiangMatchupOptimal
Wide38%62%61%56.6%±8.923% ▼
Body19%61%55%57.9%±11.319%
T43%65%71%68.4%±8.958% ▲

Optimal v Qiang Wang: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMaya winsv QiangMatchupOptimal
Wide45%62%64%59.8%±9.145%
Body16%48%63%55.7%±12.41% ▼
T39%55%69%60.0%±9.654% ▲

Optimal v Qiang Wang: +0.8±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +0.8 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.

Qiang Wang returning

1st serve to the forehand

ReturnNowTourOwnv MayaValue
FH through the middle56%+4.2+2.6+2.7+9.5±2.8
FH down the line25%+1.5+2.7+0.1+4.3±4.7
FH crosscourt18%+5.3−0.1+0.7+5.9±4.3

Lean FH through the middle: +2.0±1.9 per 100 returns v the current mix (208 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MayaValue
BH crosscourt41%+7.7+1.0+2.4+11.1±3.5
BH through the middle39%+6.0−0.9+1.1+6.2±2.7
BH down the line9%+2.2−1.3+1.8+2.7±4.3
BH slice crosscourt6%−4.2+0.3±0.0−3.8±1.6
BH slice through the middle6%−6.2±0.0−0.6−6.8±2.0

Lean BH crosscourt: +4.5±2.4 per 100 returns v the current mix (161 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv MayaValue
BH crosscourt45%+1.5+0.6+1.0+3.1±3.6
BH through the middle38%−2.6+1.6+0.3−0.7±2.7
BH down the line16%−0.5+1.2−1.9−1.3±4.9

Lean BH crosscourt: +2.2±2.4 per 100 returns v the current mix (104 returns charted, inside the 90% margin)

Maya Joint returning

1st serve to the forehand

ReturnNowTourOwnv QiangValue
FH through the middle49%+4.2−1.2+0.1+3.1±2.9
FH crosscourt27%+5.3−1.1+4.1+8.4±4.3
FH down the line11%+1.5−1.3+1.6+1.8±4.6
FH slice through the middle9%−6.7+0.1−0.3−6.9±2.3
FH slice crosscourt3%−6.6−0.3−0.7−7.6±1.8

Lean FH crosscourt: +5.5±3.5 per 100 returns v the current mix (241 returns charted)

1st serve to the backhand

ReturnNowTourOwnv QiangValue
BH through the middle54%+6.0−2.3−1.0+2.7±2.7
BH crosscourt28%+7.7−4.3+3.4+6.9±3.7
BH down the line9%+2.2−1.4−0.3+0.5±4.4
BH slice through the middle7%−6.2−0.8+0.3−6.7±2.0
BH slice crosscourt3%−4.2−1.4−0.4−6.0±1.9

Lean BH crosscourt: +4.1±3.0 per 100 returns v the current mix (259 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv QiangValue
FH through the middle50%−3.2+0.1−0.3−3.3±3.0
FH crosscourt38%+0.5−1.9+2.3+0.9±4.2
FH down the line13%−0.6−0.6+2.7+1.5±4.3

Lean FH crosscourt: +2.1±3.1 per 100 returns v the current mix (48 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv QiangValue
BH through the middle47%−2.6+0.3+1.0−1.3±2.8
BH crosscourt34%+1.5+3.8+1.9+7.2±3.7
BH down the line19%−0.5−1.4+6.5+4.6±5.1

Lean BH crosscourt: +4.5±2.9 per 100 returns v the current mix (121 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 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.

Qiang Wang

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+6.2±3.9+1.7+4.5
FH to the middle · return+3.9±3.7+3.4+0.4
BH to their backhand · rally+3.2±4.3+0.7+2.5
BH to the middle · return+2.6±3.0−0.3+2.9
BH to their backhand · serve +1+2.5±4.7+0.8+1.7
BH to their backhand · return +1+2.1±4.6−1.1+3.2

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−5.8±5.6−6.0+0.2
FH to their forehand · return +1−5.1±4.7−0.5−4.6
FH to their forehand · serve +1−1.9±4.9−1.3−0.6
BH to the middle · rally−1.6±3.1−1.8+0.2
BH to the middle · serve +1−0.8±3.1+0.2−0.9

Maya Joint

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+6.4±5.8+2.3+4.0
BH to their backhand · serve +1+4.0±4.8−1.5+5.5
FH to their backhand · return+3.7±5.1+0.8+2.9
FH to their forehand · serve +1+3.6±5.0+2.8+0.8
BH to their forehand · return+3.5±5.2−1.1+4.7
FH to their backhand · rally+3.5±5.0−0.9+4.4

Avoid

ShotEdgeOwnTheirs
BH to the middle · return−3.1±3.4−3.6+0.5
BH to the middle · rally−2.5±3.1−3.7+1.2
BH to the middle · return +1−2.4±3.0−2.6+0.1
FH to the middle · serve +1−1.3±3.1−1.0−0.3
FH to the middle · rally−1.1±3.3−2.9+1.7

Against Maya Joint-like opponents

Qiang Wang vMatchesServe pts wonReturn pts won
All charted opponents–50.4%40.5%
Players most similar to Maya Joint2 55.7%39.4%

Similar by tactical fingerprint: Jasmine Paolini, Shuai Zhang, Robin Montgomery, Yue Yuan, Bernarda Pera, Rebecca Marino, Irina Camelia Begu, Anett Kontaveit, Garbine Muguruza, Andrea Petkovic. When two players have rarely met, their records against these lookalikes fill the gap.