RallyIQ

Game plan

Qiang Wang v Na Li

Every number combines what Qiang Wang does well with what Na Li 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 16%90%: 3%–47% · best of 5: 11%
Serve points won 52.8% / 60.4% Qiang / Na · tour 56.3%
Strengths only, no similarity priors 16%serve 52.8% / 60.4%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerQiangNa
Direction choice+0.04 ±0.17
better than 61%
+0.21 ±0.11
better than 85%
Shot selection+0.37 ±0.16
better than 87%
+0.55 ±0.15
better than 95%
Execution+0.03 ±0.80
better than 62%
−0.31 ±0.59
better than 44%
Points left on the table2.24 ±0.25
lower than 90%
2.18 ±0.16
lower than 95%

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 NaMatchupOptimal
Wide36%61%71%66.3%±8.851% ▲
Body24%59%54%56.1%±11.39% ▼
T40%65%63%59.3%±10.540%

Optimal v Na Li: +0.3±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowQiang winsv NaMatchupOptimal
Wide29%60%70%64.6%±10.844% ▲
Body20%50%53%47.0%±12.64% ▼
T52%52%65%52.9%±9.152%

Optimal v Na Li: +1.1±1.3 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +9.5 per 100 first serves in before the returner adjusts.

Na Li serving

Deuce court

1st serveNowNa winsv QiangMatchupOptimal
Wide42%62%61%57.1%±9.042%
Body18%58%55%55.4%±11.93% ▼
T40%66%71%69.9%±8.955% ▲

Optimal v Qiang Wang: +1.1±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +8.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowNa winsv QiangMatchupOptimal
Wide32%69%64%67.9%±9.347% ▲
Body6%61%63%67.7%±13.40% ▼
T62%64%69%68.7%±8.153% ▼

Optimal v Qiang Wang: +0.5±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +0.3 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.

Qiang Wang returning

1st serve to the forehand

ReturnNowTourOwnv NaValue
FH through the middle56%+4.2+2.6+0.7+7.5±2.8
FH down the line25%+1.5+2.7−0.6+3.6±4.7
FH crosscourt18%+5.3−0.1+1.7+6.9±4.2

Lean FH through the middle: +1.1±1.9 per 100 returns v the current mix (208 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv NaValue
BH crosscourt41%+7.7+1.0+2.3+11.0±3.6
BH through the middle39%+6.0−0.9+2.1+7.2±2.8
BH down the line9%+2.2−1.3−0.1+0.8±4.3
BH slice crosscourt6%−4.2+0.3−0.3−4.1±2.2
BH slice through the middle6%−6.2±0.0−0.6−6.8±2.1

Lean BH crosscourt: +4.2±2.4 per 100 returns v the current mix (161 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv NaValue
BH crosscourt45%+1.5+0.6±0.0+2.1±3.6
BH through the middle38%−2.6+1.6−0.6−1.6±2.8
BH down the line16%−0.5+1.2−1.0−0.4±4.9

Lean BH crosscourt: +1.8±2.4 per 100 returns v the current mix (104 returns charted, inside the 90% margin)

Na Li returning

1st serve to the forehand

ReturnNowTourOwnv QiangValue
FH through the middle51%+4.2−2.7+0.1+1.6±2.8
FH down the line27%+1.5−0.7+1.6+2.4±4.7
FH crosscourt18%+5.3−4.6+4.1+4.9±4.4
FH slice down the line2%−10.5−0.2±0.0−10.7±1.5
FH slice through the middle2%−6.7−1.4−0.3−8.4±1.9

Lean FH crosscourt: +2.9±4.0 per 100 returns v the current mix (292 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv QiangValue
BH through the middle41%+6.0−0.6−1.0+4.4±2.8
BH crosscourt35%+7.7−2.0+3.4+9.2±3.7
BH down the line8%+2.2−0.9−0.3+1.0±4.1
BH slice through the middle7%−6.2+0.3+0.3−5.7±1.9
BH slice crosscourt6%−4.2−2.5−0.4−7.1±2.0

Lean BH crosscourt: +4.8±2.7 per 100 returns v the current mix (167 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv QiangValue
FH through the middle39%−3.2−2.2−0.3−5.7±3.1
FH crosscourt31%+0.5−2.8+2.3±0.0±4.4
FH down the line30%−0.6+0.3+2.7+2.4±5.0

Lean FH down the line: +3.9±4.0 per 100 returns v the current mix (80 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv QiangValue
BH crosscourt53%+1.5−1.6+1.9+1.9±3.7
BH through the middle37%−2.6−1.7+1.0−3.3±2.8
BH down the line10%−0.5+0.4+6.5+6.4±4.7

Lean BH crosscourt: +1.5±2.1 per 100 returns v the current mix (115 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.

Qiang Wang

Favour

ShotEdgeOwnTheirs
FH to the middle · return+5.3±2.6+3.4+2.0
FH to their backhand · serve +1+5.0±4.2+0.9+4.1
FH to their backhand · return+3.8±4.4+2.9+0.9
FH to the middle · serve +1+3.4±2.7+1.8+1.7
BH to their backhand · return+2.7±3.1+1.7+1.0
BH to their backhand · rally+1.8±2.6+0.5+1.3

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−5.0±4.2−4.2−0.9
FH to their forehand · return +1−4.5±3.8−0.5−4.0
FH to their backhand · rally−2.8±3.3−1.8−1.0
BH to their backhand · return +1−1.4±3.5−1.4±0.0
BH to the middle · rally−1.3±2.1−1.1−0.1

Na Li

Favour

ShotEdgeOwnTheirs
BH to their backhand · serve +1+5.6±3.5+1.5+4.1
FH to their backhand · rally+4.2±3.2+1.6+2.6
FH to their backhand · serve +1+3.4±4.1+0.4+3.0
FH to their backhand · return+2.6±4.2−0.3+2.9
BH to their forehand · serve +1+1.7±4.6+0.9+0.9
FH to the middle · rally+1.6±2.3+0.7+0.9

Avoid

ShotEdgeOwnTheirs
BH to the middle · return−1.9±2.5−2.0+0.1
BH to the middle · return +1−1.8±2.6−2.0+0.1
FH to their forehand · return +1−1.6±3.9−2.1+0.5
FH to the middle · return−1.5±2.5−1.6+0.1
FH to their forehand · rally−0.5±2.5−0.7+0.2

Against Na Li-like opponents

Qiang Wang vMatchesServe pts wonReturn pts won
All charted opponents–50.4%40.5%

Similar by tactical fingerprint: Naomi Osaka, Ashlyn Krueger, Belinda Bencic, Rebecca Sramkova, Shuai Zhang, Katerina Siniakova, Venus Williams, Maria Sharapova, Lindsay Davenport. When two players have rarely met, their records against these lookalikes fill the gap.