RallyIQ

Game plan

Lin Zhu v Emma Raducanu

Every number combines what Lin Zhu does well with what Emma Raducanu allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Lin Zhu wins, best of 3 15%90%: 5%–34% · best of 5: 10%
Serve points won 53.1% / 60.9% Lin / Emma · tour 56.4%
Strengths only, no similarity priors 15%serve 53.1% / 60.9%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Lin Zhu's record against Emma Raducanu'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

CareerLinEmma
Direction choice−0.19 ±0.13
better than 18%
−0.02 ±0.05
better than 50%
Shot selection+0.09 ±0.16
better than 53%
−0.09 ±0.09
better than 35%
Execution−0.19 ±0.84
better than 50%
+0.76 ±0.31
better than 86%
Points left on the table2.64 ±0.17
lower than 45%
2.53 ±0.07
lower than 63%

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.

Lin Zhu serving

Deuce court

1st serveNowLin winsv EmmaMatchupOptimal
Wide50%62%64%60.1%±6.751%
Body23%64%57%63.3%±8.538% ▲
T26%57%68%56.7%±9.111% ▼

Optimal v Emma Raducanu: +0.3±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +3.4 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLin winsv EmmaMatchupOptimal
Wide34%56%64%53.8%±8.419% ▼
Body25%55%59%57.9%±8.636% ▲
T41%61%64%60.0%±7.545% ▲

Optimal v Emma Raducanu: +0.6±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.6 per 100 first serves in before the returner adjusts.

Emma Raducanu serving

Deuce court

1st serveNowEmma winsv LinMatchupOptimal
Wide46%68%74%76.3%±5.761% ▲
Body25%56%59%57.8%±9.210% ▼
T29%68%73%73.4%±7.729%

Optimal v Lin Zhu: +1.5±0.9 per 100 first serves (faults included) over the current mix. Serving wide every time would read +5.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowEmma winsv LinMatchupOptimal
Wide44%71%71%75.9%±6.560% ▲
Body17%51%60%54.4%±9.61% ▼
T39%63%64%62.7%±8.339%

Optimal v Lin Zhu: +1.6±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +8.7 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.

Lin Zhu returning

1st serve to the forehand

ReturnNowTourOwnv EmmaValue
FH through the middle48%+4.2+1.1+2.2+7.4±2.5
FH crosscourt24%+5.3−1.8+0.7+4.2±3.8
FH down the line16%+1.5−0.6−0.9±0.0±4.2
FH slice through the middle6%−6.7−0.6+2.1−5.2±2.1
FH slice crosscourt5%−6.6−0.6+0.3−6.9±2.5

Lean FH through the middle: +3.5±1.7 per 100 returns v the current mix (185 returns charted)

1st serve to the backhand

ReturnNowTourOwnv EmmaValue
BH through the middle46%+6.0±0.0−1.0+5.0±2.4
BH crosscourt32%+7.7−0.5−0.8+6.4±3.2
BH down the line11%+2.2+2.0−1.1+3.1±3.9
BH slice through the middle7%−6.2+0.1−1.0−7.1±2.2
BH slice crosscourt4%−4.2−0.2−0.6−5.0±2.5

Lean BH crosscourt: +2.5±2.5 per 100 returns v the current mix (136 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv EmmaValue
BH through the middle47%−2.6−0.2−1.2−4.0±2.4
BH crosscourt32%+1.5+0.2−1.4+0.3±3.1
BH down the line21%−0.5+0.2+0.7+0.4±4.7

Lean BH crosscourt: +2.0±2.6 per 100 returns v the current mix (57 returns charted, inside the 90% margin)

Emma Raducanu returning

1st serve to the forehand

ReturnNowTourOwnv LinValue
FH through the middle46%+4.2+2.0−1.0+5.1±2.3
FH crosscourt21%+5.3+1.9+3.1+10.3±4.0
FH down the line14%+1.5+5.0+2.1+8.6±4.5
FH slice through the middle12%−6.7+0.8+0.2−5.7±2.3
FH slice crosscourt4%−6.6+0.8+0.7−5.1±2.4

Lean FH crosscourt: +5.8±3.4 per 100 returns v the current mix (954 returns charted)

1st serve to the backhand

ReturnNowTourOwnv LinValue
BH through the middle47%+6.0+2.7+0.9+9.7±2.3
BH crosscourt28%+7.7+3.2+1.9+12.8±3.2
BH slice through the middle11%−6.2+0.7−1.8−7.3±2.3
BH down the line10%+2.2+2.5+0.9+5.6±4.6
BH slice crosscourt3%−4.2+0.8−1.2−4.6±2.4

Lean BH crosscourt: +5.3±2.6 per 100 returns v the current mix (834 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv LinValue
FH through the middle47%−3.2+2.1+3.5+2.4±3.0
FH crosscourt34%+0.5−0.1+2.6+3.0±4.2
FH down the line17%−0.6−0.2±0.0−0.8±5.2
FH slice through the middle2%−15.2+0.7±0.0−14.5±1.1

Lean FH crosscourt: +1.3±3.3 per 100 returns v the current mix (254 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LinValue
BH through the middle44%−2.6−0.5−0.4−3.5±2.5
BH crosscourt41%+1.5+0.3+2.4+4.2±3.3
BH down the line14%−0.5−2.5+4.2+1.2±5.2
BH slice through the middle1%−11.7−0.6±0.0−12.3±1.0

Lean BH crosscourt: +4.0±2.4 per 100 returns v the current mix (470 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.

Lin Zhu

Favour

ShotEdgeOwnTheirs
FH to the middle · return+4.0±2.8+3.8+0.2
BH to their backhand · serve +1+3.8±4.0+2.1+1.7
FH to their forehand · return +1+3.0±4.7−0.2+3.2
FH to the middle · serve +1+2.6±2.9+2.5+0.1
BH to the middle · rally+2.1±2.1+2.8−0.7
BH to their forehand · rally+1.5±4.9+0.2+1.3

Avoid

ShotEdgeOwnTheirs
BH to their forehand · serve +1−5.6±6.0−2.9−2.7
FH to their backhand · rally−3.6±3.3−4.4+0.8
FH to their backhand · serve +1−3.6±4.8−2.9−0.7
FH to the middle · return +1−1.4±3.4+1.6−3.0
FH to their forehand · serve +1−0.8±4.6−3.0+2.2

Emma Raducanu

Favour

ShotEdgeOwnTheirs
FH to their forehand · return+7.6±5.3+0.4+7.2
BH to their forehand · return+6.7±5.9+1.3+5.4
BH to their backhand · return +1+5.9±3.9+2.7+3.1
FH to their backhand · return+5.6±5.4+4.0+1.6
FH to their forehand · rally+5.6±2.8+0.2+5.4
FH to their backhand · serve +1+5.3±5.0+1.9+3.4

Avoid

ShotEdgeOwnTheirs
FH to the middle · return +1−4.6±3.3−1.1−3.5
BH to the middle · return +1−2.6±2.9+1.2−3.7
FH to their backhand · rally−2.3±3.7+0.3−2.7
FH to their forehand · serve +1+0.2±4.5−1.8+2.0
FH to the middle · rally+0.4±2.3+1.1−0.7

Against Emma Raducanu-like opponents

Lin Zhu vMatchesServe pts wonReturn pts won
All charted opponents–51.5%38.8%

Similar by tactical fingerprint: Coco Gauff, Iva Jovic, Anna Kalinskaya, Anna Blinkova, Marie Bouzkova, Kimberly Birrell, Emma Navarro, Nao Hibino, Andrea Petkovic. When two players have rarely met, their records against these lookalikes fill the gap.