RallyIQ

Game plan

Kimberly Birrell v Lin Zhu

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

Forecast

Kimberly Birrell wins, best of 3 29%90%: 8%–63% · best of 5: 24%
Serve points won 55.8% / 60.0% Kimberly / Lin · tour 56.3%
Strengths only, no similarity priors 29%serve 55.8% / 60.0%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerKimberlyLin
Direction choice−0.11 ±0.18
better than 34%
−0.19 ±0.13
better than 18%
Shot selection+0.29 ±0.15
better than 77%
+0.09 ±0.16
better than 53%
Execution−1.63 ±0.97
better than 8%
−0.19 ±0.84
better than 50%
Points left on the table2.49 ±0.27
lower than 67%
2.64 ±0.17
lower than 45%

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.

Kimberly Birrell serving

Deuce court

1st serveNowKimberly winsv LinMatchupOptimal
Wide43%55%74%65.1%±9.059% ▲
Body30%59%59%60.7%±11.115% ▼
T27%67%73%72.7%±10.026%

Optimal v Lin Zhu: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKimberly winsv LinMatchupOptimal
Wide32%62%71%68.0%±10.247% ▲
Body25%53%60%56.7%±11.610% ▼
T43%60%64%59.4%±10.543%

Optimal v Lin Zhu: +0.3±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +6.5 per 100 first serves in before the returner adjusts.

Lin Zhu serving

Deuce court

1st serveNowLin winsv KimberlyMatchupOptimal
Wide50%62%74%70.7%±8.566% ▲
Body23%64%60%66.8%±11.18% ▼
T26%57%75%65.5%±11.026%

Optimal v Kimberly Birrell: +0.2±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLin winsv KimberlyMatchupOptimal
Wide34%56%78%69.5%±9.934%
Body25%55%58%56.8%±12.310% ▼
T41%61%78%74.7%±8.556% ▲

Optimal v Kimberly Birrell: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.2 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.

Kimberly Birrell returning

1st serve to the forehand

ReturnNowTourOwnv LinValue
FH through the middle48%+4.2−2.6−1.0+0.6±2.9
FH down the line30%+1.5−4.7+2.1−1.1±4.7
FH crosscourt22%+5.3+0.2+3.1+8.6±4.3

Lean FH crosscourt: +6.8±3.9 per 100 returns v the current mix (151 returns charted)

1st serve to the backhand

ReturnNowTourOwnv LinValue
BH through the middle48%+6.0−1.1+0.9+5.8±2.8
BH crosscourt37%+7.7−3.1+1.9+6.5±3.6
BH down the line12%+2.2−3.0+0.9+0.1±4.3
BH slice down the line4%−12.5−0.7±0.0−13.2±1.5

Lean BH crosscourt: +1.9±2.7 per 100 returns v the current mix (128 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv LinValue
FH through the middle43%−3.2−0.8+3.5−0.5±3.0
FH crosscourt34%+0.5−0.6+2.6+2.6±4.1
FH down the line23%−0.6+0.7±0.0+0.2±4.7

Lean FH crosscourt: +1.9±3.2 per 100 returns v the current mix (56 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LinValue
BH through the middle48%−2.6+0.7−0.4−2.3±2.8
BH crosscourt40%+1.5−2.0+2.4+1.9±3.6
BH down the line13%−0.5−0.4+4.2+3.3±4.6

Lean BH crosscourt: +1.8±2.6 per 100 returns v the current mix (86 returns charted, inside the 90% margin)

Lin Zhu returning

1st serve to the forehand

ReturnNowTourOwnv KimberlyValue
FH through the middle48%+4.2+1.1+1.9+7.1±2.9
FH crosscourt24%+5.3−1.8−3.0+0.5±4.4
FH down the line16%+1.5−0.6+3.2+4.2±4.6
FH slice through the middle6%−6.7−0.6+0.5−6.8±1.9
FH slice crosscourt5%−6.6−0.6−0.4−7.6±1.8

Lean FH through the middle: +3.7±2.0 per 100 returns v the current mix (185 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KimberlyValue
BH through the middle46%+6.0±0.0+3.1+9.1±2.8
BH crosscourt32%+7.7−0.5−0.9+6.3±3.7
BH down the line11%+2.2+2.0−2.3+1.9±4.3
BH slice through the middle7%−6.2+0.1−0.1−6.2±2.1
BH slice crosscourt4%−4.2−0.2−1.5−5.9±1.8

Lean BH through the middle: +3.4±2.0 per 100 returns v the current mix (136 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv KimberlyValue
BH through the middle47%−2.6−0.2+0.7−2.1±2.7
BH crosscourt32%+1.5+0.2+3.7+5.4±3.5
BH down the line21%−0.5+0.2−2.4−2.8±4.4

Lean BH crosscourt: +5.2±2.8 per 100 returns v the current mix (57 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.

Kimberly Birrell

Favour

ShotEdgeOwnTheirs
FH to the middle · serve +1+4.7±2.8+2.8+2.0
FH to their forehand · rally+4.1±2.8±0.0+4.1
FH to their forehand · return+4.1±4.5−0.3+4.4
BH to their backhand · return +1+3.6±3.5+1.3+2.4
BH to their backhand · rally+2.9±2.6+0.7+2.2
FH to their forehand · serve +1+2.6±4.1+2.1+0.5

Avoid

ShotEdgeOwnTheirs
FH to their forehand · return +1−6.2±4.0−5.2−1.0
FH to their backhand · rally−4.5±3.5−2.1−2.4
BH to their forehand · return−2.4±4.6−6.0+3.7
BH to the middle · return +1−2.0±2.6+0.1−2.1
FH to their backhand · return−2.0±4.4−3.6+1.7

Lin Zhu

Favour

ShotEdgeOwnTheirs
FH to the middle · return+6.0±2.6+3.4+2.5
FH to the middle · serve +1+3.8±2.7+1.9+1.8
BH to their backhand · serve +1+3.7±3.7+1.4+2.3
BH to their backhand · return+3.4±3.7+1.5+1.8
BH to the middle · return +1+3.2±2.6+1.9+1.3
FH to their backhand · return+3.1±4.4−0.9+4.0

Avoid

ShotEdgeOwnTheirs
FH to their forehand · return−2.0±4.5−1.4−0.6
FH to their backhand · rally−1.3±3.4−3.4+2.1
BH to their backhand · return +1−1.2±3.6−0.9−0.3
BH to their forehand · rally−0.5±4.5−1.0+0.5
FH to their forehand · serve +1−0.1±4.2−1.4+1.3

Against Lin Zhu-like opponents

Kimberly Birrell vMatchesServe pts wonReturn pts won
All charted opponents–51.0%34.6%

Similar by tactical fingerprint: Coco Gauff, Iva Jovic, Marie Bouzkova, Emma Navarro, Emma Raducanu, Sloane Stephens, Ajla Tomljanovic, R, Elena Dementieva. When two players have rarely met, their records against these lookalikes fill the gap.