RallyIQ

Game plan

Ajla Tomljanovic v Lin Zhu

Every number combines what Ajla Tomljanovic 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

Ajla Tomljanovic wins, best of 3 61%90%: 19%–93% · best of 5: 64%
Serve points won 57.9% / 55.8% Ajla / Lin · tour 55.0%
Strengths only, no similarity priors 56%serve 57.1% / 55.9%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Ajla Tomljanovic's record against Lin Zhu's tactical lookalikes and in their charted head-to-heads (lookalikes: +15.5 on serve, +2.2 on return vs expectation (183 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

CareerAjlaLin
Direction choice−0.42 ±0.09
better than 1%
−0.19 ±0.13
better than 18%
Shot selection−0.34 ±0.13
better than 19%
+0.09 ±0.16
better than 53%
Execution+0.24 ±0.70
better than 70%
−0.19 ±0.84
better than 50%
Points left on the table3.06 ±0.13
lower than 9%
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.

Ajla Tomljanovic serving

Deuce court

1st serveNowAjla winsv LinMatchupOptimal
Wide40%64%74%72.6%±7.548% ▲
Body29%55%59%55.9%±10.514% ▼
T30%72%73%77.5%±8.038% ▲

Optimal v Lin Zhu: +1.0±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 serveNowAjla winsv LinMatchupOptimal
Wide39%60%71%65.9%±9.254% ▲
Body23%47%60%51.2%±10.98% ▼
T37%66%64%65.6%±9.438%

Optimal v Lin Zhu: +1.1±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.5 per 100 first serves in before the returner adjusts.

Lin Zhu serving

Deuce court

1st serveNowLin winsv AjlaMatchupOptimal
Wide50%62%68%64.0%±7.840% ▼
Body23%64%59%65.9%±9.838% ▲
T26%57%77%67.2%±9.922% ▼

Optimal v Ajla Tomljanovic: +0.1±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +1.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLin winsv AjlaMatchupOptimal
Wide34%56%77%68.5%±9.219% ▼
Body25%55%64%62.6%±10.236% ▲
T41%61%64%60.2%±8.745% ▲

Optimal v Ajla Tomljanovic: +0.1±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.9 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.

Ajla Tomljanovic returning

1st serve to the forehand

ReturnNowTourOwnv LinValue
FH through the middle43%+4.2−3.0−1.0+0.2±2.8
FH slice through the middle20%−6.7+2.0+0.2−4.6±2.4
FH down the line16%+1.5+2.6+2.1+6.2±4.7
FH crosscourt11%+5.3−1.5+3.1+6.9±4.3
FH slice down the line6%−10.5+0.4±0.0−10.1±2.2

Lean FH crosscourt: +6.7±4.1 per 100 returns v the current mix (312 returns charted)

1st serve to the backhand

ReturnNowTourOwnv LinValue
BH through the middle34%+6.0−2.0+0.9+4.9±2.8
BH crosscourt24%+7.7−0.9+1.9+8.7±3.6
BH down the line16%+2.2−0.9+0.9+2.2±4.5
BH slice through the middle13%−6.2−0.6−1.8−8.6±2.3
BH slice crosscourt9%−4.2−1.7−1.2−7.1±2.3

Lean BH crosscourt: +7.0±3.1 per 100 returns v the current mix (187 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv LinValue
FH through the middle51%−3.2−0.8+3.5−0.4±3.1
FH crosscourt26%+0.5−1.0+2.6+2.1±4.2
FH down the line23%−0.6±0.0±0.0−0.6±5.0

Lean FH crosscourt: +1.9±3.7 per 100 returns v the current mix (86 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LinValue
BH through the middle54%−2.6−1.8−0.4−4.8±2.8
BH crosscourt34%+1.5+0.9+2.4+4.8±3.6
BH down the line11%−0.5−3.2+4.2+0.4±4.7

Lean BH crosscourt: +5.7±2.9 per 100 returns v the current mix (105 returns charted)

Lin Zhu returning

1st serve to the forehand

ReturnNowTourOwnv AjlaValue
FH through the middle48%+4.2+1.1+1.3+6.5±2.8
FH crosscourt24%+5.3−1.8+1.6+5.1±4.3
FH down the line16%+1.5−0.6−0.1+0.8±4.6
FH slice through the middle6%−6.7−0.6+0.3−7.0±2.2
FH slice crosscourt5%−6.6−0.6+0.8−6.4±2.1

Lean FH through the middle: +2.8±2.0 per 100 returns v the current mix (185 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AjlaValue
BH through the middle46%+6.0±0.0−1.2+4.8±2.7
BH crosscourt32%+7.7−0.5−3.0+4.2±3.6
BH down the line11%+2.2+2.0+2.8+7.0±4.3
BH slice through the middle7%−6.2+0.1−0.8−6.9±2.1
BH slice crosscourt4%−4.2−0.2+0.5−4.0±1.8

Lean BH down the line: +3.3±4.1 per 100 returns v the current mix (136 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv AjlaValue
BH through the middle47%−2.6−0.2+0.1−2.7±2.7
BH crosscourt32%+1.5+0.2−1.8−0.1±3.4
BH down the line21%−0.5+0.2+1.7+1.3±4.7

Lean BH crosscourt: +0.9±2.8 per 100 returns v the current mix (57 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 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.

Ajla Tomljanovic

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+5.1±5.2+3.5+1.6
BH to their forehand · return+3.9±5.2+0.3+3.7
BH to their backhand · serve +1+3.7±4.3+2.3+1.4
FH to their forehand · rally+2.0±4.7−0.5+2.6
BH to the middle · serve +1+1.6±2.9+0.8+0.8
FH to their backhand · return+1.5±5.1−0.1+1.7

Avoid

ShotEdgeOwnTheirs
BH to the middle · return−8.4±3.4−8.5+0.1
FH to their forehand · serve +1−7.8±5.7−4.2−3.6
FH to their backhand · rally−7.1±4.9−3.5−3.6
BH to the middle · rally−5.3±2.9−3.4−2.0
FH to their backhand · serve +1−4.3±4.9−6.2+2.0

Lin Zhu

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+4.9±5.3+2.2+2.7
BH to the middle · rally+4.4±2.9+2.5+1.9
FH to the middle · return+3.7±3.7+3.4+0.3
FH to the middle · serve +1+1.8±3.1+1.9−0.1
BH to their backhand · serve +1+1.5±4.1+1.4+0.1
FH to the middle · rally+1.2±2.7+0.6+0.6

Avoid

ShotEdgeOwnTheirs
BH to their forehand · serve +1−8.0±6.3−4.8−3.2
FH to their backhand · rally−6.9±4.9−1.7−5.2
FH to their forehand · rally−5.4±4.6−2.6−2.8
BH to their forehand · rally−5.1±5.1−1.0−4.0
BH to their backhand · return−3.2±4.1+1.5−4.7

Against Lin Zhu-like opponents

Ajla Tomljanovic vMatchesServe pts wonReturn pts won
All charted opponents–54.2%39.2%
Players most similar to Lin Zhu1 69.4%42.9%

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