RallyIQ

Game plan

Ajla Tomljanovic v Lucia Bronzetti

Every number combines what Ajla Tomljanovic does well with what Lucia Bronzetti 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 76%90%: 49%–92% · best of 5: 81%
Serve points won 61.2% / 55.9% Ajla / Lucia · tour 56.4%
Strengths only, no similarity priors 75%serve 61.3% / 56.2%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Lucia Bronzetti's tactical lookalikes and in their charted head-to-heads (lookalikes: −1.1 on serve, +5.1 on return vs expectation (190 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

CareerAjlaLucia
Direction choice−0.42 ±0.09
better than 1%
−0.37 ±0.15
better than 3%
Shot selection−0.34 ±0.13
better than 19%
+0.27 ±0.15
better than 75%
Execution+0.24 ±0.70
better than 70%
+0.14 ±0.68
better than 67%
Points left on the table3.06 ±0.13
lower than 9%
2.96 ±0.22
lower than 13%

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 LuciaMatchupOptimal
Wide40%64%67%65.2%±8.140%
Body29%55%60%56.9%±11.414% ▼
T30%72%73%77.0%±8.046% ▲

Optimal v Lucia Bronzetti: +0.9±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +10.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAjla winsv LuciaMatchupOptimal
Wide39%60%66%60.3%±8.939%
Body23%47%57%48.2%±12.18% ▼
T37%66%70%71.4%±8.753% ▲

Optimal v Lucia Bronzetti: +1.4±1.2 per 100 first serves (faults included) over the current mix. Serving T every time would read +9.8 per 100 first serves in before the returner adjusts.

Lucia Bronzetti serving

Deuce court

1st serveNowLucia winsv AjlaMatchupOptimal
Wide48%73%68%74.3%±7.248%
Body20%56%59%58.3%±11.35% ▼
T32%70%77%78.6%±7.947% ▲

Optimal v Ajla Tomljanovic: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLucia winsv AjlaMatchupOptimal
Wide42%52%77%65.1%±9.242%
Body17%63%64%70.7%±10.332% ▲
T41%60%64%59.5%±9.526% ▼

Optimal v Ajla Tomljanovic: +0.3±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +7.0 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 LuciaValue
FH through the middle43%+4.2−3.0+0.2+1.4±3.0
FH slice through the middle20%−6.7+2.0+1.7−3.1±2.4
FH down the line16%+1.5+2.6−3.0+1.1±4.6
FH crosscourt11%+5.3−1.5+1.4+5.2±4.3
FH slice down the line6%−10.5+0.4+1.0−9.1±2.6

Lean FH crosscourt: +5.1±4.2 per 100 returns v the current mix (312 returns charted)

1st serve to the backhand

ReturnNowTourOwnv LuciaValue
BH through the middle34%+6.0−2.0+2.1+6.2±2.8
BH crosscourt24%+7.7−0.9+0.4+7.2±3.7
BH down the line16%+2.2−0.9−3.1−1.8±4.6
BH slice through the middle13%−6.2−0.6+1.1−5.7±2.2
BH slice crosscourt9%−4.2−1.7−0.6−6.5±2.5

Lean BH crosscourt: +5.5±3.1 per 100 returns v the current mix (187 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv LuciaValue
FH through the middle51%−3.2−0.8+1.0−2.9±3.1
FH crosscourt26%+0.5−1.0−1.7−2.2±4.2
FH down the line23%−0.6±0.0+0.4−0.1±4.9

Lean FH down the line: +1.9±4.2 per 100 returns v the current mix (86 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LuciaValue
BH through the middle54%−2.6−1.8+1.4−3.0±2.8
BH crosscourt34%+1.5+0.9+1.6+4.0±3.7
BH down the line11%−0.5−3.2+1.2−2.6±4.7

Lean BH crosscourt: +4.6±2.9 per 100 returns v the current mix (105 returns charted)

Lucia Bronzetti returning

1st serve to the forehand

ReturnNowTourOwnv AjlaValue
FH through the middle56%+4.2+1.0+1.3+6.4±2.8
FH crosscourt25%+5.3−2.3+1.6+4.5±4.3
FH down the line19%+1.5−5.7−0.1−4.3±4.7

Lean FH through the middle: +2.5±1.9 per 100 returns v the current mix (204 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AjlaValue
BH through the middle54%+6.0−0.4−1.2+4.4±2.6
BH crosscourt30%+7.7+0.6−3.0+5.3±3.6
BH down the line16%+2.2−2.3+2.8+2.7±4.5

Lean BH crosscourt: +0.9±3.0 per 100 returns v the current mix (171 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv AjlaValue
BH through the middle51%−2.6+1.3+0.1−1.2±2.8
BH crosscourt27%+1.5−1.1−1.8−1.5±3.4
FH through the middle15%−2.7±0.0−0.6−3.3±2.2
BH down the line6%−0.5+1.0+1.7+2.2±4.2

Lean BH through the middle: +0.2±1.7 per 100 returns v the current mix (78 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 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.

Ajla Tomljanovic

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+5.3±5.8+5.2+0.1
BH to their backhand · serve +1+5.3±5.2+2.1+3.2
FH to their forehand · serve +1+5.2±5.5+2.9+2.3
FH to their forehand · return +1+5.0±5.5+1.2+3.8
FH to the middle · return +1+4.4±3.8+3.1+1.3
FH to their forehand · rally+3.6±3.3+2.9+0.7

Avoid

ShotEdgeOwnTheirs
BH to their forehand · return−3.3±6.3+1.3−4.6
FH to the middle · return−2.6±3.5−3.5+1.0
FH to their backhand · serve +1−1.0±5.5−6.3+5.3
FH to the middle · serve +1−1.0±3.6+0.2−1.1
BH to the middle · serve +1−0.7±3.5−0.7±0.0

Lucia Bronzetti

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+6.3±5.5+1.7+4.6
FH to the middle · serve +1+4.6±3.6+4.9−0.3
BH to their backhand · serve +1+4.3±5.1+3.1+1.2
FH to their forehand · return +1+3.9±5.2+4.5−0.7
FH to their forehand · serve +1+2.8±5.2+1.4+1.5
FH to the middle · rally+2.2±2.5+2.4−0.2

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−6.4±5.5−2.0−4.3
FH to their forehand · return−5.3±5.6−3.2−2.1
FH to their backhand · return−2.7±6.1−4.1+1.4
BH to their backhand · return−2.5±5.0+0.2−2.6
BH to the middle · serve +1−2.3±3.6+0.6−2.8

Against Lucia Bronzetti-like opponents

Ajla Tomljanovic vMatchesServe pts wonReturn pts won
All charted opponents–54.2%39.2%
Players most similar to Lucia Bronzetti1 51.8%45.7%

Similar by tactical fingerprint: Elina Svitolina, Suzan Lamens, Eva Vedder, Emma Navarro, Yafan Wang, Nao Hibino, Jennifer Brady, Rebecca Peterson, Johanna Larsson. When two players have rarely met, their records against these lookalikes fill the gap.