RallyIQ

Game plan

Caroline Garcia v Lulu Sun

Every number combines what Caroline Garcia does well with what Lulu Sun allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Caroline Garcia wins, best of 3 60%90%: 21%–90% · best of 5: 62%
Serve points won 60.4% / 58.5% Caroline / Lulu · tour 55.0%
Strengths only, no similarity priors 63%serve 61.1% / 58.6%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Caroline Garcia's record against Lulu Sun's tactical lookalikes and in their charted head-to-heads (lookalikes: −14.9 on serve, +1.7 on return vs expectation (127 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

CareerCarolineLulu
Direction choice+0.13 ±0.06
better than 76%
−0.04 ±0.13
better than 48%
Shot selection+0.74 ±0.11
better than 98%
+0.31 ±0.35
better than 79%
Execution−1.52 ±0.45
better than 10%
−0.39 ±1.24
better than 39%
Points left on the table2.53 ±0.09
lower than 61%
2.91 ±0.34
lower than 16%

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.

Caroline Garcia serving

Deuce court

1st serveNowCaroline winsv LuluMatchupOptimal
Wide35%70%70%74.2%±6.450% ▲
Body16%55%53%50.6%±10.61% ▼
T49%67%71%70.0%±9.249%

Optimal v Lulu Sun: +1.8±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +5.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowCaroline winsv LuluMatchupOptimal
Wide40%75%71%79.3%±6.855% ▲
Body12%59%58%60.0%±11.211%
T49%64%65%64.9%±7.834% ▼

Optimal v Lulu Sun: +1.1±1.0 per 100 first serves (faults included) over the current mix. Serving wide every time would read +9.3 per 100 first serves in before the returner adjusts.

Lulu Sun serving

Deuce court

1st serveNowLulu winsv CarolineMatchupOptimal
Wide30%67%70%71.0%±8.834% ▲
Body24%53%58%53.6%±10.09% ▼
T45%69%74%75.2%±7.357% ▲

Optimal v Caroline Garcia: +1.0±1.0 per 100 first serves (faults included) over the current mix. Serving T every time would read +6.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLulu winsv CarolineMatchupOptimal
Wide42%73%69%76.4%±7.341%
Body16%62%56%61.9%±11.31% ▼
T42%84%69%86.8%±5.858% ▲

Optimal v Caroline Garcia: +1.5±1.1 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.

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.

Caroline Garcia returning

1st serve to the forehand

ReturnNowTourOwnv LuluValue
FH through the middle44%+4.2−2.7−0.1+1.3±2.8
FH crosscourt29%+5.3−1.7−2.7+0.9±4.0
FH down the line18%+1.5−0.8−2.8−2.1±4.4
FH slice through the middle5%−6.7−0.3−1.5−8.6±2.4
FH slice crosscourt3%−6.6−0.3+0.6−6.3±2.4

Lean FH through the middle: +1.6±2.1 per 100 returns v the current mix (536 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv LuluValue
BH through the middle46%+6.0−2.7+1.2+4.5±2.6
BH crosscourt32%+7.7−1.6−1.4+4.7±3.3
BH down the line10%+2.2−5.8−5.2−8.9±4.7
BH slice through the middle6%−6.2−2.2+1.3−7.1±2.3
BH slice crosscourt4%−4.2−0.8+2.1−2.9±2.3

Lean BH crosscourt: +3.0±2.6 per 100 returns v the current mix (457 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv LuluValue
FH through the middle43%−3.2+2.0+2.4+1.3±3.1
FH crosscourt32%+0.5+2.3−1.6+1.2±4.0
FH down the line25%−0.6+2.8−7.2−5.0±5.1

Lean FH through the middle: +1.6±2.5 per 100 returns v the current mix (114 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LuluValue
BH crosscourt46%+1.5+1.4+2.5+5.3±3.6
BH through the middle35%−2.6+0.3−0.1−2.4±2.7
BH down the line19%−0.5−1.5−2.3−4.3±5.1

Lean BH crosscourt: +4.5±2.3 per 100 returns v the current mix (224 returns charted)

Lulu Sun returning

1st serve to the forehand

ReturnNowTourOwnv CarolineValue
FH through the middle59%+4.2−0.2−0.5+3.5±2.7
FH crosscourt26%+5.3+0.9−0.5+5.7±4.1
FH down the line15%+1.5−3.0−2.1−3.6±4.1

Lean FH crosscourt: +2.7±3.5 per 100 returns v the current mix (95 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv CarolineValue
BH through the middle46%+6.0−1.6+0.1+4.5±2.4
BH crosscourt27%+7.7+2.1−3.0+6.8±3.5
BH slice through the middle12%−6.2±0.0−0.8−7.0±2.5
BH slice crosscourt8%−4.2−3.5−3.3−11.0±2.8
BH down the line7%+2.2−0.5+0.3+2.0±4.1

Lean BH crosscourt: +4.5±2.9 per 100 returns v the current mix (214 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv CarolineValue
BH crosscourt48%+1.5−1.3−1.5−1.3±3.6
BH through the middle46%−2.6−0.1+1.1−1.6±2.4
FH through the middle6%−2.7−0.5−0.9−4.0±2.0

Lean BH crosscourt: +0.3±2.1 per 100 returns v the current mix (87 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.

Caroline Garcia

Favour

ShotEdgeOwnTheirs
FH to their forehand · rally+5.8±4.8+0.7+5.2
FH to their backhand · serve +1+4.7±5.2+3.7+1.0
FH to their backhand · return +1+4.1±4.9+0.5+3.5
BH to their forehand · return+0.9±5.2+0.1+0.8
FH to their forehand · serve +1+0.6±4.5−2.6+3.3
BH to the middle · serve +1+0.3±3.4−1.4+1.7

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−5.1±3.2−5.0−0.1
FH to their backhand · rally−3.7±5.2−2.7−1.0
FH to the middle · serve +1−3.2±3.1−2.6−0.6
FH to the middle · rally−3.1±3.4−2.6−0.6
BH to the middle · return−2.4±2.8−2.6+0.2

Lulu Sun

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+3.2±4.8−0.8+4.1
FH to their forehand · serve +1+2.7±4.7+3.2−0.5
FH to their backhand · return +1+1.2±4.9+0.8+0.4
BH to their forehand · return+1.1±5.0+2.3−1.2
FH to the middle · rally+0.6±3.4+1.0−0.4
FH to their backhand · rally+0.4±5.2−2.1+2.4

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−6.5±3.2−3.8−2.7
FH to the middle · serve +1−2.2±3.1−1.4−0.9
FH to the middle · return−0.6±3.6−1.6+0.9
FH to their forehand · rally±0.0±5.1+0.5−0.5
BH to their forehand · rally+0.2±5.3−1.0+1.1

Against Lulu Sun-like opponents

Caroline Garcia vMatchesServe pts wonReturn pts won
All charted opponents–57.9%38.9%
Players most similar to Lulu Sun1 43.1%43.6%

Similar by tactical fingerprint: Alexandra Eala, Diana Shnaider, Xin Yu Wang, Marta Kostyuk, Robin Montgomery, Katie Boulter, Arianne Hartono, Jule Niemeier, Eugenie Bouchard. When two players have rarely met, their records against these lookalikes fill the gap.