RallyIQ

Game plan

Louisa Chirico v Paula Badosa

Every number combines what Louisa Chirico does well with what Paula Badosa allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Louisa Chirico wins, best of 3 28%90%: 9%–59% · best of 5: 24%
Serve points won 54.9% / 59.2% Louisa / Paula · tour 56.4%
Strengths only, no similarity priors 28%serve 54.9% / 59.2%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerLouisaPaula
Direction choice−0.13 ±0.14
better than 30%
−0.24 ±0.05
better than 13%
Shot selection+0.09 ±0.19
better than 54%
+0.29 ±0.09
better than 77%
Execution−0.96 ±1.01
better than 19%
+0.75 ±0.35
better than 86%

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.

Louisa Chirico serving

Deuce court

1st serveNowLouisa winsv PaulaMatchupOptimal
Wide42%66%67%66.8%±9.057% ▲
Body29%64%54%61.0%±10.323% ▼
T28%68%65%65.1%±10.920% ▼

Optimal v Paula Badosa: +0.5±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLouisa winsv PaulaMatchupOptimal
Wide33%67%62%63.0%±9.739% ▲
Body26%55%55%54.1%±10.911% ▼
T42%65%62%62.3%±9.550% ▲

Optimal v Paula Badosa: +0.3±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.6 per 100 first serves in before the returner adjusts.

Paula Badosa serving

Deuce court

1st serveNowPaula winsv LouisaMatchupOptimal
Wide47%67%69%69.7%±9.063% ▲
Body19%62%57%61.2%±10.74% ▼
T33%73%62%67.8%±9.833%

Optimal v Louisa Chirico: +0.7±1.1 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 serveNowPaula winsv LouisaMatchupOptimal
Wide26%67%58%60.2%±9.926%
Body16%53%56%52.0%±12.11% ▼
T58%72%72%77.9%±7.873% ▲

Optimal v Louisa Chirico: +1.6±1.1 per 100 first serves (faults included) over the current mix. Serving T 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.

Louisa Chirico returning

1st serve to the forehand

ReturnNowTourOwnv PaulaValue
FH through the middle65%+4.2−1.2+1.2+4.2±2.6
FH crosscourt17%+5.3−2.1−1.4+1.8±3.6
FH down the line12%+1.5+0.8−0.5+1.8±3.7
FH slice through the middle6%−6.7−0.3+0.5−6.5±1.9

Lean FH through the middle: +1.3±1.2 per 100 returns v the current mix (83 returns charted)

1st serve to the backhand

ReturnNowTourOwnv PaulaValue
BH through the middle52%+6.0−1.3−0.9+3.8±2.5
BH crosscourt20%+7.7+0.5−0.1+8.1±3.1
BH down the line12%+2.2−0.2−2.7−0.8±4.0
BH slice through the middle10%−6.2+0.3−0.9−6.8±2.2
BH slice crosscourt6%−4.2−0.8−0.8−5.8±2.4

Lean BH crosscourt: +5.6±2.9 per 100 returns v the current mix (90 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv PaulaValue
BH crosscourt47%+1.5+0.3−0.3+1.5±3.3
BH through the middle38%−2.6+1.1+2.0+0.5±2.3
FH through the middle15%−2.7−0.3+1.4−1.6±2.1

Lean BH crosscourt: +0.9±2.0 per 100 returns v the current mix (66 returns charted, inside the 90% margin)

Paula Badosa returning

1st serve to the forehand

ReturnNowTourOwnv LouisaValue
FH through the middle45%+4.2+0.5−3.6+1.0±2.7
FH down the line21%+1.5+1.4−0.9+2.0±4.2
FH crosscourt15%+5.3+0.1−3.9+1.4±4.1
FH slice through the middle12%−6.7−0.7−0.5−7.9±2.3
FH slice crosscourt4%−6.6+1.1±0.0−5.6±1.9

Lean FH down the line: +2.4±3.6 per 100 returns v the current mix (726 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv LouisaValue
BH through the middle49%+6.0+1.3−0.9+6.4±2.5
BH crosscourt24%+7.7+1.9+0.4+10.0±3.2
BH down the line16%+2.2+4.0−4.6+1.6±4.3
BH slice through the middle7%−6.2−0.4−0.2−6.8±2.5
BH slice crosscourt2%−4.2+0.5+0.3−3.3±2.3

Lean BH crosscourt: +5.0±2.8 per 100 returns v the current mix (767 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv LouisaValue
FH through the middle54%−3.2+1.0−0.2−2.4±3.0
FH crosscourt30%+0.5−0.8+0.7+0.4±4.4
FH down the line13%−0.6−2.2−3.0−5.8±4.9
FH slice through the middle3%−15.2+0.6±0.0−14.6±1.2

Lean FH crosscourt: +2.8±3.5 per 100 returns v the current mix (217 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LouisaValue
BH through the middle45%−2.6+2.0−0.1−0.7±2.5
BH crosscourt38%+1.5+3.3+2.2+7.0±3.3
BH down the line8%−0.5+0.7−1.2−1.0±5.0
FH through the middle4%−2.7−1.1−0.2−4.0±2.5
FH inside-out3%+1.4+0.8−3.0−0.9±3.8

Lean BH crosscourt: +4.9±2.4 per 100 returns v the current mix (450 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.

Louisa Chirico

Favour

ShotEdgeOwnTheirs
FH to their forehand · serve +1+4.2±4.9+2.7+1.5
FH to their backhand · rally+2.7±4.6+1.4+1.3
BH to their forehand · rally+2.0±5.6+0.2+1.8
FH to their backhand · serve +1+1.4±5.2−1.4+2.8
BH to the middle · rally+1.3±2.9+0.9+0.4
FH to the middle · rally±0.0±3.0+1.3−1.3

Avoid

ShotEdgeOwnTheirs
FH to the middle · return−3.4±3.3−4.2+0.9
FH to their forehand · rally−1.8±3.9−2.4+0.6
BH to the middle · return−0.5±3.1−0.4−0.1
BH to their backhand · rally±0.0±3.5+0.9−0.9
FH to the middle · rally±0.0±3.0+1.3−1.3

Paula Badosa

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+7.0±5.2+3.2+3.9
FH to the middle · rally+2.3±3.0+1.9+0.4
BH to the middle · return+0.1±3.1+0.5−0.4
BH to the middle · rally−0.4±2.8+1.7−2.1
FH to their forehand · rally−0.6±3.9+0.3−0.9
BH to their backhand · rally−0.9±3.8+1.3−2.2

Avoid

ShotEdgeOwnTheirs
FH to the middle · return−2.8±3.4−0.3−2.5
FH to their backhand · rally−2.1±4.5+0.8−2.8
BH to their backhand · rally−0.9±3.8+1.3−2.2
FH to their forehand · rally−0.6±3.9+0.3−0.9
BH to the middle · rally−0.4±2.8+1.7−2.1

Against Paula Badosa-like opponents

Louisa Chirico vMatchesServe pts wonReturn pts won
All charted opponents–57.5%45.7%

Similar by tactical fingerprint: Coco Gauff, Marie Bouzkova, Kimberly Birrell, Jaqueline Cristian, Anna Bondar, Lin Zhu, Qiang Wang, Jennifer Brady, Carla Suarez Navarro. When two players have rarely met, their records against these lookalikes fill the gap.