RallyIQ

Game plan

Camila Giorgi v Robin Montgomery

Every number combines what Camila Giorgi does well with what Robin Montgomery allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Camila Giorgi wins, best of 3 77%90%: 45%–95% · best of 5: 82%
Serve points won 64.1% / 58.3% Camila / Robin · tour 58.1%
Strengths only, no similarity priors 76%serve 63.8% / 58.3%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Camila Giorgi's record against Robin Montgomery's tactical lookalikes and in their charted head-to-heads (lookalikes: +3.2 on serve, −0.1 on return vs expectation (270 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

CareerCamilaRobin
Direction choice−0.25 ±0.09
better than 11%
−0.38 ±0.19
better than 2%
Shot selection+0.61 ±0.13
better than 97%
+0.05 ±0.23
better than 50%
Execution−1.67 ±0.48
better than 7%
−1.17 ±1.25
better 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.

Camila Giorgi serving

Deuce court

1st serveNowCamila winsv RobinMatchupOptimal
Wide28%69%69%71.8%±8.228%
Body29%60%61%63.6%±9.614% ▼
T43%72%74%77.5%±8.158% ▲

Optimal v Robin Montgomery: +0.6±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowCamila winsv RobinMatchupOptimal
Wide32%66%65%65.5%±9.347% ▲
Body35%60%43%47.5%±11.420% ▼
T33%70%63%68.6%±9.433%

Optimal v Robin Montgomery: +1.2±1.1 per 100 first serves (faults included) over the current mix. Serving T every time would read +8.4 per 100 first serves in before the returner adjusts.

Robin Montgomery serving

Deuce court

1st serveNowRobin winsv CamilaMatchupOptimal
Wide32%72%69%74.3%±8.947% ▲
Body28%62%61%64.8%±9.713% ▼
T40%69%71%71.9%±8.440%

Optimal v Camila Giorgi: +0.5±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowRobin winsv CamilaMatchupOptimal
Wide39%61%69%64.4%±9.039%
Body21%60%55%58.9%±11.16% ▼
T40%73%65%73.6%±8.355% ▲

Optimal v Camila Giorgi: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.7 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.

Camila Giorgi returning

1st serve to the forehand

ReturnNowTourOwnv RobinValue
FH through the middle52%+4.2−4.1+1.2+1.2±2.7
FH crosscourt23%+5.3+0.7−2.1+3.9±3.8
FH down the line17%+1.5−2.3−5.1−5.8±4.5
FH slice through the middle5%−6.7−6.0−0.6−13.3±2.4
FH slice down the line2%−10.5−1.3+0.1−11.7±2.7

Lean FH crosscourt: +4.4±3.4 per 100 returns v the current mix (595 returns charted)

1st serve to the backhand

ReturnNowTourOwnv RobinValue
BH through the middle52%+6.0−1.7+1.4+5.8±2.7
BH crosscourt26%+7.7+0.1+1.7+9.6±3.5
BH down the line10%+2.2−1.3−3.4−2.5±4.5
BH slice through the middle5%−6.2+1.5−1.4−6.1±2.2
BH slice down the line4%−12.5−3.1±0.0−15.6±2.1

Lean BH crosscourt: +5.5±3.0 per 100 returns v the current mix (382 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv RobinValue
FH through the middle59%−3.2−2.9+1.4−4.7±3.0
FH crosscourt29%+0.5−1.3−4.5−5.2±4.0
FH down the line11%−0.6+0.5+0.1±0.0±4.3

Lean FH down the line: +4.3±4.3 per 100 returns v the current mix (150 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv RobinValue
BH through the middle48%−2.6−2.2−0.1−4.8±2.7
BH crosscourt36%+1.5+4.5−0.1+5.9±3.6
BH down the line16%−0.5−1.9−2.3−4.8±4.4

Lean BH crosscourt: +6.9±2.8 per 100 returns v the current mix (203 returns charted)

Robin Montgomery returning

1st serve to the forehand

ReturnNowTourOwnv CamilaValue
FH through the middle65%+4.2+0.2+0.1+4.5±2.8
FH down the line20%+1.5−1.1−1.7−1.2±4.4
FH crosscourt14%+5.3±0.0−3.1+2.2±3.9

Lean FH through the middle: +1.5±1.4 per 100 returns v the current mix (98 returns charted)

1st serve to the backhand

ReturnNowTourOwnv CamilaValue
BH through the middle45%+6.0+0.1−1.7+4.4±2.5
BH crosscourt24%+7.7−0.2−2.9+4.6±3.5
BH slice through the middle14%−6.2−3.1−1.8−11.1±2.4
BH down the line13%+2.2−1.7+0.2+0.7±3.9
BH slice crosscourt5%−4.2−1.1+0.5−4.7±2.1

Lean BH crosscourt: +3.2±2.9 per 100 returns v the current mix (118 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv CamilaValue
BH through the middle59%−2.6+0.5−0.6−2.7±2.4
BH crosscourt22%+1.5−1.8−3.9−4.2±3.3
BH down the line19%−0.5−3.1−3.9−7.5±4.3

Lean BH through the middle: +1.2±1.5 per 100 returns v the current mix (63 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 grass. Each player's grass record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Camila Giorgi

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+1.8±5.1+3.7−1.9
BH to their forehand · rally+1.2±5.3+2.3−1.1
FH to their forehand · serve +1+1.1±4.7+2.1−1.0
FH to their forehand · rally−0.1±4.0+1.2−1.4
BH to the middle · rally−0.3±2.8−1.6+1.3
FH to the middle · return−1.2±3.3−2.6+1.4

Avoid

ShotEdgeOwnTheirs
FH to the middle · serve +1−5.1±3.2−6.4+1.4
BH to the middle · return−3.9±2.9−4.2+0.3
FH to the middle · rally−3.4±2.9−3.7+0.3
BH to their forehand · return−2.8±5.5−3.7+0.9
FH to their backhand · rally−1.6±4.7−1.6±0.0

Robin Montgomery

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally−0.1±4.6+1.3−1.4
BH to their forehand · rally−1.1±5.4−0.3−0.7
FH to the middle · rally−1.4±2.9−0.7−0.7
BH to the middle · return−1.5±2.9+0.6−2.1
FH to their forehand · return−2.0±5.2−2.3+0.3
FH to the middle · return−2.1±3.3−0.7−1.4

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−10.0±5.1−5.6−4.4
FH to their backhand · return +1−4.7±5.3−0.6−4.0
FH to their forehand · rally−4.4±4.3−2.0−2.5
FH to their forehand · serve +1−4.1±5.0−3.1−1.0
BH to the middle · rally−2.5±2.8−1.8−0.7

Against Robin Montgomery-like opponents

Camila Giorgi vMatchesServe pts wonReturn pts won
All charted opponents–57.6%42.4%
Players most similar to Robin Montgomery2 63.0%45.9%

Similar by tactical fingerprint: Alexandra Eala, Jasmine Paolini, Maya Joint, Shuai Zhang, Lulu Sun, Xiyu Wang, Nao Hibino, Bernarda Pera, Irina Camelia Begu, Dominika Cibulkova. When two players have rarely met, their records against these lookalikes fill the gap.