RallyIQ

Game plan

Kamilla Rakhimova v Rebecca Marino

Every number combines what Kamilla Rakhimova does well with what Rebecca Marino allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Kamilla Rakhimova wins, best of 3 50%90%: 11%–89% · best of 5: 49%
Serve points won 56.6% / 56.7% Kamilla / Rebecca · tour 55.0%
Strengths only, no similarity priors 50%serve 56.6% / 56.7%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerKamillaRebecca
Direction choice+0.11 ±0.08
better than 75%
−0.18 ±0.10
better than 20%
Shot selection−0.26 ±0.13
better than 23%
+0.03 ±0.15
better than 48%
Execution−0.33 ±0.69
better than 43%
−0.84 ±0.52
better than 23%
Points left on the table2.22 ±0.10
lower than 93%
2.70 ±0.11
lower than 33%

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.

Kamilla Rakhimova serving

Deuce court

1st serveNowKamilla winsv RebeccaMatchupOptimal
Wide35%64%63%61.4%±9.836%
Body29%47%55%45.2%±11.614% ▼
T35%69%65%66.2%±11.050% ▲

Optimal v Rebecca Marino: +1.1±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +7.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKamilla winsv RebeccaMatchupOptimal
Wide47%68%63%65.7%±10.456% ▲
Body17%55%52%51.2%±13.22% ▼
T36%63%65%63.8%±10.942% ▲

Optimal v Rebecca Marino: +0.4±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.1 per 100 first serves in before the returner adjusts.

Rebecca Marino serving

Deuce court

1st serveNowRebecca winsv KamillaMatchupOptimal
Wide45%65%62%60.6%±9.960% ▲
Body11%65%60%67.7%±12.07% ▼
T44%75%62%69.4%±10.233% ▼

Optimal v Kamilla Rakhimova: +0.2±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +4.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowRebecca winsv KamillaMatchupOptimal
Wide48%77%64%75.6%±8.163% ▲
Body10%56%61%60.8%±13.70% ▼
T42%72%68%74.7%±9.437% ▼

Optimal v Kamilla Rakhimova: +0.7±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +1.9 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.

Kamilla Rakhimova returning

1st serve to the forehand

ReturnNowTourOwnv RebeccaValue
FH through the middle47%+4.2−0.7+1.3+4.8±3.0
FH crosscourt31%+5.3±0.0−3.9+1.4±4.3
FH down the line9%+1.5−0.7−3.0−2.2±4.1
FH slice through the middle9%−6.7−0.6−0.1−7.3±2.2
FH slice crosscourt5%−6.6+0.6−0.8−6.8±2.0

Lean FH through the middle: +3.3±2.1 per 100 returns v the current mix (94 returns charted)

1st serve to the backhand

ReturnNowTourOwnv RebeccaValue
BH through the middle49%+6.0+1.0−1.9+5.2±2.9
BH crosscourt46%+7.7+0.1−2.6+5.2±3.7
BH down the line5%+2.2−1.1−2.0−0.9±3.8

Lean BH crosscourt: +0.3±2.4 per 100 returns v the current mix (111 returns charted, inside the 90% margin)

Rebecca Marino returning

1st serve to the forehand

ReturnNowTourOwnv KamillaValue
FH through the middle61%+4.2−0.1+1.5+5.6±2.9
FH crosscourt20%+5.3−2.3−2.6+0.4±4.3
FH down the line18%+1.5−0.2−1.1+0.3±4.5
FH slice through the middle1%−6.7−1.4+0.4−7.7±1.8

Lean FH through the middle: +2.2±1.6 per 100 returns v the current mix (337 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KamillaValue
BH through the middle49%+6.0−0.1−0.2+5.7±2.9
BH crosscourt26%+7.7+3.8−2.0+9.5±3.6
BH down the line12%+2.2−1.3+3.4+4.3±4.5
BH slice through the middle7%−6.2−1.5−1.2−8.9±2.0
BH slice crosscourt6%−4.2−0.7+0.5−4.3±2.1

Lean BH crosscourt: +4.5±3.1 per 100 returns v the current mix (198 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv KamillaValue
FH through the middle57%−3.2+1.8−2.6−4.0±3.1
FH crosscourt35%+0.5+2.1+3.6+6.2±4.1
FH down the line9%−0.6−1.7+0.4−1.8±4.2

Lean FH crosscourt: +6.4±3.2 per 100 returns v the current mix (69 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv KamillaValue
BH through the middle47%−2.6+1.6+0.1−0.9±2.8
BH crosscourt41%+1.5+0.4+0.8+2.6±3.5
BH down the line12%−0.5+1.0+0.7+1.2±4.4

Lean BH crosscourt: +1.8±2.5 per 100 returns v the current mix (93 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.

Kamilla Rakhimova

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+4.4±5.7+5.7−1.3
BH to the middle · rally+2.9±3.2+2.3+0.6
BH to the middle · return+1.3±3.6+2.8−1.6
FH to the middle · return+1.0±3.9−0.5+1.5
FH to the middle · rally+0.9±3.0−0.1+1.0
BH to their forehand · rally+0.8±5.6+3.1−2.4

Avoid

ShotEdgeOwnTheirs
FH to their forehand · serve +1−7.2±5.1−6.2−1.0
FH to their forehand · rally−3.5±5.0−1.0−2.5
BH to their backhand · rally−2.6±4.4−0.7−2.0
FH to their backhand · serve +1−2.5±5.2−2.2−0.4
FH to their backhand · rally−1.9±4.9−1.9±0.0

Rebecca Marino

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+3.2±5.1+2.2+1.0
BH to their forehand · rally+0.6±5.7−2.6+3.2
FH to the middle · return+0.1±3.8−0.5+0.6
BH to the middle · return−0.6±3.6−0.2−0.4
FH to the middle · serve +1−1.1±3.3−0.8−0.3
BH to the middle · rally−1.7±3.2−2.2+0.5

Avoid

ShotEdgeOwnTheirs
BH to their backhand · rally−5.1±4.5−2.1−2.9
FH to their forehand · serve +1−4.7±5.8−5.9+1.2
FH to their backhand · rally−4.2±5.0−4.5+0.4
FH to their forehand · rally−2.8±4.9−1.8−1.0
FH to the middle · rally−2.0±3.3−2.9+0.9

Against Rebecca Marino-like opponents

Kamilla Rakhimova vMatchesServe pts wonReturn pts won
All charted opponents–54.1%43.9%

Similar by tactical fingerprint: Elena Rybakina, Barbora Krejcikova, Maya Joint, Shuai Zhang, Robin Montgomery, Veronika Kudermetova, Bernarda Pera, Irina Camelia Begu, Anett Kontaveit, Coco Vandeweghe. When two players have rarely met, their records against these lookalikes fill the gap.