RallyIQ

Game plan

R v Katerina Siniakova

Every number combines what R does well with what Katerina Siniakova allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

R wins, best of 3 51%90%: 13%–89% · best of 5: 52%
Serve points won 56.9% / 56.6% R / Katerina · tour 58.1%
Strengths only, no similarity priors 51%serve 56.9% / 56.6%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerRKaterina
Direction choice+0.09 ±0.26
better than 71%
+0.04 ±0.09
better than 63%
Shot selection+0.24 ±0.21
better than 72%
−0.18 ±0.14
better than 30%
Execution−6.35 ±0.39
better than 0%
−0.21 ±0.48
better than 48%
Points left on the table2.35 ±0.12
lower than 82%
2.42 ±0.13
lower than 72%

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.

R serving

Deuce court

1st serveNowR winsv KaterinaMatchupOptimal
Wide42%74%68%75.7%±6.357% ▲
Body24%53%56%51.2%±9.89% ▼
T34%75%69%76.3%±7.534%

Optimal v Katerina Siniakova: +1.5±1.0 per 100 first serves (faults included) over the current mix. Serving T every time would read +6.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowR winsv KaterinaMatchupOptimal
Wide33%67%69%70.4%±8.348% ▲
Body24%51%58%52.9%±10.412% ▼
T43%59%58%52.5%±8.140% ▼

Optimal v Katerina Siniakova: +1.1±1.0 per 100 first serves (faults included) over the current mix. Serving wide every time would read +11.9 per 100 first serves in before the returner adjusts.

Katerina Siniakova serving

Deuce court

1st serveNowKaterina winsv RMatchupOptimal
Wide35%59%74%68.4%±7.850% ▲
Body23%55%53%50.1%±9.38% ▼
T42%59%75%67.3%±8.842%

Optimal v R: +1.2±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +4.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKaterina winsv RMatchupOptimal
Wide23%60%67%61.9%±9.938% ▲
Body30%60%51%54.8%±9.415% ▼
T47%63%59%57.9%±8.147%

Optimal v R: +0.6±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.0 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.

R returning

1st serve to the forehand

ReturnNowTourOwnv KaterinaValue
FH through the middle39%+4.2−18.3+1.3−12.8±2.6
FH crosscourt20%+5.3−20.2+2.2−12.7±4.2
FH down the line17%+1.5−15.0+1.9−11.6±4.6
FH slice through the middle13%−6.7−3.2+2.9−7.1±2.5
FH slice crosscourt7%−6.6−4.7−1.6−12.9±2.3

Lean FH slice through the middle: +4.8±2.6 per 100 returns v the current mix (242 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KaterinaValue
BH through the middle47%+6.0−17.8+2.8−9.0±2.5
BH crosscourt33%+7.7−13.7+2.0−4.0±3.5
BH down the line10%+2.2−11.3−0.4−9.5±4.1
BH slice crosscourt5%−4.2−3.6−0.7−8.4±2.0
BH slice through the middle5%−6.2+0.6+2.5−3.2±2.1

Lean BH crosscourt: +3.1±2.7 per 100 returns v the current mix (146 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv KaterinaValue
FH crosscourt41%+0.5−12.4−2.4−14.3±4.3
FH through the middle38%−3.2−8.4−0.6−12.1±2.9
FH down the line21%−0.6−6.8−5.1−12.4±4.9

Lean FH through the middle: +1.0±2.8 per 100 returns v the current mix (82 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv KaterinaValue
BH through the middle45%−2.6−1.4+1.9−2.1±2.5
BH crosscourt44%+1.5−2.4+1.0+0.1±3.5
BH down the line11%−0.5−0.4+2.2+1.2±4.4

Lean BH crosscourt: +0.9±2.3 per 100 returns v the current mix (55 returns charted, inside the 90% margin)

Katerina Siniakova returning

1st serve to the forehand

ReturnNowTourOwnv RValue
FH through the middle34%+4.2−1.0−17.1−14.0±2.7
FH slice through the middle26%−6.7+1.3−0.7−6.1±2.4
FH crosscourt18%+5.3−2.8−19.3−16.8±4.2
FH slice crosscourt11%−6.6+1.3−3.7−9.1±2.7
FH down the line8%+1.5+1.0−14.4−11.9±4.7

Lean FH slice through the middle: +5.5±2.2 per 100 returns v the current mix (630 returns charted)

1st serve to the backhand

ReturnNowTourOwnv RValue
BH through the middle49%+6.0+0.9−14.2−7.3±2.6
BH crosscourt36%+7.7+3.5−12.2−1.0±3.5
BH down the line8%+2.2+0.8−12.7−9.8±4.6
BH slice through the middle5%−6.2−0.8+0.8−6.1±2.0
BH slice crosscourt2%−4.2−1.5−4.2−9.9±2.2

Lean BH crosscourt: +4.2±2.6 per 100 returns v the current mix (362 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv RValue
FH through the middle53%−3.2−1.2−7.6−12.0±3.1
FH crosscourt26%+0.5−2.1−12.3−13.8±4.4
FH down the line21%−0.6+0.1−8.0−8.5±5.1

Lean FH down the line: +3.3±4.5 per 100 returns v the current mix (128 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv RValue
BH crosscourt48%+1.5+1.3−3.6−0.9±3.6
BH through the middle32%−2.6+1.3−4.6−6.0±2.8
BH down the line20%−0.5+0.7−1.1−0.9±5.1

Lean BH crosscourt: +1.6±2.3 per 100 returns v the current mix (155 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.

R

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+5.0±5.0+2.7+2.3
BH to their backhand · serve +1+4.8±4.0+4.5+0.2
FH to the middle · rally+1.9±2.9−0.8+2.7
BH to the middle · rally+1.9±2.8+0.1+1.8
FH to the middle · return +1+0.3±3.2−0.5+0.8
FH to the middle · serve +1−0.4±3.3−1.7+1.2

Avoid

ShotEdgeOwnTheirs
FH to their forehand · return−20.0±5.1−20.7+0.7
FH to their backhand · return−17.7±5.4−15.7−2.0
FH to the middle · return−14.1±3.2−16.1+2.1
BH to their forehand · rally−11.6±5.3−10.0−1.6
FH to their backhand · rally−10.0±4.3−7.4−2.6

Katerina Siniakova

Favour

ShotEdgeOwnTheirs
BH to their backhand · serve +1+5.1±4.2+0.6+4.5
FH to their backhand · serve +1+2.1±5.2−0.6+2.7
BH to the middle · rally±0.0±2.8−0.1+0.1
BH to the middle · serve +1−0.2±3.1+2.1−2.3
FH to the middle · return +1−1.2±3.3−0.7−0.5
FH to the middle · rally−2.3±2.9−1.6−0.8

Avoid

ShotEdgeOwnTheirs
FH to their forehand · return−22.9±5.1−2.3−20.7
FH to their backhand · return−19.4±5.4−3.7−15.7
FH to the middle · return−13.7±3.3+2.4−16.1
BH to the middle · return−11.5±2.9+0.8−12.3
FH to their backhand · rally−11.1±4.4−3.6−7.4

Against Katerina Siniakova-like opponents

R vMatchesServe pts wonReturn pts won
All charted opponents–58.7%41.3%

Similar by tactical fingerprint: Ashlyn Krueger, Jessica Pegula, Rebecca Sramkova, Jaqueline Cristian, Shuai Zhang, Qiang Wang, Simona Halep, Tsvetana Pironkova, Kate Makarova. When two players have rarely met, their records against these lookalikes fill the gap.