RallyIQ

Game plan

Karolina Muchova v Magdalena Rybarikova

Every number combines what Karolina Muchova does well with what Magdalena Rybarikova allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Karolina Muchova wins, best of 3 89%90%: 63%–98% · best of 5: 94%
Serve points won 59.5% / 50.3% Karolina / Magdalena · tour 55.0%
Strengths only, no similarity priors 88%serve 59.3% / 50.6%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Karolina Muchova's record against Magdalena Rybarikova's tactical lookalikes and in their charted head-to-heads (lookalikes: +2.1 on serve, +7.0 on return vs expectation (159 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

CareerKarolinaMagdalena
Direction choice−0.01 ±0.05
better than 52%
−0.24 ±0.20
better than 12%
Shot selection−0.17 ±0.09
better than 30%
−0.43 ±0.35
better than 14%
Execution+0.22 ±0.28
better than 70%
+0.07 ±0.86
better than 64%

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.

Karolina Muchova serving

Deuce court

1st serveNowKarolina winsv MagdalenaMatchupOptimal
Wide40%68%74%76.3%±7.740%
Body22%58%66%67.2%±10.17% ▼
T38%74%63%69.8%±8.953% ▲

Optimal v Magdalena Rybarikova: +0.8±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKarolina winsv MagdalenaMatchupOptimal
Wide48%68%63%65.8%±9.146% ▼
Body14%57%53%53.0%±11.10% ▼
T39%67%70%72.7%±8.554% ▲

Optimal v Magdalena Rybarikova: +0.8±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.0 per 100 first serves in before the returner adjusts.

Magdalena Rybarikova serving

Deuce court

1st serveNowMagdalena winsv KarolinaMatchupOptimal
Wide53%58%65%56.7%±8.653%
Body20%57%58%57.6%±11.25% ▼
T27%67%69%67.4%±10.442% ▲

Optimal v Karolina Muchova: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +7.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMagdalena winsv KarolinaMatchupOptimal
Wide38%67%66%67.2%±9.853% ▲
Body15%58%54%55.1%±12.50% ▼
T47%54%64%52.9%±9.347%

Optimal v Karolina Muchova: +0.6±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +8.5 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.

Karolina Muchova returning

1st serve to the forehand

ReturnNowTourOwnv MagdalenaValue
FH through the middle41%+4.2+1.1+2.3+7.5±2.6
FH slice through the middle18%−6.7+3.0+0.1−3.6±1.9
FH crosscourt15%+5.3−0.7+0.2+4.8±3.6
FH down the line14%+1.5−2.9+2.6+1.3±3.9
FH slice crosscourt9%−6.6+2.2±0.0−4.5±1.9

Lean FH through the middle: +4.9±1.8 per 100 returns v the current mix (1182 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MagdalenaValue
BH through the middle46%+6.0−0.4−2.1+3.6±2.4
BH crosscourt20%+7.7+0.9+0.6+9.2±3.0
BH slice through the middle14%−6.2+0.2−0.8−6.8±2.1
BH down the line12%+2.2−1.2+2.0+3.0±4.0
BH slice crosscourt5%−4.2−0.8±0.0−5.0±2.1

Lean BH crosscourt: +6.9±2.7 per 100 returns v the current mix (1188 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv MagdalenaValue
FH through the middle43%−3.2−1.4+0.9−3.7±2.5
FH crosscourt28%+0.5−0.4±0.0+0.2±3.1
FH down the line21%−0.6−1.9±0.0−2.5±3.7
FH slice through the middle5%−15.2+0.1±0.0−15.1±1.5
FH slice crosscourt3%−14.9+1.3±0.0−13.6±1.3

Lean FH crosscourt: +3.4±2.6 per 100 returns v the current mix (247 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv MagdalenaValue
BH through the middle40%−2.6+1.0+0.8−0.8±2.5
BH crosscourt31%+1.5−0.4+3.5+4.6±3.4
BH down the line20%−0.5−0.3+1.6+0.8±4.8
FH inside-in5%+0.7−1.9±0.0−1.1±3.2
FH through the middle3%−2.7+1.7+0.9−0.1±2.1

Lean BH crosscourt: +3.4±2.7 per 100 returns v the current mix (582 returns charted)

Magdalena Rybarikova returning

1st serve to the forehand

ReturnNowTourOwnv KarolinaValue
FH down the line30%+1.5+2.7−0.7+3.5±4.0
FH through the middle30%+4.2−2.0−0.1+2.0±2.5
FH slice through the middle25%−6.7−0.5−1.7−8.9±2.3
FH slice crosscourt8%−6.6+0.5−2.5−8.6±2.4
FH crosscourt7%+5.3+0.2−1.4+4.1±3.1

Lean FH down the line: +4.4±3.0 per 100 returns v the current mix (89 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KarolinaValue
BH through the middle63%+6.0+0.4+0.1+6.5±2.3
BH crosscourt21%+7.7+2.8+0.3+10.9±2.7
BH slice through the middle17%−6.2+0.4−1.1−7.0±2.2

Lean BH crosscourt: +5.7±2.6 per 100 returns v the current mix (72 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 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.

Karolina Muchova

Favour

ShotEdgeOwnTheirs
BH to their backhand · serve +1+4.7±4.0+2.4+2.3
BH to their forehand · rally+4.3±4.9+1.0+3.2
FH to the middle · return+3.8±2.9+0.2+3.6
BH to their backhand · return+2.5±3.6±0.0+2.4
BH to their backhand · rally+1.3±3.2−0.4+1.7
FH to their forehand · rally+0.5±3.6+0.6−0.1

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−2.5±2.5−1.3−1.3
BH to the middle · return−1.8±2.6±0.0−1.8
FH to the middle · rally−1.2±2.7−2.6+1.4
FH to their backhand · rally±0.0±3.8−0.7+0.7
FH to their forehand · rally+0.5±3.6+0.6−0.1

Magdalena Rybarikova

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+3.0±4.3+2.2+0.8
BH to the middle · rally+1.4±2.5+0.9+0.4
BH to the middle · return+1.0±2.6±0.0+0.9
BH slice to their backhand · rally−0.8±3.3+2.7−3.4
BH slice to the middle · rally−2.7±2.7−1.2−1.5
FH to the middle · rally−3.7±2.6−3.3−0.5

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−5.9±3.7−3.7−2.1
FH to their forehand · rally−4.5±3.7−1.9−2.6
FH to the middle · rally−3.7±2.6−3.3−0.5
BH slice to the middle · rally−2.7±2.7−1.2−1.5
BH slice to their backhand · rally−0.8±3.3+2.7−3.4

Against Magdalena Rybarikova-like opponents

Karolina Muchova vMatchesServe pts wonReturn pts won
All charted opponents–61.6%42.9%
Players most similar to Magdalena Rybarikova2 63.0%49.4%

Similar by tactical fingerprint: Mirra Andreeva, Marie Bouzkova, Elise Mertens, Anna Bondar, Shelby Rogers, Petra Martic, Clara Burel, Alison Van Uytvanck, Saisai Zheng. When two players have rarely met, their records against these lookalikes fill the gap.