RallyIQ

Game plan

Magdalena Rybarikova v Mirra Andreeva

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

Forecast

Magdalena Rybarikova wins, best of 3 5%90%: 1%–18% · best of 5: 2%
Serve points won 47.9% / 60.5% Magdalena / Mirra · tour 55.0%
Strengths only, no similarity priors 5%serve 48.5% / 60.8%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Magdalena Rybarikova's record against Mirra Andreeva's tactical lookalikes and in their charted head-to-heads (lookalikes: −19.0 on serve, +8.6 on return vs expectation (110 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

CareerMagdalenaMirra
Direction choice−0.24 ±0.20
better than 12%
+0.12 ±0.04
better than 75%
Shot selection−0.43 ±0.35
better than 14%
−0.43 ±0.08
better than 15%
Execution+0.07 ±0.86
better than 64%
+1.22 ±0.22
better than 95%

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.

Magdalena Rybarikova serving

Deuce court

1st serveNowMagdalena winsv MirraMatchupOptimal
Wide53%58%60%51.0%±8.538% ▼
Body20%57%56%55.4%±11.220%
T27%67%61%59.8%±11.242% ▲

Optimal v Mirra Andreeva: +0.1±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMagdalena winsv MirraMatchupOptimal
Wide38%67%64%64.8%±10.050% ▲
Body15%58%47%48.2%±12.50% ▼
T47%54%62%51.0%±9.050% ▲

Optimal v Mirra Andreeva: +0.7±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +9.1 per 100 first serves in before the returner adjusts.

Mirra Andreeva serving

Deuce court

1st serveNowMirra winsv MagdalenaMatchupOptimal
Wide51%69%74%76.8%±7.466% ▲
Body13%59%66%68.0%±10.00% ▼
T36%73%63%68.6%±8.934% ▼

Optimal v Magdalena Rybarikova: +0.6±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMirra winsv MagdalenaMatchupOptimal
Wide33%67%63%64.3%±9.234%
Body21%61%53%58.0%±10.16% ▼
T45%65%70%71.0%±8.660% ▲

Optimal v Magdalena Rybarikova: +0.5±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.0 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.

Magdalena Rybarikova returning

1st serve to the forehand

ReturnNowTourOwnv MirraValue
FH down the line30%+1.5+2.7−1.6+2.6±3.8
FH through the middle30%+4.2−2.0+0.1+2.2±2.3
FH slice through the middle25%−6.7−0.5−0.9−8.1±2.1
FH slice crosscourt8%−6.6+0.5−0.6−6.7±2.2
FH crosscourt7%+5.3+0.2−1.9+3.6±2.6

Lean FH down the line: +3.4±2.8 per 100 returns v the current mix (89 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MirraValue
BH through the middle63%+6.0+0.4−0.6+5.8±2.3
BH crosscourt21%+7.7+2.8−0.8+9.7±2.6
BH slice through the middle17%−6.2+0.4+0.1−5.8±2.1

Lean BH crosscourt: +5.0±2.5 per 100 returns v the current mix (72 returns charted)

Mirra Andreeva returning

1st serve to the forehand

ReturnNowTourOwnv MagdalenaValue
FH through the middle34%+4.2+1.5+2.3+8.0±2.4
FH down the line24%+1.5+3.1+2.6+7.2±3.2
FH slice through the middle19%−6.7+0.1+0.1−6.5±1.7
FH slice crosscourt11%−6.6+0.6±0.0−6.0±1.4
FH crosscourt8%+5.3−3.2+0.2+2.3±3.5

Lean FH through the middle: +5.5±1.8 per 100 returns v the current mix (2589 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MagdalenaValue
BH through the middle46%+6.0+1.9−2.1+5.9±2.3
BH crosscourt24%+7.7+0.2+0.6+8.5±2.7
BH down the line14%+2.2+3.5+2.0+7.7±3.6
BH slice through the middle7%−6.2−2.0−0.8−9.0±2.1
BH slice crosscourt6%−4.2−2.7±0.0−6.9±2.0

Lean BH crosscourt: +4.0±2.4 per 100 returns v the current mix (1925 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv MagdalenaValue
FH through the middle37%−3.2+1.0+0.9−1.3±2.1
FH down the line34%−0.6±0.0±0.0−0.6±2.8
FH crosscourt15%+0.5−5.4±0.0−4.9±2.9
BH through the middle5%−1.0+0.5+0.8+0.3±2.4
BH inside-in3%+2.0+0.6+3.5+6.0±3.8

Lean BH inside-in: +8.0±3.9 per 100 returns v the current mix (743 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv MagdalenaValue
BH through the middle37%−2.6+1.0+0.8−0.7±2.3
BH down the line31%−0.5+0.8+1.6+2.0±4.2
BH crosscourt25%+1.5−1.4+3.5+3.6±3.2
FH through the middle3%−2.7−1.2+0.9−3.0±2.2
FH inside-in2%+0.7+0.3±0.0+1.0±3.0

Lean BH crosscourt: +2.5±2.9 per 100 returns v the current mix (1071 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.

Magdalena Rybarikova

Favour

ShotEdgeOwnTheirs
BH to the middle · rally+1.4±2.0+0.9+0.4
FH to their backhand · serve +1+0.9±3.6+2.2−1.4
BH slice to their backhand · rally−0.3±2.9+2.7−2.9
BH to the middle · return−1.0±2.1±0.0−1.0
FH to the middle · rally−1.5±2.1−3.3+1.7
BH slice to the middle · rally−2.2±2.2−1.2−1.0

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−4.1±3.2−3.7−0.3
FH to their forehand · rally−2.7±3.0−1.9−0.8
BH slice to the middle · rally−2.2±2.2−1.2−1.0
FH to the middle · rally−1.5±2.1−3.3+1.7
BH to the middle · return−1.0±2.1±0.0−1.0

Mirra Andreeva

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+5.0±3.8+1.7+3.2
FH to the middle · return+4.7±2.4+1.1+3.6
BH to their backhand · rally+3.8±2.4+2.1+1.7
BH to their backhand · serve +1+3.7±3.0+1.5+2.3
BH to their backhand · return+3.5±3.0+1.0+2.4
FH to their backhand · rally+1.4±3.2+0.7+0.7

Avoid

ShotEdgeOwnTheirs
BH to the middle · return−0.8±2.2+1.0−1.8
BH to the middle · rally−0.6±2.0+0.6−1.3
FH to their forehand · rally+0.3±2.9+0.4−0.1
FH to the middle · rally+1.0±2.2−0.4+1.4
FH to their backhand · rally+1.4±3.2+0.7+0.7

Against Mirra Andreeva-like opponents

Magdalena Rybarikova vMatchesServe pts wonReturn pts won
All charted opponents–48.0%39.3%
Players most similar to Mirra Andreeva1 31.3%50.0%

Similar by tactical fingerprint: Belinda Bencic, Elise Mertens, Sorana Cirstea, Victoria Azarenka, Simona Halep, Svetlana Kuznetsova, Christina Mchale, Timea Bacsinszky, Agnieszka Radwanska, Elena Dementieva. When two players have rarely met, their records against these lookalikes fill the gap.