RallyIQ

Game plan

Magdalena Rybarikova v Sara Sorribes Tormo

Every number combines what Magdalena Rybarikova does well with what Sara Sorribes Tormo 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 46%90%: 11%–85% · best of 5: 45%
Serve points won 52.3% / 53.1% Magdalena / Sara · tour 58.1%
Strengths only, no similarity priors 46%serve 52.3% / 53.1%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Sara Sorribes Tormo'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

CareerMagdalenaSara
Direction choice−0.24 ±0.20
better than 12%
−0.48 ±0.10
better than 0%
Shot selection−0.43 ±0.35
better than 14%
−0.84 ±0.19
better than 5%
Execution+0.07 ±0.86
better than 64%
+2.10 ±0.36
better than 99%

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 SaraMatchupOptimal
Wide53%58%62%53.6%±9.453%
Body20%57%51%50.5%±11.65% ▼
T27%67%60%59.1%±12.042% ▲

Optimal v Sara Sorribes Tormo: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +4.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMagdalena winsv SaraMatchupOptimal
Wide38%67%60%61.5%±11.053% ▲
Body15%58%55%56.2%±12.715%
T47%54%57%45.4%±10.032% ▼

Optimal v Sara Sorribes Tormo: +0.6±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +8.4 per 100 first serves in before the returner adjusts.

Sara Sorribes Tormo serving

Deuce court

1st serveNowSara winsv MagdalenaMatchupOptimal
Wide44%61%74%69.8%±9.359% ▲
Body41%51%66%60.9%±10.928% ▼
T15%59%63%53.2%±12.113% ▼

Optimal v Magdalena Rybarikova: +0.5±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +6.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowSara winsv MagdalenaMatchupOptimal
Wide24%58%63%54.9%±10.926% ▲
Body52%53%53%49.8%±10.537% ▼
T24%54%70%60.5%±11.037% ▲

Optimal v Magdalena Rybarikova: +0.8±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.9 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.

Magdalena Rybarikova returning

1st serve to the forehand

ReturnNowTourOwnv SaraValue
FH down the line30%+1.5+2.7+2.4+6.7±4.1
FH through the middle30%+4.2−2.0+1.5+3.7±2.5
FH slice through the middle25%−6.7−0.5+0.9−6.3±2.4
FH slice crosscourt8%−6.6+0.5±0.0−6.1±1.3
FH crosscourt7%+5.3+0.2+3.5+9.0±3.2

Lean FH down the line: +5.0±3.0 per 100 returns v the current mix (89 returns charted)

1st serve to the backhand

ReturnNowTourOwnv SaraValue
BH through the middle63%+6.0+0.4+1.6+8.1±2.5
BH crosscourt21%+7.7+2.8+0.5+11.0±2.9
BH slice through the middle17%−6.2+0.4−0.1−5.9±2.1

Lean BH crosscourt: +4.6±2.8 per 100 returns v the current mix (72 returns charted)

Sara Sorribes Tormo returning

1st serve to the forehand

ReturnNowTourOwnv MagdalenaValue
FH through the middle55%+4.2+3.8+2.3+10.2±2.7
FH down the line19%+1.5+2.0+2.6+6.2±4.1
FH crosscourt14%+5.3+3.1+0.2+8.7±4.0
FH slice through the middle8%−6.7+0.1+0.1−6.4±2.2
FH slice crosscourt4%−6.6+0.1±0.0−6.6±1.8

Lean FH through the middle: +3.1±1.6 per 100 returns v the current mix (518 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MagdalenaValue
BH through the middle49%+6.0+2.4−2.1+6.4±2.6
BH crosscourt22%+7.7+4.0+0.6+12.3±3.3
BH down the line12%+2.2+3.2+2.0+7.4±4.3
BH slice through the middle8%−6.2+1.9−0.8−5.1±2.3
BH slice crosscourt3%−4.2−0.8±0.0−5.0±1.9

Lean BH crosscourt: +6.1±2.9 per 100 returns v the current mix (530 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv MagdalenaValue
FH through the middle50%−3.2+1.6+0.9−0.6±2.6
FH crosscourt27%+0.5±0.0±0.0+0.5±3.1
FH down the line23%−0.6+1.9±0.0+1.3±3.5

Lean FH down the line: +1.2±3.1 per 100 returns v the current mix (113 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv MagdalenaValue
FH inside-out34%+1.4+0.7±0.0+2.0±3.0
FH through the middle31%−2.7+2.4+0.9+0.6±2.2
BH through the middle18%−2.6+1.7+0.8−0.1±2.8
BH crosscourt8%+1.5−0.1+3.5+4.9±3.5
BH down the line3%−0.5−1.9+1.6−0.8±4.3

Lean BH crosscourt: +3.9±3.5 per 100 returns v the current mix (261 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 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.

Magdalena Rybarikova

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+4.6±5.9+3.0+1.7
BH to the middle · return+4.1±3.9−0.2+4.3
BH to the middle · rally+2.2±3.3+1.3+0.9
FH to the middle · rally−1.3±3.7−3.1+1.8
FH to their forehand · rally−2.2±5.1−2.2±0.0
BH slice to their backhand · rally−2.4±4.1+2.5−4.9

Avoid

ShotEdgeOwnTheirs
BH slice to the middle · rally−2.8±3.3−1.2−1.7
FH to their backhand · rally−2.4±5.2−4.8+2.4
BH slice to their backhand · rally−2.4±4.1+2.5−4.9
FH to their forehand · rally−2.2±5.1−2.2±0.0
FH to the middle · rally−1.3±3.7−3.1+1.8

Sara Sorribes Tormo

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+10.6±6.5+5.2+5.3
BH to their backhand · rally+9.7±4.7+5.1+4.6
BH to their backhand · return+9.6±5.1+6.2+3.4
FH to the middle · return+8.6±4.2+4.8+3.8
FH to their backhand · rally+6.2±5.4+4.6+1.5
BH to their backhand · serve +1+5.9±4.8+2.2+3.7

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally+1.4±3.5+2.5−1.1
BH to the middle · return+3.8±3.9+4.4−0.6
FH to their forehand · rally+4.1±4.9+3.3+0.7
FH to the middle · rally+5.7±3.6+3.7+2.0
BH to their backhand · serve +1+5.9±4.8+2.2+3.7

Against Sara Sorribes Tormo-like opponents

Magdalena Rybarikova vMatchesServe pts wonReturn pts won
All charted opponents–48.0%39.3%

Similar by tactical fingerprint: Tamara Zidansek, Marie Bouzkova, Viktorija Golubic, Daria Kasatkina, Katie Volynets, Sara Errani, Alize Cornet, Madison Brengle, Saisai Zheng, Jennifer Capriati. When two players have rarely met, their records against these lookalikes fill the gap.