RallyIQ

Game plan

Sara Bejlek v Marta Kostyuk

Every number combines what Sara Bejlek does well with what Marta Kostyuk allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Sara Bejlek wins, best of 3 2%90%: 0%–9% · best of 5: 0%
Serve points won 45.4% / 61.3% Sara / Marta · tour 56.4%
Strengths only, no similarity priors 2%serve 45.4% / 61.3%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerSaraMarta
Direction choice−0.14 ±0.15
better than 27%
−0.06 ±0.05
better than 43%
Shot selection+0.24 ±0.28
better than 72%
+0.13 ±0.09
better than 60%
Execution−0.48 ±0.87
better than 34%
−0.34 ±0.29
better than 41%

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.

Sara Bejlek serving

Deuce court

1st serveNowSara winsv MartaMatchupOptimal
Wide29%59%63%56.2%±10.229%
Body36%51%57%50.4%±9.621% ▼
T35%56%68%56.4%±9.450% ▲

Optimal v Marta Kostyuk: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowSara winsv MartaMatchupOptimal
Wide49%65%64%63.3%±8.764% ▲
Body32%57%58%58.4%±9.924% ▼
T19%53%61%49.2%±12.312% ▼

Optimal v Marta Kostyuk: +0.3±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.3 per 100 first serves in before the returner adjusts.

Marta Kostyuk serving

Deuce court

1st serveNowMarta winsv SaraMatchupOptimal
Wide36%70%67%70.9%±7.051% ▲
Body24%60%53%55.3%±10.89% ▼
T40%72%74%77.2%±8.340%

Optimal v Sara Bejlek: +1.4±1.0 per 100 first serves (faults included) over the current mix. Serving T every time would read +7.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMarta winsv SaraMatchupOptimal
Wide47%73%68%75.0%±7.762% ▲
Body26%58%57%58.8%±11.211% ▼
T27%64%65%64.5%±9.227%

Optimal v Sara Bejlek: +1.1±1.0 per 100 first serves (faults included) over the current mix. Serving wide every time would read +7.0 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.

Sara Bejlek returning

1st serve to the forehand

ReturnNowTourOwnv MartaValue
FH through the middle35%+4.2−0.9+0.7+4.0±2.4
FH crosscourt29%+5.3+1.3−2.7+3.9±3.7
FH down the line28%+1.5+1.6−3.9−0.8±3.9
FH slice through the middle9%−6.7−0.1−0.7−7.5±2.0

Lean FH through the middle: +2.3±2.2 per 100 returns v the current mix (69 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MartaValue
BH through the middle50%+6.0+0.1−0.2+6.0±2.3
BH crosscourt28%+7.7+0.6−2.8+5.5±3.3
BH down the line22%+2.2−0.3−1.5+0.4±3.9

Lean BH through the middle: +1.3±1.7 per 100 returns v the current mix (134 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv MartaValue
BH through the middle35%−2.6−0.3−0.1−3.0±2.3
BH crosscourt31%+1.5−0.3−1.3−0.1±3.4
FH through the middle19%−2.7+0.5+0.3−1.8±1.9
BH down the line15%−0.5−0.5−2.8−3.8±4.0

Lean BH crosscourt: +1.9±2.6 per 100 returns v the current mix (62 returns charted, inside the 90% margin)

Marta Kostyuk returning

1st serve to the forehand

ReturnNowTourOwnv SaraValue
FH through the middle41%+4.2+0.6+1.1+5.9±2.6
FH crosscourt19%+5.3+0.5+2.9+8.6±3.8
FH slice through the middle17%−6.7+1.4±0.0−5.3±1.4
FH down the line12%+1.5+4.8±0.0+6.3±4.2
FH slice crosscourt8%−6.6+0.9±0.0−5.7±1.9

Lean FH crosscourt: +5.4±3.3 per 100 returns v the current mix (1346 returns charted)

1st serve to the backhand

ReturnNowTourOwnv SaraValue
BH through the middle52%+6.0−0.2+1.8+7.7±2.3
BH crosscourt29%+7.7−2.1+3.8+9.4±3.2
BH down the line10%+2.2−1.1+2.1+3.2±4.5
BH slice through the middle4%−6.2−0.1±0.0−6.3±1.8
BH slice crosscourt3%−4.2−0.7±0.0−4.9±2.1

Lean BH crosscourt: +2.9±2.6 per 100 returns v the current mix (969 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv SaraValue
FH through the middle41%−3.2+1.5+0.2−1.5±2.9
FH crosscourt28%+0.5−1.0+1.5+1.0±4.2
FH down the line25%−0.6+0.7+1.3+1.4±4.7
FH slice through the middle4%−15.2+0.6±0.0−14.6±1.5
FH slice crosscourt2%−14.9+0.6±0.0−14.3±1.5

Lean FH down the line: +2.3±3.9 per 100 returns v the current mix (362 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv SaraValue
BH through the middle46%−2.6−2.6−0.5−5.6±2.5
BH crosscourt30%+1.5−1.1+2.8+3.2±3.3
BH down the line19%−0.5+1.0−0.1+0.4±4.6
FH inside-out2%+1.4±0.0+1.3+2.7±3.4
FH through the middle1%−2.7−0.2+0.2−2.6±2.2

Lean BH crosscourt: +4.7±2.8 per 100 returns v the current mix (471 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 hard. Each player's hard record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Sara Bejlek

Favour

ShotEdgeOwnTheirs
BH to the middle · return+3.1±3.1+1.7+1.4
FH to the middle · rally+2.4±3.0+2.5−0.2
FH to their backhand · serve +1+0.1±4.8+0.2±0.0
BH to the middle · rally−1.0±2.8+0.2−1.2
BH to their backhand · rally−1.4±3.8−0.7−0.7
FH to their backhand · return +1−1.7±5.4+3.0−4.6

Avoid

ShotEdgeOwnTheirs
FH to their forehand · rally−3.9±4.1−1.4−2.5
FH to the middle · serve +1−2.8±3.4−1.2−1.5
BH to their forehand · rally−2.4±5.3−1.3−1.0
FH to their backhand · rally−2.0±4.2+0.2−2.2
FH to the middle · return−1.8±3.4−1.6−0.2

Marta Kostyuk

Favour

ShotEdgeOwnTheirs
FH to their backhand · return +1+7.6±5.3+4.1+3.5
BH to their forehand · return+6.3±5.6−0.3+6.6
BH to their forehand · rally+4.8±5.2−0.2+5.1
FH to their backhand · return+4.3±5.4+0.7+3.6
FH to the middle · serve +1+3.5±3.3+2.1+1.4
BH to their backhand · rally+2.6±3.9+1.1+1.5

Avoid

ShotEdgeOwnTheirs
FH to their forehand · serve +1−5.9±5.0−1.0−4.9
FH to their backhand · serve +1−0.7±4.8+1.5−2.1
BH to the middle · return±0.0±2.7−1.3+1.3
BH to the middle · rally+0.1±2.8−0.5+0.6
FH to the middle · rally+0.6±3.0+1.4−0.8

Against Marta Kostyuk-like opponents

Sara Bejlek vMatchesServe pts wonReturn pts won
All charted opponents–45.0%40.9%

Similar by tactical fingerprint: Alexandra Eala, Diana Shnaider, Xin Yu Wang, Bianca Andreescu, Leylah Fernandez, Jasmine Paolini, Olga Danilovic, Kaja Juvan, Katie Boulter, Nao Hibino. When two players have rarely met, their records against these lookalikes fill the gap.