RallyIQ

Game plan

Alina Korneeva v Sara Bejlek

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

Forecast

Alina Korneeva wins, best of 3 90%90%: 59%–99% · best of 5: 95%
Serve points won 52.7% / 43.0% Alina / Sara · tour 55.0%
Strengths only, no similarity priors 92%serve 53.1% / 42.4%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Alina Korneeva's record against Sara Bejlek's tactical lookalikes and in their charted head-to-heads (lookalikes: −10.9 on serve, −22.1 on return vs expectation (97 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

CareerAlinaSara
Direction choice−0.01 ±0.10
better than 51%
−0.14 ±0.15
better than 27%
Shot selection+0.25 ±0.25
better than 73%
+0.24 ±0.28
better than 72%
Execution+0.66 ±0.76
better than 84%
−0.48 ±0.87
better than 34%

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.

Alina Korneeva serving

Deuce court

1st serveNowAlina winsv SaraMatchupOptimal
Wide36%66%67%66.8%±9.751% ▲
Body36%54%53%49.1%±12.521% ▼
T29%66%74%72.1%±11.428%

Optimal v Sara Bejlek: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +10.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAlina winsv SaraMatchupOptimal
Wide48%59%68%62.4%±11.149%
Body26%62%57%62.6%±13.010% ▼
T26%70%65%70.0%±11.141% ▲

Optimal v Sara Bejlek: +0.7±1.3 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.6 per 100 first serves in before the returner adjusts.

Sara Bejlek serving

Deuce court

1st serveNowSara winsv AlinaMatchupOptimal
Wide29%59%60%52.9%±11.829%
Body36%51%52%45.7%±11.421% ▼
T35%56%64%51.5%±12.650% ▲

Optimal v Alina Korneeva: +0.5±1.3 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowSara winsv AlinaMatchupOptimal
Wide49%65%60%60.0%±11.064% ▲
Body32%57%51%51.6%±11.932%
T19%53%51%38.8%±13.44% ▼

Optimal v Alina Korneeva: +0.2±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +6.8 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.

Alina Korneeva returning

1st serve to the forehand

ReturnNowTourOwnv SaraValue
FH through the middle42%+4.2+2.6+1.1+7.8±3.1
FH crosscourt33%+5.3+1.8+2.9+10.0±4.3
FH slice through the middle10%−6.7+0.8±0.0−5.9±1.7
FH down the line9%+1.5+2.4±0.0+3.9±4.3
FH slice crosscourt6%−6.6+0.7±0.0−6.0±1.6

Lean FH crosscourt: +4.1±3.2 per 100 returns v the current mix (206 returns charted)

1st serve to the backhand

ReturnNowTourOwnv SaraValue
BH through the middle61%+6.0+2.3+1.8+10.2±2.9
BH crosscourt24%+7.7+1.9+3.8+13.5±3.6
BH down the line12%+2.2+0.5+2.1+4.8±4.3
BH slice through the middle4%−6.2−0.8±0.0−7.1±1.0

Lean BH crosscourt: +3.8±3.3 per 100 returns v the current mix (127 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv SaraValue
FH crosscourt46%+0.5+0.3+1.5+2.3±4.2
FH through the middle30%−3.2+0.3+0.2−2.7±2.8
FH down the line24%−0.6+3.7+1.3+4.5±4.3

Lean FH crosscourt: +1.0±2.6 per 100 returns v the current mix (50 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv SaraValue
BH crosscourt44%+1.5−0.8+2.8+3.5±3.4
BH through the middle37%−2.6+0.2−0.5−2.8±2.7
BH down the line19%−0.5−4.1−0.1−4.8±4.0

Lean BH crosscourt: +3.9±2.3 per 100 returns v the current mix (52 returns charted)

Sara Bejlek returning

1st serve to the forehand

ReturnNowTourOwnv AlinaValue
FH through the middle35%+4.2−0.9+0.2+3.5±3.0
FH crosscourt29%+5.3+1.3+0.7+7.3±4.2
FH down the line28%+1.5+1.6+1.2+4.3±4.5
FH slice through the middle9%−6.7−0.1+0.1−6.7±1.9

Lean FH crosscourt: +3.4±3.4 per 100 returns v the current mix (69 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AlinaValue
BH through the middle50%+6.0+0.1+1.6+7.7±2.8
BH crosscourt28%+7.7+0.6+2.0+10.3±3.7
BH down the line22%+2.2−0.3−0.2+1.6±4.6

Lean BH crosscourt: +3.2±3.1 per 100 returns v the current mix (134 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv AlinaValue
BH through the middle35%−2.6−0.3+1.5−1.3±2.7
BH crosscourt31%+1.5−0.3−1.8−0.7±3.4
FH through the middle19%−2.7+0.5+2.4+0.2±2.4
BH down the line15%−0.5−0.5+2.5+1.5±4.5

Lean BH crosscourt: −0.2±2.7 per 100 returns v the current mix (62 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.

Alina Korneeva

Favour

ShotEdgeOwnTheirs
BH to the middle · return+4.4±3.1+1.1+3.3
BH to their forehand · return+4.2±5.7−1.6+5.8
FH to the middle · return+3.9±3.5+2.5+1.3
FH to their backhand · rally+3.1±3.8+1.8+1.3
FH to the middle · serve +1+2.9±3.4+2.2+0.7
FH to their forehand · rally+2.9±4.0+1.2+1.7

Avoid

ShotEdgeOwnTheirs
FH to their forehand · serve +1−7.5±5.2−0.3−7.3
BH to their backhand · rally−0.1±3.9−1.0+0.9
BH to the middle · rally+0.3±2.9±0.0+0.3
FH to their backhand · return +1+0.9±5.2−0.6+1.4
FH to their backhand · serve +1+2.3±4.9+2.3±0.0

Sara Bejlek

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+5.0±5.7+4.3+0.7
FH to the middle · rally+3.8±3.0+0.6+3.2
BH to their forehand · rally+3.6±5.4+1.8+1.8
BH to the middle · return+3.3±3.2+0.7+2.6
FH to their backhand · rally+2.4±4.2+3.8−1.4
FH to the middle · serve +1+2.1±3.3+1.1+1.0

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−3.7±5.0−2.0−1.7
FH to their backhand · return +1−1.6±5.3−0.2−1.5
FH to their forehand · rally−0.4±4.1−1.1+0.7
BH to the middle · rally−0.3±2.9−0.4+0.1
FH to the middle · return+0.2±3.5−0.4+0.6

Against Sara Bejlek-like opponents

Alina Korneeva vMatchesServe pts wonReturn pts won
All charted opponents–54.6%50.6%
Players most similar to Sara Bejlek1 46.0%25.5%

Similar by tactical fingerprint: Diana Shnaider, Marta Kostyuk, Leylah Fernandez, Jil Teichmann, Olga Danilovic, Victoria Jimenez Kasintseva, Beatriz Haddad Maia, Anhelina Kalinina, Martina Trevisan. When two players have rarely met, their records against these lookalikes fill the gap.