RallyIQ

Game plan

Olga Danilovic v Sara Bejlek

Every number combines what Olga Danilovic 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

Olga Danilovic wins, best of 3 83%90%: 54%–97% · best of 5: 89%
Serve points won 58.3% / 51.1% Olga / Sara · tour 56.4%
Strengths only, no similarity priors 83%serve 58.3% / 51.1%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerOlgaSara
Direction choice−0.03 ±0.07
better than 49%
−0.14 ±0.15
better than 27%
Shot selection+0.44 ±0.13
better than 92%
+0.24 ±0.28
better than 72%
Execution−1.00 ±0.59
better than 18%
−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.

Olga Danilovic serving

Deuce court

1st serveNowOlga winsv SaraMatchupOptimal
Wide26%64%67%64.8%±9.135% ▲
Body29%54%53%49.0%±11.414% ▼
T45%64%74%70.5%±10.151% ▲

Optimal v Sara Bejlek: +0.8±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +7.7 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowOlga winsv SaraMatchupOptimal
Wide58%61%68%64.1%±9.558%
Body18%57%57%57.0%±12.43% ▼
T23%73%65%73.1%±9.639% ▲

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 +8.2 per 100 first serves in before the returner adjusts.

Sara Bejlek serving

Deuce court

1st serveNowSara winsv OlgaMatchupOptimal
Wide29%59%66%58.7%±10.629%
Body36%51%65%58.4%±10.721% ▼
T35%56%72%61.0%±10.550% ▲

Optimal v Olga Danilovic: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +1.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowSara winsv OlgaMatchupOptimal
Wide49%65%72%72.1%±8.464% ▲
Body32%57%51%51.8%±11.517% ▼
T19%53%71%59.9%±12.719%

Optimal v Olga Danilovic: +1.2±1.2 per 100 first serves (faults included) over the current mix. Serving wide every time would read +8.8 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.

Olga Danilovic returning

1st serve to the forehand

ReturnNowTourOwnv SaraValue
FH through the middle46%+4.2+0.6+1.1+5.9±3.0
FH crosscourt23%+5.3+2.3±0.0+7.7±4.2
FH down the line14%+1.5−3.6+2.9+0.8±4.7
FH slice through the middle8%−6.7−1.6±0.0−8.3±1.7
FH slice crosscourt5%−6.6−2.0±0.0−8.6±1.7

Lean FH crosscourt: +4.5±3.5 per 100 returns v the current mix (304 returns charted)

1st serve to the backhand

ReturnNowTourOwnv SaraValue
BH through the middle39%+6.0−1.7+1.8+6.2±2.6
BH crosscourt25%+7.7−1.6+2.1+8.3±3.5
BH down the line16%+2.2−1.3+3.8+4.8±4.6
BH slice through the middle10%−6.2−2.3±0.0−8.5±1.8
BH slice crosscourt7%−4.2−1.4±0.0−5.6±2.1

Lean BH crosscourt: +4.6±2.9 per 100 returns v the current mix (485 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv SaraValue
FH crosscourt42%+0.5−0.2+1.3+1.6±4.0
FH through the middle38%−3.2−0.5+0.2−3.4±3.0
FH down the line20%−0.6−2.5+1.5−1.6±4.7

Lean FH crosscourt: +2.6±2.8 per 100 returns v the current mix (74 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv SaraValue
BH through the middle47%−2.6+0.7−0.5−2.3±2.6
BH crosscourt36%+1.5+0.9−0.1+2.2±3.1
BH down the line9%−0.5−6.6+2.8−4.3±5.0
FH inside-in4%+0.7+0.9+1.3+3.0±3.9
FH through the middle3%−2.7+0.7+0.2−1.8±2.3

Lean BH crosscourt: +2.8±2.4 per 100 returns v the current mix (294 returns charted)

Sara Bejlek returning

1st serve to the forehand

ReturnNowTourOwnv OlgaValue
FH through the middle35%+4.2−0.9−2.8+0.4±2.8
FH crosscourt29%+5.3+1.3−1.6+5.1±4.2
FH down the line28%+1.5+1.6+0.5+3.6±4.4
FH slice through the middle9%−6.7−0.1+1.3−5.5±2.1

Lean FH crosscourt: +2.9±3.4 per 100 returns v the current mix (69 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv OlgaValue
BH through the middle50%+6.0+0.1+0.2+6.3±2.5
BH crosscourt28%+7.7+0.6−3.8+4.6±3.5
BH down the line22%+2.2−0.3+1.4+3.2±4.1

Lean BH through the middle: +1.1±1.8 per 100 returns v the current mix (134 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv OlgaValue
BH through the middle35%−2.6−0.3+0.3−2.6±2.6
BH crosscourt31%+1.5−0.3−1.0+0.2±3.4
FH through the middle19%−2.7+0.5−1.7−3.9±2.1
BH down the line15%−0.5−0.5−2.4−3.4±4.4

Lean BH crosscourt: +2.3±2.6 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 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.

Olga Danilovic

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+7.3±6.0+0.7+6.6
FH to their backhand · return +1+4.1±5.7+0.6+3.5
BH to their forehand · rally+3.1±5.6−2.0+5.1
FH to their backhand · return+2.8±6.1−0.8+3.6
FH to their backhand · rally+2.3±4.4+1.7+0.6
FH to their forehand · rally+1.2±4.5−1.2+2.4

Avoid

ShotEdgeOwnTheirs
FH to their forehand · serve +1−11.4±5.6−6.5−4.9
FH to their backhand · serve +1−5.9±5.2−3.8−2.1
FH to the middle · rally−3.5±3.5−2.6−0.8
BH to the middle · rally−2.8±3.2−3.4+0.6
BH to their backhand · rally−2.0±4.7−3.5+1.5

Sara Bejlek

Favour

ShotEdgeOwnTheirs
FH to their backhand · return +1+3.4±6.0+3.0+0.4
FH to the middle · rally+3.1±3.4+2.5+0.6
BH to their forehand · return+2.6±5.9+2.1+0.5
BH to the middle · return+2.4±3.3+1.7+0.7
FH to their backhand · serve +1+1.8±5.6+0.2+1.6
BH to the middle · rally+1.5±3.2+0.2+1.3

Avoid

ShotEdgeOwnTheirs
FH to the middle · return−8.1±3.8−1.6−6.5
BH to their backhand · rally−2.6±4.6−0.7−1.8
FH to the middle · serve +1−2.0±3.8−1.2−0.7
BH to their forehand · rally−0.7±5.7−1.3+0.6
FH to their forehand · rally−0.5±4.7−1.4+0.9

Against Sara Bejlek-like opponents

Olga Danilovic vMatchesServe pts wonReturn pts won
All charted opponents–55.4%40.8%

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