RallyIQ

Game plan

Kateryna Baindl v Viktorija Golubic

Every number combines what Kateryna Baindl does well with what Viktorija Golubic allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Kateryna Baindl wins, best of 3 18%90%: 4%–46% · best of 5: 13%
Serve points won 51.1% / 58.0% Kateryna / Viktorija · tour 56.4%
Strengths only, no similarity priors 18%serve 51.1% / 58.0%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerKaterynaViktorija
Direction choice−0.22 ±0.17
better than 15%
−0.29 ±0.09
better than 8%
Shot selection−0.56 ±0.33
better than 10%
−0.97 ±0.19
better than 3%
Execution−0.18 ±0.49
better than 52%
+0.87 ±0.49
better than 88%
Points left on the table2.61 ±0.17
lower than 50%
2.93 ±0.14
lower than 14%

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.

Kateryna Baindl serving

Deuce court

1st serveNowKateryna winsv ViktorijaMatchupOptimal
Wide33%59%65%58.7%±9.848% ▲
Body31%60%57%60.0%±9.727% ▼
T37%63%62%56.3%±10.225% ▼

Optimal v Viktorija Golubic: +0.2±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +1.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKateryna winsv ViktorijaMatchupOptimal
Wide33%59%73%67.5%±8.948% ▲
Body33%53%55%51.9%±10.918% ▼
T34%50%67%52.9%±9.934%

Optimal v Viktorija Golubic: +1.2±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +10.1 per 100 first serves in before the returner adjusts.

Viktorija Golubic serving

Deuce court

1st serveNowViktorija winsv KaterynaMatchupOptimal
Wide36%54%63%50.2%±9.821% ▼
Body34%65%61%67.7%±9.649% ▲
T30%55%76%64.6%±10.630%

Optimal v Kateryna Baindl: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +7.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowViktorija winsv KaterynaMatchupOptimal
Wide35%57%69%60.7%±9.850% ▲
Body30%54%62%60.2%±10.815% ▼
T35%60%60%55.6%±10.535%

Optimal v Kateryna Baindl: +0.3±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +1.9 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.

Kateryna Baindl returning

1st serve to the forehand

ReturnNowTourOwnv ViktorijaValue
FH through the middle56%+4.2+2.0+1.0+7.2±2.8
FH crosscourt26%+5.3+1.8−2.4+4.7±4.2
FH down the line10%+1.5+0.3+1.8+3.6±4.3
FH slice through the middle8%−6.7+0.2−1.0−7.5±2.0

Lean FH through the middle: +2.2±1.7 per 100 returns v the current mix (131 returns charted)

1st serve to the backhand

ReturnNowTourOwnv ViktorijaValue
BH through the middle53%+6.0+0.2+1.9+8.2±2.6
BH crosscourt27%+7.7−0.5+1.5+8.8±3.5
BH down the line12%+2.2−3.1+6.0+5.1±4.3
BH slice through the middle8%−6.2+0.5−0.9−6.7±1.7

Lean BH crosscourt: +2.1±3.0 per 100 returns v the current mix (131 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv ViktorijaValue
BH through the middle45%−2.6+0.2+0.1−2.3±2.7
BH crosscourt30%+1.5−0.1−0.7+0.7±3.5
BH down the line25%−0.5+1.1+0.5+1.1±5.0

Lean BH down the line: +1.6±4.1 per 100 returns v the current mix (77 returns charted, inside the 90% margin)

Viktorija Golubic returning

1st serve to the forehand

ReturnNowTourOwnv KaterynaValue
FH through the middle53%+4.2+2.0+0.8+7.0±2.9
FH crosscourt16%+5.3−1.5−0.1+3.8±4.4
FH down the line15%+1.5−0.3−0.7+0.5±4.7
FH slice through the middle9%−6.7+0.9+1.0−4.8±2.1
FH slice crosscourt5%−6.6−0.6+0.9−6.3±2.0

Lean FH through the middle: +3.6±1.7 per 100 returns v the current mix (280 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KaterynaValue
BH through the middle32%+6.0+0.1−2.5+3.7±2.8
BH slice through the middle31%−6.2+2.1−0.4−4.5±2.1
BH crosscourt15%+7.7+0.7+1.2+9.6±3.6
BH slice crosscourt10%−4.2+0.9±0.0−3.3±2.1
BH down the line8%+2.2−1.9−4.3−4.0±4.5

Lean BH crosscourt: +9.4±3.3 per 100 returns v the current mix (267 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv KaterynaValue
FH through the middle67%−3.2+2.4+1.6+0.8±2.8
FH crosscourt33%+0.5−0.4−0.7−0.5±4.0

Lean FH through the middle: +0.4±1.6 per 100 returns v the current mix (48 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv KaterynaValue
BH through the middle56%−2.6+2.0+0.9+0.3±2.8
BH crosscourt25%+1.5+1.6+1.4+4.5±3.6
BH down the line19%−0.5−1.8−3.0−5.3±4.9

Lean BH crosscourt: +4.2±3.3 per 100 returns v the current mix (135 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.

Kateryna Baindl

Favour

ShotEdgeOwnTheirs
BH to their forehand · return+10.0±6.3+3.7+6.3
FH to their backhand · serve +1+4.7±5.6+1.7+3.0
FH to the middle · return+4.1±3.5+1.1+3.0
BH to the middle · rally+2.7±2.8+1.3+1.4
FH to the middle · rally+2.1±3.1+0.4+1.7
BH to the middle · return+1.8±3.0−1.0+2.8

Avoid

ShotEdgeOwnTheirs
BH to their backhand · rally−5.1±3.7−3.2−1.9
FH to their forehand · serve +1−2.5±5.5−2.6+0.1
FH to their forehand · rally−2.3±4.2−0.2−2.1
BH to the middle · return +1−1.7±3.5−0.7−1.0
FH to the middle · serve +1−1.5±3.8−0.4−1.1

Viktorija Golubic

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+7.7±4.5+2.7+5.0
FH to their backhand · serve +1+7.3±5.7+2.4+4.9
BH to their forehand · serve +1+7.0±7.2+1.5+5.5
FH to the middle · return+6.0±3.6+2.7+3.3
FH to their forehand · rally+2.1±4.0+2.4−0.3
FH to the middle · serve +1+1.9±3.6+2.2−0.3

Avoid

ShotEdgeOwnTheirs
BH to their forehand · return−9.1±6.6−3.1−6.0
FH to their forehand · serve +1−4.0±5.6−1.1−2.9
BH to the middle · serve +1−2.3±3.6−0.6−1.8
BH to the middle · return−2.3±3.0+0.1−2.4
BH to their backhand · return +1−1.9±5.1−1.3−0.6

Against Viktorija Golubic-like opponents

Kateryna Baindl vMatchesServe pts wonReturn pts won
All charted opponents–50.8%40.8%

Similar by tactical fingerprint: Jessica Pegula, Marie Bouzkova, Emma Navarro, Emma Raducanu, Ajla Tomljanovic, Lin Zhu, Zarina Diyas, Alize Cornet, R. When two players have rarely met, their records against these lookalikes fill the gap.