RallyIQ

Game plan

Lesia Tsurenko v Victoria Jimenez Kasintseva

Every number combines what Lesia Tsurenko does well with what Victoria Jimenez Kasintseva allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Lesia Tsurenko wins, best of 3 79%90%: 39%–97% · best of 5: 84%
Serve points won 52.1% / 46.2% Lesia / Victoria · tour 55.0%
Strengths only, no similarity priors 79%serve 52.1% / 46.2%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Lesia Tsurenko's record against Victoria Jimenez Kasintseva'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

CareerLesiaVictoria
Direction choice−0.24 ±0.13
better than 14%
−0.24 ±0.23
better than 12%
Shot selection−0.07 ±0.18
better than 37%
+0.40 ±0.20
better than 90%
Execution+0.30 ±0.65
better than 72%
−0.70 ±0.67
better than 27%
Points left on the table2.55 ±0.10
lower than 59%
2.81 ±0.37
lower than 23%

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.

Lesia Tsurenko serving

Deuce court

1st serveNowLesia winsv VictoriaMatchupOptimal
Wide30%62%62%57.9%±9.330%
Body42%51%52%44.9%±9.127% ▼
T28%65%67%64.3%±10.443% ▲

Optimal v Victoria Jimenez Kasintseva: +0.9±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 serveNowLesia winsv VictoriaMatchupOptimal
Wide38%59%60%52.5%±9.839%
Body43%49%59%51.4%±9.928% ▼
T18%62%60%57.7%±11.133% ▲

Optimal v Victoria Jimenez Kasintseva: +0.9±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +4.8 per 100 first serves in before the returner adjusts.

Victoria Jimenez Kasintseva serving

Deuce court

1st serveNowVictoria winsv LesiaMatchupOptimal
Wide23%68%63%65.0%±10.223%
Body37%49%55%46.8%±10.722% ▼
T40%64%70%65.8%±9.355% ▲

Optimal v Lesia Tsurenko: +0.9±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +7.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowVictoria winsv LesiaMatchupOptimal
Wide49%57%64%55.2%±9.249%
Body33%53%49%46.2%±10.818% ▼
T18%65%59%59.6%±13.233% ▲

Optimal v Lesia Tsurenko: +0.8±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.6 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.

Lesia Tsurenko returning

1st serve to the forehand

ReturnNowTourOwnv VictoriaValue
FH through the middle43%+4.2−0.4−1.2+2.6±3.0
FH crosscourt21%+5.3+0.3−0.2+5.4±4.4
FH slice through the middle14%−6.7+1.1−1.1−6.7±2.4
FH down the line13%+1.5+0.2−0.6+1.1±4.7
FH slice down the line5%−10.5−0.8±0.0−11.3±2.0

Lean FH crosscourt: +4.7±3.7 per 100 returns v the current mix (297 returns charted)

1st serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH through the middle47%+6.0−2.5+1.7+5.2±2.8
BH crosscourt19%+7.7+2.7+2.5+12.9±3.7
BH down the line14%+2.2−0.3−2.0−0.2±4.6
BH slice through the middle12%−6.2−0.2−0.5−7.0±2.4
BH slice crosscourt4%−4.2−0.5−0.6−5.2±2.0

Lean BH crosscourt: +9.7±3.3 per 100 returns v the current mix (217 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv VictoriaValue
FH through the middle46%−3.2+0.5+2.1−0.6±3.1
FH crosscourt38%+0.5+1.2−0.5+1.3±4.2
FH down the line16%−0.6+0.8−1.5−1.2±4.8

Lean FH crosscourt: +1.2±3.0 per 100 returns v the current mix (92 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH through the middle43%−2.6+1.4−1.6−2.8±2.8
BH crosscourt31%+1.5−1.9−0.4−0.8±3.6
BH down the line26%−0.5−1.3−1.4−3.3±4.5

Lean BH crosscourt: +1.5±3.0 per 100 returns v the current mix (112 returns charted, inside the 90% margin)

Victoria Jimenez Kasintseva returning

1st serve to the forehand

ReturnNowTourOwnv LesiaValue
FH through the middle61%+4.2+0.3+0.6+5.1±2.9
FH crosscourt27%+5.3−1.1+0.4+4.6±4.3
FH down the line11%+1.5+1.3+1.1+3.9±4.4

Lean FH through the middle: +0.3±1.7 per 100 returns v the current mix (168 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv LesiaValue
BH through the middle59%+6.0−1.8+0.9+5.1±2.5
BH crosscourt25%+7.7+0.7+2.3+10.8±3.7
BH down the line16%+2.2−2.0+2.0+2.2±4.4

Lean BH crosscourt: +4.7±3.2 per 100 returns v the current mix (215 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv LesiaValue
BH through the middle63%−2.6+0.3−2.4−4.7±2.8
BH crosscourt30%+1.5−1.5+0.5+0.5±3.4
BH down the line7%−0.5−0.1+1.8+1.2±4.3

Lean BH crosscourt: +3.2±3.0 per 100 returns v the current mix (70 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 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.

Lesia Tsurenko

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+5.3±4.5+4.9+0.4
BH to their forehand · rally+5.2±5.3+3.8+1.4
BH to their forehand · return+4.7±6.1+2.0+2.7
BH to the middle · return +1+3.5±3.2+2.1+1.4
FH to the middle · return+3.1±3.8+2.3+0.7
FH to their forehand · rally+2.9±4.5+1.7+1.2

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−4.8±5.6−5.5+0.7
FH to their forehand · serve +1−0.5±5.6−0.8+0.3
FH to their forehand · return±0.0±6.1+2.2−2.2
BH to their backhand · return+0.3±5.1−0.1+0.3
FH to the middle · rally+0.3±3.2−1.8+2.1

Victoria Jimenez Kasintseva

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+7.6±4.2+3.3+4.3
FH to their backhand · return+4.0±5.8+2.3+1.7
BH to their forehand · return+3.9±6.4+2.6+1.3
FH to the middle · rally+2.8±3.2+0.5+2.3
BH to their backhand · rally+2.7±4.2+0.7+2.0
FH to their backhand · return +1+2.4±5.7+2.3+0.1

Avoid

ShotEdgeOwnTheirs
BH to the middle · return−5.8±3.4−5.2−0.7
BH to their forehand · rally−2.4±5.5−3.1+0.8
FH to the middle · return−2.2±3.5−0.9−1.3
BH to the middle · serve +1−1.6±3.6−2.4+0.7
FH to their forehand · serve +1−1.0±5.6−0.5−0.5

Against Victoria Jimenez Kasintseva-like opponents

Lesia Tsurenko vMatchesServe pts wonReturn pts won
All charted opponents–50.9%45.4%

Similar by tactical fingerprint: Diana Shnaider, Arantxa Rus, Jil Teichmann, Sara Bejlek, Olga Danilovic, Xiyu Wang, Nao Hibino, Beatriz Haddad Maia, Martina Trevisan. When two players have rarely met, their records against these lookalikes fill the gap.