RallyIQ

Game plan

Victoria Jimenez Kasintseva v Martina Trevisan

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

Forecast

Victoria Jimenez Kasintseva wins, best of 3 62%90%: 27%–89% · best of 5: 65%
Serve points won 51.5% / 49.2% Victoria / Martina · tour 56.4%
Strengths only, no similarity priors 66%serve 52.0% / 48.9%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Victoria Jimenez Kasintseva's record against Martina Trevisan's tactical lookalikes and in their charted head-to-heads (lookalikes: −21.9 on serve, −11.5 on return vs expectation (83 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

CareerVictoriaMartina
Direction choice−0.24 ±0.23
better than 12%
−0.17 ±0.15
better than 22%
Shot selection+0.40 ±0.20
better than 90%
+0.41 ±0.23
better than 91%
Execution−0.70 ±0.67
better than 27%
−0.41 ±0.87
better than 38%
Points left on the table2.81 ±0.37
lower than 23%
2.86 ±0.24
lower than 20%

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.

Victoria Jimenez Kasintseva serving

Deuce court

1st serveNowVictoria winsv MartinaMatchupOptimal
Wide23%68%67%69.3%±10.038% ▲
Body37%49%57%48.4%±10.922% ▼
T40%64%63%58.2%±12.140%

Optimal v Martina Trevisan: +0.9±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +12.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowVictoria winsv MartinaMatchupOptimal
Wide49%57%63%54.1%±10.449%
Body33%53%54%50.9%±11.118% ▼
T18%65%61%62.2%±13.333% ▲

Optimal v Martina Trevisan: +0.6±1.2 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.

Martina Trevisan serving

Deuce court

1st serveNowMartina winsv VictoriaMatchupOptimal
Wide23%67%62%62.5%±12.038% ▲
Body32%54%52%48.5%±10.717% ▼
T45%52%67%51.0%±10.845%

Optimal v Victoria Jimenez Kasintseva: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +9.7 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMartina winsv VictoriaMatchupOptimal
Wide54%55%60%48.5%±9.754%
Body30%48%59%51.1%±11.315% ▼
T16%66%60%61.1%±12.831% ▲

Optimal v Victoria Jimenez Kasintseva: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +9.8 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.

Victoria Jimenez Kasintseva returning

1st serve to the forehand

ReturnNowTourOwnv MartinaValue
FH through the middle61%+4.2+0.3+0.7+5.1±3.0
FH crosscourt27%+5.3−1.1−4.6−0.4±4.4
FH down the line11%+1.5+1.3+2.1+4.8±4.5

Lean FH through the middle: +1.6±1.7 per 100 returns v the current mix (168 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv MartinaValue
BH through the middle59%+6.0−1.8+2.6+6.8±2.6
BH crosscourt25%+7.7+0.7−6.2+2.3±3.7
BH down the line16%+2.2−2.0+3.3+3.4±4.7

Lean BH through the middle: +1.7±1.6 per 100 returns v the current mix (215 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv MartinaValue
BH through the middle63%−2.6+0.3+1.4−0.9±2.8
BH crosscourt30%+1.5−1.5−0.2−0.1±3.4
BH down the line7%−0.5−0.1−0.9−1.5±4.3

Lean BH crosscourt: +0.6±3.0 per 100 returns v the current mix (70 returns charted, inside the 90% margin)

Martina Trevisan returning

1st serve to the forehand

ReturnNowTourOwnv VictoriaValue
FH crosscourt43%+5.3+2.1−0.6+6.8±4.4
FH through the middle38%+4.2−0.5−1.2+2.4±3.1
FH down the line19%+1.5+2.5−0.2+3.8±4.5

Lean FH crosscourt: +2.3±2.9 per 100 returns v the current mix (103 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH through the middle45%+6.0+0.8+1.7+8.5±2.8
BH down the line22%+2.2−1.3+2.5+3.4±4.7
BH crosscourt14%+7.7−4.4−2.0+1.2±3.6
BH slice through the middle14%−6.2−3.2−0.5−9.9±2.5
BH slice down the line3%−12.5−2.0−0.6−15.0±2.3

Lean BH through the middle: +5.6±1.9 per 100 returns v the current mix (218 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH through the middle38%−2.6−1.0−1.6−5.1±2.7
BH crosscourt25%+1.5+0.9−1.4+0.9±3.0
BH down the line10%−0.5+0.8−0.4−0.2±4.4
FH inside-out10%+1.4+2.6−0.5+3.5±3.3
FH inside-in9%+0.7+0.2−1.5−0.5±3.9

Lean BH crosscourt: +2.4±2.6 per 100 returns v the current mix (68 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.

Victoria Jimenez Kasintseva

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+5.5±3.9+3.5+2.0
FH to their backhand · return +1+4.3±6.2+1.1+3.3
BH to the middle · return+4.1±3.2+0.6+3.5
FH to their backhand · return+3.9±6.5+1.5+2.4
BH to their forehand · rally+3.1±4.7+1.9+1.2
BH to their forehand · return +1+3.0±7.0+0.4+2.6

Avoid

ShotEdgeOwnTheirs
FH to their forehand · rally−1.2±4.8−3.3+2.1
BH to their backhand · rally−0.7±4.4−0.4−0.3
FH to their forehand · serve +1+0.6±5.9±0.0+0.5
BH to the middle · return +1+0.8±3.7−0.8+1.6
FH to the middle · serve +1+1.1±3.9−0.3+1.4

Martina Trevisan

Favour

ShotEdgeOwnTheirs
FH to their forehand · return+5.1±6.4+6.2−1.1
FH to their backhand · rally+5.0±3.9+2.4+2.6
FH to their forehand · serve +1+4.1±5.8+4.0+0.1
BH to the middle · return+3.8±3.5+3.2+0.5
FH to the middle · rally+3.2±3.0+0.5+2.6
FH to their backhand · return +1+2.4±5.9+1.4+1.0

Avoid

ShotEdgeOwnTheirs
BH to their backhand · return−4.7±5.5+0.4−5.1
FH to the middle · return−2.8±3.9−1.9−0.9
BH to their backhand · rally−2.6±4.5−0.3−2.4
FH to their forehand · rally−2.5±4.2−3.6+1.1
FH to the middle · serve +1−2.1±3.9−1.6−0.5

Against Martina Trevisan-like opponents

Victoria Jimenez Kasintseva vMatchesServe pts wonReturn pts won
All charted opponents–51.7%45.6%
Players most similar to Martina Trevisan1 30.3%32.0%

Similar by tactical fingerprint: Cristina Bucsa, Diana Shnaider, Arantxa Rus, Marta Kostyuk, Leylah Fernandez, Jil Teichmann, Sara Bejlek, Olga Danilovic, Kaja Juvan. When two players have rarely met, their records against these lookalikes fill the gap.