RallyIQ

Game plan

Victoria Jimenez Kasintseva v Magdalena Frech

Every number combines what Victoria Jimenez Kasintseva does well with what Magdalena Frech 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 43%90%: 16%–74% · best of 5: 41%
Serve points won 53.9% / 55.3% Victoria / Magdalena · tour 56.4%
Strengths only, no similarity priors 42%serve 53.8% / 55.4%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Victoria Jimenez Kasintseva's record against Magdalena Frech's tactical lookalikes and in their charted head-to-heads (lookalikes: +3.0 on serve, +1.9 on return vs expectation (154 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

CareerVictoriaMagdalena
Direction choice−0.24 ±0.23
better than 12%
+0.14 ±0.09
better than 78%
Shot selection+0.40 ±0.20
better than 90%
−0.42 ±0.11
better than 15%
Execution−0.70 ±0.67
better than 27%
+1.36 ±0.49
better than 96%
Points left on the table2.81 ±0.37
lower than 23%
2.39 ±0.15
lower than 78%

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 MagdalenaMatchupOptimal
Wide23%68%68%70.0%±9.438% ▲
Body37%49%57%48.9%±9.622% ▼
T40%64%71%67.2%±8.940%

Optimal v Magdalena Frech: +1.2±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +8.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowVictoria winsv MagdalenaMatchupOptimal
Wide49%57%66%58.1%±8.649%
Body33%53%61%57.7%±9.818% ▼
T18%65%68%68.6%±11.833% ▲

Optimal v Magdalena Frech: +0.8±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +8.8 per 100 first serves in before the returner adjusts.

Magdalena Frech serving

Deuce court

1st serveNowMagdalena winsv VictoriaMatchupOptimal
Wide37%62%62%57.3%±8.534% ▼
Body12%59%52%53.4%±11.40% ▼
T51%63%67%62.1%±9.466% ▲

Optimal v Victoria Jimenez Kasintseva: +0.5±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMagdalena winsv VictoriaMatchupOptimal
Wide47%66%60%60.3%±9.062% ▲
Body13%48%59%50.4%±11.70% ▼
T40%56%60%51.0%±9.438% ▼

Optimal v Victoria Jimenez Kasintseva: +0.6±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.0 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 MagdalenaValue
FH through the middle61%+4.2+0.3+2.3+6.8±2.8
FH crosscourt27%+5.3−1.1+0.5+4.7±4.3
FH down the line11%+1.5+1.3+2.0+4.8±4.4

Lean FH through the middle: +0.8±1.7 per 100 returns v the current mix (168 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv MagdalenaValue
BH through the middle59%+6.0−1.8+0.6+4.8±2.5
BH crosscourt25%+7.7+0.7−0.9+7.6±3.7
BH down the line16%+2.2−2.0+1.5+1.7±4.6

Lean BH crosscourt: +2.6±3.2 per 100 returns v the current mix (215 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv MagdalenaValue
BH through the middle63%−2.6+0.3+0.9−1.4±2.8
BH crosscourt30%+1.5−1.5+1.3+1.3±3.5
BH down the line7%−0.5−0.1−2.7−3.4±4.3

Lean BH crosscourt: +2.1±3.0 per 100 returns v the current mix (70 returns charted, inside the 90% margin)

Magdalena Frech returning

1st serve to the forehand

ReturnNowTourOwnv VictoriaValue
FH through the middle39%+4.2+2.5−1.2+5.4±3.0
FH slice through the middle25%−6.7+1.5−1.1−6.3±2.4
FH down the line16%+1.5−0.6−0.6+0.3±4.7
FH crosscourt14%+5.3+0.1−0.2+5.2±4.4
FH slice down the line4%−10.5+1.5±0.0−9.0±1.9

Lean FH through the middle: +4.6±2.1 per 100 returns v the current mix (317 returns charted)

1st serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH through the middle41%+6.0+2.1+1.7+9.8±2.7
BH crosscourt27%+7.7+1.5+2.5+11.7±3.6
BH slice through the middle13%−6.2+1.2−0.5−5.5±2.5
BH down the line8%+2.2+2.1−2.0+2.2±4.6
BH slice crosscourt8%−4.2−0.9−0.6−5.7±2.4

Lean BH crosscourt: +5.8±2.9 per 100 returns v the current mix (300 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv VictoriaValue
FH through the middle49%−3.2+1.1+2.1±0.0±3.1
FH crosscourt26%+0.5+0.1−0.5+0.1±3.9
FH down the line25%−0.6+1.8−1.5−0.3±4.9

Lean FH crosscourt: +0.1±3.5 per 100 returns v the current mix (68 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH crosscourt37%+1.5+1.2−0.4+2.2±3.7
BH down the line34%−0.5+3.5−1.4+1.6±4.7
BH through the middle29%−2.6+1.5−1.6−2.6±2.8

Lean BH crosscourt: +1.7±2.9 per 100 returns v the current mix (139 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+6.5±3.7+3.5+3.0
FH to the middle · return +1+4.9±3.9+1.5+3.4
FH to their backhand · return +1+4.4±6.1+1.1+3.4
FH to the middle · return+4.1±3.2+1.8+2.3
BH to their forehand · return +1+4.0±7.1+0.4+3.6
BH to the middle · return +1+3.8±3.6−0.8+4.6

Avoid

ShotEdgeOwnTheirs
FH to their forehand · serve +1−2.2±5.1±0.0−2.2
FH to their forehand · rally−2.0±3.5−3.3+1.3
FH to their forehand · return +1−1.6±5.5−3.2+1.6
BH to the middle · serve +1−1.4±3.8−2.2+0.8
BH to their forehand · serve +1−1.3±7.1−1.1−0.2

Magdalena Frech

Favour

ShotEdgeOwnTheirs
FH to the middle · rally+5.3±2.7+2.7+2.6
BH to their forehand · return+3.4±6.0+3.2+0.2
FH to their backhand · return +1+3.3±6.0+2.4+1.0
BH to the middle · return+2.7±3.2+2.2+0.5
FH to their forehand · rally+2.6±3.5+1.5+1.1
FH to the middle · return +1+2.6±3.9+1.0+1.5

Avoid

ShotEdgeOwnTheirs
BH to their backhand · return−5.4±5.1−0.3−5.1
FH to their forehand · return−4.7±6.3−3.6−1.1
FH to the middle · serve +1−0.9±3.8−0.5−0.5
BH to the middle · serve +1−0.8±3.7+2.4−3.2
FH to their backhand · serve +1−0.5±5.2−0.6+0.1

Against Magdalena Frech-like opponents

Victoria Jimenez Kasintseva vMatchesServe pts wonReturn pts won
All charted opponents–51.7%45.6%
Players most similar to Magdalena Frech1 56.1%45.8%

Similar by tactical fingerprint: Elina Svitolina, Daria Kasatkina, Jaqueline Cristian, Victoria Azarenka, Linda Fruhvirtova, Caroline Wozniacki, Vera Zvonareva, Agnieszka Radwanska, Flavia Pennetta, Dinara Safina. When two players have rarely met, their records against these lookalikes fill the gap.