RallyIQ

Game plan

Kurumi Nara v Anna Karolina Schmiedlova

Every number combines what Kurumi Nara does well with what Anna Karolina Schmiedlova allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Kurumi Nara wins, best of 3 17%90%: 3%–52% · best of 5: 12%
Serve points won 51.2% / 58.3% Kurumi / Anna · tour 56.3%
Strengths only, no similarity priors 17%serve 51.2% / 58.3%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Kurumi Nara's record against Anna Karolina Schmiedlova'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

CareerKurumiAnna
Direction choice−0.30 ±0.37
better than 7%
−0.07 ±0.09
better than 40%
Shot selection+0.26 ±0.28
better than 74%
+0.06 ±0.12
better than 53%
Execution−0.43 ±0.71
better than 37%
+0.53 ±0.71
better than 80%

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.

Kurumi Nara serving

Deuce court

1st serveNowKurumi winsv AnnaMatchupOptimal
Wide37%56%68%58.9%±11.752% ▲
Body35%55%56%54.1%±12.323% ▼
T28%54%73%59.8%±12.825% ▼

Optimal v Anna Karolina Schmiedlova: +0.4±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKurumi winsv AnnaMatchupOptimal
Wide39%64%70%69.3%±11.754% ▲
Body29%55%53%52.3%±12.713% ▼
T32%57%61%52.7%±12.333%

Optimal v Anna Karolina Schmiedlova: +0.9±1.3 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +10.3 per 100 first serves in before the returner adjusts.

Anna Karolina Schmiedlova serving

Deuce court

1st serveNowAnna winsv KurumiMatchupOptimal
Wide36%67%60%61.1%±10.937%
Body27%53%55%50.2%±13.512% ▼
T36%69%74%75.3%±11.151% ▲

Optimal v Kurumi Nara: +1.1±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +12.0 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAnna winsv KurumiMatchupOptimal
Wide24%61%66%60.6%±13.724%
Body26%56%61%60.3%±13.411% ▼
T50%65%72%72.2%±9.865% ▲

Optimal v Kurumi Nara: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.8 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.

Kurumi Nara returning

1st serve to the forehand

ReturnNowTourOwnv AnnaValue
FH through the middle61%+4.2−2.6+1.4+3.0±3.1
FH crosscourt22%+5.3−0.8+2.9+7.5±4.2
FH down the line17%+1.5+3.0−0.6+4.0±4.5

Lean FH crosscourt: +3.3±3.9 per 100 returns v the current mix (108 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv AnnaValue
BH through the middle50%+6.0−1.6−0.7+3.7±2.7
BH crosscourt33%+7.7−1.6−0.4+5.7±3.3
BH down the line17%+2.2+1.6+0.1+3.8±3.7

Lean BH through the middle: −0.7±1.9 per 100 returns v the current mix (42 returns charted, inside the 90% margin)

Anna Karolina Schmiedlova returning

1st serve to the forehand

ReturnNowTourOwnv KurumiValue
FH through the middle48%+4.2+1.9−1.2+4.9±3.1
FH crosscourt19%+5.3+1.2+3.6+10.1±4.3
FH down the line16%+1.5−0.3+4.3+5.6±4.4
FH slice through the middle11%−6.7−0.9±0.0−7.6±1.6
BH inside-in3%+7.3+0.7+1.3+9.3±3.0

Lean FH crosscourt: +5.9±3.9 per 100 returns v the current mix (161 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KurumiValue
BH through the middle54%+6.0+0.9+0.9+7.8±3.0
BH crosscourt26%+7.7−2.9+1.3+6.1±3.6
BH down the line11%+2.2−1.7+2.1+2.6±3.9
BH slice through the middle9%−6.2−0.6±0.0−6.8±1.3

Lean BH through the middle: +2.3±1.7 per 100 returns v the current mix (100 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv KurumiValue
BH crosscourt48%+1.5+0.2+1.8+3.4±3.5
BH through the middle43%−2.6−0.6−0.9−4.2±2.7
BH down the line8%−0.5−1.4+2.4+0.4±3.6

Lean BH crosscourt: +3.5±2.2 per 100 returns v the current mix (60 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.

Kurumi Nara

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+3.1±2.9+1.2+1.9
BH to the middle · serve +1+1.6±2.7+1.2+0.4
FH to the middle · rally+1.4±2.3+1.0+0.4
FH to the middle · return+1.2±2.8−0.7+1.9
BH to the middle · return−0.2±2.7−0.9+0.7
BH to the middle · rally−0.8±2.3−1.2+0.5

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−5.4±4.4−1.6−3.8
FH to their backhand · rally−1.4±3.7−2.3+1.0
FH to their forehand · rally−1.2±3.2±0.0−1.3
BH to their backhand · return−0.8±3.6−1.8+1.0
BH to the middle · rally−0.8±2.3−1.2+0.5

Anna Karolina Schmiedlova

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+4.3±3.5+1.8+2.6
BH to the middle · rally+3.9±2.2+2.6+1.4
FH to the middle · rally+2.1±2.3+2.2−0.2
FH to their forehand · rally+1.9±3.0+1.7+0.1
BH to the middle · return+1.0±2.7+0.3+0.7
FH to the middle · return+0.5±2.9+0.3+0.1

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−1.1±3.9−1.2+0.1
BH to their backhand · rally+0.4±2.9+0.8−0.4
FH to the middle · return+0.5±2.9+0.3+0.1
BH to the middle · return+1.0±2.7+0.3+0.7
FH to their forehand · rally+1.9±3.0+1.7+0.1

Against Anna Karolina Schmiedlova-like opponents

Kurumi Nara vMatchesServe pts wonReturn pts won
All charted opponents–49.9%42.2%

Similar by tactical fingerprint: Elina Svitolina, Jessica Pegula, Kimberly Birrell, Varvara Gracheva, Elisabetta Cocciaretto, Ana Bogdan, Caroline Wozniacki, Qiang Wang, Carla Suarez Navarro, Marion Bartoli. When two players have rarely met, their records against these lookalikes fill the gap.