RallyIQ

Game plan

Anna Karolina Schmiedlova v Maja Chwalinska

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

Forecast

Anna Karolina Schmiedlova wins, best of 3 34%90%: 7%–76% · best of 5: 31%
Serve points won 55.8% / 58.9% Anna / Maja · tour 58.1%
Strengths only, no similarity priors 31%serve 55.3% / 59.2%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Anna Karolina Schmiedlova's record against Maja Chwalinska's tactical lookalikes and in their charted head-to-heads (lookalikes: +9.8 on serve, +3.9 on return vs expectation (186 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

CareerAnnaMaja
Direction choice−0.07 ±0.09
better than 40%
−0.08 ±0.06
better than 40%
Shot selection+0.06 ±0.12
better than 53%
−0.52 ±0.28
better than 11%
Execution+0.53 ±0.71
better than 80%
+2.12 ±0.53
better than 99%
Points left on the table2.38 ±0.13
lower than 78%
2.93 ±0.10
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.

Anna Karolina Schmiedlova serving

Deuce court

1st serveNowAnna winsv MajaMatchupOptimal
Wide36%67%58%59.0%±9.737%
Body27%53%57%52.1%±11.312% ▼
T36%69%62%63.4%±10.751% ▲

Optimal v Maja Chwalinska: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +4.7 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAnna winsv MajaMatchupOptimal
Wide24%61%59%53.3%±12.624%
Body26%56%53%52.3%±11.511% ▼
T50%65%58%58.5%±9.865% ▲

Optimal v Maja Chwalinska: +0.4±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.8 per 100 first serves in before the returner adjusts.

Maja Chwalinska serving

Deuce court

1st serveNowMaja winsv AnnaMatchupOptimal
Wide26%62%68%64.2%±10.741% ▲
Body28%55%56%54.1%±11.213% ▼
T46%65%73%70.5%±9.046%

Optimal v Anna Karolina Schmiedlova: +0.5±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMaja winsv AnnaMatchupOptimal
Wide53%60%70%65.5%±10.852%
Body22%53%53%49.9%±11.67% ▼
T26%67%61%63.3%±11.241% ▲

Optimal v Anna Karolina Schmiedlova: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.0 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.

Anna Karolina Schmiedlova returning

1st serve to the forehand

ReturnNowTourOwnv MajaValue
FH through the middle48%+4.2+1.9+0.2+6.3±3.1
FH crosscourt19%+5.3+1.2+2.7+9.2±4.3
FH down the line16%+1.5−0.3+3.2+4.5±4.6
FH slice through the middle11%−6.7−0.9−1.2−8.9±2.0
BH inside-in3%+7.3+0.7+1.3+9.3±2.9

Lean FH crosscourt: +4.8±3.9 per 100 returns v the current mix (161 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MajaValue
BH through the middle54%+6.0+0.9+2.1+9.0±2.7
BH crosscourt26%+7.7−2.9+1.3+6.1±3.5
BH down the line11%+2.2−1.7+2.7+3.2±4.2
BH slice through the middle9%−6.2−0.6+0.6−6.2±1.8

Lean BH through the middle: +2.8±1.6 per 100 returns v the current mix (100 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv MajaValue
BH crosscourt48%+1.5+0.2−0.1+1.5±3.6
BH through the middle43%−2.6−0.6+1.4−1.9±2.7
BH down the line8%−0.5−1.4−1.4−3.4±3.9

Lean BH crosscourt: +1.9±2.2 per 100 returns v the current mix (60 returns charted, inside the 90% margin)

Maja Chwalinska returning

1st serve to the forehand

ReturnNowTourOwnv AnnaValue
FH through the middle41%+4.2+3.2+1.4+8.8±3.1
FH crosscourt21%+5.3+3.3−0.6+8.0±4.2
FH slice through the middle17%−6.7+1.6+0.1−5.0±2.3
FH down the line14%+1.5+1.1+2.9+5.6±4.4
FH slice crosscourt7%−6.6+1.5−0.5−5.6±1.8

Lean FH through the middle: +4.0±2.2 per 100 returns v the current mix (111 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AnnaValue
BH through the middle43%+6.0+1.2−0.7+6.4±2.8
BH crosscourt33%+7.7+2.4+0.1+10.2±3.5
BH down the line16%+2.2+3.6−0.4+5.3±4.6
BH slice through the middle3%−6.2−0.5+0.1−6.7±1.7
BH slice crosscourt3%−4.2+0.1±0.0−4.1±1.3

Lean BH crosscourt: +3.8±2.7 per 100 returns v the current mix (236 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv AnnaValue
BH through the middle31%−2.6+1.6+0.8−0.2±2.8
BH crosscourt29%+1.5±0.0+2.5+4.0±3.6
FH through the middle18%−2.7−1.1+1.8−2.1±2.6
BH down the line11%−0.5−1.3+1.8−0.1±4.8
FH inside-in5%+0.7+0.5−2.3−1.1±3.9

Lean BH crosscourt: +3.3±2.8 per 100 returns v the current mix (110 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 grass. Each player's grass record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Anna Karolina Schmiedlova

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+4.3±3.5+1.8+2.5
BH to the middle · return+3.4±3.1+0.3+3.1
BH to the middle · rally+3.2±2.8+2.6+0.6
FH to their forehand · return +1+2.6±4.0+2.1+0.5
BH to the middle · return +1+2.5±2.6+1.0+1.5
FH to the middle · rally+2.1±3.0+2.2−0.2

Avoid

ShotEdgeOwnTheirs
FH to their forehand · serve +1−2.2±3.9−0.4−1.8
BH to their backhand · rally−2.2±3.6+0.8−3.0
FH to their backhand · rally−1.8±4.6−1.2−0.5
BH to their backhand · return +1−0.6±3.5−0.7+0.1
FH to their forehand · rally−0.2±3.9+1.7−2.0

Maja Chwalinska

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+5.5±2.9+3.6+1.9
FH to their forehand · return +1+5.5±4.9+3.2+2.3
BH to the middle · return+5.3±3.2+4.6+0.7
BH to their backhand · return+3.8±3.5+2.8+1.0
FH to the middle · return+3.6±2.7+1.8+1.9
FH to the middle · serve +1+3.4±2.8+2.7+0.7

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−2.7±4.1−1.7−1.0
FH to their forehand · serve +1−2.1±5.0+2.5−4.6
BH to their forehand · rally−0.9±4.0+2.9−3.8
FH to their backhand · rally+0.4±4.5−0.6+1.0
BH to their backhand · serve +1+0.9±3.8+1.2−0.4

Against Maja Chwalinska-like opponents

Anna Karolina Schmiedlova vMatchesServe pts wonReturn pts won
All charted opponents–55.4%45.3%
Players most similar to Maja Chwalinska1 65.1%47.0%

Similar by tactical fingerprint: Magdalena Frech, Sara Bejlek, Kaja Juvan, Tiantsoa Sarah Rakotomanga Rajaonah, Sara Errani, Angelique Kerber, Brenda Fruhvirtova, Martina Trevisan, Kateryna Baindl, Agnieszka Radwanska. When two players have rarely met, their records against these lookalikes fill the gap.