RallyIQ

Game plan

Mona Barthel v Karolina Pliskova

Every number combines what Mona Barthel does well with what Karolina Pliskova allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Mona Barthel wins, best of 3 41%90%: 18%–67% · best of 5: 38%
Serve points won 57.7% / 59.5% Mona / Karolina · tour 56.4%
Strengths only, no similarity priors 41%serve 57.7% / 59.5%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerMonaKarolina
Direction choice+0.20 ±0.18
better than 84%
+0.15 ±0.05
better than 80%
Shot selection+0.21 ±0.31
better than 68%
−0.12 ±0.08
better than 34%
Execution−0.27 ±1.34
better than 45%
−0.01 ±0.35
better than 60%

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.

Mona Barthel serving

Deuce court

1st serveNowMona winsv KarolinaMatchupOptimal
Wide38%71%70%74.0%±7.253% ▲
Body17%53%56%52.4%±10.82% ▼
T45%67%69%69.0%±8.245%

Optimal v Karolina Pliskova: +1.0±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMona winsv KarolinaMatchupOptimal
Wide47%57%68%59.4%±8.545% ▼
Body12%51%55%49.2%±12.20% ▼
T40%65%69%69.8%±7.855% ▲

Optimal v Karolina Pliskova: +1.2±0.9 per 100 first serves (faults included) over the current mix. Serving T every time would read +7.5 per 100 first serves in before the returner adjusts.

Karolina Pliskova serving

Deuce court

1st serveNowKarolina winsv MonaMatchupOptimal
Wide46%67%62%62.8%±8.346%
Body15%63%55%60.8%±9.80% ▼
T39%75%73%79.3%±6.554% ▲

Optimal v Mona Barthel: +0.8±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +10.4 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKarolina winsv MonaMatchupOptimal
Wide44%74%66%74.1%±7.041% ▼
Body10%55%51%49.9%±12.00% ▼
T45%70%59%65.7%±7.859% ▲

Optimal v Mona Barthel: +1.0±0.8 per 100 first serves (faults included) over the current mix. Serving wide every time would read +6.3 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.

Mona Barthel returning

1st serve to the forehand

ReturnNowTourOwnv KarolinaValue
FH through the middle45%+4.2+0.6−0.4+4.3±2.5
FH down the line26%+1.5+3.3−2.9+1.9±4.1
FH crosscourt21%+5.3−1.9−0.8+2.6±3.6
FH slice through the middle8%−6.7−0.6−1.8−9.1±2.0

Lean FH through the middle: +2.1±1.9 per 100 returns v the current mix (121 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KarolinaValue
BH through the middle42%+6.0+0.6−0.6+6.0±2.4
BH crosscourt33%+7.7−0.9−0.4+6.5±3.2
BH down the line13%+2.2−0.9+1.2+2.5±3.9
BH slice crosscourt7%−4.2+0.4−1.5−5.3±2.6
BH slice through the middle5%−6.2+0.1−2.1−8.3±2.0

Lean BH crosscourt: +2.3±2.4 per 100 returns v the current mix (116 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv KarolinaValue
BH crosscourt49%+1.5+0.5+0.6+2.5±3.0
BH through the middle38%−2.6−2.1−0.8−5.5±2.2
BH down the line12%−0.5+0.9−0.3+0.1±3.9

Lean BH crosscourt: +3.4±1.8 per 100 returns v the current mix (65 returns charted)

Karolina Pliskova returning

1st serve to the forehand

ReturnNowTourOwnv MonaValue
FH through the middle50%+4.2−2.0−1.0+1.2±2.5
FH down the line22%+1.5−0.7−0.4+0.5±3.9
FH crosscourt19%+5.3−3.9+2.9+4.3±3.9
FH slice through the middle5%−6.7−0.6+1.2−6.1±2.2
FH slice crosscourt3%−6.6−1.2−0.4−8.3±2.3

Lean FH crosscourt: +3.6±3.5 per 100 returns v the current mix (1107 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MonaValue
BH through the middle43%+6.0+1.1+1.7+8.8±2.4
BH crosscourt23%+7.7−0.5+0.3+7.5±3.2
BH down the line15%+2.2+0.1+0.9+3.1±4.2
BH slice through the middle11%−6.2−0.5−0.3−7.0±2.0
BH slice crosscourt5%−4.2−0.9−1.1−6.2±2.6

Lean BH through the middle: +4.3±1.7 per 100 returns v the current mix (1161 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv MonaValue
FH through the middle44%−3.2−2.2−0.8−6.2±3.0
FH down the line30%−0.6+3.8+1.3+4.5±4.7
FH crosscourt24%+0.5−3.5+2.0−1.0±4.5
FH slice through the middle2%−15.2−0.1±0.0−15.3±1.1

Lean FH down the line: +6.5±3.7 per 100 returns v the current mix (248 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv MonaValue
BH crosscourt41%+1.5−1.4+1.6+1.7±3.1
BH through the middle36%−2.6+0.5−0.5−2.6±2.4
BH down the line17%−0.5−1.7−0.8−3.0±4.5
FH inside-in3%+0.7−1.8+2.0+0.9±4.5
FH through the middle2%−2.7+0.9−0.8−2.6±2.5

Lean BH crosscourt: +2.5±2.2 per 100 returns v the current mix (664 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 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.

Mona Barthel

Favour

ShotEdgeOwnTheirs
FH to their forehand · serve +1+8.4±4.9+5.1+3.2
BH to their forehand · rally+5.8±5.0+3.2+2.6
BH to their backhand · rally+4.8±3.7+4.7+0.2
FH to their forehand · rally+3.8±3.8+2.7+1.0
FH to the middle · return+1.4±3.3+2.7−1.4
BH to their backhand · serve +1+1.2±4.4+0.3+0.9

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−6.2±4.9−7.0+0.8
BH to their backhand · return−4.2±4.2−3.4−0.8
FH to their backhand · rally−2.9±4.2−3.6+0.7
BH to the middle · return−2.5±2.9−0.5−2.0
BH to the middle · rally−1.4±2.9−0.8−0.6

Karolina Pliskova

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+5.4±3.8+0.2+5.2
BH to their forehand · rally+4.7±5.4+1.2+3.5
BH to the middle · return+2.3±3.0+1.0+1.3
FH to their backhand · rally+1.8±4.4+0.6+1.2
FH to their forehand · serve +1+1.3±4.7−0.7+2.0
BH to the middle · rally−1.7±2.8±0.0−1.7

Avoid

ShotEdgeOwnTheirs
FH to their forehand · return−3.8±5.0−6.1+2.3
FH to the middle · return−3.8±3.1−1.3−2.4
FH to the middle · serve +1−1.9±3.4−1.2−0.7
FH to their forehand · rally−1.7±3.3+0.1−1.8
BH to the middle · rally−1.7±2.8±0.0−1.7

Against Karolina Pliskova-like opponents

Mona Barthel vMatchesServe pts wonReturn pts won
All charted opponents–54.8%41.5%

Similar by tactical fingerprint: Naomi Osaka, Anastasia Potapova, Belinda Bencic, Ekaterina Alexandrova, Sorana Cirstea, Shuai Zhang, Veronika Kudermetova, Shelby Rogers, Anett Kontaveit, Maria Sharapova. When two players have rarely met, their records against these lookalikes fill the gap.