RallyIQ

Game plan

Karolina Pliskova v Irina Camelia Begu

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

Forecast

Karolina Pliskova wins, best of 3 90%90%: 64%–98% · best of 5: 94%
Serve points won 62.6% / 52.9% Karolina / Irina · tour 55.0%
Strengths only, no similarity priors 90%serve 62.6% / 52.9%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerKarolinaIrina
Direction choice+0.15 ±0.05
better than 80%
−0.31 ±0.08
better than 6%
Shot selection−0.12 ±0.08
better than 34%
−0.15 ±0.15
better than 31%
Execution−0.01 ±0.35
better than 60%
−1.73 ±0.43
better than 7%
Points left on the table2.45 ±0.06
lower than 70%
2.91 ±0.10
lower than 15%

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.

Karolina Pliskova serving

Deuce court

1st serveNowKarolina winsv IrinaMatchupOptimal
Wide46%67%71%72.1%±7.346%
Body15%63%61%65.9%±8.40% ▼
T39%75%74%80.2%±5.954% ▲

Optimal v Irina Camelia Begu: +0.7±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKarolina winsv IrinaMatchupOptimal
Wide44%74%76%82.7%±6.059% ▲
Body10%55%57%55.1%±10.70% ▼
T45%70%64%69.8%±6.941% ▼

Optimal v Irina Camelia Begu: +1.0±0.9 per 100 first serves (faults included) over the current mix. Serving wide every time would read +8.7 per 100 first serves in before the returner adjusts.

Irina Camelia Begu serving

Deuce court

1st serveNowIrina winsv KarolinaMatchupOptimal
Wide42%66%70%69.6%±6.857% ▲
Body17%56%56%55.0%±9.615% ▼
T40%54%69%56.0%±7.928% ▼

Optimal v Karolina Pliskova: +0.9±0.8 per 100 first serves (faults included) over the current mix. Serving wide every time would read +8.0 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowIrina winsv KarolinaMatchupOptimal
Wide43%52%68%55.2%±7.643%
Body22%56%55%54.3%±9.46% ▼
T36%64%69%68.8%±7.351% ▲

Optimal v Karolina Pliskova: +1.0±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +8.9 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.

Karolina Pliskova returning

1st serve to the forehand

ReturnNowTourOwnv IrinaValue
FH through the middle50%+4.2−2.0+1.4+3.6±2.3
FH down the line22%+1.5−0.7+0.5+1.3±4.1
FH crosscourt19%+5.3−3.9−2.3−0.9±3.9
FH slice through the middle5%−6.7−0.6−0.9−8.2±2.4
FH slice crosscourt3%−6.6−1.2+0.2−7.7±2.3

Lean FH through the middle: +2.5±1.7 per 100 returns v the current mix (1107 returns charted)

1st serve to the backhand

ReturnNowTourOwnv IrinaValue
BH through the middle43%+6.0+1.1+2.5+9.6±2.2
BH crosscourt23%+7.7−0.5−0.4+6.9±3.2
BH down the line15%+2.2+0.1+2.8+5.0±4.1
BH slice through the middle11%−6.2−0.5+1.0−5.7±2.2
BH slice crosscourt5%−4.2−0.9+0.7−4.3±2.6

Lean BH through the middle: +4.4±1.6 per 100 returns v the current mix (1161 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv IrinaValue
FH through the middle44%−3.2−2.2−1.9−7.3±2.9
FH down the line30%−0.6+3.8−2.7+0.5±5.1
FH crosscourt24%+0.5−3.5+1.9−1.0±4.5
FH slice through the middle2%−15.2−0.1±0.0−15.3±1.1

Lean FH down the line: +4.2±3.9 per 100 returns v the current mix (248 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv IrinaValue
BH crosscourt41%+1.5−1.4−1.1−1.0±3.2
BH through the middle36%−2.6+0.5−0.3−2.4±2.5
BH down the line17%−0.5−1.7−0.6−2.8±4.6
FH inside-in3%+0.7−1.8+1.9+0.9±4.4
FH through the middle2%−2.7+0.9−1.9−3.7±2.4

Lean FH inside-in: +2.7±4.7 per 100 returns v the current mix (664 returns charted, inside the 90% margin)

Irina Camelia Begu returning

1st serve to the forehand

ReturnNowTourOwnv KarolinaValue
FH through the middle47%+4.2−1.9−0.4+1.8±2.5
FH down the line22%+1.5+1.7−2.9+0.3±4.1
FH crosscourt19%+5.3−0.8−0.8+3.7±3.6
FH slice through the middle9%−6.7−0.3−1.8−8.8±2.1
FH slice down the line3%−10.5+0.5−2.6−12.6±2.8

Lean FH crosscourt: +3.3±3.3 per 100 returns v the current mix (155 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KarolinaValue
BH through the middle53%+6.0−3.0−0.6+2.4±2.4
BH down the line26%+2.2−1.6+1.2+1.7±4.3
BH crosscourt13%+7.7−2.6−0.4+4.8±3.0
BH slice through the middle7%−6.2+0.5−2.1−7.8±2.1

Lean BH crosscourt: +2.9±3.1 per 100 returns v the current mix (126 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv KarolinaValue
BH through the middle57%−2.6−3.0−0.8−6.4±2.3
BH down the line24%−0.5−0.6−0.3−1.4±4.5
BH crosscourt19%+1.5−0.9+0.6+1.1±2.7

Lean BH crosscourt: +4.9±2.8 per 100 returns v the current mix (91 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 clay. Each player's clay record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Karolina Pliskova

Favour

ShotEdgeOwnTheirs
BH to the middle · return +1+3.9±3.1+2.2+1.7
BH to the middle · return+1.1±3.1−1.7+2.8
FH to their forehand · rally+1.1±4.3−1.9+3.0
FH to their forehand · serve +1+1.1±5.2+1.1−0.1
FH to the middle · rally+1.0±3.1−0.5+1.4
FH to their forehand · return+0.5±5.7+0.1+0.4

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−6.2±4.6−5.1−1.1
BH to their backhand · serve +1−5.3±4.7−0.3−5.0
FH to the middle · return−5.1±3.3−3.0−2.1
BH to their backhand · return +1−4.1±4.6−3.7−0.4
FH to the middle · return +1−3.8±3.4−2.7−1.1

Irina Camelia Begu

Favour

ShotEdgeOwnTheirs
FH to their forehand · return +1+3.4±5.1+1.4+2.0
BH to their backhand · serve +1+2.0±4.6+2.6−0.6
FH to the middle · return +1+0.5±3.4−0.8+1.2
BH to their backhand · rally+0.4±4.0+0.9−0.6
FH to the middle · rally−0.4±3.0−0.2−0.2
BH to the middle · serve +1−0.6±3.2+0.2−0.8

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−9.3±5.8−10.5+1.2
BH to their forehand · return−7.6±5.7−5.0−2.6
BH to the middle · return−5.7±3.1−3.2−2.5
BH to the middle · rally−5.7±2.9−2.9−2.8
FH to their backhand · rally−4.1±4.6−3.4−0.7

Against Irina Camelia Begu-like opponents

Karolina Pliskova vMatchesServe pts wonReturn pts won
All charted opponents–59.9%41.1%

Similar by tactical fingerprint: Cristina Bucsa, Maya Joint, Jaqueline Cristian, Robin Montgomery, Victoria Mboko, Nao Hibino, Shelby Rogers, Anett Kontaveit, Alison Riske Amritraj. When two players have rarely met, their records against these lookalikes fill the gap.