RallyIQ

Game plan

Jessica Pegula v Angelique Kerber

Every number combines what Jessica Pegula does well with what Angelique Kerber allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Jessica Pegula wins, best of 3 79%90%: 67%–88% · best of 5: 84%
Serve points won 59.3% / 53.2% Jessica / Angelique · tour 56.4%
Strengths only, no similarity priors 79%serve 59.4% / 53.4%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Jessica Pegula's record against Angelique Kerber's tactical lookalikes and in their charted head-to-heads (lookalikes: −0.5 on serve, +1.0 on return vs expectation (517 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

CareerJessicaAngelique
Direction choice−0.15 ±0.04
better than 25%
+0.18 ±0.05
better than 83%
Shot selection−0.24 ±0.07
better than 26%
+0.05 ±0.08
better than 50%
Execution+0.29 ±0.30
better than 72%
+1.65 ±0.29
better than 97%
Points left on the table2.72 ±0.06
lower than 31%
2.57 ±0.07
lower than 56%

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.

Structural compatibility

Expected edge per 100 rally shots from style alone: Jessica Pegula +1.67, Angelique Kerber +0.81. Each player's shot mix weighted by their own skill with each shot and by how much the other gives up against it. This is why some rankings gaps don't hold in a given matchup.

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.

Jessica Pegula serving

Deuce court

1st serveNowJessica winsv AngeliqueMatchupOptimal
Wide31%67%69%69.9%±3.846% ▲
Body29%62%59%64.1%±4.828%
T40%74%63%69.6%±4.126% ▼

Optimal v Angelique Kerber: +0.3±0.6 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +1.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowJessica winsv AngeliqueMatchupOptimal
Wide29%66%67%67.8%±4.544% ▲
Body22%61%58%62.4%±5.222%
T49%63%60%58.6%±3.934% ▼

Optimal v Angelique Kerber: +0.7±0.6 per 100 first serves (faults included) over the current mix. Serving wide every time would read +5.6 per 100 first serves in before the returner adjusts.

Angelique Kerber serving

Deuce court

1st serveNowAngelique winsv JessicaMatchupOptimal
Wide36%65%65%64.5%±4.251% ▲
Body27%56%55%52.9%±4.812% ▼
T37%59%70%61.9%±4.337%

Optimal v Jessica Pegula: +0.5±0.7 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAngelique winsv JessicaMatchupOptimal
Wide62%62%64%60.4%±3.673% ▲
Body19%54%54%51.6%±5.84% ▼
T18%71%62%69.0%±5.523% ▲

Optimal v Jessica Pegula: +1.0±0.7 per 100 first serves (faults included) over the current mix. Serving T every time would read +8.7 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.

Jessica Pegula returning

1st serve to the forehand

ReturnNowTourOwnv AngeliqueValue
FH through the middle49%+4.2−1.8+1.1+3.5±1.6
FH crosscourt19%+5.3+1.4+3.8+10.5±3.2
FH slice through the middle13%−6.7+0.3−1.0−7.4±2.4
FH down the line11%+1.5+0.4−0.6+1.3±3.9
FH slice crosscourt4%−6.6−0.2+1.0−5.8±2.8

Lean FH crosscourt: +8.3±2.8 per 100 returns v the current mix (1257 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AngeliqueValue
BH through the middle42%+6.0−3.0+1.3+4.3±1.4
BH crosscourt24%+7.7+0.5+6.5+14.8±2.3
BH slice through the middle12%−6.2−0.2+0.6−5.8±2.3
BH down the line12%+2.2−1.4−1.7−0.9±3.6
BH slice crosscourt6%−4.2−0.9+2.1−3.0±3.0

Lean BH crosscourt: +11.0±2.0 per 100 returns v the current mix (1269 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv AngeliqueValue
FH through the middle46%−3.2−1.4+2.0−2.5±2.6
FH crosscourt27%+0.5+0.2+2.0+2.7±4.2
FH down the line23%−0.6+2.7+2.4+4.5±5.1
FH slice through the middle2%−15.2−1.2±0.0−16.4±1.3
FH slice down the line2%−14.1−1.7±0.0−15.8±1.7

Lean FH down the line: +4.5±4.2 per 100 returns v the current mix (359 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv AngeliqueValue
BH through the middle48%−2.6−1.0−0.1−3.7±1.8
BH crosscourt34%+1.5−6.1+3.7−0.9±2.7
BH down the line17%−0.5−0.4+0.4−0.5±4.4
BH slice through the middle2%−11.7−0.9+1.2−11.4±2.0

Lean BH down the line: +1.8±3.9 per 100 returns v the current mix (637 returns charted, inside the 90% margin)

Angelique Kerber returning

1st serve to the forehand

ReturnNowTourOwnv JessicaValue
FH through the middle53%+4.2+2.6+0.4+7.1±1.6
FH crosscourt34%+5.3+5.1−1.3+9.1±3.1
FH down the line11%+1.5+5.5±0.0+7.0±3.8
FH slice through the middle1%−6.7−0.1+0.8−6.0±2.1
BH through the middle1%+5.2+1.3−0.2+6.3±1.6

Lean FH crosscourt: +1.6±2.2 per 100 returns v the current mix (1007 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv JessicaValue
BH through the middle48%+6.0+1.1−0.2+6.9±1.4
BH crosscourt30%+7.7−0.4−3.2+4.2±2.4
BH down the line12%+2.2+7.3−1.5+7.9±3.5
BH slice through the middle5%−6.2−0.7−1.1−8.1±2.5
BH slice crosscourt2%−4.2−0.4−1.4−6.0±3.0

Lean BH down the line: +3.0±3.3 per 100 returns v the current mix (1499 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv JessicaValue
FH crosscourt39%+0.5−1.4−0.8−1.7±3.9
FH down the line32%−0.6+1.3+2.1+2.8±4.4
FH through the middle28%−3.2+0.3−0.7−3.6±2.6

Lean FH down the line: +3.6±3.4 per 100 returns v the current mix (285 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv JessicaValue
BH crosscourt39%+1.5+2.8−0.8+3.5±2.7
BH through the middle37%−2.6+1.3+0.1−1.2±1.9
BH down the line20%−0.5+4.0−0.5+2.9±4.0
FH inside-in1%+0.7−0.9−0.8−1.0±3.6
FH through the middle1%−2.7+1.2−0.7−2.2±1.9

Lean BH crosscourt: +2.0±2.0 per 100 returns v the current mix (646 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.

Jessica Pegula

Favour

ShotEdgeOwnTheirs
BH volley to their forehand · rally+8.8±8.9+2.5+6.4
BH volley to their backhand · rally+8.3±7.7+2.7+5.5
BH to their forehand · return +1+8.2±4.3+4.3+3.8
BH to their forehand · return+6.3±3.0−0.2+6.5
FH slice to their backhand · rally+6.2±4.9+1.4+4.8
FH to their forehand · serve +1+5.9±2.5±0.0+5.9

Avoid

ShotEdgeOwnTheirs
FH slice to their forehand · rally−7.8±5.1−1.5−6.3
FH slice to the middle · return +1−6.0±3.8−1.9−4.1
BH to their backhand · return−3.2±2.5−1.4−1.8
BH slice to their backhand · rally−2.0±2.8−0.9−1.1
FH slice to the middle · rally−1.9±3.0−1.1−0.9

Angelique Kerber

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+2.7±2.5+3.6−0.9
FH to their backhand · return+2.7±3.2+4.1−1.4
BH to their forehand · rally+2.7±2.5+2.6±0.0
FH to the middle · return+2.4±1.6+2.0+0.5
BH slice to the middle · rally+2.3±2.2+2.6−0.4
BH to their forehand · serve +1+2.1±3.8+2.3−0.2

Avoid

ShotEdgeOwnTheirs
Smash to their forehand · rally−5.7±7.8−7.5+1.8
BH to their backhand · return +1−3.8±3.3−2.9−0.9
BH slice to their backhand · rally−3.6±3.0−3.0−0.6
FH to their forehand · serve +1−2.4±2.4−0.9−1.6
FH volley to their forehand · rally−2.4±8.0−0.2−2.2

Against Angelique Kerber-like opponents

Jessica Pegula vMatchesServe pts wonReturn pts won
All charted opponents–58.5%43.9%
Players most similar to Angelique Kerber4 60.2%44.9%

Similar by tactical fingerprint: Cristina Bucsa, Arantxa Rus, Leylah Fernandez, Magda Linette, Sara Bejlek, Olga Danilovic, Anhelina Kalinina, Brenda Fruhvirtova, Anna Lena Friedsam, Flavia Pennetta. When two players have rarely met, their records against these lookalikes fill the gap.