RallyIQ

Game plan

Angelique Kerber v Monica Puig

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

Forecast

Angelique Kerber wins, best of 3 98%90%: 84%–100% · best of 5: 99%
Serve points won 65.9% / 50.5% Angelique / Monica · tour 58.1%
Strengths only, no similarity priors 98%serve 66.6% / 50.7%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Angelique Kerber's record against Monica Puig's tactical lookalikes and in their charted head-to-heads (lookalikes: −3.0 on serve, +0.7 on return vs expectation (991 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

CareerAngeliqueMonica
Direction choice+0.18 ±0.05
better than 83%
−0.25 ±0.10
better than 10%
Shot selection+0.05 ±0.08
better than 50%
+0.21 ±0.30
better than 69%
Execution+1.65 ±0.29
better than 97%
−1.94 ±0.79
better than 4%

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.

Angelique Kerber serving

Deuce court

1st serveNowAngelique winsv MonicaMatchupOptimal
Wide36%65%69%67.9%±8.137%
Body27%56%59%57.7%±10.212% ▼
T37%59%80%73.2%±8.951% ▲

Optimal v Monica Puig: +0.6±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAngelique winsv MonicaMatchupOptimal
Wide62%62%74%70.3%±8.263%
Body19%54%57%55.1%±11.44% ▼
T18%71%70%75.9%±8.433% ▲

Optimal v Monica Puig: +1.2±1.1 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.

Monica Puig serving

Deuce court

1st serveNowMonica winsv AngeliqueMatchupOptimal
Wide27%60%69%62.8%±9.042% ▲
Body19%58%59%60.0%±10.319%
T54%60%63%55.1%±8.239% ▼

Optimal v Angelique Kerber: +0.6±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.7 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMonica winsv AngeliqueMatchupOptimal
Wide40%65%67%66.9%±8.355% ▲
Body16%55%58%56.3%±11.216%
T44%59%60%54.0%±8.329% ▼

Optimal v Angelique Kerber: +0.6±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +7.4 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.

Angelique Kerber returning

1st serve to the forehand

ReturnNowTourOwnv MonicaValue
FH through the middle53%+4.2+2.6−1.6+5.1±2.6
FH crosscourt34%+5.3+5.1+0.8+11.3±3.5
FH down the line11%+1.5+5.5+1.6+8.7±4.5
FH slice through the middle1%−6.7−0.1−0.2−7.0±1.9
BH through the middle1%+5.2+1.3−1.0+5.6±2.4

Lean FH crosscourt: +3.9±2.7 per 100 returns v the current mix (1007 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MonicaValue
BH through the middle48%+6.0+1.1−1.0+6.2±2.3
BH crosscourt30%+7.7−0.4−2.2+5.2±2.8
BH down the line12%+2.2+7.3−0.4+9.1±4.3
BH slice through the middle5%−6.2−0.7+0.8−6.1±2.3
BH slice crosscourt2%−4.2−0.4+0.6−4.0±2.4

Lean BH down the line: +3.9±4.1 per 100 returns v the current mix (1499 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv MonicaValue
FH crosscourt39%+0.5−1.4+1.3+0.4±4.0
FH down the line32%−0.6+1.3+1.2+1.9±5.0
FH through the middle28%−3.2+0.3+0.7−2.2±3.0

Lean FH down the line: +1.8±3.8 per 100 returns v the current mix (285 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv MonicaValue
BH crosscourt39%+1.5+2.8−1.0+3.3±3.0
BH through the middle37%−2.6+1.3+2.8+1.5±2.5
BH down the line20%−0.5+4.0+0.6+4.1±5.0
FH inside-in1%+0.7−0.9+1.3+1.1±3.7
FH through the middle1%−2.7+1.2+0.7−0.9±2.3

Lean BH down the line: +1.4±4.3 per 100 returns v the current mix (646 returns charted, inside the 90% margin)

Monica Puig returning

1st serve to the forehand

ReturnNowTourOwnv AngeliqueValue
FH through the middle62%+4.2−1.3+1.1+4.0±2.5
FH crosscourt23%+5.3−1.2+3.8+7.8±3.6
FH down the line15%+1.5−2.2−0.6−1.3±3.8

Lean FH crosscourt: +3.7±3.2 per 100 returns v the current mix (87 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AngeliqueValue
BH through the middle55%+6.0−1.2+1.3+6.2±2.2
BH crosscourt29%+7.7−2.9+6.5+11.3±2.8
BH down the line17%+2.2−2.7−1.7−2.2±3.3

Lean BH crosscourt: +5.1±2.4 per 100 returns v the current mix (66 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv AngeliqueValue
BH through the middle44%−2.6+0.9−0.1−1.9±2.1
BH crosscourt41%+1.5+1.4+3.7+6.6±3.0
BH down the line15%−0.5+1.7+0.4+1.5±3.7

Lean BH crosscourt: +4.5±2.1 per 100 returns v the current mix (54 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.

Angelique Kerber

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+6.4±3.6+3.4+3.0
FH to their forehand · rally+4.4±3.5+4.2+0.1
BH to their backhand · return+4.2±4.1+4.1+0.1
FH to their backhand · rally+4.2±3.4+2.7+1.4
BH to the middle · return+3.5±3.2+2.2+1.3
FH to the middle · rally+2.0±2.8+1.7+0.3

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally+0.8±2.4+1.4−0.6
FH to the middle · return+1.5±3.0+2.4−0.9
FH to the middle · rally+2.0±2.8+1.7+0.3
BH to the middle · return+3.5±3.2+2.2+1.3
FH to their backhand · rally+4.2±3.4+2.7+1.4

Monica Puig

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+5.5±4.3−0.5+6.0
FH to their backhand · serve +1+5.0±4.3−0.3+5.3
FH to their backhand · rally+1.8±4.5−3.4+5.2
FH to the middle · rally+1.2±2.6−0.2+1.4
BH to the middle · return−0.3±2.6−1.0+0.7
FH to the middle · return−0.4±3.1−1.3+0.8

Avoid

ShotEdgeOwnTheirs
BH to their backhand · rally−3.1±3.4−1.9−1.2
FH to their forehand · rally−2.4±4.3−2.3−0.1
BH to the middle · serve +1−2.0±3.0−2.8+0.8
BH to the middle · rally−0.5±2.4±0.0−0.5
FH to the middle · return−0.4±3.1−1.3+0.8

Against Monica Puig-like opponents

Angelique Kerber vMatchesServe pts wonReturn pts won
All charted opponents–56.1%42.6%
Players most similar to Monica Puig7 54.7%43.7%

Similar by tactical fingerprint: Alexandra Eala, Ashlyn Krueger, Maya Joint, Jaqueline Cristian, Veronika Kudermetova, Arianne Hartono, Heather Watson, Lauren Davis, Garbine Muguruza, Kate Makarova. When two players have rarely met, their records against these lookalikes fill the gap.