RallyIQ

Game plan

Monica Puig v Veronika Kudermetova

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

Forecast

Monica Puig wins, best of 3 8%90%: 2%–24% · best of 5: 4%
Serve points won 52.9% / 63.8% Monica / Veronika · tour 56.4%
Strengths only, no similarity priors 9%serve 53.2% / 63.5%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Monica Puig's record against Veronika Kudermetova's tactical lookalikes and in their charted head-to-heads (lookalikes: −4.5 on serve, −5.5 on return vs expectation (203 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

CareerMonicaVeronika
Direction choice−0.25 ±0.10
better than 10%
+0.11 ±0.06
better than 73%
Shot selection+0.21 ±0.30
better than 69%
+0.10 ±0.11
better than 55%
Execution−1.94 ±0.79
better than 4%
−0.90 ±0.37
better than 21%

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.

Monica Puig serving

Deuce court

1st serveNowMonica winsv VeronikaMatchupOptimal
Wide27%60%68%61.6%±9.642% ▲
Body19%58%62%62.4%±10.37% ▼
T54%60%70%62.9%±8.551% ▼

Optimal v Veronika Kudermetova: +0.6±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +0.4 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMonica winsv VeronikaMatchupOptimal
Wide40%65%67%66.4%±8.955% ▲
Body16%55%58%56.5%±11.71% ▼
T44%59%66%60.6%±8.644%

Optimal v Veronika Kudermetova: +0.5±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.2 per 100 first serves in before the returner adjusts.

Veronika Kudermetova serving

Deuce court

1st serveNowVeronika winsv MonicaMatchupOptimal
Wide39%63%69%66.0%±8.739%
Body16%53%59%55.5%±11.41% ▼
T45%75%80%85.2%±6.060% ▲

Optimal v Monica Puig: +1.6±1.1 per 100 first serves (faults included) over the current mix. Serving T every time would read +12.4 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowVeronika winsv MonicaMatchupOptimal
Wide41%72%74%78.8%±7.256% ▲
Body17%53%57%54.3%±12.42% ▼
T42%60%70%66.1%±9.942%

Optimal v Monica Puig: +1.2±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +9.5 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.

Monica Puig returning

1st serve to the forehand

ReturnNowTourOwnv VeronikaValue
FH through the middle62%+4.2−1.3−0.1+2.8±2.8
FH crosscourt23%+5.3−1.2+1.0+5.0±4.0
FH down the line15%+1.5−2.2+2.0+1.3±4.1

Lean FH crosscourt: +1.9±3.6 per 100 returns v the current mix (87 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv VeronikaValue
BH through the middle55%+6.0−1.2−1.1+3.8±2.5
BH crosscourt29%+7.7−2.9−1.6+3.2±3.2
BH down the line17%+2.2−2.7−2.8−3.4±4.1

Lean BH through the middle: +1.4±1.6 per 100 returns v the current mix (66 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv VeronikaValue
BH through the middle44%−2.6+0.9−0.5−2.3±2.3
BH crosscourt41%+1.5+1.4+1.1+4.1±3.1
BH down the line15%−0.5+1.7−1.5−0.3±4.2

Lean BH crosscourt: +3.5±2.2 per 100 returns v the current mix (54 returns charted)

Veronika Kudermetova returning

1st serve to the forehand

ReturnNowTourOwnv MonicaValue
FH through the middle44%+4.2−3.1−1.6−0.6±2.8
FH crosscourt30%+5.3−3.1+1.6+3.8±3.9
FH down the line17%+1.5−2.9+0.8−0.6±4.4
FH slice through the middle5%−6.7−0.6−0.2−7.4±2.2
FH slice crosscourt3%−6.6−0.3±0.0−6.9±1.8

Lean FH crosscourt: +3.7±3.1 per 100 returns v the current mix (630 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MonicaValue
BH through the middle48%+6.0−1.4−1.0+3.7±2.6
BH crosscourt29%+7.7−2.2−0.4+5.1±3.5
BH down the line13%+2.2−1.7−2.2−1.7±4.5
BH slice through the middle4%−6.2−1.2+0.8−6.6±2.2
BH slice crosscourt4%−4.2−3.0−0.2−7.4±2.4

Lean BH crosscourt: +2.7±2.8 per 100 returns v the current mix (502 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv MonicaValue
FH crosscourt45%+0.5−3.4+1.2−1.7±4.3
FH through the middle45%−3.2−2.7+0.7−5.2±3.0
FH down the line7%−0.6+2.0+1.3+2.7±4.3
BH through the middle3%−1.0+0.6+2.8+2.4±2.0

Lean FH crosscourt: +1.2±2.7 per 100 returns v the current mix (166 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv MonicaValue
BH crosscourt47%+1.5+1.3+0.6+3.4±3.5
BH through the middle39%−2.6+0.1+2.8+0.3±2.7
BH down the line12%−0.5−2.1−1.0−3.6±4.9
FH inside-out2%+1.4+0.9+1.3+3.5±3.1

Lean BH crosscourt: +2.1±2.2 per 100 returns v the current mix (290 returns charted, inside the 90% margin)

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.

Monica Puig

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+4.1±6.0−0.2+4.3
FH to the middle · rally+0.6±3.0±0.0+0.5
BH to the middle · rally+0.3±2.6+0.3±0.0
FH to their backhand · rally−0.6±4.7+1.3−1.9
BH to their backhand · rally−1.7±3.4−2.4+0.7
FH to the middle · return−1.8±3.4−1.8±0.0

Avoid

ShotEdgeOwnTheirs
FH to their forehand · rally−4.1±4.1−3.5−0.6
BH to the middle · return−3.8±3.0−1.7−2.1
FH to their backhand · serve +1−3.4±5.5−0.8−2.6
BH to the middle · serve +1−2.6±3.3−2.9+0.2
FH to the middle · return−1.8±3.4−1.8±0.0

Veronika Kudermetova

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+3.1±3.7−0.5+3.7
FH to their backhand · rally+1.0±4.4−1.6+2.7
BH to their backhand · return+0.6±4.2−0.3+0.9
FH to the middle · rally−0.1±3.2−0.5+0.3
BH to the middle · return−0.4±3.0−1.8+1.3
FH to their forehand · rally−1.0±4.0−1.0±0.0

Avoid

ShotEdgeOwnTheirs
FH to the middle · return−5.2±3.4−4.1−1.1
BH to the middle · rally−1.3±2.8−0.9−0.5
FH to their forehand · rally−1.0±4.0−1.0±0.0
BH to the middle · return−0.4±3.0−1.8+1.3
FH to the middle · rally−0.1±3.2−0.5+0.3

Against Veronika Kudermetova-like opponents

Monica Puig vMatchesServe pts wonReturn pts won
All charted opponents–52.0%36.3%
Players most similar to Veronika Kudermetova2 47.4%30.3%

Similar by tactical fingerprint: Naomi Osaka, Karolina Pliskova, Ekaterina Alexandrova, Sorana Cirstea, Serena Williams, Shuai Zhang, Clara Tauson, Anett Kontaveit, Garbine Muguruza, Eugenie Bouchard. When two players have rarely met, their records against these lookalikes fill the gap.