RallyIQ

Game plan

Paula Badosa v Mayar Sherif

Every number combines what Paula Badosa does well with what Mayar Sherif allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Paula Badosa wins, best of 3 94%90%: 79%–99% · best of 5: 97%
Serve points won 66.1% / 54.0% Paula / Mayar · tour 56.4%
Strengths only, no similarity priors 96%serve 66.4% / 53.0%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Paula Badosa's record against Mayar Sherif's tactical lookalikes and in their charted head-to-heads (lookalikes: −0.9 on serve, −3.4 on return vs expectation (1205 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

CareerPaulaMayar
Direction choice−0.24 ±0.05
better than 13%
+0.12 ±0.14
better than 76%
Shot selection+0.29 ±0.09
better than 77%
+0.77 ±0.25
better than 99%
Execution+0.75 ±0.35
better than 86%
−0.22 ±0.71
better than 47%
Points left on the table2.83 ±0.08
lower than 22%
2.53 ±0.18
lower than 63%

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.

Paula Badosa serving

Deuce court

1st serveNowPaula winsv MayarMatchupOptimal
Wide47%67%67%68.2%±6.848%
Body19%62%63%67.1%±8.24% ▼
T33%73%67%72.7%±7.648% ▲

Optimal v Mayar Sherif: +0.5±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowPaula winsv MayarMatchupOptimal
Wide26%67%64%65.5%±7.335% ▲
Body16%53%55%51.5%±10.61% ▼
T58%72%66%73.3%±7.664% ▲

Optimal v Mayar Sherif: +1.4±0.9 per 100 first serves (faults included) over the current mix. Serving T every time would read +5.5 per 100 first serves in before the returner adjusts.

Mayar Sherif serving

Deuce court

1st serveNowMayar winsv PaulaMatchupOptimal
Wide46%65%67%65.4%±6.862% ▲
Body22%54%54%50.7%±9.26% ▼
T32%63%65%59.8%±8.332%

Optimal v Paula Badosa: +0.8±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMayar winsv PaulaMatchupOptimal
Wide55%57%62%53.3%±6.954% ▼
Body13%50%55%49.5%±11.10% ▼
T31%66%62%62.9%±8.546% ▲

Optimal v Paula Badosa: +0.5±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +7.2 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.

Paula Badosa returning

1st serve to the forehand

ReturnNowTourOwnv MayarValue
FH through the middle45%+4.2+0.5+0.8+5.4±2.6
FH down the line21%+1.5+1.4+2.5+5.4±4.4
FH crosscourt15%+5.3+0.1+1.5+6.8±4.2
FH slice through the middle12%−6.7−0.7−1.6−9.0±2.4
FH slice crosscourt4%−6.6+1.1+0.2−5.3±2.3

Lean FH crosscourt: +3.7±3.9 per 100 returns v the current mix (726 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv MayarValue
BH through the middle49%+6.0+1.3+0.4+7.7±2.3
BH crosscourt24%+7.7+1.9+5.1+14.7±3.3
BH down the line16%+2.2+4.0−3.3+2.8±4.5
BH slice through the middle7%−6.2−0.4−0.5−7.2±2.2
BH slice crosscourt2%−4.2+0.5±0.0−3.6±1.9

Lean BH crosscourt: +7.9±2.8 per 100 returns v the current mix (767 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv MayarValue
FH through the middle54%−3.2+1.0+1.3−0.9±3.0
FH crosscourt30%+0.5−0.8+2.4+2.1±4.3
FH down the line13%−0.6−2.2−1.7−4.4±5.1
FH slice through the middle3%−15.2+0.6±0.0−14.6±1.2

Lean FH crosscourt: +3.0±3.5 per 100 returns v the current mix (217 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv MayarValue
BH through the middle45%−2.6+2.0+0.2−0.4±2.5
BH crosscourt38%+1.5+3.3−0.4+4.4±3.4
BH down the line8%−0.5+0.7+2.3+2.5±4.9
FH through the middle4%−2.7−1.1+1.3−2.5±2.5
FH inside-out3%+1.4+0.8−1.7+0.5±3.9

Lean BH crosscourt: +2.7±2.4 per 100 returns v the current mix (450 returns charted)

Mayar Sherif returning

1st serve to the forehand

ReturnNowTourOwnv PaulaValue
FH through the middle39%+4.2+2.0+1.2+7.3±2.6
FH crosscourt33%+5.3+1.5−1.4+5.4±4.0
FH down the line13%+1.5+1.0−0.5+2.1±4.1
FH slice crosscourt7%−6.6+0.6−2.9−9.0±2.5
FH slice through the middle7%−6.7−0.3+0.5−6.5±2.2

Lean FH through the middle: +3.5±2.1 per 100 returns v the current mix (150 returns charted)

1st serve to the backhand

ReturnNowTourOwnv PaulaValue
BH through the middle54%+6.0+0.9−0.9+6.0±2.4
BH crosscourt33%+7.7+2.2−0.1+9.7±3.4
BH down the line8%+2.2−2.0−2.7−2.6±4.1
BH slice through the middle5%−6.2−0.9−0.9−8.0±2.2

Lean BH crosscourt: +3.9±2.6 per 100 returns v the current mix (173 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv PaulaValue
FH through the middle48%−3.2+0.1+1.4−1.7±2.7
FH crosscourt41%+0.5+0.2+0.7+1.4±3.9
FH down the line11%−0.6+1.7+0.9+2.1±4.2

Lean FH crosscourt: +1.4±2.7 per 100 returns v the current mix (44 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv PaulaValue
BH through the middle45%−2.6−0.5+2.0−1.1±2.4
BH crosscourt26%+1.5−2.0−0.3−0.8±3.2
FH inside-out15%+1.4−0.9+0.9+1.4±3.8
FH through the middle8%−2.7−0.5+1.4−1.8±1.9
BH down the line6%−0.5+0.6−1.7−1.7±4.0

Lean BH crosscourt: −0.1±2.7 per 100 returns v the current mix (78 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.

Paula Badosa

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+7.3±4.2+3.9+3.4
FH to their backhand · serve +1+7.3±4.9+3.2+4.1
FH to their forehand · return +1+6.8±5.0+3.7+3.1
BH to the middle · return +1+5.5±3.0+2.9+2.6
FH to the middle · rally+3.5±2.8+1.9+1.6
FH to their forehand · rally+3.4±3.7+0.3+3.0

Avoid

ShotEdgeOwnTheirs
FH to their forehand · return+0.8±5.4−0.8+1.6
FH to their backhand · return +1+1.0±5.3+0.1+0.9
BH to the middle · return+1.2±2.8+0.5+0.7
BH to the middle · serve +1+1.4±3.2+1.6−0.1
BH to their forehand · rally+1.5±5.2+0.8+0.7

Mayar Sherif

Favour

ShotEdgeOwnTheirs
FH to their forehand · serve +1+4.5±4.7+3.0+1.5
FH to their backhand · serve +1+3.9±4.7+1.1+2.8
FH to their backhand · rally+3.0±4.2+1.7+1.3
FH to the middle · return+2.6±3.3+1.8+0.9
FH to their forehand · return +1+2.0±4.8+0.6+1.3
BH to their backhand · serve +1+1.8±4.5+2.5−0.6

Avoid

ShotEdgeOwnTheirs
FH to their forehand · return−5.4±5.3−2.7−2.7
BH to their forehand · rally−2.1±5.3−3.9+1.8
FH to the middle · serve +1−2.0±3.4−2.5+0.5
BH to their backhand · rally−1.7±3.6−0.8−0.9
BH to their backhand · return−1.7±4.2−0.2−1.4

Against Mayar Sherif-like opponents

Paula Badosa vMatchesServe pts wonReturn pts won
All charted opponents–58.7%44.2%
Players most similar to Mayar Sherif7 57.5%39.8%

Similar by tactical fingerprint: Cristina Bucsa, Karolina Muchova, Xin Yu Wang, Tamara Zidansek, Bianca Andreescu, Anhelina Kalinina, Petra Martic, Daria Saville, Kiki Bertens, Svetlana Kuznetsova. When two players have rarely met, their records against these lookalikes fill the gap.