RallyIQ

Game plan

Rebecca Marino v Mona Barthel

Every number combines what Rebecca Marino does well with what Mona Barthel allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Rebecca Marino wins, best of 3 59%90%: 24%–88% · best of 5: 61%
Serve points won 58.7% / 56.9% Rebecca / Mona · tour 56.3%
Strengths only, no similarity priors 56%serve 58.5% / 57.3%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Rebecca Marino's record against Mona Barthel's tactical lookalikes and in their charted head-to-heads (lookalikes: +5.3 on serve, +11.8 on return vs expectation (102 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

CareerRebeccaMona
Direction choice−0.18 ±0.10
better than 20%
+0.20 ±0.18
better than 84%
Shot selection+0.03 ±0.15
better than 48%
+0.21 ±0.31
better than 68%
Execution−0.84 ±0.52
better than 23%
−0.27 ±1.34
better than 45%

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.

Rebecca Marino serving

Deuce court

1st serveNowRebecca winsv MonaMatchupOptimal
Wide45%65%62%60.6%±9.955% ▲
Body11%65%55%63.2%±12.90% ▼
T44%75%73%79.3%±8.045%

Optimal v Mona Barthel: +0.3±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +10.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowRebecca winsv MonaMatchupOptimal
Wide48%77%66%77.3%±8.063% ▲
Body10%56%51%51.3%±14.80% ▼
T42%72%59%66.9%±9.437% ▼

Optimal v Mona Barthel: +1.2±1.0 per 100 first serves (faults included) over the current mix. Serving wide every time would read +7.0 per 100 first serves in before the returner adjusts.

Mona Barthel serving

Deuce court

1st serveNowMona winsv RebeccaMatchupOptimal
Wide38%71%63%68.1%±9.153% ▲
Body17%53%55%51.4%±12.42% ▼
T45%67%65%64.9%±10.845%

Optimal v Rebecca Marino: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMona winsv RebeccaMatchupOptimal
Wide47%57%63%53.8%±10.745% ▼
Body12%51%52%46.9%±13.70% ▼
T40%65%65%66.1%±9.555% ▲

Optimal v Rebecca Marino: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +8.2 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.

Rebecca Marino returning

1st serve to the forehand

ReturnNowTourOwnv MonaValue
FH through the middle61%+4.2−0.1−1.0+3.1±2.9
FH crosscourt20%+5.3−2.3+2.9+5.9±4.3
FH down the line18%+1.5−0.2−0.4+1.0±4.5
FH slice through the middle1%−6.7−1.4+1.2−7.0±1.7

Lean FH crosscourt: +2.8±3.9 per 100 returns v the current mix (337 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv MonaValue
BH through the middle49%+6.0−0.1+1.7+7.7±2.9
BH crosscourt26%+7.7+3.8+0.3+11.8±3.6
BH down the line12%+2.2−1.3+0.9+1.8±4.5
BH slice through the middle7%−6.2−1.5−0.3−8.0±1.9
BH slice crosscourt6%−4.2−0.7−1.1−6.0±2.2

Lean BH crosscourt: +5.6±3.1 per 100 returns v the current mix (198 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv MonaValue
FH through the middle57%−3.2+1.8−0.8−2.2±3.1
FH crosscourt35%+0.5+2.1+2.0+4.6±4.3
FH down the line9%−0.6−1.7+1.3−0.9±3.7

Lean FH crosscourt: +4.3±3.3 per 100 returns v the current mix (69 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv MonaValue
BH through the middle47%−2.6+1.6−0.5−1.5±2.8
BH crosscourt41%+1.5+0.4+1.6+3.5±3.6
BH down the line12%−0.5+1.0−0.8−0.3±4.1

Lean BH crosscourt: +2.8±2.5 per 100 returns v the current mix (93 returns charted)

Mona Barthel returning

1st serve to the forehand

ReturnNowTourOwnv RebeccaValue
FH through the middle45%+4.2+0.6+1.3+6.0±3.0
FH down the line26%+1.5+3.3−3.0+1.8±4.7
FH crosscourt21%+5.3−1.9−3.9−0.4±4.3
FH slice through the middle8%−6.7−0.6−0.1−7.4±2.3

Lean FH through the middle: +3.6±2.3 per 100 returns v the current mix (121 returns charted)

1st serve to the backhand

ReturnNowTourOwnv RebeccaValue
BH through the middle42%+6.0+0.6−1.9+4.8±2.9
BH crosscourt33%+7.7−0.9−2.6+4.2±3.7
BH down the line13%+2.2−0.9−2.0−0.7±4.3
BH slice crosscourt7%−4.2+0.4−2.3−6.1±2.0
BH slice through the middle5%−6.2+0.1−0.8−6.9±1.9

Lean BH through the middle: +2.2±2.1 per 100 returns v the current mix (116 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv RebeccaValue
BH crosscourt49%+1.5+0.5+0.7+2.6±3.6
BH through the middle38%−2.6−2.1+1.3−3.4±2.5
BH down the line12%−0.5+0.9+0.2+0.6±4.5

Lean BH crosscourt: +2.6±2.1 per 100 returns v the current mix (65 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.

Rebecca Marino

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+2.4±3.2−0.5+2.9
BH to the middle · return+1.5±2.5−0.2+1.7
FH to their forehand · return+1.0±4.3−1.6+2.6
BH to their forehand · rally+0.7±4.6−2.6+3.3
FH to the middle · return−0.1±2.6+1.6−1.6
FH to their backhand · rally−0.9±3.7−3.5+2.6

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−3.1±2.3−1.3−1.8
FH to their forehand · rally−1.8±2.9−1.7−0.1
FH to the middle · serve +1−1.4±2.7−0.8−0.6
FH to their forehand · serve +1−1.2±4.0−2.9+1.7
FH to their backhand · rally−0.9±3.7−3.5+2.6

Mona Barthel

Favour

ShotEdgeOwnTheirs
FH to the middle · return+2.8±2.8+2.4+0.5
FH to their forehand · rally+2.0±3.2+2.4−0.5
FH to the middle · rally+1.9±2.4+0.9+1.0
FH to their forehand · serve +1+1.5±4.2+2.5−1.0
BH to their backhand · rally−0.2±3.2+1.6−1.9
BH to their forehand · rally−0.5±4.5+1.9−2.4

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−5.7±4.2−5.3−0.4
FH to their backhand · rally−3.9±3.7−2.7−1.2
BH to their backhand · return−2.3±3.5−0.8−1.5
BH to the middle · return−1.6±2.4−0.1−1.5
BH to their backhand · serve +1−1.5±3.6−0.1−1.4

Against Mona Barthel-like opponents

Rebecca Marino vMatchesServe pts wonReturn pts won
All charted opponents–61.1%41.8%
Players most similar to Mona Barthel1 64.0%51.9%

Similar by tactical fingerprint: Karolina Pliskova, Leylah Fernandez, Elise Mertens, Ekaterina Alexandrova, Sorana Cirstea, Victoria Azarenka, Veronika Kudermetova, Vera Zvonareva, Daniela Hantuchova, Nadia Petrova. When two players have rarely met, their records against these lookalikes fill the gap.