RallyIQ

Game plan

Rebecca Marino v Danka Kovinic

Every number combines what Rebecca Marino does well with what Danka Kovinic 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 81%90%: 49%–96% · best of 5: 87%
Serve points won 64.3% / 57.4% Rebecca / Danka · tour 58.1%
Strengths only, no similarity priors 81%serve 64.3% / 57.4%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Rebecca Marino's record against Danka Kovinic's tactical lookalikes and in their charted head-to-heads. 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

CareerRebeccaDanka
Direction choice−0.18 ±0.10
better than 20%
−0.25 ±0.09
better than 12%
Shot selection+0.03 ±0.15
better than 48%
−0.05 ±0.19
better than 40%
Execution−0.84 ±0.52
better than 23%
−2.63 ±0.65
better than 2%

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 DankaMatchupOptimal
Wide45%65%75%73.5%±8.960% ▲
Body11%65%66%72.9%±11.30% ▼
T44%75%71%77.3%±8.440% ▼

Optimal v Danka Kovinic: +0.6±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowRebecca winsv DankaMatchupOptimal
Wide48%77%75%84.0%±6.663% ▲
Body10%56%60%60.3%±15.00% ▼
T42%72%69%75.2%±8.837% ▼

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

Danka Kovinic serving

Deuce court

1st serveNowDanka winsv RebeccaMatchupOptimal
Wide28%52%63%49.1%±11.728%
Body33%57%55%55.1%±11.617% ▼
T39%68%65%66.0%±11.555% ▲

Optimal v Rebecca Marino: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +8.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowDanka winsv RebeccaMatchupOptimal
Wide44%67%63%64.2%±10.644%
Body16%51%52%47.8%±13.31% ▼
T40%63%65%63.8%±10.555% ▲

Optimal v Rebecca Marino: +0.5±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.8 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 DankaValue
FH through the middle61%+4.2−0.1+1.5+5.6±2.9
FH crosscourt20%+5.3−2.3+0.5+3.6±4.2
FH down the line18%+1.5−0.2−2.6−1.3±4.0
FH slice through the middle1%−6.7−1.4+1.1−7.0±1.7

Lean FH through the middle: +1.8±1.6 per 100 returns v the current mix (337 returns charted)

1st serve to the backhand

ReturnNowTourOwnv DankaValue
BH through the middle49%+6.0−0.1−0.3+5.6±2.9
BH crosscourt26%+7.7+3.8+0.6+12.1±3.7
BH down the line12%+2.2−1.3+2.1+3.0±4.3
BH slice through the middle7%−6.2−1.5±0.0−7.7±1.5
BH slice crosscourt6%−4.2−0.7±0.0−4.8±1.7

Lean BH crosscourt: +6.5±3.1 per 100 returns v the current mix (198 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv DankaValue
FH through the middle57%−3.2+1.8−0.6−2.0±3.1
FH crosscourt35%+0.5+2.1+1.8+4.4±4.2
FH down the line9%−0.6−1.7+1.7−0.6±3.5

Lean FH crosscourt: +4.0±3.3 per 100 returns v the current mix (69 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv DankaValue
BH through the middle47%−2.6+1.6+1.6+0.6±2.8
BH crosscourt41%+1.5+0.4+1.7+3.6±3.7
BH down the line12%−0.5+1.0±0.0+0.5±4.6

Lean BH crosscourt: +1.8±2.6 per 100 returns v the current mix (93 returns charted, inside the 90% margin)

Danka Kovinic returning

1st serve to the forehand

ReturnNowTourOwnv RebeccaValue
FH through the middle40%+4.2−0.6+1.3+4.8±3.0
FH crosscourt30%+5.3−1.4−3.9+0.1±4.3
FH slice through the middle20%−6.7−1.8−0.1−8.6±2.4
FH slice crosscourt10%−6.6−0.8−0.8−8.2±2.2

Lean FH through the middle: +5.4±2.3 per 100 returns v the current mix (94 returns charted)

1st serve to the backhand

ReturnNowTourOwnv RebeccaValue
BH through the middle49%+6.0−1.4−1.9+2.8±2.9
BH crosscourt31%+7.7−4.4−2.6+0.7±3.6
BH down the line9%+2.2+0.8−2.0+1.0±4.1
BH slice crosscourt6%−4.2+1.1−2.3−5.4±1.9
BH slice through the middle5%−6.2−0.2−0.8−7.2±1.9

Lean BH through the middle: +1.8±1.9 per 100 returns v the current mix (108 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv RebeccaValue
BH crosscourt45%+1.5+0.5+0.7+2.6±3.4
BH through the middle38%−2.6+0.1+1.3−1.2±2.4
BH down the line17%−0.5+0.8+0.2+0.5±4.5

Lean BH crosscourt: +1.8±2.2 per 100 returns v the current mix (42 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 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.

Rebecca Marino

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+3.2±3.5+1.7+1.5
FH to the middle · return+1.7±2.6+1.6+0.1
BH to their backhand · rally+0.4±3.3−0.5+0.9
BH to the middle · return−0.3±2.4−0.2−0.2
FH to their backhand · serve +1−0.6±4.3+2.2−2.8
FH to their backhand · rally−1.8±3.7−3.5+1.7

Avoid

ShotEdgeOwnTheirs
FH to the middle · rally−4.6±2.4−4.2−0.4
FH to their forehand · return +1−3.6±4.0−0.8−2.8
BH to the middle · rally−2.3±2.3−1.3−1.0
FH to their forehand · rally−2.0±3.1−1.7−0.2
FH to their backhand · rally−1.8±3.7−3.5+1.7

Danka Kovinic

Favour

ShotEdgeOwnTheirs
BH to the middle · serve +1−0.1±2.5−0.6+0.6
BH to the middle · rally−0.2±2.2−0.2+0.1
FH to their forehand · rally−0.4±3.3+0.1−0.5
FH to the middle · return−0.8±2.8−1.3+0.5
FH to the middle · serve +1−1.0±2.7−1.8+0.9
FH to their backhand · rally−3.6±3.9−2.4−1.2

Avoid

ShotEdgeOwnTheirs
BH to their backhand · rally−5.0±3.3−3.2−1.9
BH to the middle · return−3.8±2.5−2.4−1.5
FH to their forehand · serve +1−3.7±4.3−2.6−1.0
FH to the middle · rally−3.6±2.3−4.6+1.0
FH to their backhand · rally−3.6±3.9−2.4−1.2

Against Danka Kovinic-like opponents

Rebecca Marino vMatchesServe pts wonReturn pts won
All charted opponents–61.1%41.8%

Similar by tactical fingerprint: Linda Noskova, Maya Joint, Anastasia Pavlyuchenkova, Robin Montgomery, Lulu Sun, Veronika Kudermetova, Irina Camelia Begu, Camila Giorgi, Coco Vandeweghe. When two players have rarely met, their records against these lookalikes fill the gap.