Game plan
Rebecca Marino v Maja Chwalinska
Every number combines what Rebecca Marino does well with what Maja Chwalinska allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective
Forecast
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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Maja Chwalinska'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
| Career | Rebecca | Maja |
|---|---|---|
| Direction choice | −0.18 ±0.10 better than 20% | −0.08 ±0.06 better than 40% |
| Shot selection | +0.03 ±0.15 better than 48% | −0.52 ±0.28 better than 11% |
| Execution | −0.84 ±0.52 better than 23% | +2.12 ±0.53 better than 99% |
| Points left on the table | 2.70 ±0.11 lower than 33% | 2.93 ±0.10 lower than 14% |
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.
Structural compatibility
Expected edge per 100 rally shots from style alone: Rebecca Marino −1.24, Maja Chwalinska +1.06. Each player's shot mix weighted by their own skill with each shot and by how much the other gives up against it. This is why some rankings gaps don't hold in a given matchup.
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 serve | Now | Rebecca wins | v Maja | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 45% | 65% | 58% | 56.6%±8.6 | 57% ▲ |
| Body | 11% | 65% | 57% | 64.5%±12.1 | 14% ▲ |
| T | 44% | 75% | 62% | 69.1%±9.7 | 29% ▼ |
Optimal v Maja Chwalinska: +0.2±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 serve | Now | Rebecca wins | v Maja | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 48% | 77% | 59% | 71.3%±9.4 | 63% ▲ |
| Body | 10% | 56% | 53% | 52.7%±13.2 | 0% ▼ |
| T | 42% | 72% | 58% | 65.9%±8.7 | 37% ▼ |
Optimal v Maja Chwalinska: +0.7±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.1 per 100 first serves in before the returner adjusts.
Maja Chwalinska serving
Deuce court
| 1st serve | Now | Maja wins | v Rebecca | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 26% | 62% | 63% | 58.8%±9.8 | 29% ▲ |
| Body | 28% | 55% | 55% | 53.3%±10.4 | 13% ▼ |
| T | 46% | 65% | 65% | 62.4%±9.6 | 58% ▲ |
Optimal v Rebecca Marino: +0.4±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.5 per 100 first serves in before the returner adjusts.
Ad court
| 1st serve | Now | Maja wins | v Rebecca | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 53% | 60% | 63% | 57.6%±9.2 | 52% |
| Body | 22% | 53% | 52% | 48.8%±11.5 | 7% ▼ |
| T | 26% | 67% | 65% | 67.8%±9.8 | 41% ▲ |
Optimal v Rebecca Marino: +0.8±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T 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.
Rebecca Marino returning
1st serve to the forehand
| Return | Now | Tour | Own | v Maja | Value |
|---|---|---|---|---|---|
| FH through the middle | 61% | +4.2 | −0.1 | +0.2 | +4.3±2.8 |
| FH crosscourt | 20% | +5.3 | −2.3 | +2.7 | +5.7±4.4 |
| FH down the line | 18% | +1.5 | −0.2 | +3.2 | +4.5±4.7 |
| FH slice through the middle | 1% | −6.7 | −1.4 | −1.2 | −9.4±1.6 |
Lean FH crosscourt: +1.3±3.9 per 100 returns v the current mix (337 returns charted, inside the 90% margin)
1st serve to the backhand
| Return | Now | Tour | Own | v Maja | Value |
|---|---|---|---|---|---|
| BH through the middle | 49% | +6.0 | −0.1 | +2.1 | +8.1±2.7 |
| BH crosscourt | 26% | +7.7 | +3.8 | +1.3 | +12.9±3.6 |
| BH down the line | 12% | +2.2 | −1.3 | +2.7 | +3.6±4.5 |
| BH slice through the middle | 7% | −6.2 | −1.5 | +0.6 | −7.1±1.9 |
| BH slice crosscourt | 6% | −4.2 | −0.7 | ±0.0 | −4.8±1.7 |
Lean BH crosscourt: +5.8±3.0 per 100 returns v the current mix (198 returns charted)
2nd serve to the forehand
| Return | Now | Tour | Own | v Maja | Value |
|---|---|---|---|---|---|
| FH through the middle | 57% | −3.2 | +1.8 | −1.2 | −2.6±3.1 |
| FH crosscourt | 35% | +0.5 | +2.1 | +0.4 | +3.0±4.2 |
| FH down the line | 9% | −0.6 | −1.7 | −1.4 | −3.7±4.1 |
Lean FH crosscourt: +3.8±3.3 per 100 returns v the current mix (69 returns charted)
2nd serve to the backhand
| Return | Now | Tour | Own | v Maja | Value |
|---|---|---|---|---|---|
| BH through the middle | 47% | −2.6 | +1.6 | +1.4 | +0.4±2.8 |
| BH crosscourt | 41% | +1.5 | +0.4 | −0.1 | +1.8±3.7 |
| BH down the line | 12% | −0.5 | +1.0 | −1.4 | −0.9±4.3 |
Lean BH crosscourt: +1.0±2.6 per 100 returns v the current mix (93 returns charted, inside the 90% margin)
Maja Chwalinska returning
1st serve to the forehand
| Return | Now | Tour | Own | v Rebecca | Value |
|---|---|---|---|---|---|
| FH through the middle | 41% | +4.2 | +3.2 | +1.3 | +8.6±3.0 |
| FH crosscourt | 21% | +5.3 | +3.3 | −3.0 | +5.5±4.2 |
| FH slice through the middle | 17% | −6.7 | +1.6 | −0.1 | −5.2±2.4 |
| FH down the line | 14% | +1.5 | +1.1 | −3.9 | −1.2±4.4 |
| FH slice crosscourt | 7% | −6.6 | +1.5 | −1.8 | −7.0±1.8 |
Lean FH through the middle: +5.6±2.1 per 100 returns v the current mix (111 returns charted)
1st serve to the backhand
| Return | Now | Tour | Own | v Rebecca | Value |
|---|---|---|---|---|---|
| BH through the middle | 43% | +6.0 | +1.2 | −1.9 | +5.3±2.8 |
| BH crosscourt | 33% | +7.7 | +2.4 | −2.0 | +8.2±3.6 |
| BH down the line | 16% | +2.2 | +3.6 | −2.6 | +3.1±4.7 |
| BH slice through the middle | 3% | −6.2 | −0.5 | −0.8 | −7.5±2.0 |
| BH slice crosscourt | 3% | −4.2 | +0.1 | ±0.0 | −4.1±1.3 |
Lean BH crosscourt: +3.4±2.8 per 100 returns v the current mix (236 returns charted)
2nd serve to the backhand
| Return | Now | Tour | Own | v Rebecca | Value |
|---|---|---|---|---|---|
| BH through the middle | 31% | −2.6 | +1.6 | +1.3 | +0.3±2.6 |
| BH crosscourt | 29% | +1.5 | ±0.0 | +0.2 | +1.8±3.6 |
| FH through the middle | 18% | −2.7 | −1.1 | +0.1 | −3.7±2.5 |
| BH down the line | 11% | −0.5 | −1.3 | +0.7 | −1.2±4.7 |
| FH inside-in | 5% | +0.7 | +0.5 | −1.9 | −0.6±3.9 |
Lean BH crosscourt: +1.9±2.8 per 100 returns v the current mix (110 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.
Rebecca Marino
Favour
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| BH to their backhand · return | +4.2±3.5 | +1.7 | +2.5 |
| FH to their backhand · serve +1 | +2.9±3.9 | +2.2 | +0.7 |
| BH to the middle · return | +2.8±2.2 | −0.2 | +3.0 |
| FH to their backhand · return | +2.2±4.4 | −0.3 | +2.4 |
| FH to the middle · return | +1.5±2.5 | +1.6 | −0.1 |
| BH to the middle · return +1 | +1.5±2.5 | ±0.0 | +1.5 |
Avoid
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to the middle · rally | −5.9±2.2 | −4.2 | −1.7 |
| FH to their backhand · return +1 | −5.2±4.1 | −3.2 | −2.0 |
| FH to their forehand · serve +1 | −4.7±3.7 | −2.9 | −1.8 |
| FH to their backhand · rally | −3.2±3.0 | −3.5 | +0.3 |
| FH to their forehand · rally | −3.2±2.7 | −1.7 | −1.4 |
Maja Chwalinska
Favour
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to the middle · serve +1 | +3.6±2.6 | +2.7 | +0.9 |
| BH to their forehand · return | +3.1±4.5 | +4.5 | −1.3 |
| FH to their forehand · rally | +3.1±2.9 | +3.6 | −0.5 |
| BH to the middle · return +1 | +2.8±2.6 | +2.1 | +0.7 |
| FH to the middle · rally | +2.3±2.1 | +1.3 | +1.0 |
| FH to the middle · return | +2.2±2.6 | +1.8 | +0.5 |
Avoid
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to their backhand · serve +1 | −2.1±3.9 | −1.7 | −0.4 |
| BH to their forehand · serve +1 | −0.6±4.8 | +3.9 | −4.5 |
| FH to their backhand · rally | −0.4±3.1 | +0.8 | −1.2 |
| FH to their backhand · return | −0.2±4.4 | +3.4 | −3.6 |
| BH to their backhand · serve +1 | −0.2±3.7 | +1.2 | −1.4 |
Against Maja Chwalinska-like opponents
| Rebecca Marino v | Matches | Serve pts won | Return pts won |
|---|---|---|---|
| All charted opponents | – | 61.1% | 41.8% |
Similar by tactical fingerprint: Magdalena Frech, Sara Bejlek, Kaja Juvan, Tiantsoa Sarah Rakotomanga Rajaonah, Sara Errani, Angelique Kerber, Brenda Fruhvirtova, Martina Trevisan, Kateryna Baindl, Agnieszka Radwanska. When two players have rarely met, their records against these lookalikes fill the gap.