Game plan
Rebecca Marino v Maya Joint
Every number combines what Rebecca Marino does well with what Maya Joint 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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Maya Joint's tactical lookalikes and in their charted head-to-heads (lookalikes: +2.4 on serve, −3.2 on return vs expectation (135 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
| Career | Rebecca | Maya |
|---|---|---|
| Direction choice | −0.18 ±0.10 better than 20% | −0.21 ±0.12 better than 16% |
| Shot selection | +0.03 ±0.15 better than 48% | +0.13 ±0.15 better than 59% |
| Execution | −0.84 ±0.52 better than 23% | −0.78 ±0.58 better than 24% |
| Points left on the table | 2.70 ±0.11 lower than 33% | 2.76 ±0.15 lower than 28% |
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 −1.50, Maya Joint −1.43. 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 Maya | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 45% | 65% | 69% | 67.5%±7.7 | 60% ▲ |
| Body | 11% | 65% | 55% | 63.1%±11.9 | 0% ▼ |
| T | 44% | 75% | 70% | 76.4%±7.4 | 40% ▼ |
Optimal v Maya Joint: +0.4±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.4 per 100 first serves in before the returner adjusts.
Ad court
| 1st serve | Now | Rebecca wins | v Maya | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 48% | 77% | 72% | 81.9%±6.0 | 63% ▲ |
| Body | 10% | 56% | 54% | 54.0%±13.0 | 0% ▼ |
| T | 42% | 72% | 68% | 74.9%±7.6 | 37% ▼ |
Optimal v Maya Joint: +1.1±1.0 per 100 first serves (faults included) over the current mix. Serving wide every time would read +5.7 per 100 first serves in before the returner adjusts.
Maya Joint serving
Deuce court
| 1st serve | Now | Maya wins | v Rebecca | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 38% | 62% | 63% | 58.9%±8.0 | 33% ▼ |
| Body | 19% | 61% | 55% | 58.6%±10.2 | 22% ▲ |
| T | 43% | 65% | 65% | 62.1%±9.2 | 45% ▲ |
Optimal v Rebecca Marino: ±0.0±0.3 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +1.9 per 100 first serves in before the returner adjusts.
Ad court
| 1st serve | Now | Maya wins | v Rebecca | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 45% | 62% | 63% | 58.9%±8.9 | 54% ▲ |
| Body | 16% | 48% | 52% | 44.6%±11.1 | 1% ▼ |
| T | 39% | 55% | 65% | 55.4%±8.7 | 45% ▲ |
Optimal v Rebecca Marino: +0.6±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.7 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 Maya | Value |
|---|---|---|---|---|---|
| FH through the middle | 61% | +4.2 | −0.1 | +2.7 | +6.8±2.6 |
| FH crosscourt | 20% | +5.3 | −2.3 | +0.7 | +3.8±4.3 |
| FH down the line | 18% | +1.5 | −0.2 | +0.1 | +1.4±4.6 |
| FH slice through the middle | 1% | −6.7 | −1.4 | +0.8 | −7.3±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
| Return | Now | Tour | Own | v Maya | Value |
|---|---|---|---|---|---|
| BH through the middle | 49% | +6.0 | −0.1 | +1.1 | +7.0±2.6 |
| BH crosscourt | 26% | +7.7 | +3.8 | +2.4 | +13.9±3.6 |
| BH down the line | 12% | +2.2 | −1.3 | +1.8 | +2.7±4.5 |
| BH slice through the middle | 7% | −6.2 | −1.5 | −0.6 | −8.3±2.1 |
| BH slice crosscourt | 6% | −4.2 | −0.7 | ±0.0 | −4.8±1.7 |
Lean BH crosscourt: +7.3±3.0 per 100 returns v the current mix (198 returns charted)
2nd serve to the forehand
| Return | Now | Tour | Own | v Maya | Value |
|---|---|---|---|---|---|
| FH through the middle | 57% | −3.2 | +1.8 | +0.8 | −0.6±3.1 |
| FH crosscourt | 35% | +0.5 | +2.1 | −0.4 | +2.2±4.3 |
| FH down the line | 9% | −0.6 | −1.7 | −1.0 | −3.2±4.3 |
Lean FH crosscourt: +2.1±3.3 per 100 returns v the current mix (69 returns charted, inside the 90% margin)
2nd serve to the backhand
| Return | Now | Tour | Own | v Maya | Value |
|---|---|---|---|---|---|
| BH through the middle | 47% | −2.6 | +1.6 | +0.3 | −0.7±2.7 |
| BH crosscourt | 41% | +1.5 | +0.4 | +1.0 | +2.9±3.6 |
| BH down the line | 12% | −0.5 | +1.0 | −1.9 | −1.4±4.7 |
Lean BH crosscourt: +2.2±2.6 per 100 returns v the current mix (93 returns charted, inside the 90% margin)
Maya Joint returning
1st serve to the forehand
| Return | Now | Tour | Own | v Rebecca | Value |
|---|---|---|---|---|---|
| FH through the middle | 49% | +4.2 | −1.2 | +1.3 | +4.3±2.9 |
| FH crosscourt | 27% | +5.3 | −1.1 | −3.9 | +0.4±4.4 |
| FH down the line | 11% | +1.5 | −1.3 | −3.0 | −2.8±4.6 |
| FH slice through the middle | 9% | −6.7 | +0.1 | −0.1 | −6.7±2.5 |
| FH slice crosscourt | 3% | −6.6 | −0.3 | −0.8 | −7.6±2.1 |
Lean FH through the middle: +3.5±2.0 per 100 returns v the current mix (241 returns charted)
1st serve to the backhand
| Return | Now | Tour | Own | v Rebecca | Value |
|---|---|---|---|---|---|
| BH through the middle | 54% | +6.0 | −2.3 | −1.9 | +1.9±2.7 |
| BH crosscourt | 28% | +7.7 | −4.3 | −2.6 | +0.8±3.7 |
| BH down the line | 9% | +2.2 | −1.4 | −2.0 | −1.2±4.5 |
| BH slice through the middle | 7% | −6.2 | −0.8 | −0.8 | −7.8±2.2 |
| BH slice crosscourt | 3% | −4.2 | −1.4 | −2.3 | −7.9±1.9 |
Lean BH through the middle: +1.5±1.7 per 100 returns v the current mix (259 returns charted, inside the 90% margin)
2nd serve to the forehand
| Return | Now | Tour | Own | v Rebecca | Value |
|---|---|---|---|---|---|
| FH through the middle | 50% | −3.2 | +0.1 | +0.1 | −2.9±3.0 |
| FH crosscourt | 38% | +0.5 | −1.9 | +0.5 | −0.9±4.1 |
| FH down the line | 13% | −0.6 | −0.6 | −1.9 | −3.1±4.3 |
Lean FH crosscourt: +1.3±3.0 per 100 returns v the current mix (48 returns charted, inside the 90% margin)
2nd serve to the backhand
| Return | Now | Tour | Own | v Rebecca | Value |
|---|---|---|---|---|---|
| BH through the middle | 47% | −2.6 | +0.3 | +1.3 | −1.0±2.6 |
| BH crosscourt | 34% | +1.5 | +3.8 | +0.7 | +5.9±3.6 |
| BH down the line | 19% | −0.5 | −1.4 | +0.2 | −1.7±5.1 |
Lean BH crosscourt: +4.7±2.9 per 100 returns v the current mix (121 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
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to the middle · return | +4.3±2.3 | +1.6 | +2.7 |
| BH to their backhand · return | +4.0±3.2 | +1.7 | +2.3 |
| FH to their backhand · serve +1 | +2.8±4.1 | +2.2 | +0.6 |
| BH to the middle · return | +0.9±2.1 | −0.2 | +1.1 |
| BH to their backhand · rally | +0.7±2.9 | −0.5 | +1.2 |
| BH to the middle · return +1 | −0.3±2.4 | ±0.0 | −0.3 |
Avoid
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to the middle · rally | −5.7±2.1 | −4.2 | −1.5 |
| FH to the middle · return +1 | −4.9±2.6 | −4.1 | −0.8 |
| FH to their forehand · rally | −4.2±2.7 | −1.7 | −2.5 |
| FH to the middle · serve +1 | −3.9±2.6 | −0.8 | −3.1 |
| FH to their forehand · return +1 | −3.9±3.8 | −0.8 | −3.1 |
Maya Joint
Favour
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to their forehand · return +1 | +1.8±4.0 | +1.7 | +0.1 |
| FH to their forehand · serve +1 | +1.4±4.0 | +2.4 | −1.0 |
| FH to their backhand · serve +1 | +0.8±4.0 | +1.2 | −0.4 |
| FH to the middle · return | +0.3±2.5 | −0.1 | +0.5 |
| FH to the middle · rally | +0.2±2.0 | −0.7 | +1.0 |
| BH to the middle · serve +1 | −0.5±2.2 | −1.0 | +0.6 |
Avoid
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to their forehand · return | −5.3±4.4 | −2.7 | −2.6 |
| BH to their forehand · serve +1 | −5.2±4.9 | −0.7 | −4.5 |
| BH to their backhand · return | −3.9±3.4 | −2.4 | −1.5 |
| BH to their backhand · serve +1 | −3.4±3.4 | −2.0 | −1.4 |
| FH to their backhand · return | −2.9±4.5 | +0.7 | −3.6 |
Against Maya Joint-like opponents
| Rebecca Marino v | Matches | Serve pts won | Return pts won |
|---|---|---|---|
| All charted opponents | – | 61.1% | 41.8% |
| Players most similar to Maya Joint | 1 | 56.3% | 37.0% |
Similar by tactical fingerprint: Jasmine Paolini, Shuai Zhang, Robin Montgomery, Yue Yuan, Bernarda Pera, Irina Camelia Begu, Anett Kontaveit, Garbine Muguruza, Andrea Petkovic. When two players have rarely met, their records against these lookalikes fill the gap.