Game plan
Garbine Muguruza v Maya Joint
Every number combines what Garbine Muguruza 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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Garbine Muguruza's record against Maya Joint'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 | Garbine | Maya |
|---|---|---|
| Direction choice | −0.04 ±0.05 better than 47% | −0.21 ±0.12 better than 16% |
| Shot selection | +0.58 ±0.06 better than 96% | +0.13 ±0.15 better than 59% |
| Execution | −0.03 ±0.27 better than 60% | −0.78 ±0.58 better than 24% |
| Points left on the table | 2.49 ±0.08 lower than 66% | 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.
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.
Garbine Muguruza serving
Deuce court
| 1st serve | Now | Garbine wins | v Maya | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 35% | 68% | 69% | 70.2%±6.0 | 35% |
| Body | 18% | 56% | 55% | 53.8%±8.4 | 3% ▼ |
| T | 47% | 74% | 70% | 75.6%±5.4 | 62% ▲ |
Optimal v Maya Joint: +1.4±0.9 per 100 first serves (faults included) over the current mix. Serving T every time would read +5.8 per 100 first serves in before the returner adjusts.
Ad court
| 1st serve | Now | Garbine wins | v Maya | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 39% | 70% | 72% | 76.0%±5.4 | 55% ▲ |
| Body | 22% | 57% | 54% | 55.2%±8.8 | 6% ▼ |
| T | 39% | 65% | 68% | 68.9%±6.9 | 39% |
Optimal v Maya Joint: +0.9±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +7.2 per 100 first serves in before the returner adjusts.
Maya Joint serving
Deuce court
| 1st serve | Now | Maya wins | v Garbine | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 38% | 62% | 67% | 62.8%±6.3 | 46% ▲ |
| Body | 19% | 61% | 57% | 60.4%±8.2 | 25% ▲ |
| T | 43% | 65% | 65% | 61.5%±6.4 | 29% ▼ |
Optimal v Garbine Muguruza: +0.1±0.7 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +1.0 per 100 first serves in before the returner adjusts.
Ad court
| 1st serve | Now | Maya wins | v Garbine | Matchup | Optimal |
|---|---|---|---|---|---|
| Wide | 45% | 62% | 65% | 61.0%±6.5 | 60% ▲ |
| Body | 16% | 48% | 56% | 48.4%±9.2 | 1% ▼ |
| T | 39% | 55% | 64% | 53.9%±6.9 | 39% |
Optimal v Garbine Muguruza: +0.8±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.7 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.
Garbine Muguruza returning
1st serve to the forehand
| Return | Now | Tour | Own | v Maya | Value |
|---|---|---|---|---|---|
| FH through the middle | 59% | +4.2 | +1.4 | +2.7 | +8.3±2.1 |
| FH crosscourt | 23% | +5.3 | −1.4 | +0.7 | +4.7±3.7 |
| FH down the line | 13% | +1.5 | +2.3 | +0.1 | +3.9±4.2 |
| FH slice through the middle | 3% | −6.7 | −2.8 | +0.8 | −8.7±2.3 |
| FH slice crosscourt | 1% | −6.6 | −0.4 | +0.6 | −6.5±2.1 |
Lean FH through the middle: +2.1±1.3 per 100 returns v the current mix (1441 returns charted)
1st serve to the backhand
| Return | Now | Tour | Own | v Maya | Value |
|---|---|---|---|---|---|
| BH through the middle | 57% | +6.0 | +1.7 | +1.1 | +8.8±2.0 |
| BH crosscourt | 28% | +7.7 | +1.7 | +2.4 | +11.8±3.0 |
| BH down the line | 12% | +2.2 | +5.5 | +1.8 | +9.5±4.4 |
| BH slice through the middle | 2% | −6.2 | +0.3 | −0.6 | −6.5±2.3 |
| BH slice crosscourt | 1% | −4.2 | −0.3 | ±0.0 | −4.5±1.9 |
Lean BH crosscourt: +2.6±2.5 per 100 returns v the current mix (1254 returns charted)
2nd serve to the forehand
| Return | Now | Tour | Own | v Maya | Value |
|---|---|---|---|---|---|
| FH through the middle | 49% | −3.2 | +1.8 | +0.8 | −0.6±2.8 |
| FH crosscourt | 31% | +0.5 | +1.1 | −0.4 | +1.3±4.2 |
| FH down the line | 15% | −0.6 | +0.9 | −1.0 | −0.6±5.2 |
| FH slice through the middle | 2% | −15.2 | −0.2 | ±0.0 | −15.4±1.3 |
| BH inside-out | 1% | +2.2 | +1.1 | −1.9 | +1.4±3.7 |
Lean FH crosscourt: +1.6±3.3 per 100 returns v the current mix (411 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 | +1.2 | +0.3 | −1.1±2.3 |
| BH crosscourt | 40% | +1.5 | +2.5 | +1.0 | +5.1±3.2 |
| BH down the line | 14% | −0.5 | +3.2 | −1.9 | +0.8±5.1 |
Lean BH crosscourt: +3.4±2.3 per 100 returns v the current mix (675 returns charted)
Maya Joint returning
1st serve to the forehand
| Return | Now | Tour | Own | v Garbine | Value |
|---|---|---|---|---|---|
| FH through the middle | 49% | +4.2 | −1.2 | +1.4 | +4.4±2.3 |
| FH crosscourt | 27% | +5.3 | −1.1 | −0.1 | +4.2±3.8 |
| FH down the line | 11% | +1.5 | −1.3 | +0.2 | +0.4±4.0 |
| FH slice through the middle | 9% | −6.7 | +0.1 | −0.4 | −7.0±2.3 |
| FH slice crosscourt | 3% | −6.6 | −0.3 | −0.4 | −7.3±2.4 |
Lean FH through the middle: +2.2±1.6 per 100 returns v the current mix (241 returns charted)
1st serve to the backhand
| Return | Now | Tour | Own | v Garbine | Value |
|---|---|---|---|---|---|
| BH through the middle | 54% | +6.0 | −2.3 | +0.3 | +4.0±2.1 |
| BH crosscourt | 28% | +7.7 | −4.3 | +1.2 | +4.7±3.1 |
| BH down the line | 9% | +2.2 | −1.4 | +2.5 | +3.3±3.9 |
| BH slice through the middle | 7% | −6.2 | −0.8 | −0.7 | −7.7±2.2 |
| BH slice crosscourt | 3% | −4.2 | −1.4 | −0.2 | −5.8±2.6 |
Lean BH crosscourt: +1.6±2.5 per 100 returns v the current mix (259 returns charted, inside the 90% margin)
2nd serve to the forehand
| Return | Now | Tour | Own | v Garbine | Value |
|---|---|---|---|---|---|
| FH through the middle | 50% | −3.2 | +0.1 | +1.1 | −1.9±2.5 |
| FH crosscourt | 38% | +0.5 | −1.9 | +0.2 | −1.1±3.7 |
| FH down the line | 13% | −0.6 | −0.6 | +2.3 | +1.2±3.7 |
Lean FH crosscourt: +0.1±2.7 per 100 returns v the current mix (48 returns charted, inside the 90% margin)
2nd serve to the backhand
| Return | Now | Tour | Own | v Garbine | Value |
|---|---|---|---|---|---|
| BH through the middle | 47% | −2.6 | +0.3 | +0.1 | −2.3±2.3 |
| BH crosscourt | 34% | +1.5 | +3.8 | +2.4 | +7.7±3.1 |
| BH down the line | 19% | −0.5 | −1.4 | +3.5 | +1.6±4.6 |
Lean BH crosscourt: +5.8±2.5 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 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.
Garbine Muguruza
Favour
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| BH to the middle · return | +5.4±3.3 | +1.5 | +3.8 |
| BH to their backhand · return | +3.9±4.6 | +1.5 | +2.4 |
| BH to their backhand · return +1 | +3.1±4.0 | +2.1 | +1.0 |
| FH to the middle · return | +3.0±3.6 | +1.3 | +1.7 |
| BH to their backhand · serve +1 | +2.8±4.0 | +2.9 | −0.1 |
| BH to their forehand · return | +2.4±5.6 | +3.4 | −1.0 |
Avoid
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to the middle · serve +1 | −5.7±3.4 | −3.2 | −2.6 |
| FH to their forehand · return +1 | −5.5±5.2 | −3.3 | −2.3 |
| FH to their forehand · rally | −2.2±4.2 | +1.4 | −3.6 |
| BH to their forehand · return +1 | −2.1±5.3 | +2.6 | −4.7 |
| FH to the middle · return +1 | −1.9±3.0 | −1.1 | −0.8 |
Maya Joint
Favour
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to their forehand · serve +1 | +5.8±5.2 | +4.9 | +0.8 |
| FH to their backhand · return | +3.5±5.0 | +0.7 | +2.8 |
| BH to the middle · return | +2.2±3.3 | +0.9 | +1.3 |
| BH to their backhand · serve +1 | +2.2±4.6 | −1.1 | +3.2 |
| FH to their backhand · rally | +1.7±4.8 | +0.2 | +1.5 |
| FH to their forehand · rally | +0.7±4.5 | +0.5 | +0.3 |
Avoid
| Shot | Edge | Own | Theirs |
|---|---|---|---|
| FH to their forehand · return | −5.0±4.8 | −2.7 | −2.3 |
| FH to their backhand · return +1 | −4.3±4.9 | −2.9 | −1.5 |
| BH to the middle · rally | −2.5±2.5 | −2.8 | +0.3 |
| FH to the middle · serve +1 | −2.4±3.5 | −2.1 | −0.3 |
| FH to their backhand · serve +1 | −1.3±5.5 | −0.4 | −0.8 |
Against Maya Joint-like opponents
| Garbine Muguruza v | Matches | Serve pts won | Return pts won |
|---|---|---|---|
| All charted opponents | – | 58.9% | 44.2% |
Similar by tactical fingerprint: Jasmine Paolini, Shuai Zhang, Robin Montgomery, Yue Yuan, Bernarda Pera, Rebecca Marino, Irina Camelia Begu, Anett Kontaveit, Andrea Petkovic. When two players have rarely met, their records against these lookalikes fill the gap.