RallyIQ

Game plan

Maya Joint v Lin Zhu

Every number combines what Maya Joint does well with what Lin Zhu allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Maya Joint wins, best of 3 66%90%: 37%–87% · best of 5: 69%
Serve points won 57.0% / 54.0% Maya / Lin · tour 56.4%
Strengths only, no similarity priors 67%serve 57.1% / 53.7%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Maya Joint's record against Lin Zhu's tactical lookalikes and in their charted head-to-heads (lookalikes: −0.8 on serve, −4.2 on return vs expectation (217 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

CareerMayaLin
Direction choice−0.21 ±0.12
better than 16%
−0.19 ±0.13
better than 18%
Shot selection+0.13 ±0.15
better than 59%
+0.09 ±0.16
better than 53%
Execution−0.78 ±0.58
better than 24%
−0.19 ±0.84
better than 50%
Points left on the table2.76 ±0.15
lower than 28%
2.64 ±0.17
lower 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.

Maya Joint serving

Deuce court

1st serveNowMaya winsv LinMatchupOptimal
Wide38%62%74%70.7%±7.853% ▲
Body19%61%59%61.8%±10.94% ▼
T43%65%73%70.6%±9.043%

Optimal v Lin Zhu: +0.6±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +1.7 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMaya winsv LinMatchupOptimal
Wide45%62%71%67.5%±8.860% ▲
Body16%48%60%52.1%±11.81% ▼
T39%55%64%54.0%±10.239%

Optimal v Lin Zhu: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +7.7 per 100 first serves in before the returner adjusts.

Lin Zhu serving

Deuce court

1st serveNowLin winsv MayaMatchupOptimal
Wide50%62%69%64.9%±8.166% ▲
Body23%64%55%62.1%±10.623%
T26%57%70%59.5%±10.411% ▼

Optimal v Maya Joint: +0.1±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowLin winsv MayaMatchupOptimal
Wide34%56%72%63.0%±9.432% ▼
Body25%55%54%52.6%±11.112% ▼
T41%61%68%64.6%±9.256% ▲

Optimal v Maya Joint: +0.3±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.6 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.

Maya Joint returning

1st serve to the forehand

ReturnNowTourOwnv LinValue
FH through the middle49%+4.2−1.2−1.0+2.0±2.8
FH crosscourt27%+5.3−1.1+3.1+7.3±4.4
FH down the line11%+1.5−1.3+2.1+2.3±4.6
FH slice through the middle9%−6.7+0.1+0.2−6.4±2.3
FH slice crosscourt3%−6.6−0.3+0.7−6.2±1.9

Lean FH crosscourt: +5.1±3.5 per 100 returns v the current mix (241 returns charted)

1st serve to the backhand

ReturnNowTourOwnv LinValue
BH through the middle54%+6.0−2.3+0.9+4.6±2.7
BH crosscourt28%+7.7−4.3+1.9+5.4±3.6
BH down the line9%+2.2−1.4+0.9+1.7±4.5
BH slice through the middle7%−6.2−0.8−1.8−8.8±2.2
BH slice crosscourt3%−4.2−1.4−1.2−6.8±2.0

Lean BH crosscourt: +2.0±3.0 per 100 returns v the current mix (259 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv LinValue
FH through the middle50%−3.2+0.1+3.5+0.4±3.0
FH crosscourt38%+0.5−1.9+2.6+1.3±4.1
FH down the line13%−0.6−0.6±0.0−1.2±4.3

Lean FH crosscourt: +0.7±3.0 per 100 returns v the current mix (48 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv LinValue
BH through the middle47%−2.6+0.3−0.4−2.7±2.8
BH crosscourt34%+1.5+3.8+2.4+7.6±3.6
BH down the line19%−0.5−1.4+4.2+2.3±5.0

Lean BH crosscourt: +5.9±2.9 per 100 returns v the current mix (121 returns charted)

Lin Zhu returning

1st serve to the forehand

ReturnNowTourOwnv MayaValue
FH through the middle48%+4.2+1.1+2.7+8.0±2.9
FH crosscourt24%+5.3−1.8+0.7+4.2±4.3
FH down the line16%+1.5−0.6+0.1+1.0±4.6
FH slice through the middle6%−6.7−0.6+0.8−6.5±2.0
FH slice crosscourt5%−6.6−0.6+0.6−6.6±1.8

Lean FH through the middle: +3.7±2.0 per 100 returns v the current mix (185 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MayaValue
BH through the middle46%+6.0±0.0+1.1+7.1±2.7
BH crosscourt32%+7.7−0.5+2.4+9.6±3.6
BH down the line11%+2.2+2.0+1.8+6.0±4.3
BH slice through the middle7%−6.2+0.1−0.6−6.7±2.0
BH slice crosscourt4%−4.2−0.2±0.0−4.4±1.2

Lean BH crosscourt: +3.3±2.7 per 100 returns v the current mix (136 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv MayaValue
BH through the middle47%−2.6−0.2+0.3−2.5±2.7
BH crosscourt32%+1.5+0.2+1.0+2.7±3.4
BH down the line21%−0.5+0.2−1.9−2.3±4.8

Lean BH crosscourt: +3.5±2.8 per 100 returns v the current mix (57 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 hard. Each player's hard record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Maya Joint

Favour

ShotEdgeOwnTheirs
FH to their backhand · serve +1+6.7±5.9+3.3+3.4
BH to their forehand · return+6.0±6.6+0.6+5.4
FH to their forehand · serve +1+4.0±5.5+2.0+2.0
FH to their forehand · return+3.5±6.1−3.6+7.2
FH to their forehand · rally+3.5±3.6−1.9+5.4
FH to their backhand · return+2.8±6.4+1.2+1.6

Avoid

ShotEdgeOwnTheirs
FH to the middle · return +1−5.7±3.8−2.2−3.5
BH to the middle · return +1−5.6±3.5−1.9−3.7
BH to the middle · rally−3.7±2.5−3.1−0.7
FH to their backhand · rally−2.3±4.4+0.3−2.7
BH to their backhand · serve +1−1.5±5.1−3.2+1.7

Lin Zhu

Favour

ShotEdgeOwnTheirs
FH to the middle · return+8.1±3.3+3.8+4.3
BH to their forehand · return+3.2±6.4+2.4+0.8
BH to their backhand · return+3.1±4.6+1.4+1.7
BH to the middle · rally+2.2±2.6+2.8−0.7
BH to their backhand · rally+1.5±3.3−0.2+1.7
BH to the middle · return +1+1.4±3.5+2.0−0.7

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−4.2±4.1−4.4+0.2
FH to their forehand · return +1−4.1±5.5−0.2−3.9
FH to their forehand · rally−3.0±3.6+0.1−3.1
FH to their forehand · serve +1−2.9±5.5−3.0±0.0
BH to their forehand · serve +1−2.7±7.2−2.9+0.2

Against Lin Zhu-like opponents

Maya Joint vMatchesServe pts wonReturn pts won
All charted opponents–54.5%42.0%
Players most similar to Lin Zhu1 54.8%38.2%

Similar by tactical fingerprint: Coco Gauff, Iva Jovic, Marie Bouzkova, Kimberly Birrell, Emma Navarro, Emma Raducanu, Sloane Stephens, Ajla Tomljanovic, R, Elena Dementieva. When two players have rarely met, their records against these lookalikes fill the gap.