RallyIQ

Game plan

Astra Sharma v Yulia Putintseva

Every number combines what Astra Sharma does well with what Yulia Putintseva allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Astra Sharma wins, best of 3 70%90%: 36%–92% · best of 5: 74%
Serve points won 57.8% / 53.8% Astra / Yulia · tour 55.0%
Strengths only, no similarity priors 70%serve 57.8% / 53.8%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Astra Sharma's record against Yulia Putintseva'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

CareerAstraYulia
Direction choice−0.06 ±0.08
better than 42%
−0.04 ±0.06
better than 48%
Shot selection+0.12 ±0.44
better than 57%
−0.42 ±0.13
better than 15%
Execution−1.59 ±1.43
better than 9%
+0.53 ±0.56
better than 80%

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.

Astra Sharma serving

Deuce court

1st serveNowAstra winsv YuliaMatchupOptimal
Wide47%61%68%63.4%±9.150% ▲
Body18%63%62%66.9%±11.03% ▼
T35%74%66%72.8%±9.147% ▲

Optimal v Yulia Putintseva: +0.4±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAstra winsv YuliaMatchupOptimal
Wide54%69%70%72.5%±8.154%
Body15%52%54%49.9%±13.10% ▼
T31%66%68%69.4%±9.846% ▲

Optimal v Yulia Putintseva: +1.2±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +4.3 per 100 first serves in before the returner adjusts.

Yulia Putintseva serving

Deuce court

1st serveNowYulia winsv AstraMatchupOptimal
Wide58%60%65%58.7%±9.758%
Body18%52%55%49.6%±12.23% ▼
T24%66%71%68.7%±9.939% ▲

Optimal v Astra Sharma: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +9.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowYulia winsv AstraMatchupOptimal
Wide49%60%57%51.8%±10.034% ▼
Body17%52%57%52.1%±11.818%
T33%59%62%56.4%±10.948% ▲

Optimal v Astra Sharma: +0.3±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.0 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.

Astra Sharma returning

1st serve to the forehand

ReturnNowTourOwnv YuliaValue
FH through the middle57%+4.2−0.2+0.1+4.0±2.7
FH crosscourt21%+5.3+0.6+1.4+7.3±3.8
FH down the line21%+1.5+1.1+6.2+8.9±4.2

Lean FH down the line: +3.1±3.7 per 100 returns v the current mix (84 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv YuliaValue
BH through the middle44%+6.0−0.8+0.9+6.1±2.6
BH crosscourt23%+7.7−0.8+1.2+8.1±3.2
BH slice crosscourt11%−4.2±0.0−0.2−4.4±2.2
BH slice through the middle11%−6.2−0.6+0.9−6.0±2.2
BH slice down the line6%−12.5+1.4+1.4−9.8±2.4

Lean BH crosscourt: +5.1±2.7 per 100 returns v the current mix (93 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv YuliaValue
FH through the middle27%−2.7+0.8+0.4−1.4±2.4
BH through the middle22%−2.6−0.6+1.2−2.1±2.3
BH crosscourt20%+1.5−0.2−2.1−0.8±3.0
FH inside-out20%+1.4±0.0+5.1+6.4±3.8
FH inside-in10%+0.7−1.6+3.6+2.7±3.8

Yulia Putintseva returning

1st serve to the forehand

ReturnNowTourOwnv AstraValue
FH through the middle47%+4.2−0.6+0.4+4.0±2.9
FH down the line25%+1.5+1.1−1.8+0.8±4.3
FH crosscourt16%+5.3−5.1−0.3−0.1±4.3
FH slice through the middle7%−6.7+0.8−0.8−6.7±2.2
FH slice crosscourt5%−6.6−1.0−1.3−8.9±2.4

Lean FH through the middle: +2.8±2.0 per 100 returns v the current mix (415 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AstraValue
BH through the middle38%+6.0+1.8−1.6+6.3±2.8
BH crosscourt28%+7.7+1.9+0.1+9.7±3.6
BH slice through the middle12%−6.2−1.6−0.9−8.8±2.2
BH down the line12%+2.2+0.5−0.7+2.0±4.4
BH slice crosscourt5%−4.2+0.2±0.0−3.9±1.9

Lean BH crosscourt: +6.2±2.8 per 100 returns v the current mix (371 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv AstraValue
FH through the middle51%−3.2−0.1−1.4−4.6±3.1
FH down the line30%−0.6−4.4−5.1−10.0±4.8
FH crosscourt19%+0.5−1.0+0.8+0.3±4.1

Lean FH crosscourt: +5.7±4.0 per 100 returns v the current mix (107 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv AstraValue
BH through the middle44%−2.6+0.6+2.2+0.2±2.7
BH crosscourt36%+1.5±0.0−3.2−1.7±3.5
BH down the line17%−0.5−2.5−2.4−5.4±4.7
FH through the middle3%−2.7+0.7−1.4−3.4±2.2

Lean BH through the middle: +1.7±2.1 per 100 returns v the current mix (230 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 clay. Each player's clay record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Astra Sharma

Favour

ShotEdgeOwnTheirs
FH to their backhand · return+5.6±5.8+0.6+5.0
FH to their forehand · serve +1+2.4±5.4−0.5+2.9
BH to the middle · return+1.3±3.6−1.0+2.3
FH to their backhand · rally+0.9±4.3−0.4+1.3
FH to their backhand · serve +1±0.0±5.3−4.7+4.8
FH to the middle · rally−0.8±3.3−1.2+0.4

Avoid

ShotEdgeOwnTheirs
FH to the middle · serve +1−4.0±3.8−3.8−0.1
FH to the middle · return−2.4±3.4−1.5−0.9
FH to their forehand · rally−1.7±4.3−1.0−0.7
FH to their forehand · return−1.7±6.0−3.9+2.2
BH to their backhand · rally−1.3±4.2−1.5+0.2

Yulia Putintseva

Favour

ShotEdgeOwnTheirs
BH to the middle · rally+2.9±3.0+1.6+1.3
FH to the middle · rally+0.5±3.4+2.2−1.8
FH to the middle · return±0.0±3.6+1.0−1.0
BH to the middle · return−0.3±3.4+0.7−1.0
BH to their backhand · return−1.1±4.5+1.5−2.6
FH to their forehand · rally−1.4±4.5+1.8−3.2

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−4.4±5.5+1.0−5.4
BH to their backhand · serve +1−2.2±5.0+0.7−2.9
BH to their backhand · rally−1.6±3.8+2.6−4.2
FH to their backhand · rally−1.5±4.6+1.0−2.5
FH to their forehand · rally−1.4±4.5+1.8−3.2

Against Yulia Putintseva-like opponents

Astra Sharma vMatchesServe pts wonReturn pts won
All charted opponents–59.5%47.5%

Similar by tactical fingerprint: Camila Osorio, Marie Bouzkova, Antonia Ruzic, Emma Navarro, Emma Raducanu, Lin Zhu, Anastasija Sevastova, Clara Burel, Alize Cornet, Elena Dementieva. When two players have rarely met, their records against these lookalikes fill the gap.