RallyIQ

Game plan

Jennifer Capriati v Anastasia Myskina

Every number combines what Jennifer Capriati does well with what Anastasia Myskina allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Jennifer Capriati wins, best of 3 69%90%: 25%–95% · best of 5: 73%
Serve points won 58.7% / 55.0% Jennifer / Anastasia · tour 58.1%
Strengths only, no similarity priors 69%serve 58.6% / 54.9%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Jennifer Capriati's record against Anastasia Myskina's tactical lookalikes and in their charted head-to-heads (lookalikes: +1.4 on serve, −1.1 on return vs expectation (255 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

CareerJenniferAnastasia
Direction choice−0.23 ±0.16
better than 14%
+0.17 ±0.16
better than 82%
Shot selection+0.02 ±0.08
better than 47%
+0.02 ±0.17
better than 47%
Execution+0.09 ±0.84
better than 65%
−0.24 ±0.72
better than 46%
Points left on the table2.76 ±0.26
lower than 26%
2.43 ±0.19
lower than 71%

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.

Jennifer Capriati serving

Deuce court

1st serveNowJennifer winsv AnastasiaMatchupOptimal
Wide50%64%64%62.6%±8.065% ▲
Body26%55%61%58.7%±12.810% ▼
T25%63%66%60.6%±12.125%

Optimal v Anastasia Myskina: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +1.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowJennifer winsv AnastasiaMatchupOptimal
Wide24%59%64%56.6%±11.723%
Body38%56%56%56.1%±12.224% ▼
T38%69%68%72.5%±7.953% ▲

Optimal v Anastasia Myskina: +1.0±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +10.0 per 100 first serves in before the returner adjusts.

Anastasia Myskina serving

Deuce court

1st serveNowAnastasia winsv JenniferMatchupOptimal
Wide55%59%63%56.2%±8.651% ▼
Body12%51%59%52.5%±12.90% ▼
T34%70%66%68.2%±10.149% ▲

Optimal v Jennifer Capriati: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +8.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAnastasia winsv JenniferMatchupOptimal
Wide27%56%67%58.0%±10.927%
Body21%60%60%63.6%±11.436% ▲
T52%55%63%54.1%±10.037% ▼

Optimal v Jennifer Capriati: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +6.5 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.

Jennifer Capriati returning

1st serve to the forehand

ReturnNowTourOwnv AnastasiaValue
FH through the middle58%+4.2+0.4+2.7+7.2±2.9
FH down the line26%+1.5+1.2−0.5+2.3±4.7
FH crosscourt17%+5.3+2.5+1.2+9.0±4.3

Lean FH crosscourt: +2.8±4.1 per 100 returns v the current mix (200 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv AnastasiaValue
BH through the middle59%+6.0−1.4+0.1+4.7±2.8
BH crosscourt23%+7.7−3.8+1.9+5.8±3.6
BH down the line18%+2.2+2.0+0.7+4.9±4.6

Lean BH crosscourt: +0.8±3.3 per 100 returns v the current mix (181 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv AnastasiaValue
FH crosscourt41%+0.5−1.2−1.3−2.0±4.3
FH through the middle41%−3.2+0.2+0.3−2.7±3.1
FH down the line19%−0.6±0.0+3.3+2.7±4.9

Lean FH down the line: +4.1±4.5 per 100 returns v the current mix (79 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv AnastasiaValue
BH through the middle53%−2.6−0.6+1.4−1.8±2.8
BH crosscourt32%+1.5−1.5+0.5+0.6±3.5
BH down the line15%−0.5+1.1−0.1+0.5±4.5

Lean BH crosscourt: +1.3±2.9 per 100 returns v the current mix (62 returns charted, inside the 90% margin)

Anastasia Myskina returning

1st serve to the forehand

ReturnNowTourOwnv JenniferValue
FH through the middle50%+4.2−1.8−1.3+1.1±2.8
FH crosscourt25%+5.3−1.9−2.5+0.9±4.3
FH down the line13%+1.5+3.4+2.8+7.8±4.6
FH slice through the middle8%−6.7+0.5+0.7−5.5±2.2
FH slice down the line2%−10.5−0.6±0.0−11.1±1.7

Lean FH down the line: +6.8±4.4 per 100 returns v the current mix (353 returns charted)

1st serve to the backhand

ReturnNowTourOwnv JenniferValue
BH through the middle62%+6.0−1.4−2.2+2.4±2.8
BH crosscourt25%+7.7−2.0−1.2+4.6±3.6
BH down the line13%+2.2±0.0−4.1−1.9±4.4

Lean BH crosscourt: +2.2±3.3 per 100 returns v the current mix (149 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv JenniferValue
FH through the middle43%−3.2−0.1+1.2−2.1±3.1
FH crosscourt42%+0.5+0.8−1.0+0.3±4.5
FH down the line14%−0.6−0.7+0.1−1.2±4.8

Lean FH crosscourt: +1.2±3.0 per 100 returns v the current mix (106 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv JenniferValue
BH through the middle50%−2.6+0.4+0.2−2.0±2.7
BH crosscourt44%+1.5+0.4±0.0+1.9±3.5
BH down the line6%−0.5+0.7+4.3+4.4±3.9

Lean BH crosscourt: +1.8±2.4 per 100 returns v the current mix (98 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 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.

Jennifer Capriati

Favour

ShotEdgeOwnTheirs
FH to their backhand · return+5.3±5.3+1.3+4.0
FH to their forehand · serve +1+4.4±4.9−0.1+4.5
BH to their backhand · return +1+2.3±5.0+1.9+0.4
BH to the middle · return+2.2±3.9+1.2+1.0
BH to their backhand · rally+1.6±4.4+0.2+1.4
FH to their forehand · rally+1.6±4.9+1.1+0.4

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−9.0±5.5−7.2−1.9
FH to their forehand · return +1−5.4±4.8−2.3−3.1
FH to their forehand · return−3.1±5.2+1.8−4.9
BH to their forehand · rally−1.6±6.0−1.1−0.5
FH to their backhand · serve +1−1.3±5.1−2.0+0.8

Anastasia Myskina

Favour

ShotEdgeOwnTheirs
FH to their backhand · return+4.9±6.2+2.4+2.5
FH to the middle · rally+1.7±3.7±0.0+1.7
BH to the middle · rally+0.4±2.9+2.2−1.8
BH to their backhand · return +1−0.2±4.4+0.5−0.7
FH to their backhand · return +1−1.5±5.3−3.0+1.6
BH to their backhand · serve +1−1.6±4.6−3.3+1.7

Avoid

ShotEdgeOwnTheirs
BH to their backhand · rally−8.1±3.6−4.8−3.2
BH to their forehand · return +1−7.1±6.0−1.6−5.5
FH to their forehand · rally−5.6±4.9−6.0+0.5
FH to their backhand · rally−4.5±5.6−3.8−0.7
BH to the middle · return−4.5±3.3−3.9−0.6

Against Anastasia Myskina-like opponents

Jennifer Capriati vMatchesServe pts wonReturn pts won
All charted opponents–54.9%44.0%
Players most similar to Anastasia Myskina1 56.1%41.6%

Similar by tactical fingerprint: Anastasia Potapova, Belinda Bencic, Elise Mertens, Paula Badosa, Qiang Wang, Kim Clijsters, Louisa Chirico, Marion Bartoli, Elena Dementieva. When two players have rarely met, their records against these lookalikes fill the gap.