RallyIQ

Game plan

Kayla Day v Samantha Stosur

Every number combines what Kayla Day does well with what Samantha Stosur allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Kayla Day wins, best of 3 54%90%: 14%–90% · best of 5: 55%
Serve points won 63.4% / 62.6% Kayla / Samantha · tour 58.1%
Strengths only, no similarity priors 54%serve 63.4% / 62.6%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerKaylaSamantha
Direction choice−0.37 ±0.16
better than 3%
+0.02 ±0.07
better than 56%
Shot selection−0.18 ±0.14
better than 29%
−0.03 ±0.15
better than 43%
Execution+0.97 ±0.91
better than 91%
−0.51 ±0.60
better than 32%
Points left on the table3.13 ±0.51
lower than 7%
2.77 ±0.11
lower than 26%

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.

Kayla Day serving

Deuce court

1st serveNowKayla winsv SamanthaMatchupOptimal
Wide35%66%74%74.7%±8.950% ▲
Body17%56%58%57.1%±12.92% ▼
T48%71%68%71.5%±8.648%

Optimal v Samantha Stosur: +1.4±1.2 per 100 first serves (faults included) over the current mix. Serving wide every time would read +4.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKayla winsv SamanthaMatchupOptimal
Wide45%61%65%59.8%±10.145%
Body25%61%58%62.6%±11.710% ▼
T30%65%69%69.4%±10.745% ▲

Optimal v Samantha Stosur: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.0 per 100 first serves in before the returner adjusts.

Samantha Stosur serving

Deuce court

1st serveNowSamantha winsv KaylaMatchupOptimal
Wide38%68%67%69.5%±8.753% ▲
Body19%56%60%58.6%±10.44% ▼
T43%64%68%64.2%±12.143%

Optimal v Kayla Day: +0.5±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.4 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowSamantha winsv KaylaMatchupOptimal
Wide38%64%61%60.2%±9.938%
Body15%59%57%59.9%±11.90% ▼
T46%73%59%68.6%±9.462% ▲

Optimal v Kayla Day: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +4.5 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.

Kayla Day returning

1st serve to the forehand

ReturnNowTourOwnv SamanthaValue
FH crosscourt42%+5.3+3.1−0.9+7.5±4.2
FH through the middle36%+4.2+0.7−0.2+4.7±2.7
FH down the line11%+1.5−0.8+0.9+1.6±3.9
FH slice through the middle11%−6.7+0.6−1.1−7.2±2.2

Lean FH crosscourt: +3.2±2.7 per 100 returns v the current mix (66 returns charted)

1st serve to the backhand

ReturnNowTourOwnv SamanthaValue
BH through the middle52%+6.0+1.1+0.5+7.7±2.6
BH crosscourt24%+7.7+1.0−2.4+6.3±3.6
BH down the line14%+2.2−0.7+1.9+3.3±4.0
BH slice down the line5%−12.5−0.3±0.0−12.8±2.9
BH slice through the middle5%−6.2+0.3−0.4−6.3±2.1

Lean BH through the middle: +2.8±1.6 per 100 returns v the current mix (110 returns charted)

Samantha Stosur returning

1st serve to the forehand

ReturnNowTourOwnv KaylaValue
FH through the middle48%+4.2−0.1−1.6+2.4±2.9
FH down the line22%+1.5−1.2−1.2−0.9±4.3
FH crosscourt20%+5.3−3.6−0.4+1.3±4.0
FH slice through the middle6%−6.7+0.4+1.4−4.9±2.2
FH slice crosscourt3%−6.6−0.9±0.0−7.5±1.5

Lean FH through the middle: +1.7±2.0 per 100 returns v the current mix (355 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv KaylaValue
BH through the middle37%+6.0+0.4+0.7+7.1±2.7
BH crosscourt19%+7.7−1.2−0.4+6.1±3.4
BH slice through the middle15%−6.2−0.3−0.4−7.0±2.3
BH down the line14%+2.2+3.5−1.2+4.5±4.6
BH slice crosscourt9%−4.2−2.1+0.8−5.4±2.5

Lean BH through the middle: +4.3±2.0 per 100 returns v the current mix (468 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv KaylaValue
FH through the middle34%−3.2+2.8+0.8+0.4±3.1
FH crosscourt32%+0.5−3.5+1.9−1.0±4.1
FH down the line29%−0.6−4.2−3.8−8.6±4.8
FH slice through the middle5%−15.2+0.6±0.0−14.6±1.1

Lean FH through the middle: +3.8±2.8 per 100 returns v the current mix (111 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv KaylaValue
FH through the middle21%−2.7±0.0+0.8−1.9±2.7
BH through the middle20%−2.6−0.5+2.1−1.0±2.8
FH inside-in18%+0.7+0.1+1.9+2.8±4.4
FH inside-out14%+1.4+0.8−3.8−1.7±3.9
BH slice crosscourt9%−7.5−2.3±0.0−9.7±1.9

Lean FH inside-in: +4.6±3.8 per 100 returns v the current mix (247 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.

Kayla Day

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+7.6±4.0+4.4+3.2
FH to their backhand · serve +1+4.0±4.9+4.9−0.9
FH to their forehand · rally+3.1±4.9+2.7+0.4
BH to the middle · return+2.9±2.3+2.4+0.5
FH to their backhand · rally+1.9±4.1+2.7−0.8
BH to the middle · rally+1.0±2.5+0.6+0.4

Avoid

ShotEdgeOwnTheirs
FH to their backhand · return−1.4±4.3+1.3−2.6
FH to the middle · rally−0.2±2.9−0.1−0.1
FH to the middle · return+0.3±4.0−2.5+2.9
BH to the middle · rally+1.0±2.5+0.6+0.4
FH to their backhand · rally+1.9±4.1+2.7−0.8

Samantha Stosur

Favour

ShotEdgeOwnTheirs
FH to their forehand · serve +1+5.8±4.6+4.1+1.7
BH to their forehand · rally+3.0±4.9−2.7+5.7
BH to the middle · rally+2.3±2.2+1.5+0.8
FH to their backhand · serve +1+2.2±5.4+0.2+2.0
BH to the middle · return+1.8±3.0+1.4+0.3
BH to their backhand · rally+0.2±3.4−1.4+1.6

Avoid

ShotEdgeOwnTheirs
FH to their forehand · rally−4.8±4.8−3.5−1.3
FH to the middle · rally−3.0±2.8±0.0−3.0
FH to their backhand · rally−1.1±4.8−1.2+0.1
FH to the middle · return±0.0±3.3+1.3−1.3
BH to their backhand · rally+0.2±3.4−1.4+1.6

Against Samantha Stosur-like opponents

Kayla Day vMatchesServe pts wonReturn pts won
All charted opponents–57.1%41.5%

Similar by tactical fingerprint: Madison Keys, Karolina Muchova, Rebecca Sramkova, Diane Parry, Peyton Stearns, Katie Boulter, Daria Saville, Svetlana Kuznetsova, Christina Mchale, Ana Ivanovic. When two players have rarely met, their records against these lookalikes fill the gap.