Game plan

Amarissa Kiara Toth v Nadia Podoroska

Every number combines what Amarissa Kiara Toth does well with what Nadia Podoroska allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

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.

Amarissa Kiara Toth serving

Deuce court

1st serve	Now	Amarissa wins	v Nadia	Matchup	Optimal
Wide	23%	61%	71%	66.7%±13.4	39% ▲
Body	61%	50%	65%	58.6%±12.7	46% ▼
T	16%	60%	69%	61.6%±15.2	15%

Optimal v Nadia Podoroska: +0.6±1.3 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.8 per 100 first serves in before the returner adjusts.

Ad court

1st serve	Now	Amarissa wins	v Nadia	Matchup	Optimal
Wide	36%	55%	70%	59.5%±13.7	49% ▲
Body	33%	60%	62%	65.9%±13.7	18% ▼
T	30%	61%	64%	60.5%±14.8	33% ▲

Optimal v Nadia Podoroska: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +4.0 per 100 first serves in before the returner adjusts.

Nadia Podoroska serving

Deuce court

1st serve	Now	Nadia wins	v Amarissa	Matchup	Optimal
Wide	36%	57%	69%	60.1%±13.6	36%
Body	21%	50%	53%	45.7%±15.1	6% ▼
T	43%	66%	68%	65.9%±14.9	58% ▲

Optimal v Amarissa Kiara Toth: +1.0±1.3 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.4 per 100 first serves in before the returner adjusts.

Ad court

1st serve	Now	Nadia wins	v Amarissa	Matchup	Optimal
Wide	34%	60%	68%	63.0%±14.3	49% ▲
Body	23%	64%	52%	59.7%±14.6	23%
T	43%	55%	59%	49.4%±14.0	28% ▼

Optimal v Amarissa Kiara Toth: +0.6±1.3 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +6.6 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.

Amarissa Kiara Toth returning

Not enough charted returns.

Nadia Podoroska returning

1st serve to the forehand

Return	Now	Tour	Own	v Amarissa	Value
FH through the middle	42%	+4.2	−0.8	+0.5	+3.9±3.2
FH crosscourt	19%	+5.3	+0.1	+1.8	+7.2±4.1
FH slice through the middle	19%	−6.7	−0.1	±0.0	−6.8±1.7
FH down the line	11%	+1.5	+2.0	+0.8	+4.3±3.5
FH slice crosscourt	8%	−6.6	+1.2	±0.0	−5.4±1.5

Lean FH crosscourt: +5.4±3.6 per 100 returns v the current mix (144 returns charted)

1st serve to the backhand

Return	Now	Tour	Own	v Amarissa	Value
BH through the middle	56%	+6.0	+0.4	+1.4	+7.8±2.8
BH crosscourt	26%	+7.7	−0.4	+1.1	+8.5±3.4
BH down the line	10%	+2.2	−1.2	+0.4	+1.4±3.6
BH slice through the middle	5%	−6.2	+0.5	±0.0	−5.8±1.3
BH slice down the line	3%	−12.5	+0.8	±0.0	−11.7±1.5

Lean BH crosscourt: +2.4±3.0 per 100 returns v the current mix (171 returns charted, inside the 90% margin)

2nd serve to the backhand

Return	Now	Tour	Own	v Amarissa	Value
BH through the middle	41%	−2.6	+0.8	+0.6	−1.3±2.3
BH crosscourt	30%	+1.5	−1.0	+0.4	+0.9±3.0
FH through the middle	13%	−2.7	±0.0	±0.0	−2.7±1.5
BH down the line	6%	−0.5	−2.2	±0.0	−2.7±2.3
FH inside-in	5%	+0.7	+0.6	+0.6	+1.9±2.7

Lean BH crosscourt: +1.5±2.3 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 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.

Amarissa Kiara Toth

Favour

Shot	Edge	Own	Theirs
FH to the middle · rally	+2.0±3.9	+4.2	−2.2
FH to their backhand · rally	+1.9±5.6	+3.6	−1.7
FH to their forehand · rally	−2.9±5.3	−0.3	−2.6
BH to their backhand · rally	−5.3±4.7	−3.6	−1.7

Avoid

Shot	Edge	Own	Theirs
BH to their backhand · rally	−5.3±4.7	−3.6	−1.7
FH to their forehand · rally	−2.9±5.3	−0.3	−2.6
FH to their backhand · rally	+1.9±5.6	+3.6	−1.7
FH to the middle · rally	+2.0±3.9	+4.2	−2.2

Nadia Podoroska

Favour

Shot	Edge	Own	Theirs
FH to their forehand · rally	+2.6±4.9	+2.5	+0.1
BH to the middle · rally	+0.4±3.5	−2.3	+2.8
FH to their backhand · rally	−0.4±5.6	+0.2	−0.6
BH to their backhand · rally	−0.7±4.8	−1.9	+1.2

Avoid

Shot	Edge	Own	Theirs
BH to their backhand · rally	−0.7±4.8	−1.9	+1.2
FH to their backhand · rally	−0.4±5.6	+0.2	−0.6
BH to the middle · rally	+0.4±3.5	−2.3	+2.8
FH to their forehand · rally	+2.6±4.9	+2.5	+0.1

Against Nadia Podoroska-like opponents

Amarissa Kiara Toth v	Matches	Serve pts won	Return pts won
All charted opponents	–	46.6%	43.5%

Similar by tactical fingerprint: Diana Shnaider, Ashlyn Krueger, Bianca Andreescu, Jasmine Paolini, Maya Joint, Maria Sakkari, Nao Hibino, Irina Camelia Begu, Andrea Petkovic, Dominika Cibulkova. When two players have rarely met, their records against these lookalikes fill the gap.