RallyIQ

Game plan

Alison Riske Amritraj v Garbine Muguruza

Every number combines what Alison Riske Amritraj does well with what Garbine Muguruza allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Alison Riske Amritraj wins, best of 3 20%90%: 4%–53% · best of 5: 15%
Serve points won 54.9% / 61.3% Alison / Garbine · tour 58.1%
Strengths only, no similarity priors 18%serve 54.9% / 62.1%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Alison Riske Amritraj's record against Garbine Muguruza's tactical lookalikes and in their charted head-to-heads (lookalikes: −4.2 on serve, +3.7 on return vs expectation (187 points); head-to-head: +5.2 on serve, +13.5 on return vs expectation (128 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

CareerAlisonGarbine
Direction choice−0.11 ±0.09
better than 34%
−0.04 ±0.05
better than 47%
Shot selection+0.32 ±0.15
better than 80%
+0.58 ±0.06
better than 96%
Execution−0.38 ±0.51
better than 39%
−0.03 ±0.27
better than 60%
Points left on the table2.65 ±0.08
lower than 44%
2.49 ±0.08
lower than 66%

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.

Alison Riske Amritraj serving

Deuce court

1st serveNowAlison winsv GarbineMatchupOptimal
Wide42%64%67%65.3%±5.257% ▲
Body17%51%57%50.4%±8.01% ▼
T42%60%65%57.1%±5.642%

Optimal v Garbine Muguruza: +0.7±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAlison winsv GarbineMatchupOptimal
Wide42%68%65%67.1%±5.757% ▲
Body24%57%56%56.8%±7.59% ▼
T34%57%64%56.6%±6.034%

Optimal v Garbine Muguruza: +0.7±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +6.0 per 100 first serves in before the returner adjusts.

Garbine Muguruza serving

Deuce court

1st serveNowGarbine winsv AlisonMatchupOptimal
Wide35%68%68%69.1%±5.435%
Body18%56%50%48.6%±7.43% ▼
T47%74%76%80.6%±4.162% ▲

Optimal v Alison Riske Amritraj: +2.5±0.9 per 100 first serves (faults included) over the current mix. Serving T every time would read +9.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowGarbine winsv AlisonMatchupOptimal
Wide39%70%68%72.0%±5.455% ▲
Body22%57%56%56.9%±7.66% ▼
T39%65%66%66.5%±6.039%

Optimal v Alison Riske Amritraj: +0.8±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.4 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.

Alison Riske Amritraj returning

1st serve to the forehand

ReturnNowTourOwnv GarbineValue
FH through the middle53%+4.2−4.2+1.4+1.3±2.1
FH crosscourt23%+5.3−0.2−0.1+5.0±3.7
FH down the line13%+1.5+0.4+0.2+2.1±4.1
FH slice through the middle6%−6.7+0.6−0.4−6.5±2.3
FH slice crosscourt3%−6.6±0.0−0.4−7.1±2.5

Lean FH crosscourt: +3.7±3.1 per 100 returns v the current mix (352 returns charted)

1st serve to the backhand

ReturnNowTourOwnv GarbineValue
BH through the middle49%+6.0+0.3+0.3+6.6±2.0
BH crosscourt26%+7.7−1.0+1.2+8.0±3.0
BH down the line17%+2.2−3.4+2.5+1.3±4.1
BH slice through the middle5%−6.2+0.2−0.7−6.8±2.1
BH slice down the line3%−12.5−2.7−2.1−17.2±3.2

Lean BH crosscourt: +3.4±2.6 per 100 returns v the current mix (322 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv GarbineValue
FH through the middle54%−3.2−0.6+1.1−2.6±2.6
FH crosscourt31%+0.5+1.7+0.2+2.5±4.0
FH down the line14%−0.6+0.1+2.3+1.9±4.3

Lean FH crosscourt: +2.9±3.1 per 100 returns v the current mix (105 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv GarbineValue
BH through the middle44%−2.6−1.0+0.1−3.5±2.2
BH crosscourt34%+1.5−0.2+2.4+3.7±3.1
BH down the line22%−0.5−0.5+3.5+2.5±4.8

Lean BH crosscourt: +3.5±2.5 per 100 returns v the current mix (202 returns charted)

Garbine Muguruza returning

1st serve to the forehand

ReturnNowTourOwnv AlisonValue
FH through the middle59%+4.2+1.4+0.4+5.9±2.0
FH crosscourt23%+5.3−1.4−0.9+3.0±3.5
FH down the line13%+1.5+2.3−0.6+3.2±4.2
FH slice through the middle3%−6.7−2.8+0.6−9.0±2.6
FH slice crosscourt1%−6.6−0.4+1.7−5.4±2.6

Lean FH through the middle: +1.6±1.3 per 100 returns v the current mix (1441 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AlisonValue
BH through the middle57%+6.0+1.7+0.9+8.7±1.8
BH crosscourt28%+7.7+1.7+0.3+9.7±3.0
BH down the line12%+2.2+5.5+0.7+8.4±4.3
BH slice through the middle2%−6.2+0.3−0.5−6.4±2.5
BH slice crosscourt1%−4.2−0.3+0.7−3.8±2.7

Lean BH crosscourt: +1.3±2.4 per 100 returns v the current mix (1254 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv AlisonValue
FH through the middle49%−3.2+1.8+1.1−0.4±2.7
FH crosscourt31%+0.5+1.1+1.9+3.6±4.3
FH down the line15%−0.6+0.9+0.2+0.6±5.2
FH slice through the middle2%−15.2−0.2+0.7−14.7±1.7
BH inside-out1%+2.2+1.1−1.2+2.1±3.7

Lean FH crosscourt: +2.9±3.3 per 100 returns v the current mix (411 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv AlisonValue
BH through the middle47%−2.6+1.2−1.5−2.9±2.2
BH crosscourt40%+1.5+2.5+1.6+5.6±3.2
BH down the line14%−0.5+3.2−1.2+1.5±5.2

Lean BH crosscourt: +4.5±2.3 per 100 returns v the current mix (675 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.

Alison Riske Amritraj

Favour

ShotEdgeOwnTheirs
BH to their backhand · serve +1+6.8±4.8+3.5+3.2
BH to their backhand · rally+4.6±4.3+4.4+0.2
FH to their forehand · rally+3.3±4.3+3.0+0.3
BH to the middle · serve +1+2.7±3.3+1.0+1.7
BH to their forehand · rally+1.2±5.6+1.6−0.3
FH to the middle · return +1+0.8±3.4+0.1+0.8

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−3.7±5.4−2.9−0.8
FH to their forehand · return−2.7±5.6−0.4−2.3
BH to the middle · return−1.5±3.3−2.8+1.3
FH to their backhand · return +1−1.4±5.8±0.0−1.5
FH to their backhand · rally−1.4±4.7−3.0+1.5

Garbine Muguruza

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+6.6±5.3+1.3+5.4
FH to their backhand · rally+6.5±5.1+1.3+5.3
FH to the middle · return+4.7±3.6+1.3+3.4
BH to their backhand · rally+3.9±4.0+0.3+3.6
BH to their forehand · return+3.3±5.8+3.4−0.1
BH to the middle · rally+3.1±2.9+1.2+1.8

Avoid

ShotEdgeOwnTheirs
FH to their backhand · return +1−6.7±5.6−2.1−4.6
FH to their forehand · return +1−5.5±5.1−3.3−2.2
FH to the middle · serve +1−3.9±3.4−3.2−0.8
FH slice to the middle · rally−3.8±3.1−3.4−0.4
FH to their backhand · serve +1−2.9±5.5−2.1−0.8

Against Garbine Muguruza-like opponents

Alison Riske Amritraj vMatchesServe pts wonReturn pts won
All charted opponents–54.7%41.2%
Players most similar to Garbine Muguruza1 55.3%47.3%
Garbine Muguruza (charted head-to-head)1 57.9%53.5%

Similar by tactical fingerprint: Alexandra Eala, Karolina Pliskova, Ashlyn Krueger, Maya Joint, Jaqueline Cristian, Shuai Zhang, Veronika Kudermetova, Irina Camelia Begu, Anett Kontaveit. When two players have rarely met, their records against these lookalikes fill the gap.

Charted head-to-head