RallyIQ

Game plan

Antonia Ruzic v Emma Raducanu

Every number combines what Antonia Ruzic does well with what Emma Raducanu allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Antonia Ruzic wins, best of 3 9%90%: 1%–37% · best of 5: 4%
Serve points won 49.9% / 60.2% Antonia / Emma · tour 55.0%
Strengths only, no similarity priors 8%serve 49.9% / 60.2%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Antonia Ruzic's record against Emma Raducanu's tactical lookalikes and in their charted head-to-heads (lookalikes: +1.6 on serve, +1.5 on return vs expectation (104 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

CareerAntoniaEmma
Direction choice+0.14 ±0.12
better than 78%
−0.02 ±0.05
better than 50%
Shot selection−0.07 ±0.20
better than 39%
−0.09 ±0.09
better than 35%
Execution−0.48 ±0.76
better than 34%
+0.76 ±0.31
better than 86%

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.

Antonia Ruzic serving

Deuce court

1st serveNowAntonia winsv EmmaMatchupOptimal
Wide52%60%64%57.9%±7.858% ▲
Body19%52%57%50.9%±10.64% ▼
T29%66%68%65.9%±10.438% ▲

Optimal v Emma Raducanu: +0.5±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +7.0 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAntonia winsv EmmaMatchupOptimal
Wide31%50%64%48.2%±10.016% ▼
Body20%50%59%53.8%±10.720%
T49%55%64%54.3%±8.764% ▲

Optimal v Emma Raducanu: +0.3±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.0 per 100 first serves in before the returner adjusts.

Emma Raducanu serving

Deuce court

1st serveNowEmma winsv AntoniaMatchupOptimal
Wide46%68%66%68.7%±8.261% ▲
Body25%56%64%63.3%±8.810% ▼
T29%68%66%66.2%±9.729%

Optimal v Antonia Ruzic: +0.6±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowEmma winsv AntoniaMatchupOptimal
Wide44%71%65%70.4%±8.560% ▲
Body17%51%61%56.0%±10.51% ▼
T39%63%58%56.9%±9.639%

Optimal v Antonia Ruzic: +1.1±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +7.6 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.

Antonia Ruzic returning

1st serve to the forehand

ReturnNowTourOwnv EmmaValue
FH through the middle53%+4.2+1.3+2.2+7.6±2.5
FH crosscourt40%+5.3+0.5+0.7+6.5±3.8
FH down the line8%+1.5+0.9−0.9+1.5±3.4

Lean FH through the middle: +0.9±1.9 per 100 returns v the current mix (91 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv EmmaValue
BH through the middle59%+6.0+0.8−1.0+5.8±2.4
BH crosscourt26%+7.7+2.4−0.8+9.4±3.0
BH down the line8%+2.2−2.0−1.1−0.9±3.4
BH slice crosscourt7%−4.2−0.7−0.6−5.5±2.5

Lean BH crosscourt: +3.9±2.6 per 100 returns v the current mix (76 returns charted)

Emma Raducanu returning

1st serve to the forehand

ReturnNowTourOwnv AntoniaValue
FH through the middle46%+4.2+2.0+2.1+8.2±2.6
FH crosscourt21%+5.3+1.9+6.4+13.7±4.0
FH down the line14%+1.5+5.0+0.2+6.7±4.5
FH slice through the middle12%−6.7+0.8+0.8−5.1±2.1
FH slice crosscourt4%−6.6+0.8+0.3−5.5±2.5

Lean FH crosscourt: +7.3±3.4 per 100 returns v the current mix (954 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AntoniaValue
BH through the middle47%+6.0+2.7+1.5+10.3±2.5
BH crosscourt28%+7.7+3.2+2.1+13.0±3.3
BH slice through the middle11%−6.2+0.7+1.6−3.9±2.1
BH down the line10%+2.2+2.5+1.1+5.7±4.5
BH slice crosscourt3%−4.2+0.8±0.0−3.4±2.0

Lean BH crosscourt: +4.8±2.7 per 100 returns v the current mix (834 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv AntoniaValue
FH through the middle47%−3.2+2.1−1.0−2.1±2.8
FH crosscourt34%+0.5−0.1−0.6−0.2±3.9
FH down the line17%−0.6−0.2−1.4−2.2±4.8
FH slice through the middle2%−15.2+0.7±0.0−14.5±1.1

Lean FH crosscourt: +1.5±3.0 per 100 returns v the current mix (254 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv AntoniaValue
BH through the middle44%−2.6−0.5+1.9−1.2±2.5
BH crosscourt41%+1.5+0.3+2.4+4.2±3.3
BH down the line14%−0.5−2.5−1.7−4.8±4.9
BH slice through the middle1%−11.7−0.6±0.0−12.3±1.0

Lean BH crosscourt: +3.8±2.4 per 100 returns v the current mix (470 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 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.

Antonia Ruzic

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+5.0±4.8+0.7+4.2
FH to their forehand · return +1+3.6±5.3+0.4+3.2
FH to their forehand · rally+3.3±4.3+1.0+2.3
BH to their backhand · serve +1+3.1±4.7+2.9+0.2
FH to their forehand · return+2.5±5.9−0.6+3.0
FH to their forehand · serve +1+1.9±5.3−2.1+4.0

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−3.7±3.1−2.2−1.5
FH to the middle · rally−3.2±3.4−1.8−1.4
BH to their forehand · rally−2.7±5.9−4.0+1.3
BH to their backhand · return−1.3±4.4−0.3−1.0
BH to the middle · return−0.7±3.2+0.5−1.2

Emma Raducanu

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+7.7±4.1+2.2+5.4
FH to their forehand · rally+6.1±4.5+2.6+3.5
FH to the middle · rally+4.7±3.3+1.3+3.4
BH to their backhand · return+4.3±4.3+1.7+2.6
BH to the middle · return+3.7±3.4+0.8+2.9
FH to their backhand · rally+3.4±4.8+0.9+2.5

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−4.0±6.0−3.9−0.1
FH to their backhand · return±0.0±5.6+1.0−1.0
BH to the middle · rally+1.2±3.0+1.3−0.1
BH to their backhand · serve +1+1.3±4.7+1.1+0.2
FH to the middle · return+2.5±3.6+0.4+2.1

Against Emma Raducanu-like opponents

Antonia Ruzic vMatchesServe pts wonReturn pts won
All charted opponents–50.5%41.8%
Players most similar to Emma Raducanu1 51.0%41.8%

Similar by tactical fingerprint: Coco Gauff, Iva Jovic, Anna Kalinskaya, Anna Blinkova, Marie Bouzkova, Kimberly Birrell, Emma Navarro, Nao Hibino, Lin Zhu, Andrea Petkovic. When two players have rarely met, their records against these lookalikes fill the gap.