RallyIQ

Game plan

Maja Chwalinska v Fiona Ferro

Every number combines what Maja Chwalinska does well with what Fiona Ferro allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Maja Chwalinska wins, best of 3 86%90%: 45%–99% · best of 5: 91%
Serve points won 60.6% / 52.5% Maja / Fiona · tour 56.4%
Strengths only, no similarity priors 85%serve 60.4% / 52.4%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Maja Chwalinska's record against Fiona Ferro's tactical lookalikes and in their charted head-to-heads (lookalikes: +4.1 on serve, −1.7 on return vs expectation (151 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

CareerMajaFiona
Direction choice−0.08 ±0.06
better than 40%
−0.15 ±0.12
better than 26%
Shot selection−0.52 ±0.28
better than 11%
+0.21 ±0.23
better than 67%
Execution+2.12 ±0.53
better than 99%
−0.74 ±0.91
better than 26%
Points left on the table2.93 ±0.10
lower than 14%
2.66 ±0.14
lower than 40%

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.

Maja Chwalinska serving

Deuce court

1st serveNowMaja winsv FionaMatchupOptimal
Wide26%62%76%72.6%±10.041% ▲
Body28%55%58%55.7%±11.913% ▼
T46%65%68%65.2%±10.046%

Optimal v Fiona Ferro: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +8.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMaja winsv FionaMatchupOptimal
Wide53%60%57%51.5%±10.452%
Body22%53%55%51.7%±13.57% ▼
T26%67%72%74.6%±10.141% ▲

Optimal v Fiona Ferro: +1.2±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +17.2 per 100 first serves in before the returner adjusts.

Fiona Ferro serving

Deuce court

1st serveNowFiona winsv MajaMatchupOptimal
Wide33%61%58%52.3%±11.340% ▲
Body28%42%57%41.8%±12.313% ▼
T39%58%62%51.6%±11.747% ▲

Optimal v Maja Chwalinska: +0.3±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowFiona winsv MajaMatchupOptimal
Wide54%59%59%51.4%±11.454%
Body25%54%53%50.4%±12.510% ▼
T21%64%58%57.8%±12.536% ▲

Optimal v Maja Chwalinska: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.3 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.

Maja Chwalinska returning

1st serve to the forehand

ReturnNowTourOwnv FionaValue
FH through the middle41%+4.2+3.2−0.1+7.3±3.2
FH crosscourt21%+5.3+3.3−0.4+8.2±4.2
FH slice through the middle17%−6.7+1.6+0.2−5.0±1.9
FH down the line14%+1.5+1.1+0.6+3.2±4.4
FH slice crosscourt7%−6.6+1.5±0.0−5.2±1.4

Lean FH crosscourt: +4.3±3.6 per 100 returns v the current mix (111 returns charted)

1st serve to the backhand

ReturnNowTourOwnv FionaValue
BH through the middle43%+6.0+1.2−0.7+6.4±2.8
BH crosscourt33%+7.7+2.4+0.8+11.0±3.5
BH down the line16%+2.2+3.6+2.7+8.5±4.7
BH slice through the middle3%−6.2−0.5±0.0−6.8±1.2
BH slice crosscourt3%−4.2+0.1±0.0−4.1±1.3

Lean BH crosscourt: +3.8±2.7 per 100 returns v the current mix (236 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv FionaValue
BH through the middle31%−2.6+1.6−0.4−1.4±2.8
BH crosscourt29%+1.5±0.0−0.6+0.9±3.5
FH through the middle18%−2.7−1.1−2.6−6.4±2.6
BH down the line11%−0.5−1.3+2.8+1.0±4.7
FH inside-in5%+0.7+0.5+0.5+1.7±3.9

Lean BH crosscourt: +2.0±2.7 per 100 returns v the current mix (110 returns charted, inside the 90% margin)

Fiona Ferro returning

1st serve to the forehand

ReturnNowTourOwnv MajaValue
FH through the middle38%+4.2−2.1+0.2+2.2±3.1
FH down the line30%+1.5−3.8+3.2+1.0±4.7
FH crosscourt16%+5.3+0.4+2.7+8.4±4.1
FH slice through the middle9%−6.7−0.5−1.2−8.4±1.8
FH slice crosscourt7%−6.6+0.1+1.0−5.5±2.0

Lean FH crosscourt: +7.0±4.0 per 100 returns v the current mix (115 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MajaValue
BH through the middle49%+6.0+1.4+2.1+9.5±2.7
BH crosscourt24%+7.7+1.3+1.3+10.4±3.6
BH down the line18%+2.2+2.1+2.7+7.0±4.5
BH slice crosscourt4%−4.2−1.6±0.0−5.8±1.2
BH slice through the middle4%−6.2+0.4+0.6−5.2±1.6

Lean BH crosscourt: +2.4±3.1 per 100 returns v the current mix (120 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv MajaValue
BH crosscourt40%+1.5±0.0−0.1+1.4±3.6
BH through the middle30%−2.6+0.1+1.4−1.1±2.7
FH inside-out12%+1.4+0.6−1.4+0.5±3.5
BH down the line9%−0.5−1.2−1.4−3.2±4.0
FH through the middle9%−2.7+0.3−1.2−3.5±2.2

Lean BH crosscourt: +1.7±2.4 per 100 returns v the current mix (67 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.

Maja Chwalinska

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+7.4±4.0+3.6+3.8
BH to their backhand · return+5.9±4.4+2.8+3.1
FH to their backhand · return+5.1±6.4+4.0+1.2
FH to the middle · rally+3.0±3.5+2.7+0.4
FH to the middle · serve +1+2.0±3.9+2.2−0.1
BH to their backhand · serve +1+1.9±4.7+1.2+0.7

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−5.7±6.0−2.6−3.1
FH to the middle · return−3.6±4.1+1.6−5.2
FH to their backhand · rally−1.0±5.4−0.6−0.4
BH to the middle · rally−0.7±3.4+1.9−2.5
FH to their forehand · rally+0.1±5.0+4.1−4.0

Fiona Ferro

Favour

ShotEdgeOwnTheirs
BH to the middle · return+7.1±3.8+4.4+2.7
BH to the middle · rally+4.6±3.3+3.0+1.6
BH to their backhand · return+4.1±4.5+1.6+2.5
BH to their backhand · serve +1+2.3±4.7+0.3+2.0
BH to their forehand · rally+2.1±6.2+2.0+0.1
FH to their backhand · rally+1.0±5.5+1.9−0.9

Avoid

ShotEdgeOwnTheirs
FH to their forehand · rally−8.9±5.1−4.1−4.7
BH to their backhand · rally−8.1±4.6−4.7−3.3
FH to their forehand · return−5.8±6.4−5.7−0.1
FH to their backhand · serve +1−5.5±6.0−4.7−0.8
FH to the middle · serve +1−4.9±4.0−3.9−0.9

Against Fiona Ferro-like opponents

Maja Chwalinska vMatchesServe pts wonReturn pts won
All charted opponents–58.7%49.2%
Players most similar to Fiona Ferro1 62.8%43.8%

Similar by tactical fingerprint: Alexandra Eala, Diana Shnaider, Marta Kostyuk, Sara Bejlek, Olga Danilovic, Kaja Juvan, Yue Yuan, Nao Hibino, Anhelina Kalinina, Svetlana Kuznetsova. When two players have rarely met, their records against these lookalikes fill the gap.