RallyIQ

Game plan

Kaja Juvan v Fiona Ferro

Every number combines what Kaja Juvan 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

Kaja Juvan wins, best of 3 72%90%: 35%–94% · best of 5: 77%
Serve points won 57.0% / 52.6% Kaja / Fiona · tour 55.0%
Strengths only, no similarity priors 72%serve 57.0% / 52.6%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerKajaFiona
Direction choice−0.08 ±0.13
better than 40%
−0.15 ±0.12
better than 26%
Shot selection−0.23 ±0.22
better than 26%
+0.21 ±0.23
better than 67%
Execution−0.59 ±0.61
better than 30%
−0.74 ±0.91
better than 26%
Points left on the table2.80 ±0.19
lower than 24%
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.

Kaja Juvan serving

Deuce court

1st serveNowKaja winsv FionaMatchupOptimal
Wide30%64%76%74.4%±8.845% ▲
Body21%62%58%62.1%±11.06% ▼
T49%66%68%66.2%±9.449%

Optimal v Fiona Ferro: +0.5±1.2 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.

Ad court

1st serveNowKaja winsv FionaMatchupOptimal
Wide55%68%57%59.8%±9.865% ▲
Body18%62%55%61.7%±12.43% ▼
T26%59%72%67.5%±10.632% ▲

Optimal v Fiona Ferro: +0.5±1.1 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.

Fiona Ferro serving

Deuce court

1st serveNowFiona winsv KajaMatchupOptimal
Wide33%61%73%68.5%±10.233%
Body28%42%51%36.0%±10.713% ▼
T39%58%69%59.3%±10.154% ▲

Optimal v Kaja Juvan: +1.3±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +12.7 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowFiona winsv KajaMatchupOptimal
Wide54%59%72%66.3%±8.970% ▲
Body25%54%57%54.9%±11.710% ▼
T21%64%67%66.2%±11.520%

Optimal v Kaja Juvan: +1.1±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.8 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.

Kaja Juvan returning

1st serve to the forehand

ReturnNowTourOwnv FionaValue
FH through the middle43%+4.2+0.2−0.1+4.3±3.1
FH slice through the middle21%−6.7+1.3+0.2−5.3±2.1
FH crosscourt14%+5.3−4.5+0.6+1.4±4.3
FH down the line9%+1.5−0.1−0.4+1.1±4.5
FH slice crosscourt8%−6.6−0.5±0.0−7.1±1.8

Lean FH through the middle: +4.3±2.0 per 100 returns v the current mix (235 returns charted)

1st serve to the backhand

ReturnNowTourOwnv FionaValue
BH through the middle59%+6.0+1.5−0.7+6.8±2.7
BH crosscourt20%+7.7−0.2+2.7+10.3±3.7
BH down the line12%+2.2−0.4+0.8+2.6±4.5
BH slice through the middle5%−6.2+0.7±0.0−5.5±1.5
BH slice crosscourt4%−4.2−1.7±0.0−5.9±1.7

Lean BH crosscourt: +4.4±3.4 per 100 returns v the current mix (294 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv FionaValue
FH crosscourt44%+0.5+0.1+0.3+0.9±4.1
FH through the middle40%−3.2+0.5−2.6−5.3±3.0
FH down the line16%−0.6−2.0+0.5−2.1±4.6

Lean FH crosscourt: +2.9±2.7 per 100 returns v the current mix (63 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv FionaValue
BH through the middle53%−2.6−1.3−0.4−4.3±2.8
BH crosscourt26%+1.5−4.6+2.8−0.3±3.6
BH down the line12%−0.5+0.3−0.6−0.9±4.7
FH inside-out6%+1.4−0.4+0.5+1.5±3.7
FH through the middle4%−2.7+0.3−2.6−5.0±2.2

Lean BH crosscourt: +2.2±3.2 per 100 returns v the current mix (137 returns charted, inside the 90% margin)

Fiona Ferro returning

1st serve to the forehand

ReturnNowTourOwnv KajaValue
FH through the middle38%+4.2−2.1−0.3+1.7±3.0
FH down the line30%+1.5−3.8+1.5−0.7±4.7
FH crosscourt16%+5.3+0.4−1.7+4.0±4.1
FH slice through the middle9%−6.7−0.5−0.9−8.1±2.2
FH slice crosscourt7%−6.6+0.1+0.8−5.7±2.0

Lean FH crosscourt: +4.0±3.9 per 100 returns v the current mix (115 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KajaValue
BH through the middle49%+6.0+1.4+1.1+8.5±2.7
BH crosscourt24%+7.7+1.3+1.3+10.3±3.5
BH down the line18%+2.2+2.1−3.2+1.0±4.5
BH slice crosscourt4%−4.2−1.6+0.7−5.1±2.1
BH slice through the middle4%−6.2+0.4−1.6−7.4±2.0

Lean BH crosscourt: +3.9±3.1 per 100 returns v the current mix (120 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv KajaValue
BH crosscourt40%+1.5±0.0−0.6+0.9±3.5
BH through the middle30%−2.6+0.1+1.1−1.4±2.6
FH inside-out12%+1.4+0.6+1.7+3.7±3.7
BH down the line9%−0.5−1.2−4.3−6.0±4.4
FH through the middle9%−2.7+0.3+1.4−0.9±2.2

Lean BH crosscourt: +1.2±2.3 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 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.

Kaja Juvan

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+7.9±4.7+1.7+6.2
FH to the middle · rally+3.8±3.5+2.8+1.0
BH to their backhand · rally+2.8±4.0+1.5+1.4
BH to their backhand · serve +1+2.6±5.3−0.5+3.1
FH to the middle · return+2.4±3.8+2.7−0.4
BH to the middle · return+2.0±3.3+2.0±0.0

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−5.4±4.7−3.9−1.6
BH to their backhand · return +1−4.2±5.0−5.2+1.0
BH to the middle · rally−1.2±3.2+0.3−1.4
FH to the middle · serve +1−1.1±3.7+0.4−1.5
FH to their forehand · rally−0.7±4.5−1.4+0.7

Fiona Ferro

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+5.6±4.0+2.0+3.5
BH to their backhand · return+5.0±4.8+3.4+1.6
FH to their backhand · return+3.6±5.8−0.3+3.9
FH to their backhand · serve +1+0.4±5.4−2.0+2.4
FH to their backhand · rally+0.4±4.3+1.1−0.7
FH to their forehand · return±0.0±6.4+1.3−1.3

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−9.2±6.2−4.7−4.5
FH to the middle · serve +1−2.7±3.8−3.3+0.6
FH to their forehand · rally−2.3±4.4−1.3−1.0
FH to the middle · return−1.9±3.8−1.8−0.1
FH to the middle · rally−1.0±3.3−1.3+0.3

Against Fiona Ferro-like opponents

Kaja Juvan vMatchesServe pts wonReturn pts won
All charted opponents–57.7%40.3%

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