RallyIQ

Game plan

Ana Konjuh v Talia Gibson

Every number combines what Ana Konjuh does well with what Talia Gibson allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Ana Konjuh wins, best of 3 33%90%: 9%–68% · best of 5: 29%
Serve points won 58.3% / 61.6% Ana / Talia · tour 56.3%
Strengths only, no similarity priors 33%serve 58.3% / 61.6%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerAnaTalia
Direction choice+0.09 ±0.13
better than 71%
+0.26 ±0.05
better than 91%
Shot selection−0.23 ±0.24
better than 26%
+0.01 ±0.09
better than 45%
Execution−1.90 ±0.91
better than 5%
−0.47 ±0.93
better than 35%
Points left on the table2.35 ±0.23
lower than 80%
2.25 ±0.11
lower than 89%

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.

Ana Konjuh serving

Deuce court

1st serveNowAna winsv TaliaMatchupOptimal
Wide37%74%72%78.8%±7.351% ▲
Body19%56%56%54.9%±11.94% ▼
T44%77%76%83.0%±7.145%

Optimal v Talia Gibson: +1.4±1.1 per 100 first serves (faults included) over the current mix. Serving T every time would read +7.0 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowAna winsv TaliaMatchupOptimal
Wide27%59%69%62.5%±10.827%
Body24%56%55%54.7%±12.69% ▼
T49%60%67%62.5%±9.064% ▲

Optimal v Talia Gibson: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +1.9 per 100 first serves in before the returner adjusts.

Talia Gibson serving

Deuce court

1st serveNowTalia winsv AnaMatchupOptimal
Wide43%68%67%68.8%±8.359% ▲
Body10%55%64%61.5%±11.70% ▼
T46%74%75%80.1%±7.541% ▼

Optimal v Ana Konjuh: +0.8±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +6.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowTalia winsv AnaMatchupOptimal
Wide51%74%72%79.4%±7.666% ▲
Body6%54%56%54.3%±13.40% ▼
T43%69%61%65.9%±9.034% ▼

Optimal v Ana Konjuh: +0.5±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +7.3 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.

Ana Konjuh returning

1st serve to the forehand

ReturnNowTourOwnv TaliaValue
FH through the middle35%+4.2−0.4+1.2+5.0±3.0
FH crosscourt25%+5.3+1.2−0.6+6.0±4.4
FH down the line17%+1.5+1.1+0.7+3.4±4.6
FH slice through the middle15%−6.7−0.5−2.2−9.5±2.5
FH slice crosscourt7%−6.6−0.2−0.2−7.0±2.4

Lean FH crosscourt: +4.1±3.5 per 100 returns v the current mix (170 returns charted)

1st serve to the backhand

ReturnNowTourOwnv TaliaValue
BH through the middle52%+6.0+2.3+1.8+10.1±2.8
BH crosscourt27%+7.7+0.2+1.3+9.2±3.6
BH slice through the middle13%−6.2−1.4+0.2−7.5±2.4
BH down the line8%+2.2+0.1−0.9+1.4±4.0

Lean BH through the middle: +3.3±1.7 per 100 returns v the current mix (104 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv TaliaValue
FH through the middle45%−3.2−1.8−0.7−5.6±2.9
FH crosscourt33%+0.5+1.9−0.9+1.5±4.1
FH down the line23%−0.6+1.9+2.5+3.8±4.5

Lean FH through the middle: −4.5±2.3 per 100 returns v the current mix (40 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv TaliaValue
BH through the middle56%−2.6±0.0−0.4−3.1±2.6
BH crosscourt22%+1.5−1.9−2.2−2.7±3.2
BH down the line22%−0.5+0.7+0.9+1.0±4.6

Lean BH through the middle: −1.0±1.7 per 100 returns v the current mix (41 returns charted, inside the 90% margin)

Talia Gibson returning

1st serve to the forehand

ReturnNowTourOwnv AnaValue
FH through the middle48%+4.2+0.7−0.9+4.0±3.0
FH crosscourt25%+5.3−3.2−3.8−1.8±4.2
FH down the line19%+1.5−3.1±0.0−1.5±4.7
FH slice through the middle5%−6.7−1.6+1.0−7.4±2.4
FH slice crosscourt3%−6.6−1.2±0.0−7.8±2.1

Lean FH through the middle: +3.4±2.1 per 100 returns v the current mix (296 returns charted)

1st serve to the backhand

ReturnNowTourOwnv AnaValue
BH through the middle35%+6.0+0.1+0.6+6.6±2.9
BH crosscourt30%+7.7−7.3−0.9−0.5±3.7
BH down the line12%+2.2+3.9−2.1+4.0±4.4
BH slice through the middle10%−6.2−0.5+0.2−6.6±2.2
BH slice crosscourt7%−4.2−0.8±0.0−4.9±1.7

Lean BH through the middle: +5.7±2.3 per 100 returns v the current mix (181 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv AnaValue
FH through the middle46%−3.2+0.4−1.3−4.1±3.1
FH crosscourt31%+0.5+3.2−1.0+2.7±4.3
FH down the line23%−0.6−3.3+3.4−0.4±4.7

Lean FH crosscourt: +3.9±3.5 per 100 returns v the current mix (84 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv AnaValue
BH crosscourt53%+1.5−0.1−2.5−1.1±3.5
BH through the middle34%−2.6+0.2−0.1−2.5±2.8
BH down the line13%−0.5+0.2−1.6−1.9±4.9

Lean BH crosscourt: +0.6±2.0 per 100 returns v the current mix (149 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.

Ana Konjuh

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+2.2±3.3−0.2+2.5
BH to their backhand · return +1+1.4±3.6−0.7+2.1
BH to their backhand · serve +1+1.3±3.7+0.3+1.0
FH to their forehand · return+1.1±4.4+2.1−1.0
FH to their forehand · return +1+0.6±4.0+0.2+0.4
FH to their backhand · serve +1−0.2±4.3−1.1+0.8

Avoid

ShotEdgeOwnTheirs
FH to the middle · rally−5.7±2.6−6.5+0.8
BH to the middle · return +1−3.7±2.6−2.7−1.1
FH to their backhand · rally−3.1±4.2−5.5+2.5
BH to their forehand · rally−2.9±4.7−5.3+2.4
BH to the middle · rally−2.8±2.4−3.6+0.8

Talia Gibson

Favour

ShotEdgeOwnTheirs
FH to their forehand · serve +1+2.1±4.0+0.5+1.6
FH to the middle · return+0.1±2.7+1.4−1.3
BH to the middle · serve +1±0.0±2.6−1.1+1.0
FH to their forehand · rally−0.6±3.5−1.1+0.5
FH to the middle · rally−1.2±2.6−1.6+0.4
BH to the middle · return−1.2±2.4−0.9−0.3

Avoid

ShotEdgeOwnTheirs
BH to their backhand · return−6.9±3.5−4.8−2.1
FH to their backhand · serve +1−5.8±4.2−1.0−4.8
BH to the middle · rally−4.0±2.5−2.0−2.1
BH to their forehand · serve +1−4.0±4.8−3.1−0.8
FH to their backhand · rally−2.3±4.0−1.9−0.5

Against Talia Gibson-like opponents

Ana Konjuh vMatchesServe pts wonReturn pts won
All charted opponents–56.3%40.8%

Similar by tactical fingerprint: Naomi Osaka, Amanda Anisimova, Jelena Ostapenko, Dayana Yastremska, Viktoria Hruncakova, Anastasia Pavlyuchenkova, Veronika Kudermetova, Danielle Collins, Petra Kvitova, Daniela Hantuchova. When two players have rarely met, their records against these lookalikes fill the gap.