RallyIQ

Game plan

Nao Hibino v Victoria Jimenez Kasintseva

Every number combines what Nao Hibino does well with what Victoria Jimenez Kasintseva allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Nao Hibino wins, best of 3 75%90%: 34%–96% · best of 5: 80%
Serve points won 54.4% / 49.5% Nao / Victoria · tour 55.0%
Strengths only, no similarity priors 71%serve 54.2% / 50.0%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Nao Hibino's record against Victoria Jimenez Kasintseva's tactical lookalikes and in their charted head-to-heads (lookalikes: +3.1 on serve, +5.1 on return vs expectation (282 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

CareerNaoVictoria
Direction choice−0.14 ±0.10
better than 27%
−0.24 ±0.23
better than 12%
Shot selection−0.13 ±0.14
better than 34%
+0.40 ±0.20
better than 90%
Execution−0.86 ±0.99
better than 22%
−0.70 ±0.67
better than 27%
Points left on the table2.71 ±0.21
lower than 32%
2.81 ±0.37
lower than 23%

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.

Nao Hibino serving

Deuce court

1st serveNowNao winsv VictoriaMatchupOptimal
Wide37%63%62%58.3%±10.037%
Body33%60%52%54.1%±10.718% ▼
T30%55%67%54.1%±11.945% ▲

Optimal v Victoria Jimenez Kasintseva: +0.2±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowNao winsv VictoriaMatchupOptimal
Wide46%66%60%59.6%±10.346%
Body17%55%59%58.1%±12.42% ▼
T37%60%60%55.8%±10.852% ▲

Optimal v Victoria Jimenez Kasintseva: +0.5±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +1.7 per 100 first serves in before the returner adjusts.

Victoria Jimenez Kasintseva serving

Deuce court

1st serveNowVictoria winsv NaoMatchupOptimal
Wide23%68%70%71.5%±10.538% ▲
Body37%49%55%46.2%±11.122% ▼
T40%64%68%64.1%±10.040%

Optimal v Nao Hibino: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +12.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowVictoria winsv NaoMatchupOptimal
Wide49%57%65%56.4%±9.549%
Body33%53%62%58.5%±11.018% ▼
T18%65%68%69.1%±13.233% ▲

Optimal v Nao Hibino: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +9.7 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.

Nao Hibino returning

1st serve to the forehand

ReturnNowTourOwnv VictoriaValue
FH through the middle40%+4.2+0.6−1.2+3.5±3.1
FH down the line22%+1.5+1.5−0.6+2.5±4.6
FH crosscourt17%+5.3−1.9−0.2+3.2±4.1
FH slice through the middle15%−6.7−2.5−1.1−10.3±2.3
FH slice crosscourt6%−6.6±0.0±0.0−6.6±1.3

Lean FH through the middle: +3.0±2.3 per 100 returns v the current mix (108 returns charted)

1st serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH through the middle47%+6.0−0.4+1.7+7.3±2.8
BH crosscourt30%+7.7−1.5+2.5+8.7±3.7
BH down the line13%+2.2−1.8−2.0−1.7±4.6
BH slice crosscourt5%−4.2+0.3−0.6−4.4±2.1
BH slice through the middle5%−6.2+1.3−0.5−5.4±2.2

Lean BH crosscourt: +3.4±2.9 per 100 returns v the current mix (186 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv VictoriaValue
FH through the middle52%−3.2−0.8+2.1−1.9±3.0
FH down the line34%−0.6−4.0−1.5−6.0±4.8
FH crosscourt14%+0.5+0.6−0.5+0.6±3.4

Lean FH through the middle: +1.1±2.2 per 100 returns v the current mix (44 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH crosscourt43%+1.5−2.8−0.4−1.8±3.5
BH through the middle36%−2.6−1.6−1.6−5.7±2.6
BH down the line21%−0.5−1.5−1.4−3.5±3.9

Lean BH crosscourt: +1.8±2.4 per 100 returns v the current mix (47 returns charted, inside the 90% margin)

Victoria Jimenez Kasintseva returning

1st serve to the forehand

ReturnNowTourOwnv NaoValue
FH through the middle61%+4.2+0.3−1.3+3.2±3.0
FH crosscourt27%+5.3−1.1+2.8+7.0±4.4
FH down the line11%+1.5+1.3+1.8+4.6±4.5

Lean FH crosscourt: +2.6±3.7 per 100 returns v the current mix (168 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv NaoValue
BH through the middle59%+6.0−1.8+0.4+4.6±2.7
BH crosscourt25%+7.7+0.7−0.3+8.1±3.7
BH down the line16%+2.2−2.0−1.9−1.7±4.7

Lean BH crosscourt: +3.7±3.3 per 100 returns v the current mix (215 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv NaoValue
BH through the middle63%−2.6+0.3+2.8+0.5±2.8
BH crosscourt30%+1.5−1.5±0.0±0.0±3.4
BH down the line7%−0.5−0.1−0.5−1.1±4.1

Lean BH through the middle: +0.3±1.5 per 100 returns v the current mix (70 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.

Nao Hibino

Favour

ShotEdgeOwnTheirs
BH to the middle · return+4.1±3.6+2.1+2.0
BH to their forehand · rally+3.5±5.6+2.0+1.4
BH to the middle · return +1+3.0±3.7+1.6+1.4
FH to their backhand · serve +1+2.7±5.6+2.1+0.7
FH to the middle · rally+2.7±3.4+0.6+2.1
FH to their backhand · rally+2.3±4.4+1.9+0.4

Avoid

ShotEdgeOwnTheirs
BH to their forehand · return−2.7±6.3−5.4+2.7
BH to their backhand · return−2.7±5.2−3.1+0.3
FH to their forehand · serve +1−1.6±5.9−1.9+0.3
FH to their backhand · return +1−0.3±5.8−1.7+1.4
FH to the middle · serve +1+0.1±3.8+0.7−0.7

Victoria Jimenez Kasintseva

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+6.7±4.0+3.3+3.4
FH to their backhand · return +1+5.2±5.8+2.3+2.9
BH to their forehand · return+3.2±6.5+2.6+0.6
FH to their forehand · return +1+2.5±5.9+0.1+2.4
BH to their backhand · rally+1.7±4.6+0.7+1.0
FH to the middle · rally+1.2±3.4+0.5+0.7

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−4.3±5.6−3.1−1.2
FH to their forehand · serve +1−3.9±5.8−0.5−3.4
BH to the middle · return−2.8±3.4−5.2+2.4
FH to their forehand · return−2.7±6.5−1.8−0.9
FH to the middle · return−2.3±3.7−0.9−1.4

Against Victoria Jimenez Kasintseva-like opponents

Nao Hibino vMatchesServe pts wonReturn pts won
All charted opponents–53.3%41.1%
Players most similar to Victoria Jimenez Kasintseva2 59.0%47.3%

Similar by tactical fingerprint: Diana Shnaider, Arantxa Rus, Jil Teichmann, Sara Bejlek, Olga Danilovic, Xiyu Wang, Beatriz Haddad Maia, Lesia Tsurenko, Martina Trevisan. When two players have rarely met, their records against these lookalikes fill the gap.