RallyIQ

Game plan

Kaia Kanepi v Tereza Valentova

Every number combines what Kaia Kanepi does well with what Tereza Valentova allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Kaia Kanepi wins, best of 3 28%90%: 7%–64% · best of 5: 24%
Serve points won 55.2% / 59.6% Kaia / Tereza · tour 56.3%
Strengths only, no similarity priors 30%serve 55.5% / 59.4%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Kaia Kanepi's record against Tereza Valentova's tactical lookalikes and in their charted head-to-heads (lookalikes: −6.3 on serve, −5.0 on return vs expectation (121 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

CareerKaiaTereza
Direction choice−0.07 ±0.12
better than 41%
−0.19 ±0.10
better than 19%
Shot selection−0.07 ±0.17
better than 38%
−0.15 ±0.18
better than 33%
Execution−0.92 ±0.56
better than 20%
−0.48 ±0.63
better than 34%
Points left on the table2.82 ±0.16
lower than 22%
2.94 ±0.16
lower than 14%

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.

Kaia Kanepi serving

Deuce court

1st serveNowKaia winsv TerezaMatchupOptimal
Wide55%70%62%65.8%±8.969% ▲
Body10%53%59%54.7%±12.90% ▼
T35%68%61%60.7%±10.131% ▼

Optimal v Tereza Valentova: +0.4±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKaia winsv TerezaMatchupOptimal
Wide50%67%63%64.6%±9.050%
Body9%64%58%65.8%±13.30% ▼
T40%69%66%70.1%±8.950% ▲

Optimal v Tereza Valentova: +0.1±0.8 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.2 per 100 first serves in before the returner adjusts.

Tereza Valentova serving

Deuce court

1st serveNowTereza winsv KaiaMatchupOptimal
Wide58%67%77%78.6%±7.361% ▲
Body13%58%68%68.2%±11.30% ▼
T29%74%70%75.3%±9.039% ▲

Optimal v Kaia Kanepi: +0.3±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowTereza winsv KaiaMatchupOptimal
Wide32%70%77%80.4%±7.448% ▲
Body13%51%64%59.5%±12.912%
T55%62%61%58.6%±10.140% ▼

Optimal v Kaia Kanepi: +1.0±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +14.6 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.

Kaia Kanepi returning

1st serve to the forehand

ReturnNowTourOwnv TerezaValue
FH through the middle38%+4.2−1.5+1.0+3.7±3.0
FH slice through the middle18%−6.7+0.4±0.0−6.3±2.1
FH crosscourt16%+5.3+2.3−2.4+5.2±4.1
FH down the line14%+1.5−3.2−2.4−4.1±4.3
FH slice down the line7%−10.5±0.0±0.0−10.4±1.6

Lean FH crosscourt: +5.7±3.7 per 100 returns v the current mix (104 returns charted)

1st serve to the backhand

ReturnNowTourOwnv TerezaValue
BH through the middle45%+6.0+0.5−0.8+5.8±2.9
BH crosscourt23%+7.7−1.3−0.5+6.0±3.6
BH down the line15%+2.2+1.4−0.4+3.2±4.5
BH slice crosscourt10%−4.2−0.7−0.7−5.5±2.2
BH slice through the middle6%−6.2−1.6−0.7−8.5±2.0

Lean BH crosscourt: +2.6±3.1 per 100 returns v the current mix (125 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv TerezaValue
BH through the middle49%−2.6−1.3+1.6−2.3±2.7
BH crosscourt33%+1.5+0.7+1.0+3.2±3.6
BH down the line18%−0.5−2.1−0.1−2.7±4.8

Lean BH crosscourt: +3.8±2.9 per 100 returns v the current mix (76 returns charted)

Tereza Valentova returning

1st serve to the forehand

ReturnNowTourOwnv KaiaValue
FH through the middle48%+4.2−0.1−2.2+1.9±3.1
FH crosscourt18%+5.3±0.0−1.8+3.4±4.3
FH down the line15%+1.5+2.2−0.2+3.6±4.6
FH slice through the middle10%−6.7−1.0−0.5−8.2±2.1
FH slice crosscourt7%−6.6+0.9±0.0−5.7±1.6

Lean FH down the line: +3.0±4.2 per 100 returns v the current mix (210 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv KaiaValue
BH through the middle46%+6.0+0.6−3.0+3.7±2.8
BH crosscourt21%+7.7+0.8+1.3+9.8±3.7
BH down the line21%+2.2+0.5+1.4+4.0±4.5
BH slice through the middle7%−6.2−0.3−2.0−8.5±2.3
BH slice crosscourt3%−4.2−1.5−0.3−5.9±2.1

Lean BH crosscourt: +6.4±3.3 per 100 returns v the current mix (239 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv KaiaValue
FH through the middle63%−3.2+0.1+0.8−2.3±3.0
FH crosscourt23%+0.5−1.5−1.5−2.4±3.7
FH down the line14%−0.6+1.6+0.5+1.6±3.7

Lean FH through the middle: −0.5±1.5 per 100 returns v the current mix (43 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv KaiaValue
BH through the middle40%−2.6+1.1+1.1−0.4±2.8
BH crosscourt30%+1.5−1.3−1.3−1.1±3.6
BH down the line29%−0.5−3.0−2.6−6.1±5.1

Lean BH through the middle: +1.9±2.5 per 100 returns v the current mix (139 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.

Kaia Kanepi

Favour

ShotEdgeOwnTheirs
BH to their backhand · return+2.0±3.5+1.5+0.5
FH to their forehand · serve +1+1.0±4.0+0.3+0.7
BH to their forehand · rally+0.4±4.6+2.4−2.1
FH to the middle · rally−0.2±2.4+0.7−0.9
BH to their backhand · rally−0.5±3.4−0.9+0.4
FH to the middle · return−0.6±2.7−0.8+0.2

Avoid

ShotEdgeOwnTheirs
FH to their backhand · rally−5.9±3.7−5.2−0.7
FH to their forehand · return +1−5.2±4.1−1.7−3.6
FH to their forehand · rally−4.9±3.4−1.8−3.1
FH to the middle · serve +1−4.4±2.6−1.4−3.0
BH to the middle · serve +1−4.4±2.6−1.4−3.0

Tereza Valentova

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+7.1±3.9+0.8+6.3
FH to their forehand · serve +1+0.6±4.2+2.0−1.4
FH to the middle · serve +1+0.6±2.6+1.4−0.8
FH to the middle · rally−0.2±2.4−1.4+1.2
BH to their backhand · return−0.4±3.4+0.3−0.6
BH to their backhand · serve +1−0.8±3.9−2.8+2.0

Avoid

ShotEdgeOwnTheirs
BH to the middle · return +1−3.7±2.6−3.6−0.1
FH to their forehand · return−3.3±4.5−1.2−2.1
FH to their forehand · return +1−3.1±4.0+1.7−4.9
FH to their forehand · rally−3.1±3.4−3.7+0.5
BH to their backhand · rally−2.5±3.3−1.5−1.0

Against Tereza Valentova-like opponents

Kaia Kanepi vMatchesServe pts wonReturn pts won
All charted opponents–58.2%39.2%
Players most similar to Tereza Valentova1 51.5%35.7%

Similar by tactical fingerprint: Iva Jovic, Karolina Pliskova, Madison Keys, Solana Sierra, Jessica Bouzas Maneiro, Shelby Rogers, Irina Camelia Begu, Alison Van Uytvanck, Sabine Lisicki. When two players have rarely met, their records against these lookalikes fill the gap.