RallyIQ

Game plan

Eva Vedder v Kiki Bertens

Every number combines what Eva Vedder does well with what Kiki Bertens allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Eva Vedder wins, best of 3 13%90%: 2%–43% · best of 5: 8%
Serve points won 55.8% / 64.5% Eva / Kiki · tour 58.1%
Strengths only, no similarity priors 13%serve 55.8% / 64.5%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerEvaKiki
Direction choice−0.22 ±0.20
better than 15%
+0.08 ±0.04
better than 70%
Shot selection+0.01 ±0.33
better than 46%
−0.41 ±0.09
better than 17%
Execution−1.19 ±0.71
better than 15%
−0.19 ±0.21
better than 50%
Points left on the table2.90 ±0.34
lower than 16%
2.67 ±0.06
lower than 38%

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.

Eva Vedder serving

Deuce court

1st serveNowEva winsv KikiMatchupOptimal
Wide39%61%66%61.2%±8.654% ▲
Body23%49%59%50.6%±10.09% ▼
T38%64%64%60.3%±9.437%

Optimal v Kiki Bertens: +0.5±0.9 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.

Ad court

1st serveNowEva winsv KikiMatchupOptimal
Wide49%61%63%58.1%±7.941% ▼
Body7%57%58%58.7%±12.20% ▼
T43%66%64%65.8%±8.859% ▲

Optimal v Kiki Bertens: +0.5±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +4.3 per 100 first serves in before the returner adjusts.

Kiki Bertens serving

Deuce court

1st serveNowKiki winsv EvaMatchupOptimal
Wide49%71%65%70.4%±7.547% ▼
Body9%62%66%70.0%±8.15% ▼
T42%70%69%71.8%±7.948% ▲

Optimal v Eva Vedder: ±0.0±0.3 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +0.9 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKiki winsv EvaMatchupOptimal
Wide56%70%72%76.4%±6.171% ▲
Body5%50%65%59.4%±12.20% ▼
T39%68%64%67.7%±8.629% ▼

Optimal v Eva Vedder: +0.7±0.8 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.3 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.

Eva Vedder returning

1st serve to the forehand

ReturnNowTourOwnv KikiValue
FH through the middle52%+4.2+0.8−1.0+3.9±2.5
FH slice through the middle25%−6.7−0.1−0.7−7.5±2.1
FH crosscourt14%+5.3−0.9+0.5+4.9±3.3
FH down the line10%+1.5+0.5−3.8−1.8±3.4

Lean FH through the middle: +3.2±1.4 per 100 returns v the current mix (102 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KikiValue
BH through the middle50%+6.0−4.0−0.8+1.2±2.3
BH crosscourt24%+7.7−0.6−2.2+4.9±3.0
BH down the line14%+2.2−2.4−4.8−5.0±3.9
BH slice through the middle8%−6.2+0.8−0.4−5.8±1.9
BH slice crosscourt3%−4.2−1.4−3.0−8.6±2.3

Lean BH crosscourt: +4.6±2.6 per 100 returns v the current mix (143 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv KikiValue
BH through the middle33%−2.6+2.7−0.9−0.7±2.0
BH crosscourt23%+1.5+0.9−2.1+0.3±2.5
FH through the middle19%−2.7−0.1+0.1−2.7±1.8
BH down the line13%−0.5−2.2−2.9−5.6±3.6
FH inside-out12%+1.4+0.8−2.3−0.2±2.9

Lean BH through the middle: +0.7±1.6 per 100 returns v the current mix (52 returns charted, inside the 90% margin)

Kiki Bertens returning

1st serve to the forehand

ReturnNowTourOwnv EvaValue
FH through the middle46%+4.2+1.8+1.1+7.1±2.5
FH crosscourt16%+5.3+0.4+0.8+6.5±3.6
FH slice through the middle15%−6.7−4.0−0.4−11.2±2.1
FH down the line12%+1.5−1.2−0.1+0.2±4.1
FH slice crosscourt10%−6.6+1.5±0.0−5.1±1.6

Lean FH through the middle: +5.1±1.6 per 100 returns v the current mix (1802 returns charted)

1st serve to the backhand

ReturnNowTourOwnv EvaValue
BH through the middle43%+6.0±0.0−0.8+5.2±2.3
BH crosscourt18%+7.7+0.2+2.0+9.9±3.1
BH slice through the middle15%−6.2−0.2+0.7−5.8±1.9
BH down the line14%+2.2−2.0−0.5−0.3±4.0
BH slice crosscourt5%−4.2−0.9+0.4−4.6±2.4

Lean BH crosscourt: +7.5±2.8 per 100 returns v the current mix (2080 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv EvaValue
FH through the middle45%−3.2+1.1+0.6−1.6±2.8
FH crosscourt35%+0.5−1.1−0.5−1.0±4.0
FH down the line15%−0.6−0.8−2.5−3.8±5.1
FH slice through the middle3%−15.2+0.2±0.0−15.0±1.5
FH slice crosscourt1%−14.9+0.9±0.0−14.0±1.3

Lean FH crosscourt: +1.3±3.0 per 100 returns v the current mix (483 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv EvaValue
BH through the middle40%−2.6−0.4−1.1−4.1±2.3
BH crosscourt32%+1.5−0.4−0.6+0.5±3.1
FH through the middle9%−2.7−0.4+0.6−2.5±2.6
FH inside-in8%+0.7+1.3−0.5+1.6±4.5
BH down the line7%−0.5−2.9+1.2−2.3±5.0

Lean FH inside-in: +3.4±4.4 per 100 returns v the current mix (1083 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 grass. Each player's grass record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Eva Vedder

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+2.1±4.0+2.9−0.8
BH to the middle · rally+0.2±2.5+0.7−0.5
FH to their backhand · serve +1−0.8±4.2−1.2+0.5
FH to the middle · rally−1.3±3.2−0.8−0.4
FH to the middle · serve +1−1.6±3.5−2.7+1.1
FH to their forehand · serve +1−2.9±4.7+2.2−5.2

Avoid

ShotEdgeOwnTheirs
BH to their backhand · return−7.1±4.0±0.0−7.1
FH to their backhand · rally−5.2±4.8−3.9−1.3
FH to their forehand · rally−4.0±4.4−4.6+0.6
BH to the middle · return−3.4±3.3−0.7−2.7
FH to the middle · return−3.0±3.5−0.1−2.9

Kiki Bertens

Favour

ShotEdgeOwnTheirs
FH to their forehand · serve +1+3.2±4.9+0.9+2.3
FH to their backhand · rally+1.6±4.7+0.3+1.4
BH to the middle · rally+1.5±3.1+2.0−0.6
FH to the middle · return+1.0±2.9+0.7+0.3
FH to their forehand · return +1+0.4±4.2±0.0+0.4
FH to the middle · rally+0.3±3.3−0.9+1.2

Avoid

ShotEdgeOwnTheirs
BH to the middle · return−4.5±3.3−1.3−3.1
FH to their backhand · serve +1−4.0±5.0−3.2−0.7
BH to their backhand · rally−0.9±4.1−2.4+1.5
BH to their backhand · return +1−0.9±4.1+0.4−1.2
BH to their backhand · return−0.6±3.8−1.5+0.9

Against Kiki Bertens-like opponents

Eva Vedder vMatchesServe pts wonReturn pts won
All charted opponents–54.7%35.9%

Similar by tactical fingerprint: Karolina Muchova, Xin Yu Wang, Linda Klimovicova, Marta Kostyuk, Maria Sakkari, Kaja Juvan, Anastasija Sevastova, Petra Martic, Alison Van Uytvanck, Johanna Konta. When two players have rarely met, their records against these lookalikes fill the gap.