RallyIQ

Game plan

Polona Hercog v Eva Vedder

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

Forecast

Polona Hercog wins, best of 3 91%90%: 58%–99% · best of 5: 96%
Serve points won 65.1% / 54.5% Polona / Eva · tour 58.1%
Strengths only, no similarity priors 89%serve 64.9% / 55.3%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Polona Hercog's record against Eva Vedder's tactical lookalikes and in their charted head-to-heads (lookalikes: +1.6 on serve, +5.7 on return vs expectation (464 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

CareerPolonaEva
Direction choice+0.08 ±0.14
better than 69%
−0.22 ±0.20
better than 15%
Shot selection−0.78 ±0.38
better than 5%
+0.01 ±0.33
better than 46%
Execution−0.50 ±0.45
better than 33%
−1.19 ±0.71
better than 15%
Points left on the table2.72 ±0.13
lower than 30%
2.90 ±0.34
lower than 16%

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.

Polona Hercog serving

Deuce court

1st serveNowPolona winsv EvaMatchupOptimal
Wide34%69%65%68.4%±9.540% ▲
Body17%61%66%69.8%±10.121% ▲
T50%67%69%68.0%±9.639% ▼

Optimal v Eva Vedder: ±0.0±0.7 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +1.4 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowPolona winsv EvaMatchupOptimal
Wide49%59%72%66.7%±8.864% ▲
Body18%58%65%66.8%±12.53% ▼
T33%62%64%61.4%±11.333%

Optimal v Eva Vedder: +0.4±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving body every time would read +1.8 per 100 first serves in before the returner adjusts.

Eva Vedder serving

Deuce court

1st serveNowEva winsv PolonaMatchupOptimal
Wide39%61%68%63.5%±10.054% ▲
Body23%49%52%43.9%±11.68% ▼
T38%64%60%56.1%±11.138%

Optimal v Polona Hercog: +0.8±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +7.4 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowEva winsv PolonaMatchupOptimal
Wide49%61%63%58.0%±9.458% ▲
Body7%57%57%57.8%±14.30% ▼
T43%66%58%59.4%±11.142% ▼

Optimal v Polona Hercog: +0.2±0.6 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +0.8 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.

Polona Hercog returning

1st serve to the forehand

ReturnNowTourOwnv EvaValue
FH through the middle53%+4.2+0.2+1.1+5.5±3.0
FH crosscourt22%+5.3+1.8+0.8+7.8±4.2
FH slice through the middle12%−6.7+1.0−0.4−6.2±2.4
FH down the line9%+1.5−0.7−0.1+0.7±4.4
FH slice crosscourt2%−6.6+0.1±0.0−6.5±1.2

Lean FH crosscourt: +4.3±3.6 per 100 returns v the current mix (257 returns charted)

1st serve to the backhand

ReturnNowTourOwnv EvaValue
BH slice through the middle29%−6.2+2.3+0.7−3.3±2.3
BH through the middle21%+6.0−0.4−0.8+4.8±2.9
BH crosscourt17%+7.7−3.3+2.0+6.3±3.7
BH slice crosscourt8%−4.2−0.3+0.4−4.0±2.4
FH through the middle7%+5.1+0.1+1.1+6.3±2.7

Lean BH crosscourt: +5.5±3.2 per 100 returns v the current mix (298 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv EvaValue
FH through the middle54%−3.2+1.3+0.6−1.4±3.1
FH crosscourt31%+0.5−1.1−0.5−1.0±4.2
FH down the line8%−0.6±0.0−2.5−3.1±4.3
FH slice through the middle7%−15.2−1.1±0.0−16.3±1.1

Lean FH crosscourt: +1.5±3.4 per 100 returns v the current mix (85 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv EvaValue
FH through the middle37%−2.7+1.0+0.6−1.2±2.7
FH inside-out34%+1.4+1.2−2.5±0.0±4.2
BH through the middle11%−2.6+0.2−1.1−3.5±2.6
FH inside-in10%+0.7+1.2−0.5+1.5±4.3
BH crosscourt5%+1.5±0.0−0.6+0.9±3.2

Lean FH inside-in: +2.6±4.2 per 100 returns v the current mix (161 returns charted, inside the 90% margin)

Eva Vedder returning

1st serve to the forehand

ReturnNowTourOwnv PolonaValue
FH through the middle52%+4.2+0.8−0.1+4.9±3.0
FH slice through the middle25%−6.7−0.1+0.5−6.3±2.5
FH crosscourt14%+5.3−0.9±0.0+4.4±4.0
FH down the line10%+1.5+0.5±0.0+2.0±4.2

Lean FH through the middle: +3.1±1.7 per 100 returns v the current mix (102 returns charted)

1st serve to the backhand

ReturnNowTourOwnv PolonaValue
BH through the middle50%+6.0−4.0+1.3+3.3±2.7
BH crosscourt24%+7.7−0.6+1.1+8.3±3.6
BH down the line14%+2.2−2.4−0.5−0.8±4.5
BH slice through the middle8%−6.2+0.8−0.1−5.5±2.3
BH slice crosscourt3%−4.2−1.4−2.5−8.1±2.0

Lean BH crosscourt: +5.5±3.1 per 100 returns v the current mix (143 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv PolonaValue
BH through the middle33%−2.6+2.7+1.5+1.6±2.4
BH crosscourt23%+1.5+0.9−0.5+1.9±3.1
FH through the middle19%−2.7−0.1−0.1−2.9±2.3
BH down the line13%−0.5−2.2−1.0−3.8±4.5
FH inside-out12%+1.4+0.8−0.2+1.9±3.6

Lean BH through the middle: +1.5±2.0 per 100 returns v the current mix (52 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.

Polona Hercog

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+2.0±3.7+0.5+1.5
FH to their forehand · serve +1+1.3±5.5−1.0+2.3
BH to the middle · rally+0.3±3.0+0.8−0.6
FH to their backhand · rally+0.1±5.4−1.2+1.4
FH to their backhand · serve +1±0.0±5.7+0.8−0.7
BH to their backhand · return +1±0.0±3.7+1.2−1.2

Avoid

ShotEdgeOwnTheirs
FH to their forehand · rally−5.0±4.7−4.6−0.4
FH to the middle · return−2.5±3.1−2.9+0.3
BH to the middle · return−2.0±3.4+1.1−3.1
FH to their forehand · return +1−1.0±4.6−1.4+0.4
BH to their backhand · return−0.8±3.7−1.7+0.9

Eva Vedder

Favour

ShotEdgeOwnTheirs
FH to their forehand · serve +1+2.8±4.5+2.2+0.5
FH to the middle · rally+0.1±3.6−0.8+0.9
BH to their backhand · rally−0.2±4.5+2.9−3.1
BH to their backhand · return−0.2±4.3±0.0−0.3
BH to the middle · return−0.3±3.8−0.7+0.4
BH to the middle · rally−0.9±2.8+0.7−1.6

Avoid

ShotEdgeOwnTheirs
FH to their forehand · rally−8.4±5.2−4.6−3.8
FH to their backhand · rally−6.7±5.5−3.9−2.8
FH to their backhand · serve +1−6.3±4.9−1.2−5.1
FH to the middle · serve +1−3.3±3.3−2.7−0.6
FH to the middle · return−1.6±3.9−0.1−1.5

Against Eva Vedder-like opponents

Polona Hercog vMatchesServe pts wonReturn pts won
All charted opponents–56.3%44.4%
Players most similar to Eva Vedder3 57.7%47.5%

Similar by tactical fingerprint: Xin Yu Wang, Tamara Zidansek, Suzan Lamens, Nadia Podoroska, Maria Sakkari, Lucia Bronzetti, Jennifer Brady, Kiki Bertens, Johanna Larsson. When two players have rarely met, their records against these lookalikes fill the gap.