RallyIQ

Game plan

Clara Burel v Eva Vedder

Every number combines what Clara Burel 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

Clara Burel wins, best of 3 85%90%: 50%–98% · best of 5: 90%
Serve points won 61.0% / 53.2% Clara / Eva · tour 56.4%
Strengths only, no similarity priors 85%serve 61.0% / 53.2%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerClaraEva
Direction choice−0.27 ±0.09
better than 9%
−0.22 ±0.20
better than 15%
Shot selection−0.09 ±0.22
better than 35%
+0.01 ±0.33
better than 46%
Execution+0.01 ±0.51
better than 60%
−1.19 ±0.71
better than 15%
Points left on the table2.89 ±0.26
lower than 18%
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.

Clara Burel serving

Deuce court

1st serveNowClara winsv EvaMatchupOptimal
Wide53%56%65%55.3%±11.253%
Body27%50%66%59.6%±11.712% ▼
T20%69%69%70.8%±12.235% ▲

Optimal v Eva Vedder: +0.4±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +11.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowClara winsv EvaMatchupOptimal
Wide40%66%72%72.3%±9.753% ▲
Body21%54%65%62.9%±14.05% ▼
T39%61%64%61.3%±11.942% ▲

Optimal v Eva Vedder: +0.9±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +6.2 per 100 first serves in before the returner adjusts.

Eva Vedder serving

Deuce court

1st serveNowEva winsv ClaraMatchupOptimal
Wide39%61%63%57.9%±11.445% ▲
Body23%49%53%44.3%±12.48% ▼
T38%64%61%56.6%±12.547% ▲

Optimal v Clara Burel: +0.6±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +3.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowEva winsv ClaraMatchupOptimal
Wide49%61%56%51.4%±10.941% ▼
Body7%57%48%49.3%±15.60% ▼
T43%66%59%60.9%±11.759% ▲

Optimal v Clara Burel: +0.4±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +5.5 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.

Clara Burel returning

1st serve to the forehand

ReturnNowTourOwnv EvaValue
FH through the middle51%+4.2+4.1+1.1+9.4±3.1
FH crosscourt23%+5.3+0.2+0.8+6.3±4.1
FH down the line18%+1.5−0.2−0.1+1.2±4.4
FH slice through the middle8%−6.7−0.9−0.4−8.0±2.2

Lean FH through the middle: +3.6±2.0 per 100 returns v the current mix (146 returns charted)

1st serve to the backhand

ReturnNowTourOwnv EvaValue
BH through the middle54%+6.0+0.9−0.8+6.1±2.9
BH crosscourt25%+7.7+1.8+2.0+11.5±3.6
BH slice through the middle11%−6.2−0.3+0.7−5.9±2.1
BH down the line10%+2.2+1.1−0.5+2.8±4.2

Lean BH crosscourt: +5.7±3.2 per 100 returns v the current mix (131 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv EvaValue
FH through the middle49%−3.2+1.3+0.6−1.3±3.0
FH crosscourt32%+0.5−1.7−0.5−1.6±4.0
FH down the line19%−0.6+2.9−2.5−0.2±4.4

Lean FH through the middle: −0.1±2.2 per 100 returns v the current mix (47 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv EvaValue
BH through the middle59%−2.6−0.2−1.1−3.9±2.8
BH crosscourt31%+1.5−1.3−0.6−0.5±3.4
BH down the line10%−0.5−0.3+1.2+0.3±4.1

Lean BH crosscourt: +2.0±2.9 per 100 returns v the current mix (49 returns charted, inside the 90% margin)

Eva Vedder returning

1st serve to the forehand

ReturnNowTourOwnv ClaraValue
FH through the middle52%+4.2+0.8+2.5+7.5±3.2
FH slice through the middle25%−6.7−0.1±0.0−6.8±1.7
FH crosscourt14%+5.3−0.9+1.9+6.3±4.0
FH down the line10%+1.5+0.5+2.3+4.3±4.1

Lean FH through the middle: +4.0±1.7 per 100 returns v the current mix (102 returns charted)

1st serve to the backhand

ReturnNowTourOwnv ClaraValue
BH through the middle50%+6.0−4.0+0.4+2.4±2.9
BH crosscourt24%+7.7−0.6+0.5+7.7±3.7
BH down the line14%+2.2−2.4+0.9+0.7±4.1
BH slice through the middle8%−6.2+0.8±0.0−5.4±1.4
BH slice crosscourt3%−4.2−1.4±0.0−5.6±1.2

Lean BH crosscourt: +5.2±3.2 per 100 returns v the current mix (143 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv ClaraValue
BH through the middle33%−2.6+2.7−2.0−1.9±2.6
BH crosscourt23%+1.5+0.9+0.7+3.1±3.3
FH through the middle19%−2.7−0.1+2.2−0.6±2.4
BH down the line13%−0.5−2.2−1.5−4.2±3.8
FH inside-out12%+1.4+0.8+3.5+5.6±3.5

Lean BH through the middle: −1.9±2.1 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 hard. Each player's hard record is shrunk toward their all-surface one, so a thin sample on it moves the numbers only a little.

Clara Burel

Favour

ShotEdgeOwnTheirs
FH to their backhand · rally+7.0±5.7+6.7+0.3
FH to the middle · return+5.5±4.1+3.5+2.0
FH to the middle · rally+0.8±3.6+0.5+0.2
BH to their backhand · rally±0.0±4.8−0.2+0.2
BH to the middle · return−0.4±3.9+1.9−2.3
FH to their forehand · rally−1.4±4.9+1.2−2.6

Avoid

ShotEdgeOwnTheirs
BH to the middle · rally−2.9±3.4−0.5−2.4
BH to their backhand · return−2.8±5.4−2.5−0.2
FH to their backhand · serve +1−2.4±6.4−1.6−0.8
FH to their forehand · rally−1.4±4.9+1.2−2.6
BH to the middle · return−0.4±3.9+1.9−2.3

Eva Vedder

Favour

ShotEdgeOwnTheirs
FH to their forehand · serve +1+3.0±6.1+5.3−2.3
FH to the middle · return+2.6±4.1−0.6+3.2
BH to their backhand · return−0.1±5.3−0.6+0.5
FH to their backhand · serve +1−0.2±6.3−0.1−0.1
FH to the middle · rally−0.7±3.6−0.5−0.2
BH to the middle · rally−1.6±3.5−1.5−0.1

Avoid

ShotEdgeOwnTheirs
BH to their backhand · rally−4.0±4.7−2.0−1.9
FH to the middle · serve +1−3.1±4.1−4.2+1.1
FH to their backhand · rally−2.9±5.5+0.2−3.1
BH to the middle · return−2.5±3.9−1.8−0.8
FH to their forehand · rally−2.5±4.8−2.2−0.3

Against Eva Vedder-like opponents

Clara Burel vMatchesServe pts wonReturn pts won
All charted opponents–52.2%47.7%

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