RallyIQ

Game plan

Clara Burel v Kate Makarova

Every number combines what Clara Burel does well with what Kate Makarova 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 53%90%: 14%–88% · best of 5: 53%
Serve points won 51.2% / 50.7% Clara / Kate · tour 55.0%
Strengths only, no similarity priors 53%serve 51.2% / 50.7%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Kate Makarova'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

CareerClaraKate
Direction choice−0.27 ±0.09
better than 9%
−0.06 ±0.08
better than 43%
Shot selection−0.09 ±0.22
better than 35%
−0.02 ±0.17
better than 44%
Execution+0.01 ±0.51
better than 60%
−0.16 ±0.83
better than 54%
Points left on the table2.89 ±0.26
lower than 18%
2.73 ±0.18
lower than 29%

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 KateMatchupOptimal
Wide53%56%70%61.1%±10.153%
Body27%50%60%52.7%±12.212% ▼
T20%69%68%69.8%±11.435% ▲

Optimal v Kate Makarova: +1.0±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +9.2 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowClara winsv KateMatchupOptimal
Wide40%66%70%69.7%±9.941%
Body21%54%50%48.4%±13.35% ▼
T39%61%67%64.2%±10.154% ▲

Optimal v Kate Makarova: +1.3±1.2 per 100 first serves (faults included) over the current mix. Serving wide every time would read +6.5 per 100 first serves in before the returner adjusts.

Kate Makarova serving

Deuce court

1st serveNowKate winsv ClaraMatchupOptimal
Wide26%67%63%64.5%±10.741% ▲
Body24%55%53%50.2%±11.09% ▼
T50%62%61%55.0%±10.150%

Optimal v Clara Burel: +0.7±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +8.3 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKate winsv ClaraMatchupOptimal
Wide28%57%56%47.4%±10.528%
Body35%51%48%43.4%±11.519% ▼
T38%71%59%66.8%±9.753% ▲

Optimal v Clara Burel: +1.2±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +13.5 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 KateValue
FH through the middle51%+4.2+4.1−1.0+7.3±2.9
FH crosscourt23%+5.3+0.2+4.9+10.4±4.3
FH down the line18%+1.5−0.2−2.6−1.3±4.6
FH slice through the middle8%−6.7−0.9−1.6−9.2±1.9

Lean FH crosscourt: +5.3±3.8 per 100 returns v the current mix (146 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KateValue
BH through the middle54%+6.0+0.9+0.7+7.7±2.7
BH crosscourt25%+7.7+1.8+0.7+10.2±3.6
BH slice through the middle11%−6.2−0.3−0.6−7.1±2.3
BH down the line10%+2.2+1.1−0.8+2.5±4.3

Lean BH crosscourt: +4.0±3.1 per 100 returns v the current mix (131 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv KateValue
FH through the middle49%−3.2+1.3−1.0−2.8±3.0
FH crosscourt32%+0.5−1.7+2.8+1.7±4.0
FH down the line19%−0.6+2.9−2.9−0.6±4.6

Lean FH crosscourt: +2.6±3.2 per 100 returns v the current mix (47 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv KateValue
BH through the middle59%−2.6−0.2−1.7−4.5±2.7
BH crosscourt31%+1.5−1.3−2.5−2.4±3.4
BH down the line10%−0.5−0.3+1.0+0.2±4.2

Lean BH crosscourt: +1.0±2.9 per 100 returns v the current mix (49 returns charted, inside the 90% margin)

Kate Makarova returning

1st serve to the forehand

ReturnNowTourOwnv ClaraValue
FH through the middle50%+4.2−0.3+2.5+6.4±3.1
FH crosscourt20%+5.3−1.4+2.3+6.2±4.3
FH down the line15%+1.5−1.4+1.9+2.0±4.6
FH slice through the middle6%−6.7−0.5±0.0−7.3±1.4
FH slice crosscourt5%−6.6−0.8±0.0−7.5±1.4

Lean FH through the middle: +2.2±1.9 per 100 returns v the current mix (181 returns charted)

1st serve to the backhand

ReturnNowTourOwnv ClaraValue
BH through the middle52%+6.0+0.3+0.4+6.7±2.8
BH crosscourt36%+7.7−2.2+0.9+6.4±3.4
BH down the line6%+2.2+0.2+0.5+2.9±4.2
BH slice through the middle6%−6.2−1.6±0.0−7.8±1.4

Lean BH through the middle: +1.2±1.8 per 100 returns v the current mix (179 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv ClaraValue
FH through the middle52%−3.2+1.4+2.2+0.5±3.1
FH down the line26%−0.6+0.5+3.0+2.9±5.1
FH crosscourt23%+0.5−0.4+3.5+3.7±4.2

Lean FH crosscourt: +1.8±3.8 per 100 returns v the current mix (93 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv ClaraValue
BH crosscourt55%+1.5−0.8−1.5−0.8±3.2
BH through the middle45%−2.6−0.3−2.0−4.9±2.7

Lean BH crosscourt: +1.9±1.9 per 100 returns v the current mix (51 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.

Clara Burel

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+11.1±6.1+7.8+3.3
FH to their backhand · rally+8.1±4.8+2.8+5.3
BH to the middle · return+5.9±3.6+4.1+1.8
FH to their backhand · return+5.5±6.1−1.2+6.7
BH to the middle · serve +1+2.3±3.6+1.4+0.9
BH to their backhand · rally+1.6±4.9+1.2+0.4

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−4.8±5.8−0.6−4.2
BH to the middle · return +1−3.0±3.6+1.1−4.1
FH to their forehand · return−3.0±6.1+1.7−4.7
FH to their forehand · rally−2.0±4.8−3.9+2.0
FH to the middle · return +1−1.7±3.4−2.5+0.8

Kate Makarova

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+8.4±5.9+6.9+1.5
FH to the middle · return+3.6±3.8+0.4+3.2
BH to their backhand · rally+3.0±4.5+4.7−1.7
BH to the middle · return+2.2±3.6+0.8+1.3
FH to their forehand · return+1.4±6.1−2.9+4.2
FH to the middle · rally+1.2±3.5+1.3−0.1

Avoid

ShotEdgeOwnTheirs
FH to their forehand · serve +1−5.4±5.8−2.9−2.5
BH to their backhand · serve +1−4.3±5.1−2.3−2.0
FH to their forehand · rally−2.2±4.8−1.8−0.3
FH to the middle · serve +1−0.4±3.7−0.7+0.3
FH to their backhand · rally+0.4±4.7+2.5−2.1

Against Kate Makarova-like opponents

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

Similar by tactical fingerprint: Alexandra Eala, Diana Shnaider, Ashlyn Krueger, Jaqueline Cristian, Katerina Siniakova, Venus Williams, Victoria Azarenka, Garbine Muguruza, Simona Halep, Vera Zvonareva. When two players have rarely met, their records against these lookalikes fill the gap.