RallyIQ

Game plan

Marion Bartoli v Kimberly Birrell

Every number combines what Marion Bartoli does well with what Kimberly Birrell allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Marion Bartoli wins, best of 3 71%90%: 27%–96% · best of 5: 76%
Serve points won 62.1% / 57.8% Marion / Kimberly · tour 58.1%
Strengths only, no similarity priors 71%serve 62.1% / 57.8%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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

CareerMarionKimberly
Direction choice−0.05 ±0.28
better than 45%
−0.11 ±0.18
better than 34%
Shot selection+0.45 ±0.27
better than 93%
+0.29 ±0.15
better than 77%
Execution+0.11 ±1.26
better than 66%
−1.63 ±0.97
better than 8%

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.

Marion Bartoli serving

Deuce court

1st serveNowMarion winsv KimberlyMatchupOptimal
Wide50%63%74%71.9%±9.650%
Body16%49%60%51.7%±14.21% ▼
T34%61%75%69.3%±11.249% ▲

Optimal v Kimberly Birrell: +1.1±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.1 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMarion winsv KimberlyMatchupOptimal
Wide27%63%78%75.6%±10.142% ▲
Body28%52%58%54.7%±13.713% ▼
T45%58%78%72.6%±9.945%

Optimal v Kimberly Birrell: +1.4±1.3 per 100 first serves (faults included) over the current mix. Serving wide every time would read +7.3 per 100 first serves in before the returner adjusts.

Kimberly Birrell serving

Deuce court

1st serveNowKimberly winsv MarionMatchupOptimal
Wide43%55%75%65.2%±10.759% ▲
Body30%59%54%56.3%±12.815% ▼
T27%67%66%65.4%±12.126%

Optimal v Marion Bartoli: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +2.8 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowKimberly winsv MarionMatchupOptimal
Wide32%62%73%69.8%±12.132%
Body25%53%62%59.0%±12.710% ▼
T43%60%71%66.5%±10.958% ▲

Optimal v Marion Bartoli: +0.5±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +4.1 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.

Marion Bartoli returning

1st serve to the forehand

ReturnNowTourOwnv KimberlyValue
FH through the middle60%+4.2+0.5+1.9+6.6±3.0
FH crosscourt34%+5.3−2.2−3.0+0.1±4.3
FH down the line5%+1.5−0.6+3.2+4.1±3.8

Lean FH through the middle: +2.4±1.9 per 100 returns v the current mix (93 returns charted)

1st serve to the backhand

ReturnNowTourOwnv KimberlyValue
BH through the middle57%+6.0+0.6+3.1+9.7±2.8
BH crosscourt36%+7.7+2.5−0.9+9.3±3.5
BH slice through the middle7%−6.2−0.7−0.1−7.0±1.9

Lean BH through the middle: +1.4±1.8 per 100 returns v the current mix (67 returns charted, inside the 90% margin)

Kimberly Birrell returning

1st serve to the forehand

ReturnNowTourOwnv MarionValue
FH through the middle48%+4.2−2.6−2.2−0.6±3.1
FH down the line30%+1.5−4.7+2.8−0.4±4.7
FH crosscourt22%+5.3+0.2−0.5+5.0±4.0

Lean FH crosscourt: +4.3±3.8 per 100 returns v the current mix (151 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MarionValue
BH through the middle48%+6.0−1.1+0.3+5.2±3.0
BH crosscourt37%+7.7−3.1+1.5+6.1±3.6
BH down the line12%+2.2−3.0−0.5−1.4±4.0
BH slice down the line4%−12.5−0.7±0.0−13.2±1.5

Lean BH crosscourt: +2.0±2.7 per 100 returns v the current mix (128 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv MarionValue
FH through the middle43%−3.2−0.8+0.4−3.6±3.0
FH crosscourt34%+0.5−0.6−0.2−0.3±4.0
FH down the line23%−0.6+0.7−1.3−1.1±4.7

Lean FH crosscourt: +1.6±3.1 per 100 returns v the current mix (56 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv MarionValue
BH through the middle48%−2.6+0.7−0.9−2.8±2.7
BH crosscourt40%+1.5−2.0−1.1−1.6±3.3
BH down the line13%−0.5−0.4+1.1+0.2±3.8

Lean BH crosscourt: +0.3±2.4 per 100 returns v the current mix (86 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.

Marion Bartoli

Favour

ShotEdgeOwnTheirs
FH to the middle · return+6.1±4.3+2.1+4.0
FH to their forehand · rally+4.4±5.2+2.1+2.3
FH to the middle · rally+2.8±3.8+2.1+0.7
BH to the middle · serve +1+2.1±3.8+1.9+0.2
BH to the middle · return+2.0±4.0−1.0+3.1
FH to their backhand · rally+2.0±5.8+1.1+0.9

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−3.6±6.4−3.3−0.4
BH to the middle · rally−1.6±3.5−0.3−1.3
BH to their backhand · serve +1−0.7±5.7−2.0+1.3
FH to their forehand · serve +1+0.4±6.0−2.4+2.8
BH to their backhand · rally+1.2±4.8+0.9+0.3

Kimberly Birrell

Favour

ShotEdgeOwnTheirs
FH to their forehand · serve +1+10.7±5.9+2.4+8.2
BH to their backhand · rally+8.9±4.8−0.6+9.5
BH to their forehand · rally+2.0±6.6−3.3+5.3
BH to the middle · rally+1.0±3.5−0.3+1.2
BH to the middle · return+0.9±4.1+0.3+0.6
FH to the middle · rally−1.3±3.7−0.5−0.8

Avoid

ShotEdgeOwnTheirs
FH to the middle · return−5.6±4.4−2.6−3.0
FH to their forehand · rally−3.0±5.2−2.6−0.4
FH to their backhand · rally−1.5±5.8−1.5+0.1
FH to their backhand · return−1.4±6.4−4.3+2.9
FH to the middle · rally−1.3±3.7−0.5−0.8

Against Kimberly Birrell-like opponents

Marion Bartoli vMatchesServe pts wonReturn pts won
All charted opponents–50.9%37.5%

Similar by tactical fingerprint: Iva Jovic, Anastasia Potapova, Eva Lys, Anna Blinkova, Emma Navarro, Shuai Zhang, Emma Raducanu, Varvara Gracheva, Lin Zhu, R. When two players have rarely met, their records against these lookalikes fill the gap.