RallyIQ

Game plan

Mona Barthel v Monica Seles

Every number combines what Mona Barthel does well with what Monica Seles allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Mona Barthel wins, best of 3 32%90%: 11%–61% · best of 5: 28%
Serve points won 55.3% / 58.9% Mona / Monica · tour 56.4%
Strengths only, no similarity priors 29%serve 54.9% / 59.1%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Mona Barthel's record against Monica Seles's tactical lookalikes and in their charted head-to-heads (lookalikes: +9.9 on serve, +5.2 on return vs expectation (123 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

CareerMonaMonica
Direction choice+0.20 ±0.18
better than 84%
+0.28 ±0.09
better than 93%
Shot selection+0.21 ±0.31
better than 68%
+0.21 ±0.12
better than 67%
Execution−0.27 ±1.34
better than 45%
+0.14 ±0.30
better than 68%

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.

Mona Barthel serving

Deuce court

1st serveNowMona winsv MonicaMatchupOptimal
Wide38%71%65%69.5%±8.353% ▲
Body17%53%58%53.8%±11.62% ▼
T45%67%65%64.4%±9.345%

Optimal v Monica Seles: +0.7±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.0 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMona winsv MonicaMatchupOptimal
Wide47%57%59%49.7%±9.445% ▼
Body12%51%54%48.8%±13.20% ▼
T40%65%63%64.2%±8.955% ▲

Optimal v Monica Seles: +0.7±1.0 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +8.8 per 100 first serves in before the returner adjusts.

Monica Seles serving

Deuce court

1st serveNowMonica winsv MonaMatchupOptimal
Wide30%67%62%62.7%±9.530%
Body16%57%55%55.0%±11.11% ▼
T55%62%73%67.9%±8.769% ▲

Optimal v Mona Barthel: +0.3±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +3.6 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowMonica winsv MonaMatchupOptimal
Wide49%66%66%66.5%±8.542% ▼
Body8%63%51%58.5%±13.40% ▼
T43%68%59%63.5%±8.958% ▲

Optimal v Mona Barthel: +0.5±0.9 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +2.0 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.

Mona Barthel returning

1st serve to the forehand

ReturnNowTourOwnv MonicaValue
FH through the middle45%+4.2+0.6−3.8+1.0±2.9
FH down the line26%+1.5+3.3−1.8+3.0±4.6
FH crosscourt21%+5.3−1.9+2.9+6.3±4.0
FH slice through the middle8%−6.7−0.6+2.2−5.1±2.3

Lean FH crosscourt: +4.2±3.6 per 100 returns v the current mix (121 returns charted)

1st serve to the backhand

ReturnNowTourOwnv MonicaValue
BH through the middle42%+6.0+0.6−2.6+4.1±2.6
BH crosscourt33%+7.7−0.9−0.2+6.7±3.5
BH down the line13%+2.2−0.9−1.9−0.6±4.1
BH slice crosscourt7%−4.2+0.4+5.9+2.2±2.4
BH slice through the middle5%−6.2+0.1+4.1−2.0±1.8

Lean BH crosscourt: +2.8±2.7 per 100 returns v the current mix (116 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv MonicaValue
BH crosscourt49%+1.5+0.5+2.3+4.3±3.5
BH through the middle38%−2.6−2.1−2.1−6.8±2.6
BH down the line12%−0.5+0.9+3.0+3.4±4.5

Lean BH crosscourt: +4.4±2.1 per 100 returns v the current mix (65 returns charted)

Monica Seles returning

1st serve to the forehand

ReturnNowTourOwnv MonaValue
FH through the middle45%+4.2−2.2−1.0+0.9±2.8
FH crosscourt42%+5.3+0.8−0.4+5.7±3.7
FH down the line10%+1.5+1.0+2.9+5.4±4.7
FH slice through the middle2%−6.7+0.6+1.2−5.0±1.9
FH slice crosscourt1%−6.6+0.4±0.0−6.2±1.1

Lean FH crosscourt: +2.5±2.5 per 100 returns v the current mix (525 returns charted, inside the 90% margin)

1st serve to the backhand

ReturnNowTourOwnv MonaValue
BH through the middle47%+6.0−2.1+1.7+5.7±2.5
BH crosscourt32%+7.7−2.5+0.9+6.1±3.3
BH down the line10%+2.2−0.6+0.3+1.8±4.6
BH slice through the middle4%−6.2+1.6−0.3−4.9±2.1
BH slice crosscourt3%−4.2−2.6+0.4−6.4±2.4

Lean BH crosscourt: +1.7±2.6 per 100 returns v the current mix (646 returns charted, inside the 90% margin)

2nd serve to the forehand

ReturnNowTourOwnv MonaValue
FH crosscourt47%+0.5+1.7+1.3+3.6±4.0
FH through the middle27%−3.2−1.0−0.8−5.0±3.1
FH down the line26%−0.6+1.9+2.0+3.3±5.3

Lean FH crosscourt: +2.3±2.6 per 100 returns v the current mix (158 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv MonaValue
BH crosscourt48%+1.5+3.0−0.8+3.7±3.2
BH through the middle36%−2.6+1.6−0.5−1.5±2.7
BH down the line14%−0.5+1.9+1.6+3.0±5.1
FH inside-in3%+0.7+3.9+1.3+6.0±3.4

Lean BH crosscourt: +1.9±2.0 per 100 returns v the current mix (219 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.

Mona Barthel

Favour

ShotEdgeOwnTheirs
BH to their forehand · rally+6.5±5.2+3.2+3.3
FH to their forehand · serve +1+4.3±5.2+5.1−0.9
FH to the middle · rally+1.3±3.2+1.8−0.4
FH to their forehand · rally+0.8±4.0+2.7−2.0
BH to their backhand · rally−0.3±4.3+4.7−5.0
BH to the middle · rally−1.1±3.2−0.8−0.3

Avoid

ShotEdgeOwnTheirs
FH to their backhand · serve +1−7.5±5.3−7.0−0.5
FH to their backhand · rally−5.3±4.3−3.6−1.7
BH to the middle · return−2.9±3.2−0.5−2.4
BH to their backhand · return−2.3±4.7−3.4+1.0
BH to their backhand · serve +1−1.8±5.2+0.3−2.2

Monica Seles

Favour

ShotEdgeOwnTheirs
FH to their forehand · return+6.3±5.6+4.1+2.3
BH to their backhand · rally+3.9±4.0−1.3+5.2
BH to their forehand · rally+3.7±5.4+0.2+3.5
BH to the middle · return+1.2±3.2±0.0+1.3
FH to their backhand · rally+0.4±4.4−0.8+1.2
FH to their forehand · serve +1−1.4±5.2−3.5+2.0

Avoid

ShotEdgeOwnTheirs
FH to the middle · return−4.8±3.3−2.3−2.4
BH to the middle · rally−3.9±3.0−2.3−1.7
FH to their forehand · rally−3.2±3.6−1.5−1.8
FH to the middle · serve +1−3.1±3.7−2.4−0.7
FH to their forehand · serve +1−1.4±5.2−3.5+2.0

Against Monica Seles-like opponents

Mona Barthel vMatchesServe pts wonReturn pts won
All charted opponents–54.8%41.5%
Players most similar to Monica Seles1 64.5%47.5%

Similar by tactical fingerprint: Leylah Fernandez, Linda Fruhvirtova, Angelique Kerber, Qiang Wang, Simona Halep, Vera Zvonareva, Kate Makarova, Dinara Safina, Lindsay Davenport. When two players have rarely met, their records against these lookalikes fill the gap.