RallyIQ

Game plan

Irina Camelia Begu v Victoria Mboko

Every number combines what Irina Camelia Begu does well with what Victoria Mboko allows, each measured against the tour average and shrunk toward it when the sample is thin. Flip perspective

Forecast

Irina Camelia Begu wins, best of 3 9%90%: 1%–33% · best of 5: 5%
Serve points won 49.9% / 59.9% Irina / Victoria · tour 56.3%
Strengths only, no similarity priors 9%serve 49.9% / 59.9%

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 At least one of them is no longer active or has too little charted in the last three seasons, so both are compared on their careers.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 Irina Camelia Begu's record against Victoria Mboko'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

CareerIrinaVictoria
Direction choice−0.31 ±0.08
better than 6%
−0.14 ±0.12
better than 29%
Shot selection−0.15 ±0.15
better than 31%
−0.26 ±0.13
better than 23%
Execution−1.73 ±0.43
better than 7%
+0.45 ±0.62
better than 77%
Points left on the table2.91 ±0.10
lower than 15%
2.70 ±0.15
lower than 34%

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.

Irina Camelia Begu serving

Deuce court

1st serveNowIrina winsv VictoriaMatchupOptimal
Wide42%66%64%63.4%±8.157% ▲
Body17%56%58%56.7%±11.218%
T40%54%66%52.6%±9.725% ▼

Optimal v Victoria Mboko: +0.5±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving wide every time would read +5.5 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowIrina winsv VictoriaMatchupOptimal
Wide43%52%62%48.5%±9.243%
Body22%56%49%49.0%±11.76% ▼
T36%64%64%63.7%±8.751% ▲

Optimal v Victoria Mboko: +0.6±1.2 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +9.7 per 100 first serves in before the returner adjusts.

Victoria Mboko serving

Deuce court

1st serveNowVictoria winsv IrinaMatchupOptimal
Wide44%67%71%71.9%±8.144%
Body22%60%61%63.0%±9.87% ▼
T34%74%74%79.5%±7.149% ▲

Optimal v Irina Camelia Begu: +1.0±1.1 per 100 first serves (faults included) over the current mix, inside the 90% margin. Serving T every time would read +7.0 per 100 first serves in before the returner adjusts.

Ad court

1st serveNowVictoria winsv IrinaMatchupOptimal
Wide50%71%76%80.6%±7.165% ▲
Body24%58%57%58.0%±11.08% ▼
T27%68%64%67.2%±9.027%

Optimal v Irina Camelia Begu: +1.3±1.1 per 100 first serves (faults included) over the current mix. Serving wide every time would read +8.9 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.

Irina Camelia Begu returning

1st serve to the forehand

ReturnNowTourOwnv VictoriaValue
FH through the middle47%+4.2−1.9−0.4+1.8±2.9
FH down the line22%+1.5+1.7−4.0−0.8±4.6
FH crosscourt19%+5.3−0.8+0.6+5.1±4.3
FH slice through the middle9%−6.7−0.3−2.0−8.9±2.4
FH slice down the line3%−10.5+0.5+0.8−9.2±2.4

Lean FH crosscourt: +4.6±3.9 per 100 returns v the current mix (155 returns charted)

1st serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH through the middle53%+6.0−3.0±0.0+3.0±2.6
BH down the line26%+2.2−1.6−3.3−2.8±4.6
BH crosscourt13%+7.7−2.6−0.5+4.7±3.3
BH slice through the middle7%−6.2+0.5−1.6−7.2±2.2

Lean BH crosscourt: +3.7±3.4 per 100 returns v the current mix (126 returns charted)

2nd serve to the backhand

ReturnNowTourOwnv VictoriaValue
BH through the middle57%−2.6−3.0−2.0−7.6±2.8
BH down the line24%−0.5−0.6−0.4−1.5±4.9
BH crosscourt19%+1.5−0.9+1.4+2.0±3.5

Lean BH crosscourt: +6.4±3.4 per 100 returns v the current mix (91 returns charted)

Victoria Mboko returning

1st serve to the forehand

ReturnNowTourOwnv IrinaValue
FH through the middle45%+4.2+0.4+1.4+6.0±2.7
FH crosscourt21%+5.3+0.3−2.3+3.4±4.2
FH down the line17%+1.5−3.0+0.5−1.0±4.6
FH slice through the middle8%−6.7−0.6−0.9−8.3±2.4
FH slice crosscourt7%−6.6+1.0+0.2−5.4±2.3

Lean FH through the middle: +4.0±1.9 per 100 returns v the current mix (453 returns charted)

1st serve to the backhand

ReturnNowTourOwnv IrinaValue
BH through the middle39%+6.0+2.4+2.5+10.9±2.7
BH crosscourt23%+7.7+0.7−0.4+8.0±3.6
BH slice through the middle17%−6.2+1.0+1.0−4.3±2.3
BH down the line10%+2.2+1.8+2.8+6.8±4.4
BH slice crosscourt7%−4.2−2.1+0.7−5.6±2.4

Lean BH through the middle: +5.5±2.0 per 100 returns v the current mix (261 returns charted)

2nd serve to the forehand

ReturnNowTourOwnv IrinaValue
FH through the middle44%−3.2+0.3−1.9−4.7±3.1
FH down the line35%−0.6+0.4−2.7−2.9±5.1
FH crosscourt21%+0.5−3.0+1.9−0.5±4.3

Lean FH crosscourt: +2.7±4.1 per 100 returns v the current mix (116 returns charted, inside the 90% margin)

2nd serve to the backhand

ReturnNowTourOwnv IrinaValue
BH crosscourt45%+1.5−0.9−1.1−0.5±3.6
BH through the middle42%−2.6−1.2−0.3−4.2±2.8
BH down the line14%−0.5+1.1−0.6±0.0±4.6

Lean BH down the line: +2.0±4.4 per 100 returns v the current mix (173 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.

Irina Camelia Begu

Favour

ShotEdgeOwnTheirs
BH to their backhand · rally+2.6±2.7+1.2+1.4
BH to their forehand · serve +1+1.3±4.8−1.5+2.8
FH to their backhand · serve +1+0.1±4.0−0.8+1.0
FH to the middle · return +1+0.1±2.7+0.6−0.5
FH to the middle · rally±0.0±2.0−0.6+0.6
FH to the middle · serve +1−0.2±2.6−0.6+0.4

Avoid

ShotEdgeOwnTheirs
BH to their forehand · rally−12.1±4.2−8.4−3.7
BH to their forehand · return−6.0±4.5−3.4−2.6
FH to their backhand · rally−4.2±3.2−4.0−0.3
BH to the middle · return−4.2±2.2−4.4+0.2
BH to the middle · rally−3.5±2.0−2.9−0.6

Victoria Mboko

Favour

ShotEdgeOwnTheirs
BH to the middle · return +1+2.8±2.5+1.6+1.2
BH to the middle · return+2.4±2.2−0.3+2.7
FH to their forehand · rally+2.2±2.7−0.3+2.5
FH to the middle · serve +1+1.9±2.6+1.3+0.7
BH to the middle · rally+1.9±2.0+1.3+0.6
BH to their backhand · rally+1.6±2.8+1.3+0.3

Avoid

ShotEdgeOwnTheirs
BH to their backhand · serve +1−2.0±3.6+1.4−3.4
FH to the middle · return +1−1.8±2.6−0.8−1.0
FH to their forehand · return−1.1±4.3−0.6−0.4
FH to their backhand · serve +1−0.9±4.0−1.8+1.0
FH to their forehand · serve +1−0.5±4.0+1.2−1.6

Against Victoria Mboko-like opponents

Irina Camelia Begu vMatchesServe pts wonReturn pts won
All charted opponents–50.2%39.0%

Similar by tactical fingerprint: Coco Gauff, Cristina Bucsa, Karolina Pliskova, Belinda Bencic, Ekaterina Alexandrova, Sorana Cirstea, Emma Raducanu, Shelby Rogers, Alison Riske Amritraj. When two players have rarely met, their records against these lookalikes fill the gap.