Victoria Wang vs Sophia Dumitrascu — score & stats

Set by set

Player	Set 1	Set 2	Sets
V. Wang	1	0	0
S. Dumitrascu	6	6	2

Momentum — game by game, ● = break

▲ V. Wang ▼ S. Dumitrascu

Match facts

Tournament

UTR PTT Newport Beach Women 11

V. Wang

L C. Celebrini 1–2 L Y. Zhytelna 0–2

S. Dumitrascu

L C. Celebrini 1–2 W S. Barnhart 0–0 W C. Simunyu 2–1 L D. Evtimova 0–2 L D. Rizvi 1–2

V. Wang · match stats · S. Dumitrascu

0Aces0

0Double faults1

67%First serve in54%

20%First-serve points won71%

100%Second serve in96%

40%Second-serve points won46%

0%Break points saved89%

11%Break points converted100%

40%Return points won73%

12Service points won31

21Receiver points won33

33Total points won64

1Games won12

0Service games won5

0Tiebreaks won0

3Longest point streak11

1Longest game streak7

Per set · aces & double faults

Set	Aces	Double faults
Set 1	0–0	0–0
Set 2	0–0	0–1

Figures read V. Wang first.

Set 26 games · newest first

0-6 game 6 V. Wang serving BREAK

0-15 0-30 0-40

0-5 game 5 S. Dumitrascu serving

0-15 0-30 15-30 30-30 40-30 40-40 40-A

0-4 game 4 V. Wang serving BREAK

0-15 15-15 15-30 15-40

0-3 game 3 S. Dumitrascu serving

15-0 15-15 15-30 15-40 30-40

0-2 game 2 V. Wang serving BREAK

0-15 15-15 15-30 30-30 40-30 40-40 A-40 40-40 A-40 40-40 A-40 40-40 40-A

0-1 game 1 S. Dumitrascu serving

0-15 15-15 15-30 30-30 30-40 40-40 A-40 40-40 A-40 40-40 40-A

Set 17 games · newest first

1-6 game 7 V. Wang serving BREAK

0-15 0-30 0-40

1-5 game 6 S. Dumitrascu serving BREAK

15-0 30-0 40-0 40-15 40-30 40-40 A-40 40-40 A-40 40-40 40-A 40-40 A-40

0-5 game 5 V. Wang serving BREAK

0-15 0-30 0-40

0-4 game 4 S. Dumitrascu serving

0-15 0-30 0-40

0-3 game 3 V. Wang serving BREAK

0-15 15-15 30-15 30-30 40-30 40-40 40-A

0-2 game 2 S. Dumitrascu serving

0-15 15-15 15-30 30-30 30-40 40-40 40-A

0-1 game 1 V. Wang serving BREAK

15-0 15-15 15-30 30-30 30-40

Point chips read V. Wang first · highlighted border marks the point winner.

Win probability

V. WangS. Dumitrascu

45%

55%

Model confidence

Why the model thinks so

Very evenly matched