Straight-set wins — full ATP & WTA ranking

Leader

Anastasia Safta

95.8% of their wins came in straight sets

Average: 72.5%
Std. dev.: 7.8
Leader sits at: 3.0σ
Range: 42.9 … 95.8

1011 players

#	Player	Value	Pctl	Sample
1	AS Anastasia Safta	95.8%	100.0	24
2	DP Dominika Podhajecka	95.2%	99.9	21
3	NS Neus Torner Sensano	95.2%	99.8	21
4	ET Evelyne Christelle Atticia Tiron	95.0%	99.7	20
5	AH Ava Hrastar	92.6%	99.6	27
6	SR Sofia Camila Rojas	92.6%	99.5	27
7	KM Karolina Mrazikova	92.0%	99.4	25
8	DH Dalayna Hewitt	90.9%	99.3	33
9	JS Jana Stojanova	90.9%	99.2	22
10	AZ Allura Zamarripa	90.5%	99.1	21
11	BE Basak Eraydin	90.5%	99.0	21
12	OS Oliwia Szymczuch	90.5%	98.9	21
13	VM Vlada Mincheva	90.0%	98.8	40
14	Ana Grubor	89.7%	98.7	39
15	GS Gaia Squarcialupi	89.7%	98.6	39
16	ES Ekaterina Shalimova	89.7%	98.5	29
17	YH Yuka Hosoki	89.3%	98.4	28
18	RZ Ruirui Zou	89.2%	98.3	37
19	AF Anastasiia Firman	88.9%	98.2	36
20	HH Holly Hutchinson	88.9%	98.1	27
21	AZ Alian Zack	88.5%	98.0	26
22	MS Maria Sholokhova	88.5%	97.9	26
23	PO Pemra Ozgen	88.2%	97.8	34
24	NS Natalija Senic	88.2%	97.7	93
25	PT Patricia Maria Tig	88.0%	97.6	83
26	PG Patricia Georgiana Goina	87.9%	97.5	33
27	AO Andrea Obradovic	87.5%	97.4	24
28	KN Kristina Novak	87.5%	97.3	24
29	LA Lucciana Perez Alarcon	87.1%	97.2	62
30	FL Fangzhou Liu	87.0%	97.1	54
31	NM Noelia Zeballos Melgar	87.0%	97.0	46
32	CM Carlotta Moccia	87.0%	96.9	23
33	FL Fang An Lin	87.0%	96.8	23
34	GM Ginevra Parentini Vallega Montebruno	87.0%	96.7	23
35	MP Manca Pislak	87.0%	96.6	23
36	RG Rachel Gailis	87.0%	96.5	23
37	VO Victoria Osuigwe	87.0%	96.4	23
38	Ariana Arseneault	86.8%	96.3	38
39	DV Dune Vaissaud	86.8%	96.2	38
40	JG Jimar Geraldine Gerald Gonzalez	86.8%	96.1	38
41	YH Yujia Huang	86.8%	96.0	38
42	SB Sophia Biolay	86.4%	95.9	22
43	AG Alba Rey Garcia	86.3%	95.8	51
44	AG Anastasiia Grechkina	86.2%	95.7	29
45	AR Alice Robbe	86.1%	95.6	36
46	DF Daria Frayman	86.0%	95.5	43
47	PK Punnin Kovapitukted	85.7%	95.4	49
48	NJ Noka Juric	85.7%	95.3	28
49	CB Camilla Bossi	85.7%	95.2	21
50	FN Friederike Nolte	85.7%	95.1	21

How this is calculated

Minimum 20 wins. Below that the figure is noise and publishing it would be faking precision. Each family of metrics carries its own counter — 200 matches is not 200 tiebreaks.
Window. Five years of results. These rows need only the scoreline, which exists far further back than serve statistics do.
One division at a time. A player belongs to the Challenger/ITF division when more than half of their matches since 2024 are played there. Mixing the two ranks schedules rather than players: the raw list of closers was headed by an ITF player at 100% over twenty matches.
Not a betting signal. It describes a player, not a price. Our own prediction engine is measured against the market and loses: 0.632 log loss against 0.593.

Leader

Anastasia Safta

95.8% of their wins came in straight sets

Average: 72.5%
Std. dev.: 7.8
Leader sits at: 3.0σ
Range: 42.9 … 95.8

1011 players

#	Player	Value	Pctl	Sample
1	AS Anastasia Safta	95.8%	100.0	24
2	DP Dominika Podhajecka	95.2%	99.9	21
3	NS Neus Torner Sensano	95.2%	99.8	21
4	ET Evelyne Christelle Atticia Tiron	95.0%	99.7	20
5	AH Ava Hrastar	92.6%	99.6	27
6	SR Sofia Camila Rojas	92.6%	99.5	27
7	KM Karolina Mrazikova	92.0%	99.4	25
8	DH Dalayna Hewitt	90.9%	99.3	33
9	JS Jana Stojanova	90.9%	99.2	22
10	AZ Allura Zamarripa	90.5%	99.1	21
11	BE Basak Eraydin	90.5%	99.0	21
12	OS Oliwia Szymczuch	90.5%	98.9	21
13	VM Vlada Mincheva	90.0%	98.8	40
14	Ana Grubor	89.7%	98.7	39
15	GS Gaia Squarcialupi	89.7%	98.6	39
16	ES Ekaterina Shalimova	89.7%	98.5	29
17	YH Yuka Hosoki	89.3%	98.4	28
18	RZ Ruirui Zou	89.2%	98.3	37
19	AF Anastasiia Firman	88.9%	98.2	36
20	HH Holly Hutchinson	88.9%	98.1	27
21	AZ Alian Zack	88.5%	98.0	26
22	MS Maria Sholokhova	88.5%	97.9	26
23	PO Pemra Ozgen	88.2%	97.8	34
24	NS Natalija Senic	88.2%	97.7	93
25	PT Patricia Maria Tig	88.0%	97.6	83
26	PG Patricia Georgiana Goina	87.9%	97.5	33
27	AO Andrea Obradovic	87.5%	97.4	24
28	KN Kristina Novak	87.5%	97.3	24
29	LA Lucciana Perez Alarcon	87.1%	97.2	62
30	FL Fangzhou Liu	87.0%	97.1	54
31	NM Noelia Zeballos Melgar	87.0%	97.0	46
32	CM Carlotta Moccia	87.0%	96.9	23
33	FL Fang An Lin	87.0%	96.8	23
34	GM Ginevra Parentini Vallega Montebruno	87.0%	96.7	23
35	MP Manca Pislak	87.0%	96.6	23
36	RG Rachel Gailis	87.0%	96.5	23
37	VO Victoria Osuigwe	87.0%	96.4	23
38	Ariana Arseneault	86.8%	96.3	38
39	DV Dune Vaissaud	86.8%	96.2	38
40	JG Jimar Geraldine Gerald Gonzalez	86.8%	96.1	38
41	YH Yujia Huang	86.8%	96.0	38
42	SB Sophia Biolay	86.4%	95.9	22
43	AG Alba Rey Garcia	86.3%	95.8	51
44	AG Anastasiia Grechkina	86.2%	95.7	29
45	AR Alice Robbe	86.1%	95.6	36
46	DF Daria Frayman	86.0%	95.5	43
47	PK Punnin Kovapitukted	85.7%	95.4	49
48	NJ Noka Juric	85.7%	95.3	28
49	CB Camilla Bossi	85.7%	95.2	21
50	FN Friederike Nolte	85.7%	95.1	21

How this is calculated

Minimum 20 wins. Below that the figure is noise and publishing it would be faking precision. Each family of metrics carries its own counter — 200 matches is not 200 tiebreaks.
Window. Five years of results. These rows need only the scoreline, which exists far further back than serve statistics do.
One division at a time. A player belongs to the Challenger/ITF division when more than half of their matches since 2024 are played there. Mixing the two ranks schedules rather than players: the raw list of closers was headed by an ITF player at 100% over twenty matches.
Not a betting signal. It describes a player, not a price. Our own prediction engine is measured against the market and loses: 0.632 log loss against 0.593.