Bagels handed out — full ATP & WTA ranking

Leader

Xiangruyi Ji

59.1% of the sets they win are 6-0

Average: 25.4%
Std. dev.: 7.0
Leader sits at: 4.8σ
Range: 2.9 … 59.1

1317 players

#	Player	Value	Pctl	Sample
1	XJ Xiangruyi Ji	59.1%	100.0	31
2	PK Polina Kaibekova	55.6%	99.9	50
3	LR Liel Marlies Rothensteiner	51.9%	99.8	55
4	MP Manca Pislak	49.1%	99.8	43
5	DP Doroteja Petrovic	47.8%	99.7	31
6	ML Maria Eduarda Lages	46.4%	99.6	36
7	MC Marian Gomez Pezuela Cano	46.2%	99.5	47
8	MI Margarita Ignatjeva	45.9%	99.5	33
9	DP Denisa Elena Plesa	45.5%	99.4	33
10	JA Jacquline Nylander Altelius	45.5%	99.3	31
11	JG Jovana Grujic	45.3%	99.2	38
12	KY Kseniya Yersh	45.2%	99.2	50
13	NK Na Hyun Kang	45.2%	99.1	31
14	AA Abigail Amos	44.4%	99.0	32
15	LL Lola De Leon	44.4%	98.9	32
16	KK Karolina Krajmer	44.4%	98.9	30
17	AI Anastasia Iamachkine	44.3%	98.8	61
18	MS Meshkatolzahra Safi	43.4%	98.7	49
19	MA Mariam Atia	43.2%	98.6	48
20	JS Jana Stojanova	43.1%	98.6	39
21	AP Alessia Popescu	42.9%	98.5	53
22	MK Mariam Karadzhaeva	42.9%	98.4	36
23	AG Anna Gabric	42.1%	98.3	40
24	AI Alexia Shara Iancu	41.9%	98.3	34
25	SL Sabastiani Leon	41.8%	98.2	65
26	LR Lexington Reed	41.5%	98.1	34
27	KT Kaili Demi Teso	41.5%	98.0	33
28	SO Sabrina Olimjanova	41.2%	97.9	38
29	DS Daria Shubina	41.2%	97.9	32
30	BB Bianca Bulat	41.0%	97.8	34
31	DF Daria Frayman	40.7%	97.7	65
32	SS Sebastianna Scilipoti	40.3%	97.6	54
33	EC Eleni Christofi	40.0%	97.6	46
34	SR Stella Remander	40.0%	97.5	42
35	MM Maria Victoria Marchesini	40.0%	97.4	38
36	GG Grete Gull	40.0%	97.3	32
37	KN Koharu Niimi	40.0%	97.3	30
38	JD Jenny Duerst	39.9%	97.2	114
39	AS Anastasia Safta	39.6%	97.1	42
40	LA Lucciana Perez Alarcon	39.2%	97.0	78
41	SF Shuo Feng	39.2%	97.0	39
42	SB Sophia Biolay	39.2%	96.9	36
43	CN Chloe Noel	39.1%	96.8	50
44	JH Jiangxue Han	39.0%	96.7	65
45	AV Alexandra Vasilyeva	38.9%	96.7	79
46	GP Gabriella Price	38.9%	96.6	32
47	AW Amelia Waligora	38.9%	96.5	31
48	PR Pawinee Ruamrak	38.9%	96.4	30
49	NJ Noka Juric	38.8%	96.4	59
50	IV Ingrid Vojcinakova	38.8%	96.3	46

How this is calculated

Minimum 30 matches with a recorded score. 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.
The denominator is sets, not matches. It is 6-0 sets over sets WON, so it does not reward whoever plays more. The minimum is applied to matches with a recorded score, because the count of sets won is not stored as its own column.
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

Xiangruyi Ji

59.1% of the sets they win are 6-0

Average: 25.4%
Std. dev.: 7.0
Leader sits at: 4.8σ
Range: 2.9 … 59.1

1317 players

#	Player	Value	Pctl	Sample
1	XJ Xiangruyi Ji	59.1%	100.0	31
2	PK Polina Kaibekova	55.6%	99.9	50
3	LR Liel Marlies Rothensteiner	51.9%	99.8	55
4	MP Manca Pislak	49.1%	99.8	43
5	DP Doroteja Petrovic	47.8%	99.7	31
6	ML Maria Eduarda Lages	46.4%	99.6	36
7	MC Marian Gomez Pezuela Cano	46.2%	99.5	47
8	MI Margarita Ignatjeva	45.9%	99.5	33
9	DP Denisa Elena Plesa	45.5%	99.4	33
10	JA Jacquline Nylander Altelius	45.5%	99.3	31
11	JG Jovana Grujic	45.3%	99.2	38
12	KY Kseniya Yersh	45.2%	99.2	50
13	NK Na Hyun Kang	45.2%	99.1	31
14	AA Abigail Amos	44.4%	99.0	32
15	LL Lola De Leon	44.4%	98.9	32
16	KK Karolina Krajmer	44.4%	98.9	30
17	AI Anastasia Iamachkine	44.3%	98.8	61
18	MS Meshkatolzahra Safi	43.4%	98.7	49
19	MA Mariam Atia	43.2%	98.6	48
20	JS Jana Stojanova	43.1%	98.6	39
21	AP Alessia Popescu	42.9%	98.5	53
22	MK Mariam Karadzhaeva	42.9%	98.4	36
23	AG Anna Gabric	42.1%	98.3	40
24	AI Alexia Shara Iancu	41.9%	98.3	34
25	SL Sabastiani Leon	41.8%	98.2	65
26	LR Lexington Reed	41.5%	98.1	34
27	KT Kaili Demi Teso	41.5%	98.0	33
28	SO Sabrina Olimjanova	41.2%	97.9	38
29	DS Daria Shubina	41.2%	97.9	32
30	BB Bianca Bulat	41.0%	97.8	34
31	DF Daria Frayman	40.7%	97.7	65
32	SS Sebastianna Scilipoti	40.3%	97.6	54
33	EC Eleni Christofi	40.0%	97.6	46
34	SR Stella Remander	40.0%	97.5	42
35	MM Maria Victoria Marchesini	40.0%	97.4	38
36	GG Grete Gull	40.0%	97.3	32
37	KN Koharu Niimi	40.0%	97.3	30
38	JD Jenny Duerst	39.9%	97.2	114
39	AS Anastasia Safta	39.6%	97.1	42
40	LA Lucciana Perez Alarcon	39.2%	97.0	78
41	SF Shuo Feng	39.2%	97.0	39
42	SB Sophia Biolay	39.2%	96.9	36
43	CN Chloe Noel	39.1%	96.8	50
44	JH Jiangxue Han	39.0%	96.7	65
45	AV Alexandra Vasilyeva	38.9%	96.7	79
46	GP Gabriella Price	38.9%	96.6	32
47	AW Amelia Waligora	38.9%	96.5	31
48	PR Pawinee Ruamrak	38.9%	96.4	30
49	NJ Noka Juric	38.8%	96.4	59
50	IV Ingrid Vojcinakova	38.8%	96.3	46

How this is calculated

Minimum 30 matches with a recorded score. 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.
The denominator is sets, not matches. It is 6-0 sets over sets WON, so it does not reward whoever plays more. The minimum is applied to matches with a recorded score, because the count of sets won is not stored as its own column.
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.