Bagels handed out — full ATP & WTA ranking

Leader

Jesper de Jong

17.8% of the sets they win are 6-0

Average: 10.6%
Std. dev.: 3.2
Leader sits at: 2.3σ
Range: 1.8 … 17.8

132 players

#	Player	Value	Pctl	Sample
1	Jesper de Jong#105	17.8%	100.0	177
2	Learner Tien#19	17.5%	99.2	189
3	Arthur Cazaux#138	17.3%	98.5	116
4	Mariano Navone#44	17.2%	97.7	176
5	Zachary Svajda#87	17.1%	96.9	181
6	Alex de Minaur#7	16.2%	96.2	348
7	Jaume Munar#45	16.0%	95.4	227
8	RN Rafael Nadal	15.9%	94.7	70
9	Luciano Darderi#22	15.5%	93.9	187
10	Jannik Sinner#1	15.4%	93.1	348
11	Damir Dzumhur#104	15.4%	92.4	196
12	Carlos Alcaraz#2	15.3%	91.6	330
13	Marton Fucsovics#83	14.9%	90.8	215
14	GM Gael Monfils	14.8%	90.1	150
15	Vit Kopriva#71	14.6%	89.3	188
16	SERNovak Djokovic#5	14.6%	88.5	244
17	Alejandro Tabilo#26	14.5%	87.8	213
18	Cristian Garin#136	14.0%	87.0	222
19	Cameron Norrie#37	14.0%	86.3	308
20	Valentin Vacherot#18	13.9%	85.5	150
21	Roberto Bautista Agut	13.8%	84.7	229
22	Pavel Kotov	13.8%	84.0	141
23	Jack Draper#143	13.8%	83.2	167
24	Jenson Brooksby#74	13.6%	82.4	142
25	Pablo Carreno Busta#68	13.5%	81.7	206
26	Jacob Fearnley#110	13.5%	80.9	142
27	Sebastian Baez#48	13.4%	80.2	263
28	Roberto Carballes Baena	13.3%	79.4	212
29	Mackenzie McDonald#144	12.9%	78.6	270
30	RG Richard Gasquet	12.9%	77.9	166
31	SK Sebastian Korda#50	12.9%	77.1	219
32	Grigor Dimitrov#137	12.9%	76.3	230
33	AM Andy Murray	12.8%	75.6	114
34	Gabriel Diallo#108	12.8%	74.8	173
35	Wu Yibing#116	12.7%	74.0	112
36	Mattia Bellucci#80	12.7%	73.3	185
37	Casper Ruud#14	12.7%	72.5	329
38	Fabian Marozsan#61	12.7%	71.8	163
39	Camilo Ugo Carabelli#76	12.7%	71.0	188
40	GZ Giulio Zeppieri	12.7%	70.2	117
41	Botic Van De Zandschulp#70	12.5%	69.5	284
42	ER Emil Ruusuvuori	12.3%	68.7	187
43	Karen Khachanov#39	12.1%	67.9	275
44	Tommy Paul#21	12.0%	67.2	305
45	Lorenzo Musetti#13	11.9%	66.4	296
46	Alexander Zverev#3	11.9%	65.6	371
47	Daniil Medvedev#6	11.9%	64.9	341
48	JM John Millman	11.8%	64.1	48
49	Denis Shapovalov#43	11.7%	63.4	233
50	SERHamad Medjedovic#73	11.6%	62.6	152

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

Jesper de Jong

17.8% of the sets they win are 6-0

Average: 10.6%
Std. dev.: 3.2
Leader sits at: 2.3σ
Range: 1.8 … 17.8

132 players

#	Player	Value	Pctl	Sample
1	Jesper de Jong#105	17.8%	100.0	177
2	Learner Tien#19	17.5%	99.2	189
3	Arthur Cazaux#138	17.3%	98.5	116
4	Mariano Navone#44	17.2%	97.7	176
5	Zachary Svajda#87	17.1%	96.9	181
6	Alex de Minaur#7	16.2%	96.2	348
7	Jaume Munar#45	16.0%	95.4	227
8	RN Rafael Nadal	15.9%	94.7	70
9	Luciano Darderi#22	15.5%	93.9	187
10	Jannik Sinner#1	15.4%	93.1	348
11	Damir Dzumhur#104	15.4%	92.4	196
12	Carlos Alcaraz#2	15.3%	91.6	330
13	Marton Fucsovics#83	14.9%	90.8	215
14	GM Gael Monfils	14.8%	90.1	150
15	Vit Kopriva#71	14.6%	89.3	188
16	SERNovak Djokovic#5	14.6%	88.5	244
17	Alejandro Tabilo#26	14.5%	87.8	213
18	Cristian Garin#136	14.0%	87.0	222
19	Cameron Norrie#37	14.0%	86.3	308
20	Valentin Vacherot#18	13.9%	85.5	150
21	Roberto Bautista Agut	13.8%	84.7	229
22	Pavel Kotov	13.8%	84.0	141
23	Jack Draper#143	13.8%	83.2	167
24	Jenson Brooksby#74	13.6%	82.4	142
25	Pablo Carreno Busta#68	13.5%	81.7	206
26	Jacob Fearnley#110	13.5%	80.9	142
27	Sebastian Baez#48	13.4%	80.2	263
28	Roberto Carballes Baena	13.3%	79.4	212
29	Mackenzie McDonald#144	12.9%	78.6	270
30	RG Richard Gasquet	12.9%	77.9	166
31	SK Sebastian Korda#50	12.9%	77.1	219
32	Grigor Dimitrov#137	12.9%	76.3	230
33	AM Andy Murray	12.8%	75.6	114
34	Gabriel Diallo#108	12.8%	74.8	173
35	Wu Yibing#116	12.7%	74.0	112
36	Mattia Bellucci#80	12.7%	73.3	185
37	Casper Ruud#14	12.7%	72.5	329
38	Fabian Marozsan#61	12.7%	71.8	163
39	Camilo Ugo Carabelli#76	12.7%	71.0	188
40	GZ Giulio Zeppieri	12.7%	70.2	117
41	Botic Van De Zandschulp#70	12.5%	69.5	284
42	ER Emil Ruusuvuori	12.3%	68.7	187
43	Karen Khachanov#39	12.1%	67.9	275
44	Tommy Paul#21	12.0%	67.2	305
45	Lorenzo Musetti#13	11.9%	66.4	296
46	Alexander Zverev#3	11.9%	65.6	371
47	Daniil Medvedev#6	11.9%	64.9	341
48	JM John Millman	11.8%	64.1	48
49	Denis Shapovalov#43	11.7%	63.4	233
50	SERHamad Medjedovic#73	11.6%	62.6	152

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.