Win percentage — full ATP & WTA ranking

Leader

Pierre Yves Bailly

77.4% of matches won

Average: 49.3%
Std. dev.: 11.7
Leader sits at: 2.4σ
Range: 11.1 … 77.4

1250 players

#	Player	Value	Pctl	Sample
1	PB Pierre Yves Bailly	77.4%	100.0	31
2	LM Lilian Marmousez	76.2%	99.9	147
3	VB Viacheslav Bielinskyi	75.2%	99.8	125
4	Toby Samuel#109	75.0%	99.8	108
5	Jakub Nicod	74.5%	99.7	102
6	Philip Henning	74.0%	99.6	177
7	Max Basing	72.9%	99.5	96
8	Rafael Jodar#15	72.8%	99.4	125
9	DB Devin Badenhorst	72.7%	99.4	33
10	NK Nicola Kuhn	72.4%	99.3	58
11	YB Yan Bai	71.6%	99.2	109
12	IM Igor Ribeiro Marcondes	70.4%	99.1	81
13	Chris Rodesch#150	70.2%	99.0	141
14	Raphael Collignon#38	70.2%	99.0	208
15	PM Pedro Vives Marcos	70.2%	98.9	57
16	OM Ognjen Milic	70.1%	98.8	87
17	PS Patrick Schoen	70.0%	98.7	60
18	OT Oliver Tarvet	70.0%	98.6	50
19	DS Dhakshineswar Suresh	69.7%	98.6	66
20	Arthur Fery#36	69.5%	98.5	118
21	MG Marek Gengel	69.5%	98.4	190
22	DM Dragos Nicolae Madaras	69.4%	98.3	36
23	ET Evgenii Tiurnev	69.2%	98.2	104
24	SB Samir Banerjee	69.2%	98.2	65
25	AC Altug Celikbilek	69.1%	98.1	68
26	Michael Zheng#113	69.0%	98.0	84
27	NL Nam Hoang Ly	68.9%	97.9	45
28	Ilia Simakin	68.9%	97.8	183
29	SG Svyatoslav Gulin	68.8%	97.8	157
30	JM Johannus Monday	68.7%	97.7	115
31	Daniel Milavsky	68.4%	97.6	114
32	SC Sean Cuenin	68.3%	97.5	82
33	Andrej Nedic	68.2%	97.4	157
34	AG Alastair Gray	67.9%	97.4	131
35	Radu Mihai Papoe	67.7%	97.3	93
36	RC Roberto Cid	67.6%	97.2	105
37	JF Jay Dylan Hara Friend	67.6%	97.1	71
38	MS Marat Sharipov	67.5%	97.0	154
39	PF Peter Fajta	67.5%	97.0	117
40	BB Blu Baker	67.5%	96.9	40
41	TL Timo Legout	67.4%	96.8	43
42	DT Dan Alexandru Tomescu	67.2%	96.7	116
43	Mili Poljicak	67.0%	96.6	176
44	SD Stefan Dostanic	67.0%	96.6	100
45	AP Alessandro Pecci	66.9%	96.5	124
46	Khumoun Sultanov	66.9%	96.4	151
47	Guido Ivan Justo	66.7%	96.3	177
48	Akira Santillan	66.7%	96.2	105
49	CS Christian Sigsgaard	66.7%	96.2	57
50	Felix Gill	66.5%	96.1	158

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.
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

Pierre Yves Bailly

77.4% of matches won

Average: 49.3%
Std. dev.: 11.7
Leader sits at: 2.4σ
Range: 11.1 … 77.4

1250 players

#	Player	Value	Pctl	Sample
1	PB Pierre Yves Bailly	77.4%	100.0	31
2	LM Lilian Marmousez	76.2%	99.9	147
3	VB Viacheslav Bielinskyi	75.2%	99.8	125
4	Toby Samuel#109	75.0%	99.8	108
5	Jakub Nicod	74.5%	99.7	102
6	Philip Henning	74.0%	99.6	177
7	Max Basing	72.9%	99.5	96
8	Rafael Jodar#15	72.8%	99.4	125
9	DB Devin Badenhorst	72.7%	99.4	33
10	NK Nicola Kuhn	72.4%	99.3	58
11	YB Yan Bai	71.6%	99.2	109
12	IM Igor Ribeiro Marcondes	70.4%	99.1	81
13	Chris Rodesch#150	70.2%	99.0	141
14	Raphael Collignon#38	70.2%	99.0	208
15	PM Pedro Vives Marcos	70.2%	98.9	57
16	OM Ognjen Milic	70.1%	98.8	87
17	PS Patrick Schoen	70.0%	98.7	60
18	OT Oliver Tarvet	70.0%	98.6	50
19	DS Dhakshineswar Suresh	69.7%	98.6	66
20	Arthur Fery#36	69.5%	98.5	118
21	MG Marek Gengel	69.5%	98.4	190
22	DM Dragos Nicolae Madaras	69.4%	98.3	36
23	ET Evgenii Tiurnev	69.2%	98.2	104
24	SB Samir Banerjee	69.2%	98.2	65
25	AC Altug Celikbilek	69.1%	98.1	68
26	Michael Zheng#113	69.0%	98.0	84
27	NL Nam Hoang Ly	68.9%	97.9	45
28	Ilia Simakin	68.9%	97.8	183
29	SG Svyatoslav Gulin	68.8%	97.8	157
30	JM Johannus Monday	68.7%	97.7	115
31	Daniel Milavsky	68.4%	97.6	114
32	SC Sean Cuenin	68.3%	97.5	82
33	Andrej Nedic	68.2%	97.4	157
34	AG Alastair Gray	67.9%	97.4	131
35	Radu Mihai Papoe	67.7%	97.3	93
36	RC Roberto Cid	67.6%	97.2	105
37	JF Jay Dylan Hara Friend	67.6%	97.1	71
38	MS Marat Sharipov	67.5%	97.0	154
39	PF Peter Fajta	67.5%	97.0	117
40	BB Blu Baker	67.5%	96.9	40
41	TL Timo Legout	67.4%	96.8	43
42	DT Dan Alexandru Tomescu	67.2%	96.7	116
43	Mili Poljicak	67.0%	96.6	176
44	SD Stefan Dostanic	67.0%	96.6	100
45	AP Alessandro Pecci	66.9%	96.5	124
46	Khumoun Sultanov	66.9%	96.4	151
47	Guido Ivan Justo	66.7%	96.3	177
48	Akira Santillan	66.7%	96.2	105
49	CS Christian Sigsgaard	66.7%	96.2	57
50	Felix Gill	66.5%	96.1	158

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.
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.