Comebacks — full ATP & WTA ranking

Leader

Kyle Kang

40.7% of matches won after losing the first set

Average: 16.8%
Std. dev.: 7.4
Leader sits at: 3.2σ
Range: 0.0 … 40.7

1173 players

#	Player	Value	Pctl	Sample
1	KK Kyle Kang	40.7%	100.0	27
2	PR Pedro Rodrigues	38.5%	99.9	26
3	Petr Nesterov	38.0%	99.8	79
4	JF Jay Dylan Hara Friend	37.9%	99.7	29
5	Stefanos Sakellaridis	36.3%	99.7	91
6	Bernard Tomic	36.0%	99.6	75
7	AC Altug Celikbilek	36.0%	99.5	25
8	Leandro Riedi#128	35.1%	99.4	37
9	LM Lilian Marmousez	35.0%	99.3	40
10	Michael Zheng#113	34.4%	99.2	32
11	Facundo Diaz Acosta#84	33.8%	99.1	65
12	Andrej Martin	33.3%	99.1	54
13	MG Matteo Gigante	33.3%	99.0	51
14	AP Alessandro Pecci	33.3%	98.9	48
15	JG Juan Sebastian Gomez	33.3%	98.8	36
16	LP Lucas Pouille	33.3%	98.7	33
17	Max Basing	33.3%	98.6	27
18	Marco Trungelliti#90	32.9%	98.5	85
19	MK Milos Karol	32.9%	98.5	76
20	Andrea Guerrieri	32.9%	98.4	70
21	Kyrian Jacquet#121	32.6%	98.3	46
22	Arthur Fery#36	32.6%	98.2	43
23	Jaime Faria#79	32.5%	98.1	77
24	Eliot Spizzirri#115	32.4%	98.0	71
25	FB Florian Broska	32.3%	98.0	62
26	VD Vlad Andrei Dancu	32.3%	97.9	31
27	AB Arthur Bouquier	32.0%	97.8	50
28	Yanki Erel	32.0%	97.7	50
29	Vilius Gaubas#120	31.6%	97.6	98
30	EC Enzo Couacaud	31.6%	97.5	38
31	Frederico Ferreira Silva	31.3%	97.4	80
32	TB Thomas Braithwaite	31.3%	97.4	32
33	EA Egor Agafonov	31.0%	97.3	58
34	Jakub Nicod	31.0%	97.2	29
35	JP Julio Cesar Porras	31.0%	97.1	29
36	MM Martin Van Der Meerschen	31.0%	97.0	42
37	RB Roman Burruchaga	30.8%	96.9	78
38	IM Igor Ribeiro Marcondes	30.8%	96.8	26
39	Mark Lajal	30.7%	96.8	75
40	Alexander Blockx#32	30.6%	96.7	72
41	Max Houkes	30.6%	96.6	72
42	SD Stefan Dostanic	30.6%	96.5	36
43	Rio Noguchi	30.4%	96.4	92
44	KB Karim Bennani	30.4%	96.3	23
45	Rafael Jodar#15	30.2%	96.2	43
46	Maks Kasnikowski	30.2%	96.2	63
47	CP Cezar Gabriel Papoe	30.0%	96.1	20
48	SM Shunsuke Mitsui	30.0%	96.0	20
49	Dane Sweeny#122	29.5%	95.9	78
50	LD Louis Dussin	29.4%	95.8	34

How this is calculated

Minimum 20 matches after losing the first set. 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

Kyle Kang

40.7% of matches won after losing the first set

Average: 16.8%
Std. dev.: 7.4
Leader sits at: 3.2σ
Range: 0.0 … 40.7

1173 players

#	Player	Value	Pctl	Sample
1	KK Kyle Kang	40.7%	100.0	27
2	PR Pedro Rodrigues	38.5%	99.9	26
3	Petr Nesterov	38.0%	99.8	79
4	JF Jay Dylan Hara Friend	37.9%	99.7	29
5	Stefanos Sakellaridis	36.3%	99.7	91
6	Bernard Tomic	36.0%	99.6	75
7	AC Altug Celikbilek	36.0%	99.5	25
8	Leandro Riedi#128	35.1%	99.4	37
9	LM Lilian Marmousez	35.0%	99.3	40
10	Michael Zheng#113	34.4%	99.2	32
11	Facundo Diaz Acosta#84	33.8%	99.1	65
12	Andrej Martin	33.3%	99.1	54
13	MG Matteo Gigante	33.3%	99.0	51
14	AP Alessandro Pecci	33.3%	98.9	48
15	JG Juan Sebastian Gomez	33.3%	98.8	36
16	LP Lucas Pouille	33.3%	98.7	33
17	Max Basing	33.3%	98.6	27
18	Marco Trungelliti#90	32.9%	98.5	85
19	MK Milos Karol	32.9%	98.5	76
20	Andrea Guerrieri	32.9%	98.4	70
21	Kyrian Jacquet#121	32.6%	98.3	46
22	Arthur Fery#36	32.6%	98.2	43
23	Jaime Faria#79	32.5%	98.1	77
24	Eliot Spizzirri#115	32.4%	98.0	71
25	FB Florian Broska	32.3%	98.0	62
26	VD Vlad Andrei Dancu	32.3%	97.9	31
27	AB Arthur Bouquier	32.0%	97.8	50
28	Yanki Erel	32.0%	97.7	50
29	Vilius Gaubas#120	31.6%	97.6	98
30	EC Enzo Couacaud	31.6%	97.5	38
31	Frederico Ferreira Silva	31.3%	97.4	80
32	TB Thomas Braithwaite	31.3%	97.4	32
33	EA Egor Agafonov	31.0%	97.3	58
34	Jakub Nicod	31.0%	97.2	29
35	JP Julio Cesar Porras	31.0%	97.1	29
36	MM Martin Van Der Meerschen	31.0%	97.0	42
37	RB Roman Burruchaga	30.8%	96.9	78
38	IM Igor Ribeiro Marcondes	30.8%	96.8	26
39	Mark Lajal	30.7%	96.8	75
40	Alexander Blockx#32	30.6%	96.7	72
41	Max Houkes	30.6%	96.6	72
42	SD Stefan Dostanic	30.6%	96.5	36
43	Rio Noguchi	30.4%	96.4	92
44	KB Karim Bennani	30.4%	96.3	23
45	Rafael Jodar#15	30.2%	96.2	43
46	Maks Kasnikowski	30.2%	96.2	63
47	CP Cezar Gabriel Papoe	30.0%	96.1	20
48	SM Shunsuke Mitsui	30.0%	96.0	20
49	Dane Sweeny#122	29.5%	95.9	78
50	LD Louis Dussin	29.4%	95.8	34

How this is calculated

Minimum 20 matches after losing the first set. 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.