Comebacks — full ATP & WTA ranking

Leader

Elena Ruse

43.5% of matches won after losing the first set

Average: 21.1%
Std. dev.: 7.1
Leader sits at: 3.2σ
Range: 0.0 … 43.5

151 players

#	Player	Value	Pctl	Sample
1	ER Elena Ruse	43.5%	100.0	23
2	Aryna Sabalenka#1	40.5%	99.3	84
3	Iga Swiatek#8	39.5%	98.7	81
4	Elena Rybakina#2	39.3%	98.0	89
5	Mirra Andreeva#5	37.9%	97.3	58
6	Jessica Pegula#3	36.6%	96.7	112
7	Belinda Bencic#14	36.5%	96.0	74
8	Elina Svitolina#9	34.2%	95.3	76
9	Anastasia Potapova#27	31.9%	94.7	113
10	Amanda Anisimova#10	31.8%	94.0	66
11	Madison Keys#23	31.6%	93.3	76
12	Coco Gauff#4	31.5%	92.7	108
13	Rebeka Masarova	30.7%	92.0	88
14	McCartney Kessler#48	30.1%	91.3	73
15	Paula Badosa#84	29.7%	90.7	74
16	Qinwen Zheng	29.5%	90.0	88
17	Jasmine Paolini#15	29.3%	89.3	123
18	Jelena Ostapenko#29	29.1%	88.7	103
19	Marta Kostyuk#11	29.1%	88.0	103
20	KK Kaia Kanepi	28.9%	87.3	45
21	Katie Volynets#91	28.6%	86.7	91
22	IB Irina Camelia Begu	27.7%	86.0	47
23	Zeynep Sonmez#52	27.5%	85.3	80
24	Karolina Muchova#6	27.4%	84.7	62
25	Hailey Baptiste#32	27.0%	84.0	74
26	OJ Ons Jabeur	26.8%	83.3	71
27	Karolina Pliskova#66	26.3%	82.7	76
28	Beatriz Haddad Maia	26.2%	82.0	122
29	Jaqueline Cristian#39	26.0%	81.3	104
30	CG Caroline Garcia	25.6%	80.7	82
31	Daria Kasatkina#61	25.6%	80.0	133
32	MV Marketa Vondrousova#140	25.5%	79.3	47
33	Robin Montgomery	25.5%	78.7	47
34	Moyuka Uchijima#111	25.5%	78.0	106
35	Danielle Collins	25.5%	77.3	55
36	Ajla Tomljanovic#95	25.3%	76.7	99
37	Emma Navarro#28	25.3%	76.0	99
38	Ann Li#31	25.2%	75.3	107
39	Barbora Krejcikova#26	25.0%	74.7	72
40	VA Victoria Azarenka	25.0%	74.0	64
41	Naomi Osaka#13	24.5%	73.3	49
42	LT Lesia Tsurenko	24.4%	72.7	45
43	Katie Boulter#72	24.4%	72.0	82
44	Diana Shnaider#17	24.1%	71.3	83
45	Elise Mertens#24	24.1%	70.7	108
46	Ella Seidel#105	23.7%	70.0	76
47	Wang Xinyu#41	23.7%	69.3	38
48	Laura Siegemund#88	23.4%	68.7	77
49	Taylor Townsend#110	23.3%	68.0	60
50	Maya Joint#75	23.3%	67.3	73

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

Elena Ruse

43.5% of matches won after losing the first set

Average: 21.1%
Std. dev.: 7.1
Leader sits at: 3.2σ
Range: 0.0 … 43.5

151 players

#	Player	Value	Pctl	Sample
1	ER Elena Ruse	43.5%	100.0	23
2	Aryna Sabalenka#1	40.5%	99.3	84
3	Iga Swiatek#8	39.5%	98.7	81
4	Elena Rybakina#2	39.3%	98.0	89
5	Mirra Andreeva#5	37.9%	97.3	58
6	Jessica Pegula#3	36.6%	96.7	112
7	Belinda Bencic#14	36.5%	96.0	74
8	Elina Svitolina#9	34.2%	95.3	76
9	Anastasia Potapova#27	31.9%	94.7	113
10	Amanda Anisimova#10	31.8%	94.0	66
11	Madison Keys#23	31.6%	93.3	76
12	Coco Gauff#4	31.5%	92.7	108
13	Rebeka Masarova	30.7%	92.0	88
14	McCartney Kessler#48	30.1%	91.3	73
15	Paula Badosa#84	29.7%	90.7	74
16	Qinwen Zheng	29.5%	90.0	88
17	Jasmine Paolini#15	29.3%	89.3	123
18	Jelena Ostapenko#29	29.1%	88.7	103
19	Marta Kostyuk#11	29.1%	88.0	103
20	KK Kaia Kanepi	28.9%	87.3	45
21	Katie Volynets#91	28.6%	86.7	91
22	IB Irina Camelia Begu	27.7%	86.0	47
23	Zeynep Sonmez#52	27.5%	85.3	80
24	Karolina Muchova#6	27.4%	84.7	62
25	Hailey Baptiste#32	27.0%	84.0	74
26	OJ Ons Jabeur	26.8%	83.3	71
27	Karolina Pliskova#66	26.3%	82.7	76
28	Beatriz Haddad Maia	26.2%	82.0	122
29	Jaqueline Cristian#39	26.0%	81.3	104
30	CG Caroline Garcia	25.6%	80.7	82
31	Daria Kasatkina#61	25.6%	80.0	133
32	MV Marketa Vondrousova#140	25.5%	79.3	47
33	Robin Montgomery	25.5%	78.7	47
34	Moyuka Uchijima#111	25.5%	78.0	106
35	Danielle Collins	25.5%	77.3	55
36	Ajla Tomljanovic#95	25.3%	76.7	99
37	Emma Navarro#28	25.3%	76.0	99
38	Ann Li#31	25.2%	75.3	107
39	Barbora Krejcikova#26	25.0%	74.7	72
40	VA Victoria Azarenka	25.0%	74.0	64
41	Naomi Osaka#13	24.5%	73.3	49
42	LT Lesia Tsurenko	24.4%	72.7	45
43	Katie Boulter#72	24.4%	72.0	82
44	Diana Shnaider#17	24.1%	71.3	83
45	Elise Mertens#24	24.1%	70.7	108
46	Ella Seidel#105	23.7%	70.0	76
47	Wang Xinyu#41	23.7%	69.3	38
48	Laura Siegemund#88	23.4%	68.7	77
49	Taylor Townsend#110	23.3%	68.0	60
50	Maya Joint#75	23.3%	67.3	73

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.