Top 100 Most Popular Girl Baby Names by Pronunciation in the US 1957 - Combined Name Rankings

About Distortion Index

The Distortion Index shows how much the ranking might be skewed by alternative spellings of the same pronunciation. A higher index (closer to 1.0) means the main name represents a smaller portion of the total, indicating the ranking could be misleading. A lower index (closer to 0.0) means the main name dominates, making the ranking more accurate.

Low Distortion (0.07): Jayden (1,000) + Zayden (50) + Jaden (30) = Main name dominates
High Distortion (0.91): Jayden (100) + Zayden (800) + Jaden (200) = Alternative spellings dominate
Distortion Index Color Guide:
0.0-0.29 Low Distortion (Green) - Main name dominates
0.3-0.69 Medium Distortion (Orange) - Moderate alternative spellings
0.7-1.0 High Distortion (Red) - Alternative spellings dominate
Rank Adjustment Explanation:

Adjusted Rank: The primary rank shown reflects the combined count of all similar pronunciation names, providing a more accurate representation of the name's true popularity. Original Rank: The rank in parentheses shows the original ranking based on the main name only, before grouping similar pronunciations.

Rank Change Indicators:
📈 Rank improved (moved up)
📉 Rank declined (moved down)
➡️ Rank unchanged

Advanced Pronunciation Algorithm

We've developed a revolutionary pronunciation comparison algorithm that intelligently groups baby names with similar sounds and pronunciations. Our sophisticated system automatically corrects common typos and misspellings, ensuring accurate name grouping based on phonetic similarity rather than just spelling.

This cutting-edge algorithm uses advanced phonetic analysis to identify names that sound alike but may have different spellings, providing you with the most comprehensive and accurate baby name rankings by pronunciation. 💡 Understanding the Distortion Index is crucial for interpreting these results accurately. While our algorithm is highly accurate, if you notice any grouping errors, please let us know and we'll promptly resolve them.

🔍 Intelligent phonetic analysis and grouping
✏️ Automatic typo correction and misspelling detection
🎯 Accurate pronunciation-based name categorization

Girl Names

Ranking Name Distortion Index Count
1 ➡️
(Org: 1)
(61,626)
0.01 61,626
2 ➡️
(Org: 2)
(51,886)
0.11 51,886
3 ➡️
(Org: 3)
(46,386)
0.04 46,386
4 ➡️
(Org: 4)
(43,213)
0.01 43,213
5 📈
(Org: 6)
(42,844)
0.06 42,844
6 📉
(Org: 5)
(41,078)
0.01 41,078
7 ➡️
(Org: 7)
(39,513)
0 39,513
8 ➡️
(Org: 8)
(39,288)
0 39,288
9 📈
(Org: 20)
(32,462)
0.38 32,462
10 📉
(Org: 9)
(29,699)
0 29,699
11 📉
(Org: 10)
(28,540)
0.02 28,540
12 📉
(Org: 11)
(27,874)
0.01 27,874
13 📈
(Org: 24)
(27,328)
0.43 27,328
14 📈
(Org: 16)
(27,214)
0.17 27,214
15 📈
(Org: 37)
(26,727)
0.58 26,727
16 📉
(Org: 12)
(25,432)
0.02 25,432
17 📉
(Org: 14)
(24,705)
0.02 24,705
18 📉
(Org: 13)
(24,338)
- 24,338
19 📉
(Org: 18)
(23,683)
0.08 23,683
20 📉
(Org: 15)
(23,088)
0.01 23,088
21 📉
(Org: 17)
(22,089)
- 22,089
22 📉
(Org: 19)
(21,376)
0.05 21,376
23 📉
(Org: 21)
(21,094)
0.06 21,094
24 📈
(Org: 51)
(18,685)
0.57 18,685
25 📉
(Org: 22)
(17,848)
0.02 17,848
26 📈
(Org: 45)
(17,613)
0.49 17,613
27 📉
(Org: 23)
(16,571)
0.05 16,571
28 📈
(Org: 60)
(16,016)
0.53 16,016
29 📉
(Org: 27)
(15,788)
0.09 15,788
30 📉
(Org: 26)
(15,619)
0.03 15,619
31 📉
(Org: 25)
(15,231)
0.01 15,231
32 📈
(Org: 68)
(14,911)
0.53 14,911
33 📉
(Org: 30)
(14,234)
0.12 14,234
34 📉
(Org: 28)
(13,897)
0.02 13,897
35 📉
(Org: 29)
(13,183)
0.01 13,183
36 📉
(Org: 31)
(12,931)
0.06 12,931
37 📉
(Org: 32)
(11,968)
- 11,968
38 📉
(Org: 34)
(11,816)
0.03 11,816
39 📉
(Org: 33)
(11,740)
0.01 11,740
40 📈
(Org: 41)
(11,583)
0.17 11,583
41 📉
(Org: 35)
(11,427)
- 11,427
42 📈
(Org: 54)
(11,380)
0.3 11,380
43 📉
(Org: 38)
(10,872)
0.03 10,872
44 📈
(Org: 79)
(10,848)
0.45 10,848
45 📉
(Org: 42)
(10,823)
0.13 10,823
46 📉
(Org: 40)
(10,171)
0 10,171
47 📈
(Org: 61)
(10,168)
0.27 10,168
48 📉
(Org: 43)
(9,443)
0 9,443
49 📈
(Org: 56)
(9,349)
0.17 9,349
50 ➡️
(Org: 50)
(9,185)
0.08 9,185
51 📈
(Org: 52)
(9,114)
0.12 9,114
52 📉
(Org: 44)
(9,050)
0 9,050
53 📈
(Org: 55)
(9,038)
0.14 9,038
54 📉
(Org: 47)
(8,870)
0.01 8,870
55 📉
(Org: 46)
(8,869)
0 8,869
56 📉
(Org: 48)
(8,623)
0.01 8,623
57 📉
(Org: 49)
(8,550)
0 8,550
58 📉
(Org: 57)
(8,150)
0.04 8,150
59 📉
(Org: 53)
(8,043)
0 8,043
60 📉
(Org: 59)
(7,766)
0.01 7,766
61 📉
(Org: 58)
(7,764)
- 7,764
62 📈
(Org: 64)
(7,562)
0.04 7,562
63 📉
(Org: 62)
(7,559)
0.02 7,559
64 📉
(Org: 63)
(7,370)
0 7,370
65 📈
(Org: 67)
(7,135)
0.01 7,135
66 ➡️
(Org: 66)
(7,120)
- 7,120
67 📈
(Org: 91)
(7,027)
0.25 7,027
68 📈
(Org: 69)
(6,901)
0 6,901
69 📈
(Org: 71)
(6,799)
0 6,799
70 📈
(Org: 77)
(6,540)
0.07 6,540
71 📈
(Org: 74)
(6,419)
0 6,419
72 📈
(Org: 75)
(6,268)
0 6,268
73 📈
(Org: 76)
(6,263)
0.01 6,263
74 📈
(Org: 120)
(6,210)
0.4 6,210
75 📈
(Org: 78)
(6,191)
0.03 6,191
76 📈
(Org: 80)
(6,127)
0.04 6,127
77 📈
(Org: 116)
(6,096)
0.35 6,096
78 📈
(Org: 82)
(6,047)
0.05 6,047
79 📈
(Org: 83)
(5,895)
0.03 5,895
80 📈
(Org: 81)
(5,816)
0 5,816
81 📈
(Org: 87)
(5,683)
0.03 5,683
82 📈
(Org: 84)
(5,675)
0 5,675
83 📈
(Org: 85)
(5,616)
0 5,616
84 📈
(Org: 90)
(5,540)
0.04 5,540
85 📈
(Org: 95)
(5,508)
0.09 5,508
86 ➡️
(Org: 86)
(5,486)
- 5,486
87 📈
(Org: 89)
(5,349)
0 5,349
88 ➡️
(Org: 88)
(5,334)
0 5,334
89 📈
(Org: 98)
(5,290)
0.08 5,290
90 📈
(Org: 93)
(5,249)
0.02 5,249
91 📈
(Org: 107)
(5,188)
0.16 5,188
92 📈
(Org: 210)
(5,163)
0.69 5,163
93 📈
(Org: 148)
(5,145)
0.48 5,145
94 📈
(Org: 96)
(5,082)
0.01 5,082
95 📈
(Org: 128)
(4,999)
0.31 4,999
96 📈
(Org: 101)
(4,931)
0.05 4,931
97 ➡️
(Org: 97)
(4,920)
0.01 4,920
98 📈
(Org: 139)
(4,784)
0.37 4,784
99 📈
(Org: 100)
(4,779)
0.01 4,779
100 📈
(Org: 103)
(4,661)
0.01 4,661