Top 100 Most Popular Girl Baby Names by Pronunciation in the US 1990 - 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: 2)
(48,596)
0.06 48,596
2 📉
(Org: 1)
(47,429)
0.02 47,429
3 ➡️
(Org: 3)
(39,832)
0.08 39,832
4 📈
(Org: 6)
(34,879)
0.26 34,879
5 📉
(Org: 4)
(34,430)
0 34,430
6 📈
(Org: 7)
(27,729)
0.1 27,729
7 📉
(Org: 5)
(25,939)
0 25,939
8 📈
(Org: 26)
(24,618)
0.53 24,618
9 📈
(Org: 50)
(22,886)
0.69 22,886
10 📉
(Org: 8)
(22,881)
0.03 22,881
11 📈
(Org: 42)
(21,895)
0.63 21,895
12 📉
(Org: 9)
(21,610)
0.04 21,610
13 📉
(Org: 11)
(21,349)
0.05 21,349
14 📉
(Org: 10)
(21,049)
0.03 21,049
15 📉
(Org: 13)
(20,665)
0.13 20,665
16 📉
(Org: 12)
(20,230)
0.04 20,230
17 📉
(Org: 16)
(19,357)
0.19 19,357
18 📈
(Org: 25)
(18,747)
0.36 18,747
19 📉
(Org: 17)
(18,462)
0.17 18,462
20 📉
(Org: 14)
(18,146)
0.03 18,146
21 📈
(Org: 24)
(17,111)
0.25 17,111
22 📉
(Org: 21)
(16,473)
0.17 16,473
23 📉
(Org: 15)
(15,901)
0 15,901
24 📉
(Org: 18)
(15,266)
0.06 15,266
25 📉
(Org: 22)
(14,756)
0.09 14,756
26 📉
(Org: 20)
(14,605)
0.04 14,605
27 📉
(Org: 23)
(14,270)
0.08 14,270
28 📉
(Org: 19)
(14,228)
0 14,228
29 📈
(Org: 36)
(14,068)
0.39 14,068
30 📉
(Org: 27)
(13,052)
0.14 13,052
31 📈
(Org: 39)
(12,876)
0.36 12,876
32 📈
(Org: 44)
(12,770)
0.42 12,770
33 📉
(Org: 28)
(12,727)
0.13 12,727
34 📈
(Org: 41)
(11,962)
0.32 11,962
35 📉
(Org: 32)
(11,617)
0.18 11,617
36 📉
(Org: 29)
(11,192)
0.02 11,192
37 📉
(Org: 31)
(11,081)
0.08 11,081
38 📉
(Org: 30)
(11,059)
0.07 11,059
39 📈
(Org: 48)
(10,716)
0.33 10,716
40 📈
(Org: 61)
(9,990)
0.43 9,990
41 📈
(Org: 67)
(9,677)
0.46 9,677
42 📉
(Org: 33)
(9,151)
0 9,151
43 📈
(Org: 49)
(9,010)
0.22 9,010
44 📉
(Org: 37)
(8,937)
0.05 8,937
45 📉
(Org: 35)
(8,761)
0.01 8,761
46 📈
(Org: 51)
(8,469)
0.2 8,469
47 📉
(Org: 38)
(8,464)
- 8,464
48 📉
(Org: 43)
(8,398)
0.05 8,398
49 📉
(Org: 47)
(8,311)
0.14 8,311
50 📉
(Org: 40)
(8,214)
- 8,214
51 📈
(Org: 55)
(8,025)
0.19 8,025
52 📈
(Org: 54)
(7,874)
0.17 7,874
53 📉
(Org: 46)
(7,744)
0.06 7,744
54 📉
(Org: 45)
(7,332)
0.01 7,332
55 📉
(Org: 53)
(7,211)
0.1 7,211
56 ➡️
(Org: 56)
(6,745)
0.05 6,745
57 📉
(Org: 52)
(6,711)
0.01 6,711
58 📈
(Org: 63)
(6,474)
0.13 6,474
59 📈
(Org: 106)
(6,381)
0.49 6,381
60 📉
(Org: 59)
(6,353)
0.06 6,353
61 📈
(Org: 103)
(6,238)
0.46 6,238
62 📉
(Org: 58)
(6,214)
0.03 6,214
63 📉
(Org: 60)
(6,130)
0.04 6,130
64 📈
(Org: 108)
(6,080)
0.47 6,080
65 📉
(Org: 64)
(5,605)
0.05 5,605
66 ➡️
(Org: 66)
(5,373)
0.02 5,373
67 📈
(Org: 69)
(5,363)
0.06 5,363
68 📈
(Org: 94)
(5,356)
0.33 5,356
69 📉
(Org: 68)
(5,251)
0.01 5,251
70 ➡️
(Org: 70)
(5,215)
0.04 5,215
71 📈
(Org: 87)
(5,084)
0.25 5,084
72 📈
(Org: 248)
(4,831)
0.77 4,831
73 📈
(Org: 76)
(4,731)
0.06 4,731
74 📈
(Org: 82)
(4,660)
0.16 4,660
75 📉
(Org: 72)
(4,650)
0 4,650
76 📈
(Org: 168)
(4,628)
0.63 4,628
77 📈
(Org: 80)
(4,585)
0.14 4,585
78 📉
(Org: 73)
(4,572)
- 4,572
79 📈
(Org: 81)
(4,515)
0.12 4,515
80 📈
(Org: 142)
(4,506)
0.52 4,506
81 📈
(Org: 163)
(4,481)
0.6 4,481
82 📈
(Org: 110)
(4,476)
0.32 4,476
83 📈
(Org: 89)
(4,465)
0.16 4,465
84 📉
(Org: 79)
(4,270)
0.04 4,270
85 ➡️
(Org: 85)
(4,118)
0.07 4,118
86 📈
(Org: 88)
(4,083)
0.07 4,083
87 📉
(Org: 84)
(4,057)
0.06 4,057
88 📉
(Org: 86)
(4,002)
0.05 4,002
89 📈
(Org: 235)
(3,985)
0.7 3,985
90 📈
(Org: 100)
(3,955)
0.11 3,955
91 📉
(Org: 83)
(3,931)
0 3,931
92 ➡️
(Org: 92)
(3,928)
0.07 3,928
93 📈
(Org: 141)
(3,900)
0.44 3,900
94 📈
(Org: 99)
(3,883)
0.09 3,883
95 📉
(Org: 90)
(3,824)
0.03 3,824
96 📉
(Org: 93)
(3,730)
0.02 3,730
96 📉
(Org: 91)
(3,730)
0.01 3,730
98 📉
(Org: 95)
(3,712)
0.03 3,712
99 📉
(Org: 96)
(3,672)
0.02 3,672
100 📉
(Org: 97)
(3,609)
0.01 3,609