Top 100 Most Popular Girl Baby Names by Pronunciation in the US 1985 - 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)
(49,701)
0.05 49,701
2 📉
(Org: 1)
(48,972)
0.01 48,972
3 ➡️
(Org: 3)
(43,654)
0.02 43,654
4 ➡️
(Org: 4)
(39,131)
0 39,131
5 ➡️
(Org: 5)
(35,510)
0.3 35,510
6 📈
(Org: 7)
(26,701)
0.14 26,701
7 📉
(Org: 6)
(25,871)
0.1 25,871
8 📈
(Org: 38)
(25,393)
0.67 25,393
9 📈
(Org: 12)
(24,528)
0.32 24,528
10 📈
(Org: 32)
(22,819)
0.56 22,819
11 📈
(Org: 21)
(22,053)
0.35 22,053
12 📉
(Org: 9)
(21,437)
0.04 21,437
13 📉
(Org: 10)
(21,178)
0.06 21,178
14 📉
(Org: 8)
(21,165)
0 21,165
15 📉
(Org: 11)
(20,728)
0.04 20,728
16 📉
(Org: 13)
(19,780)
0.17 19,780
17 ➡️
(Org: 17)
(16,930)
0.09 16,930
18 ➡️
(Org: 18)
(16,615)
0.07 16,615
19 📉
(Org: 14)
(16,462)
0.03 16,462
20 📉
(Org: 15)
(16,137)
0.02 16,137
21 📉
(Org: 19)
(15,798)
0.06 15,798
22 ➡️
(Org: 22)
(15,553)
0.1 15,553
23 📉
(Org: 16)
(15,543)
0 15,543
24 📈
(Org: 28)
(15,205)
0.27 15,205
25 ➡️
(Org: 25)
(14,987)
0.15 14,987
26 📉
(Org: 23)
(14,855)
0.08 14,855
27 📉
(Org: 20)
(14,429)
- 14,429
28 📈
(Org: 34)
(13,738)
0.31 13,738
29 📉
(Org: 24)
(13,677)
0.04 13,677
30 📉
(Org: 27)
(12,382)
0.11 12,382
31 📉
(Org: 26)
(11,622)
0.02 11,622
32 📉
(Org: 29)
(10,710)
0 10,710
33 📈
(Org: 46)
(10,531)
0.33 10,531
34 📉
(Org: 31)
(10,294)
0.01 10,294
35 📈
(Org: 37)
(10,227)
0.17 10,227
36 📉
(Org: 33)
(10,058)
0 10,058
37 📈
(Org: 60)
(9,897)
0.47 9,897
38 📉
(Org: 35)
(9,356)
0.01 9,356
39 📉
(Org: 36)
(9,274)
0.03 9,274
40 📈
(Org: 43)
(8,936)
0.16 8,936
41 📈
(Org: 67)
(8,906)
0.54 8,906
42 📉
(Org: 39)
(8,839)
0.1 8,839
43 📈
(Org: 69)
(8,818)
0.54 8,818
44 📈
(Org: 145)
(8,392)
0.76 8,392
45 📉
(Org: 40)
(8,313)
0.05 8,313
46 📈
(Org: 77)
(7,887)
0.54 7,887
47 📉
(Org: 41)
(7,851)
0.02 7,851
48 📉
(Org: 42)
(7,800)
0.03 7,800
49 📉
(Org: 45)
(7,792)
0.09 7,792
50 ➡️
(Org: 50)
(7,186)
0.1 7,186
51 📉
(Org: 47)
(6,691)
- 6,691
52 📈
(Org: 56)
(6,575)
0.15 6,575
53 📉
(Org: 51)
(6,500)
0.06 6,500
54 📉
(Org: 52)
(6,360)
0.05 6,360
55 📈
(Org: 86)
(6,301)
0.49 6,301
56 📈
(Org: 58)
(6,042)
0.08 6,042
57 📉
(Org: 54)
(5,915)
0.01 5,915
58 📉
(Org: 55)
(5,865)
0.02 5,865
59 📈
(Org: 62)
(5,664)
0.17 5,664
60 📉
(Org: 59)
(5,440)
0 5,440
61 📈
(Org: 84)
(5,347)
0.4 5,347
62 📉
(Org: 61)
(5,169)
0.03 5,169
63 📈
(Org: 75)
(5,097)
0.29 5,097
64 📉
(Org: 63)
(4,708)
0.01 4,708
65 📈
(Org: 107)
(4,598)
0.42 4,598
66 📈
(Org: 114)
(4,508)
0.45 4,508
67 📈
(Org: 98)
(4,461)
0.37 4,461
68 📉
(Org: 64)
(4,441)
0.01 4,441
69 📈
(Org: 132)
(4,416)
0.5 4,416
70 📉
(Org: 68)
(4,391)
0.07 4,391
71 📉
(Org: 66)
(4,290)
0.04 4,290
72 📈
(Org: 76)
(4,228)
0.15 4,228
73 📈
(Org: 78)
(4,178)
0.15 4,178
74 📈
(Org: 124)
(4,047)
0.42 4,047
75 📉
(Org: 73)
(3,979)
0.04 3,979
76 📉
(Org: 72)
(3,954)
0.02 3,954
77 📈
(Org: 106)
(3,940)
0.32 3,940
78 📉
(Org: 71)
(3,896)
0 3,896
79 ➡️
(Org: 79)
(3,854)
0.08 3,854
80 📈
(Org: 99)
(3,850)
0.28 3,850
81 📉
(Org: 74)
(3,709)
0.01 3,709
82 📈
(Org: 83)
(3,666)
0.1 3,666
83 📈
(Org: 130)
(3,648)
0.38 3,648
84 📈
(Org: 96)
(3,592)
0.18 3,592
85 📉
(Org: 82)
(3,473)
0.04 3,473
86 📈
(Org: 170)
(3,451)
0.52 3,451
87 📈
(Org: 103)
(3,423)
0.21 3,423
88 📉
(Org: 81)
(3,409)
0 3,409
89 📈
(Org: 93)
(3,371)
0.12 3,371
90 📈
(Org: 134)
(3,312)
0.34 3,312
91 📉
(Org: 88)
(3,229)
0.03 3,229
92 ➡️
(Org: 92)
(3,225)
0.06 3,225
92 📉
(Org: 85)
(3,225)
0.01 3,225
94 📉
(Org: 87)
(3,224)
0.03 3,224
95 📉
(Org: 91)
(3,148)
0.03 3,148
96 📈
(Org: 147)
(3,100)
0.35 3,100
97 📉
(Org: 90)
(3,098)
0.01 3,098
98 📉
(Org: 94)
(3,066)
0.04 3,066
99 📈
(Org: 101)
(2,945)
0.07 2,945
100 📈
(Org: 152)
(2,926)
0.35 2,926