Confusing Names - Paul Kernfeld dot com

Martin Lee Allen: a whole law firm or just one person's name? I bring you a data-driven answer to this question.

Based on the 1990 U.S. census data, here is a list of names that are equally common as first names and last names:

Name	Total Popularity (%)	Popularity as First (%)	Popularity as Last (%)	Closeness (%)
MARTIN	0.491	0.218	0.273	80
LEE	0.433	0.213	0.220	97
ALLEN	0.375	0.176	0.199	88
JORDAN	0.146	0.068	0.078	87
ARNOLD	0.128	0.072	0.056	78
AUSTIN	0.097	0.045	0.052	87
OLIVER	0.090	0.040	0.050	80
WADE	0.085	0.045	0.040	89
NEAL	0.076	0.037	0.039	95
LUCAS	0.069	0.031	0.038	82
BLAKE	0.065	0.037	0.028	76
MACK	0.056	0.025	0.031	81
SHERMAN	0.055	0.028	0.027	96
SIMON	0.052	0.026	0.026	100
OWEN	0.051	0.026	0.025	96
DOYLE	0.049	0.022	0.027	81
CLAY	0.044	0.021	0.023	91
ROMAN	0.043	0.020	0.023	87
BRADFORD	0.043	0.021	0.022	95
CAREY	0.041	0.022	0.019	86
MOSES	0.039	0.020	0.019	95
HEATH	0.038	0.017	0.021	81
LARA	0.033	0.016	0.017	94
HALEY	0.030	0.014	0.016	88
SOLOMON	0.029	0.013	0.016	81

Methodology

I wanted a way to decide if a name was equally common as a first or last name. I decided to use the formula min(first / last, last / first), which I'm calling "closeness." It probably has at least one other name, if not more.

I wanted to filter out all names with closeness < c. First I binned the names by closeness. Within each bin, I hid the first and last popularity and guessed whether that name was predominantly a first name or a last name. I could guess accurately below c > 0.75 but not above it, so I set c to 0.75.

Finally, I ranked all qualifying names by total popularity. The total popularity figure that I'm using is actually just the sum of the name's popularity as a first name and as a last name. This will be a slight overestimate since it doesn't account for people whose first names are the same as their last names ("Martin Martin?"). This is probably not a big deal.