The birthplace effect describes the over-representation of high performance athletes from regions with medium-to-large population sizes, and the under-representation of athletes from regions with very small and very large population sizes (Côté et al., 2006). However, it is not clear how homogenous regions are within population categories (< 2,500; 2,500-4,999; 5,000-9,999; 10,000-29,999; 30,000-99,999; 100,000-249,999; 250,000-499,999; 500,000-999,999; >1,000,000) with respect to athlete development. Birthplace data were collected for players drafted into the National Hockey League from 2000-2014 from 6 provincial regions: British Columbia (n=191), Alberta (n=218), Central Provinces (n=218), Ontario (n = 562), Quebec (n = 242), and Atlantic Provinces (n=74). For each provincial region, variability within each population category was explored using mean, standard deviation (SD) and min/max values by comparing the number of athletes that emerged from each city within each population category. Inferential statistics were not run because these were complete populations. Overall, little variability existed within the 4 smallest population categories. Greater variability existed between cities within the larger population categories. For example, in Ontario the five cities within the 250,000-499,999 population category in Ontario produced an average of 20.6 players with a SD of 12.2 and a range of 4 to 37, and the four cities with the 500,000-999,999 population category produced an average of 31.8 players with a SD of 38.2 and a range of 3 to 87. These results reinforce the necessity to study the environmental constraints that make specific regions within population categories more and less conducive to athlete development, and that it may be necessary to reconsider the categorization methodology (i.e., population categories) currently used.