Top 1000 Women in the Boston Marathon 2004
Yesterday I looked at the top 1000 men in last year's Boston Marathon. Today, I'll look at the top 1000 women. Like those stats, these are based on results from marathonguide.com.
One thing different about the women is that they were more geographically spread out. For example, there were only 4 North American cities with 10 or more women. For the men there were 8. So I lowered the cutoff from 10 to 5 to show more cities. It was a similar case for the countries. There were only two countries outside the US which had 5 or more women in the top 1000: Canada with 63 and Japan with 8. There were 7 for the men. So I lowered this cutoff from 5 to 1.
Times By Countries
Out of the 1000 top women, 906 were from the US, 63 from Canada, and 8 from Japan. No other country had more than 2 top women. The winning time came from a woman in Kenya with a time of 2:24:07. The top US time was 2:34:08 from a women in Florida. The average US time was 3:37:19 which beat Canada's 3:39:40. Japan had a fast group of 8 women at the race. Their fastest time was 2:34:32 and the average was 3:14:13.
Times by US States
Out of the 1000 top women, there were 18 states with 20 or more runners. Like the men, Massachusetts had the most with 113. But unlike the men, the next highest was California with 90 (only 54 California men made it in the top 1000). The third highest was New York with 73. The fastest state was Florida. The 31 women from Florida averaged 3:31:37. Also the fastest US time (2:34:08) came from Florida.
Times by Cities
Out of the 1000 top women, 28 North American cities had 5 or more runners. Just like the men, New York City had the most with 27 (3 more than the men). New York was followed by Chicago with 22 and Boston with 14. Florida wins again in average times. The city of St. Petersburg had 5 women who averaged 3:21:00.
Age Groups
Like the men, the largest age group for the women was the thirties. This breaks a trend that I've seen in several races in which the women's twenties age group is the largest. Perhaps if I looked at all the women not just the top 1000, I would see this same trend. However, there were a higher percentage of women in their twenties than men (38% vs 28%).
For average times, the three major age groups (20's, 30's, and 40's) were all within 2 minutes of each other. The fastest was the twenties age group with 3:35:44 and the slowest was the thirties with 3:37:35. Interesting, the forties age group averaged faster than the thirties with 3:36:09. For the men, the averages for these three groups were over five minutes apart with the longer times with age.
Below are the detailed stats.
Average/Best Times By Country
Average/Best Times By State
Average/Best Times By Cities
Female Runners by Times - Move mouse over cells to see best times
Average/Best Times By Female Age Groups
One thing different about the women is that they were more geographically spread out. For example, there were only 4 North American cities with 10 or more women. For the men there were 8. So I lowered the cutoff from 10 to 5 to show more cities. It was a similar case for the countries. There were only two countries outside the US which had 5 or more women in the top 1000: Canada with 63 and Japan with 8. There were 7 for the men. So I lowered this cutoff from 5 to 1.
Times By Countries
Out of the 1000 top women, 906 were from the US, 63 from Canada, and 8 from Japan. No other country had more than 2 top women. The winning time came from a woman in Kenya with a time of 2:24:07. The top US time was 2:34:08 from a women in Florida. The average US time was 3:37:19 which beat Canada's 3:39:40. Japan had a fast group of 8 women at the race. Their fastest time was 2:34:32 and the average was 3:14:13.
Times by US States
Out of the 1000 top women, there were 18 states with 20 or more runners. Like the men, Massachusetts had the most with 113. But unlike the men, the next highest was California with 90 (only 54 California men made it in the top 1000). The third highest was New York with 73. The fastest state was Florida. The 31 women from Florida averaged 3:31:37. Also the fastest US time (2:34:08) came from Florida.
Times by Cities
Out of the 1000 top women, 28 North American cities had 5 or more runners. Just like the men, New York City had the most with 27 (3 more than the men). New York was followed by Chicago with 22 and Boston with 14. Florida wins again in average times. The city of St. Petersburg had 5 women who averaged 3:21:00.
Age Groups
Like the men, the largest age group for the women was the thirties. This breaks a trend that I've seen in several races in which the women's twenties age group is the largest. Perhaps if I looked at all the women not just the top 1000, I would see this same trend. However, there were a higher percentage of women in their twenties than men (38% vs 28%).
For average times, the three major age groups (20's, 30's, and 40's) were all within 2 minutes of each other. The fastest was the twenties age group with 3:35:44 and the slowest was the thirties with 3:37:35. Interesting, the forties age group averaged faster than the thirties with 3:36:09. For the men, the averages for these three groups were over five minutes apart with the longer times with age.
Below are the detailed stats.
Average/Best Times By Country
| Country | Number | Percent | Mean Time | Best Time |
|---|---|---|---|---|
| Australia | 2 | 0% | 3:23:12 | 3:14:56 |
| Canada | 63 | 6% | 3:39:40 | 2:57:06 |
| Costa Rica | 2 | 0% | 3:32:07 | 3:14:11 |
| El Salvador | 1 | 0% | 3:41:33 | 3:41:33 |
| Ethiopia | 1 | 0% | 2:24:43 | 2:24:43 |
| Finland | 1 | 0% | 3:47:21 | 3:47:21 |
| France | 1 | 0% | 3:41:50 | 3:41:50 |
| Guatemala | 1 | 0% | 3:45:24 | 3:45:24 |
| Japan | 8 | 1% | 3:14:13 | 2:34:32 |
| Kenya | 1 | 0% | 2:24:27 | 2:24:27 |
| Korea | 1 | 0% | 3:37:34 | 3:37:34 |
| Latvia | 1 | 0% | 2:30:16 | 2:30:16 |
| Lebanon | 1 | 0% | 3:39:56 | 3:39:56 |
| Mexico | 2 | 0% | 3:03:34 | 2:50:57 |
| Poland | 1 | 0% | 2:32:23 | 2:32:23 |
| Romania | 1 | 0% | 2:30:44 | 2:30:44 |
| Russia | 2 | 0% | 2:32:18 | 2:31:17 |
| Serbia | 1 | 0% | 2:27:34 | 2:27:34 |
| Singapore | 1 | 0% | 3:05:38 | 3:05:38 |
| USA | 906 | 91% | 3:37:19 | 2:34:08 |
| United Kingdom | 2 | 0% | 3:40:25 | 3:36:10 |
| Total | 1000 | 100% | 3:36:38 | 2:24:27 |
Average/Best Times By State
| State | Number | Percent | Mean Time | Best Time |
|---|---|---|---|---|
| CA | 90 | 9% | 3:37:22 | 2:59:19 |
| CO | 34 | 3% | 3:38:40 | 3:10:26 |
| CT | 23 | 2% | 3:35:53 | 3:03:30 |
| FL | 31 | 3% | 3:31:37 | 2:34:08 |
| IL | 43 | 4% | 3:39:18 | 3:04:35 |
| MA | 113 | 11% | 3:35:20 | 3:00:18 |
| MD | 25 | 3% | 3:37:55 | 2:58:59 |
| MI | 36 | 4% | 3:38:02 | 3:13:08 |
| MN | 26 | 3% | 3:38:11 | 3:13:27 |
| NC | 27 | 3% | 3:38:23 | 3:01:31 |
| NJ | 34 | 3% | 3:38:15 | 3:13:51 |
| NY | 73 | 7% | 3:36:06 | 2:53:37 |
| OH | 23 | 2% | 3:36:37 | 3:08:46 |
| PA | 38 | 4% | 3:36:48 | 2:50:59 |
| TX | 43 | 4% | 3:41:24 | 3:08:43 |
| VA | 28 | 3% | 3:37:31 | 3:03:22 |
| WA | 21 | 2% | 3:37:22 | 3:05:25 |
| WI | 25 | 3% | 3:37:48 | 2:55:43 |
| outside US | 94 | 9% | 3:30:03 | 2:24:27 |
| Below Cutoff | 173 | 17% | 3:37:57 | 3:02:15 |
| Total | 1000 | 100% | 3:36:38 | 2:24:27 |
Average/Best Times By Cities
| City | Number | Percent | Mean Time | Best Time |
|---|---|---|---|---|
| Albuquerque, NM | 5 | 1% | 3:33:10 | 3:15:33 |
| Atlanta, GA | 7 | 1% | 3:40:53 | 3:35:16 |
| Austin, TX | 6 | 1% | 3:40:00 | 3:28:04 |
| Boston, MA | 14 | 1% | 3:28:20 | 3:01:57 |
| Boulder, CO | 8 | 1% | 3:35:59 | 3:15:54 |
| Brighton, MA | 7 | 1% | 3:42:40 | 3:27:53 |
| Brookline, MA | 5 | 1% | 3:37:36 | 3:25:19 |
| Brooklyn, NY | 6 | 1% | 3:29:47 | 3:14:51 |
| Cambridge, MA | 7 | 1% | 3:32:29 | 3:09:59 |
| Charlotte, NC | 6 | 1% | 3:41:04 | 3:33:28 |
| Chicago, IL | 22 | 2% | 3:37:17 | 3:04:35 |
| Cincinnati, OH | 5 | 1% | 3:45:01 | 3:37:27 |
| Houston, TX | 8 | 1% | 3:39:37 | 3:08:43 |
| Lexington, KY | 5 | 1% | 3:38:57 | 3:29:07 |
| London, ON | 8 | 1% | 3:35:34 | 2:57:06 |
| Milwaukee, WI | 5 | 1% | 3:36:45 | 3:19:08 |
| Minneapolis, MN | 6 | 1% | 3:31:05 | 3:13:27 |
| New York, NY | 27 | 3% | 3:38:07 | 3:00:00 |
| Philadelphia, PA | 7 | 1% | 3:33:25 | 2:50:59 |
| Pittsburgh, PA | 5 | 1% | 3:40:38 | 3:24:24 |
| Portland, OR | 9 | 1% | 3:38:25 | 3:23:21 |
| San Diego, CA | 6 | 1% | 3:39:35 | 3:27:11 |
| San Francisco, CA | 13 | 1% | 3:42:04 | 3:21:08 |
| Seattle, WA | 5 | 1% | 3:40:15 | 3:24:40 |
| Somerville, MA | 6 | 1% | 3:37:03 | 3:28:55 |
| St. Petersburg, FL | 5 | 1% | 3:21:00 | 2:57:58 |
| Toronto, ON | 9 | 1% | 3:38:26 | 3:22:33 |
| Washington, DC | 5 | 1% | 3:31:23 | 3:13:24 |
| outside US/Canada | 31 | 3% | 3:10:31 | 2:24:27 |
| Below Cutoff | 742 | 74% | 3:37:41 | 2:34:08 |
| Total | 1000 | 100% | 3:36:38 | 2:24:27 |
Female Runners by Times - Move mouse over cells to see best times
| Ages | under 3:00 | 3:00 to 3:20 | 3:20 to 3:40 | over 3:40 |
|---|---|---|---|---|
| teens | 0 (0%) | 1 (20%) | 3 (60%) | 1 (20%) |
| twenties | 9 (2%) | 33 (9%) | 166 (43%) | 174 (46%) |
| thirties | 8 (2%) | 20 (5%) | 163 (40%) | 218 (53%) |
| forties | 4 (2%) | 16 (9%) | 82 (44%) | 85 (45%) |
| fifties | 0 (0%) | 1 (6%) | 3 (18%) | 13 (76%) |
| Total | 21 (2%) | 71 (7%) | 417 (42%) | 491 (49%) |
Average/Best Times By Female Age Groups
| Ages | Number | Percent | Mean Time | Best Time |
|---|---|---|---|---|
| teens | 5 | 1% | 3:31:24 | 3:15:54 |
| twenties | 382 | 38% | 3:35:44 | 2:24:43 |
| thirties | 409 | 41% | 3:37:35 | 2:24:27 |
| forties | 187 | 19% | 3:36:09 | 2:34:08 |
| fifties | 17 | 2% | 3:41:12 | 3:16:06 |
| Total | 1000 | 100% | 3:36:38 | 2:24:27 |
posted by Ken at
7:10 AM
2 Comments:
Based on the US city participation, it appears that the women come from more cities than the men. Same thing for the countries. The men seem to be "pocketed" around the US.
Given all the marathons in the world, I wonder what country would be World Champion? Kenya place high in the higher profile marathons but given the sheer number of US marathon, I wonder if the US would have the most first place finshes
By
Anonymous, at 8:39 AM
Thanks for the comments.
About the world champion country, that'll be something that I'll look into. One useful resource for this is at iaaf. They have all-time toplists for all types of races. I'll see if I can look at world champion countries based on these lists.
By
Ken, at 3:46 PM
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