2006 Freescale Austin Marathon - Men's Stats
The temperatures were around 30°F with some frozen drizzle, but the marathon went on as planned except for a 30 minute delay. According to this RunTex news article, there were many PRs. The winner for the men was Mindi Pukstas of Lithuania with a personal record time of 2:13:43. He just finished ahead of Abebe Yimer of Ethiopia who had a time of 2:13:52 which was a PR by more than 4 minutes.
The most extraordinary performance was the women's winner, Tatyana Pozdnyakova of the Ukraine, who won with a time of 2:34:23. Her winning time is five minutes slower than her PR, but she has an excuse: Pozdnyakova is 50 years old. Her time might be a new world age group record.
One thing to note is that the number of timed finishers went down this year. This is likely due to the weather. However, the total number of finishers have also gone down the last two years so it might not all be weather related. The percentage of females went up from last year but is slightly less than in 2004 and 2003. The details are shown below:
The Statistics for the Men
The Freescale Marathon website changed formats on me so I had to use the race data from the MarathonGuide.com. This slowed me down, So for today's post, I only have the men's stats. Later in the week, I'll put together the women's stats.
The cold weather seemed to have a positive effect on finish times. The total mean time was 4:20:44 last year. This year it was 4:15:31. There was even more difference in the total median times: 2006 4:05:40 vs 2005 4:15:13. Some of this difference may be due to the serious runners being more likely to ignore the bad weather than the recreational runners. However, the number of sub-3:00-hour and sub-4:00-hour runners also increased:
I'll have to use this data to continue my look at temperature effects on running. My guess is that there won't be much difference between temperatures of 30°F to 55°F. Over 55°F I would think the times would start to slow down. The question I would like to find is how should you adjust your pace based on temperature. The more precise you are able to select your ideal pace, the better race you should have. Refer to this post for some detailed stats on this issue.
Below are the stats for the men stats from the 2006 Austin Marathon data at MarathonGuide.com. Note, some runners were excluded who were listed as unknowns without names or ages. Last year's stats are available in this previous post.
Male Runners - 2006 Freescale Austin Marathon
Total Runners by Times
Male Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.
Average/Best Times By Male Age Groups
Average/Best Times By State (cutoff=10)
The most extraordinary performance was the women's winner, Tatyana Pozdnyakova of the Ukraine, who won with a time of 2:34:23. Her winning time is five minutes slower than her PR, but she has an excuse: Pozdnyakova is 50 years old. Her time might be a new world age group record.
One thing to note is that the number of timed finishers went down this year. This is likely due to the weather. However, the total number of finishers have also gone down the last two years so it might not all be weather related. The percentage of females went up from last year but is slightly less than in 2004 and 2003. The details are shown below:
Year Total Male Female % Female Total Change
2006 4788 2866 1922 40.1% -3.6%
2005 4967 3034 1930 38.9% -5.2%
2004 5238 3126 2112 40.3% -2.3%
2003 5360 3171 2189 40.8%
The Statistics for the Men
The Freescale Marathon website changed formats on me so I had to use the race data from the MarathonGuide.com. This slowed me down, So for today's post, I only have the men's stats. Later in the week, I'll put together the women's stats.
The cold weather seemed to have a positive effect on finish times. The total mean time was 4:20:44 last year. This year it was 4:15:31. There was even more difference in the total median times: 2006 4:05:40 vs 2005 4:15:13. Some of this difference may be due to the serious runners being more likely to ignore the bad weather than the recreational runners. However, the number of sub-3:00-hour and sub-4:00-hour runners also increased:
Year Sub-3:00 Sub-4:00
2006 153 (5%) 433 (15%)
2005 106 (4%) 373 (12%)
I'll have to use this data to continue my look at temperature effects on running. My guess is that there won't be much difference between temperatures of 30°F to 55°F. Over 55°F I would think the times would start to slow down. The question I would like to find is how should you adjust your pace based on temperature. The more precise you are able to select your ideal pace, the better race you should have. Refer to this post for some detailed stats on this issue.
Below are the stats for the men stats from the 2006 Austin Marathon data at MarathonGuide.com. Note, some runners were excluded who were listed as unknowns without names or ages. Last year's stats are available in this previous post.
Male Runners - 2006 Freescale Austin Marathon
Total Runners by Times
| under 3:00 | 3:00 to 3:30 | 3:30 to 4:00 | over 4:00 |
|---|---|---|---|
| 153 (5%) | 433 (15%) | 708 (25%) | 1540 (54%) |
Male Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.
| Ages | under 3:00 | 3:00 to 3:30 | 3:30 to 4:00 | over 4:00 |
|---|---|---|---|---|
| 13-19 | 1 (5%) | 5 (24%) | 3 (14%) | 12 (57%) |
| 20-29 | 46 (10%) | 75 (16%) | 105 (22%) | 251 (53%) |
| 30-39 | 63 (7%) | 147 (16%) | 232 (26%) | 462 (51%) |
| 40-49 | 37 (4%) | 162 (18%) | 240 (27%) | 454 (51%) |
| 50-59 | 5 (1%) | 37 (9%) | 116 (27%) | 271 (63%) |
| 60-69 | 1 (1%) | 7 (7%) | 11 (11%) | 80 (81%) |
| 70-79 | 0 (0%) | 0 (0%) | 1 (11%) | 8 (89%) |
| 80+ | 0 (0%) | 0 (0%) | 0 (0%) | 2 (100%) |
| Total | 153 (5%) | 433 (15%) | 708 (25%) | 1540 (54%) |
Average/Best Times By Male Age Groups
| Ages | Number | Percent | Mean Time | Best Time |
|---|---|---|---|---|
| 13-19 | 21 | 1% | 4:13:47 | 2:55:30 |
| 20-29 | 477 | 17% | 4:09:47 | 2:13:43 |
| 30-39 | 904 | 32% | 4:09:45 | 2:14:54 |
| 40-49 | 893 | 32% | 4:12:03 | 2:26:42 |
| 50-59 | 429 | 15% | 4:29:33 | 2:51:43 |
| 60-69 | 99 | 3% | 4:59:09 | 2:55:18 |
| 70-79 | 9 | 0% | 5:17:57 | 3:49:25 |
| 80+ | 2 | 0% | 5:33:36 | 5:33:36 |
| Total | 2834 | 100% | 4:15:31 | 2:13:43 |
| Fastest Ages (by average) | ||||
| 30-39 | 904 | 32% | 4:09:45 | 2:14:54 |
Average/Best Times By State (cutoff=10)
| State | Number | Percent | Mean Time | Best Time |
|---|---|---|---|---|
| California | 48 | 2% | 4:22:23 | 2:19:45 |
| Colorado | 28 | 1% | 4:35:03 | 2:45:05 |
| Florida | 24 | 1% | 3:54:47 | 3:01:38 |
| Georgia | 13 | 0% | 4:03:49 | 2:50:19 |
| Illinois | 28 | 1% | 3:56:13 | 2:28:49 |
| Kansas | 23 | 1% | 3:43:55 | 2:27:50 |
| Louisiana | 40 | 1% | 4:06:57 | 2:58:25 |
| Maryland | 11 | 0% | 4:20:53 | 3:22:22 |
| Massachusetts | 12 | 0% | 3:06:37 | 2:15:28 |
| Michigan | 12 | 0% | 3:16:07 | 2:20:41 |
| Missouri | 19 | 1% | 3:42:22 | 2:34:28 |
| New Mexico | 13 | 0% | 3:42:16 | 2:37:15 |
| New York | 23 | 1% | 4:01:03 | 2:59:15 |
| North Carolina | 13 | 0% | 4:13:23 | 2:19:47 |
| Ohio | 15 | 1% | 3:51:02 | 2:14:28 |
| Oklahoma | 41 | 1% | 4:20:11 | 2:43:06 |
| Oregon | 12 | 0% | 3:07:32 | 2:22:18 |
| Pennsylvania | 16 | 1% | 3:50:55 | 2:24:32 |
| Tennessee | 10 | 0% | 3:48:43 | 3:00:28 |
| Texas | 2213 | 78% | 4:19:37 | 2:19:47 |
| Unknown | 103 | 4% | 4:00:47 | 2:13:43 |
| Virginia | 10 | 0% | 4:10:37 | 2:17:37 |
| Washington | 10 | 0% | 4:13:35 | 2:53:33 |
| Below Cutoff | 97 | 3% | 3:59:22 | 2:14:16 |
| Total | 2834 | 100% | 4:15:31 | 2:13:43 |
| Fastest State (by average) | ||||
| Massachusetts | 12 | 0% | 3:06:37 | 2:15:28 |
posted by Ken at
6:33 AM
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