Green Bay Marathon and Half Marathon Stats
For any marathon runners who are Packers fans, this race is for you. Runners of all the races got to run a lap around Lambeau Field in the last mile. For half marathon runners, you get to finish at the same place as the marathon runners. That meant if you were able to finish the half before about 2:15, you got to see the marathon winners.
The only problem about having the half and the full marathons start and finish at the same place is that the half marathon course has to split off from the full marathon course. This can cause confusion. It happened last March to the half marathon runner Wesley Ochoro in the Valley of the Sun Half Marathon. And it happened in Green Bay to David Busieni who finished 2nd in the marathon last Sunday after he ran about 200 yards off course where the two races forked. Luckily, the half marathon leader asked Busieni what race he was running before he lost too much ground. But this was a major factor. Before the fork at mile 8, Busieni and Wilson Komen (who won the marathon) were within seconds of each other. Then at mile 11, Komen had about a 1.5 minute lead.
The Stats
I've included both the full and half marathon stats from last Sunday's Cellcom Green Bay Marathon. The age-group/times tables now includes median times and the top-10 mean times. As explained in my last post, the top-10 mean time is the mean of the top 10 finishers in the age category. It is intended to provide for a better comparison between age groups. Both the mean and median can be affected by a large number of walkers. So the top-10 mean should allow for a better comparison between groups.
Half-Marathons More Popular with Women
There's one trend that stands out race after race, and that's the popularity of half marathons with women. There were over twice the number of men in the full marathon as women (2.16 to 1). However, for the half marathon, women outnumber men (just barely 1.01 to 1). This is very similar to Cleveland (marathon: 2.29 men to 1 woman, half: 1.13 women to 1 man).
Marathons Attract More Out-of-State Runners
And in another stat that's similar to Cleveland, 37% of the marathon runners were from outside of Wisconsin vs. 19% of the half marathon runners. Clevelands numbers were 39% and 21%.
Men Slow Down More with Age?
As I mentioned in the last post, by comparing age groups using the top-10 mean, the observation about women slowing down more with age, doesn't hold up. At least not at Green Bay. For both the full and half marathons, there was more slowdown with the men than the women for the 20s, 30s, and 40s age groups.
Total Runners by Times
Male Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.
Female Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.
Average/Best Times By Age Groups (Male and Female)
Average/Best Times By Male Age Groups
Average/Best Times By Female Age Groups
Average/Best Times By Cities (cutoff=20)
Average/Best Times By State (cutoff=20)
Total Runners by Times
Male Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.
Female Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.
Average/Best Times By Age Groups (Male and Female)
Average/Best Times By Male Age Groups
Average/Best Times By Female Age Groups
Average/Best Times By Cities (cutoff=20)
Average/Best Times By State (cutoff=20)
The above stats are based on 5/22/05 results from the Cellcom Green Bay Marathon results page.
The only problem about having the half and the full marathons start and finish at the same place is that the half marathon course has to split off from the full marathon course. This can cause confusion. It happened last March to the half marathon runner Wesley Ochoro in the Valley of the Sun Half Marathon. And it happened in Green Bay to David Busieni who finished 2nd in the marathon last Sunday after he ran about 200 yards off course where the two races forked. Luckily, the half marathon leader asked Busieni what race he was running before he lost too much ground. But this was a major factor. Before the fork at mile 8, Busieni and Wilson Komen (who won the marathon) were within seconds of each other. Then at mile 11, Komen had about a 1.5 minute lead.
The Stats
I've included both the full and half marathon stats from last Sunday's Cellcom Green Bay Marathon. The age-group/times tables now includes median times and the top-10 mean times. As explained in my last post, the top-10 mean time is the mean of the top 10 finishers in the age category. It is intended to provide for a better comparison between age groups. Both the mean and median can be affected by a large number of walkers. So the top-10 mean should allow for a better comparison between groups.
Half-Marathons More Popular with Women
There's one trend that stands out race after race, and that's the popularity of half marathons with women. There were over twice the number of men in the full marathon as women (2.16 to 1). However, for the half marathon, women outnumber men (just barely 1.01 to 1). This is very similar to Cleveland (marathon: 2.29 men to 1 woman, half: 1.13 women to 1 man).
Marathons Attract More Out-of-State Runners
And in another stat that's similar to Cleveland, 37% of the marathon runners were from outside of Wisconsin vs. 19% of the half marathon runners. Clevelands numbers were 39% and 21%.
Men Slow Down More with Age?
As I mentioned in the last post, by comparing age groups using the top-10 mean, the observation about women slowing down more with age, doesn't hold up. At least not at Green Bay. For both the full and half marathons, there was more slowdown with the men than the women for the 20s, 30s, and 40s age groups.
Full Marathon
Total Runners by Times
| under 3:00 | 3:00 to 4:00 | 4:00 to 5:00 | over 5:00 |
|---|---|---|---|
| 31 (3%) | 331 (37%) | 390 (43%) | 148 (16%) |
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 4:00 | 4:00 to 5:00 | over 5:00 |
|---|---|---|---|---|
| teens | 0 (0%) | 3 (30%) | 4 (40%) | 3 (30%) |
| twenties | 7 (9%) | 37 (47%) | 30 (38%) | 5 (6%) |
| thirties | 13 (8%) | 85 (50%) | 54 (32%) | 19 (11%) |
| forties | 3 (1%) | 110 (54%) | 80 (39%) | 12 (6%) |
| fifties | 0 (0%) | 30 (30%) | 49 (49%) | 22 (22%) |
| sixties | 0 (0%) | 2 (9%) | 12 (52%) | 9 (39%) |
| seventies | 0 (0%) | 0 (0%) | 1 (50%) | 1 (50%) |
| unknown | 1 (4%) | 3 (13%) | 10 (42%) | 10 (42%) |
| Total | 24 (4%) | 270 (44%) | 240 (39%) | 81 (13%) |
Female 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 4:00 | 4:00 to 5:00 | over 5:00 |
|---|---|---|---|---|
| teens | 0 (0%) | 0 (0%) | 5 (100%) | 0 (0%) |
| twenties | 3 (4%) | 22 (30%) | 39 (53%) | 9 (12%) |
| thirties | 2 (2%) | 25 (27%) | 49 (53%) | 16 (17%) |
| forties | 2 (2%) | 13 (14%) | 48 (51%) | 31 (33%) |
| fifties | 0 (0%) | 1 (7%) | 5 (33%) | 9 (60%) |
| sixties | 0 (0%) | 0 (0%) | 1 (50%) | 1 (50%) |
| seventies | 0 (0%) | 0 (0%) | 0 (0%) | 1 (100%) |
| unknown | 0 (0%) | 0 (0%) | 3 (100%) | 0 (0%) |
| Total | 7 (2%) | 61 (21%) | 150 (53%) | 67 (24%) |
Average/Best Times By Age Groups (Male and Female)
| Ages | # | % | Mean | Best | ||
|---|---|---|---|---|---|---|
| teens | 15 | 2% | 4:28:42 | 3:17:01 | ||
| twenties | 152 | 17% | 4:05:25 | 2:17:30 | ||
| thirties | 263 | 29% | 4:08:02 | 2:19:34 | ||
| forties | 299 | 33% | 4:14:30 | 2:37:45 | ||
| fifties | 116 | 13% | 4:34:22 | 3:12:31 | ||
| sixties | 25 | 3% | 5:04:01 | 3:44:06 | ||
| seventies | 3 | 0% | 6:08:35 | 4:46:27 | ||
| unknown | 27 | 3% | 4:50:30 | 2:56:09 | ||
| Total | 900 | 100% | 4:16:43 | 2:17:30 | ||
| Fastest Ages (by overall mean) | ||||||
| twenties | 152 | 17% | 4:05:25 | 2:17:30 | ||
Average/Best Times By Male Age Groups
| Ages | # | % | Mean | Median | Top10 Mean | Best |
|---|---|---|---|---|---|---|
| teens | 10 | 2% | 4:28:12 | 4:40:01 | 4:28:12 | 3:17:01 |
| twenties | 79 | 13% | 3:55:17 | 3:56:38 | 2:45:54 | 2:17:30 |
| thirties | 171 | 28% | 3:59:51 | 3:54:17 | 2:37:29 | 2:19:34 |
| forties | 205 | 33% | 4:01:09 | 3:53:55 | 3:01:00 | 2:37:45 |
| fifties | 101 | 16% | 4:26:24 | 4:21:14 | 3:29:30 | 3:12:31 |
| sixties | 23 | 4% | 5:05:03 | 4:54:59 | 4:24:39 | 3:44:06 |
| seventies | 2 | 0% | 5:59:09 | 5:59:09 | 5:59:09 | 4:46:27 |
| unknown | 24 | 4% | 4:55:30 | 4:37:16 | 3:51:35 | 2:56:09 |
| Total | 615 | 100% | 4:09:31 | 4:03:26 | 2:28:10 | 2:17:30 |
| Fastest Ages (by overall mean) | ||||||
| twenties | 79 | 13% | 3:55:17 | 3:56:38 | 2:45:54 | 2:17:30 |
Average/Best Times By Female Age Groups
| Ages | # | % | Mean | Median | Top10 Mean | Best |
|---|---|---|---|---|---|---|
| teens | 5 | 2% | 4:29:44 | 4:42:14 | 4:29:44 | 4:06:35 |
| twenties | 73 | 26% | 4:16:24 | 4:18:41 | 3:17:29 | 2:52:37 |
| thirties | 92 | 32% | 4:23:14 | 4:16:28 | 3:24:03 | 2:40:57 |
| forties | 94 | 33% | 4:43:39 | 4:36:36 | 3:20:25 | 2:53:22 |
| fifties | 15 | 5% | 5:28:02 | 5:03:53 | 4:56:48 | 3:50:29 |
| sixties | 2 | 1% | 4:52:02 | 4:52:02 | 4:52:02 | 4:29:36 |
| seventies | 1 | 0% | 6:27:27 | 6:27:27 | 6:27:27 | 6:27:27 |
| unknown | 3 | 1% | 4:10:34 | 4:08:41 | 4:10:34 | 4:05:49 |
| Total | 285 | 100% | 4:32:15 | 4:22:26 | 2:57:56 | 2:40:57 |
| Fastest Ages (by overall mean) | ||||||
| twenties | 73 | 26% | 4:16:24 | 4:18:41 | 3:17:29 | 2:52:37 |
Average/Best Times By Cities (cutoff=20)
| City | # | % | Mean | Best | ||
|---|---|---|---|---|---|---|
| Appleton, WI | 35 | 4% | 4:15:20 | 3:02:37 | ||
| De Pere, WI | 34 | 4% | 4:18:25 | 2:47:50 | ||
| Green Bay, WI | 117 | 13% | 4:13:10 | 2:42:18 | ||
| Madison, WI | 24 | 3% | 4:08:33 | 3:09:52 | ||
| Below Cutoff | 690 | 77% | 4:17:35 | 2:17:30 | ||
| Total | 900 | 100% | 4:16:43 | 2:17:30 | ||
| Fastest City (by overall mean) | ||||||
| Madison, WI | 24 | 3% | 4:08:33 | 3:09:52 | ||
Average/Best Times By State (cutoff=20)
| State | # | % | Mean | Best | ||
|---|---|---|---|---|---|---|
| IA | 32 | 4% | 4:35:27 | 3:06:35 | ||
| IL | 83 | 9% | 4:18:49 | 2:51:07 | ||
| MI | 39 | 4% | 4:05:35 | 2:37:45 | ||
| MN | 64 | 7% | 4:13:27 | 2:31:21 | ||
| WI | 569 | 63% | 4:16:10 | 2:31:32 | ||
| Below Cutoff | 113 | 13% | 4:18:18 | 2:17:30 | ||
| Total | 900 | 100% | 4:16:43 | 2:17:30 | ||
| Fastest State (by overall mean) | ||||||
| MI | 39 | 4% | 4:05:35 | 2:37:45 | ||
Half-Marathon
Total Runners by Times
| under 1:30 | 1:30 to 2:00 | 2:00 to 2:30 | over 2:30 |
|---|---|---|---|
| 62 (4%) | 621 (40%) | 616 (39%) | 262 (17%) |
Male Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.
| Ages | under 1:30 | 1:30 to 2:00 | 2:00 to 2:30 | over 2:30 |
|---|---|---|---|---|
| teens | 3 (13%) | 14 (61%) | 6 (26%) | 0 (0%) |
| twenties | 21 (16%) | 68 (52%) | 30 (23%) | 13 (10%) |
| thirties | 19 (9%) | 121 (57%) | 58 (27%) | 13 (6%) |
| forties | 10 (5%) | 135 (66%) | 48 (23%) | 12 (6%) |
| fifties | 3 (3%) | 47 (49%) | 33 (35%) | 12 (13%) |
| sixties | 0 (0%) | 5 (17%) | 16 (55%) | 8 (28%) |
| seventies | 0 (0%) | 0 (0%) | 1 (20%) | 4 (80%) |
| unknown | 1 (1%) | 25 (33%) | 36 (48%) | 13 (17%) |
| Total | 57 (7%) | 415 (54%) | 228 (29%) | 75 (10%) |
Female Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.
| Ages | under 1:30 | 1:30 to 2:00 | 2:00 to 2:30 | over 2:30 |
|---|---|---|---|---|
| teens | 0 (0%) | 12 (38%) | 14 (44%) | 6 (19%) |
| twenties | 3 (2%) | 55 (28%) | 112 (56%) | 30 (15%) |
| thirties | 0 (0%) | 85 (31%) | 141 (51%) | 48 (18%) |
| forties | 2 (1%) | 46 (23%) | 87 (44%) | 63 (32%) |
| fifties | 0 (0%) | 5 (10%) | 20 (41%) | 24 (49%) |
| sixties | 0 (0%) | 0 (0%) | 5 (56%) | 4 (44%) |
| seventies | 0 (0%) | 0 (0%) | 1 (100%) | 0 (0%) |
| unknown | 0 (0%) | 3 (13%) | 8 (35%) | 12 (52%) |
| Total | 5 (1%) | 206 (26%) | 388 (49%) | 187 (24%) |
Average/Best Times By Age Groups (Male and Female)
| Ages | # | % | Mean | Best | ||
|---|---|---|---|---|---|---|
| teens | 55 | 4% | 2:04:15 | 1:21:08 | ||
| twenties | 332 | 21% | 2:05:03 | 1:06:55 | ||
| thirties | 485 | 31% | 2:05:07 | 1:08:48 | ||
| forties | 403 | 26% | 2:09:06 | 1:19:50 | ||
| fifties | 144 | 9% | 2:16:29 | 1:25:05 | ||
| sixties | 38 | 2% | 2:28:17 | 1:43:14 | ||
| seventies | 6 | 0% | 2:52:43 | 2:06:39 | ||
| unknown | 98 | 6% | 2:17:42 | 1:29:51 | ||
| Total | 1561 | 100% | 2:08:41 | 1:06:55 | ||
| Fastest Ages (by overall mean) | ||||||
| teens | 55 | 4% | 2:04:15 | 1:21:08 | ||
Average/Best Times By Male Age Groups
| Ages | # | % | Mean | Median | Top10 Mean | Best |
|---|---|---|---|---|---|---|
| teens | 23 | 3% | 1:49:13 | 1:42:07 | 1:34:04 | 1:21:08 |
| twenties | 132 | 17% | 1:55:34 | 1:52:38 | 1:11:31 | 1:06:55 |
| thirties | 211 | 27% | 1:55:14 | 1:52:42 | 1:13:43 | 1:08:48 |
| forties | 205 | 26% | 1:55:40 | 1:50:51 | 1:26:15 | 1:19:50 |
| fifties | 95 | 12% | 2:05:58 | 1:59:00 | 1:33:51 | 1:25:05 |
| sixties | 29 | 4% | 2:23:27 | 2:13:27 | 1:55:40 | 1:43:14 |
| seventies | 5 | 1% | 2:57:18 | 3:10:17 | 2:57:18 | 2:06:39 |
| unknown | 75 | 10% | 2:11:51 | 2:07:00 | 1:41:19 | 1:29:51 |
| Total | 775 | 100% | 1:59:36 | 1:54:29 | 1:09:12 | 1:06:55 |
| Fastest Ages (by overall mean) | ||||||
| teens | 23 | 3% | 1:49:13 | 1:42:07 | 1:34:04 | 1:21:08 |
Average/Best Times By Female Age Groups
| Ages | # | % | Mean | Median | Top10 Mean | Best |
|---|---|---|---|---|---|---|
| teens | 32 | 4% | 2:15:03 | 2:09:32 | 1:51:03 | 1:40:00 |
| twenties | 200 | 25% | 2:11:19 | 2:06:48 | 1:33:07 | 1:20:36 |
| thirties | 274 | 35% | 2:12:44 | 2:06:51 | 1:38:08 | 1:35:09 |
| forties | 198 | 25% | 2:23:01 | 2:15:27 | 1:37:50 | 1:25:13 |
| fifties | 49 | 6% | 2:36:52 | 2:29:18 | 1:57:21 | 1:38:22 |
| sixties | 9 | 1% | 2:43:52 | 2:28:59 | 2:43:52 | 2:17:30 |
| seventies | 1 | 0% | 2:29:46 | 2:29:46 | 2:29:46 | 2:29:46 |
| unknown | 23 | 3% | 2:36:47 | 2:31:53 | 2:10:59 | 1:42:50 |
| Total | 786 | 100% | 2:17:39 | 2:10:44 | 1:29:49 | 1:20:36 |
| Fastest Ages (by overall mean) | ||||||
| twenties | 200 | 25% | 2:11:19 | 2:06:48 | 1:33:07 | 1:20:36 |
Average/Best Times By Cities (cutoff=20)
| City | # | % | Mean | Best | ||
|---|---|---|---|---|---|---|
| Appleton, WI | 94 | 6% | 2:04:47 | 1:15:07 | ||
| De Pere, WI | 61 | 4% | 2:12:57 | 1:33:31 | ||
| Depere, WI | 24 | 2% | 2:07:17 | 1:34:23 | ||
| Green Bay, WI | 306 | 20% | 2:13:13 | 1:18:41 | ||
| Madison, WI | 26 | 2% | 1:59:35 | 1:07:07 | ||
| Milwaukee, WI | 39 | 2% | 2:08:11 | 1:08:48 | ||
| Neenah, WI | 31 | 2% | 2:06:26 | 1:13:18 | ||
| Oshkosh, WI | 33 | 2% | 2:02:41 | 1:18:45 | ||
| Sheboygan, WI | 27 | 2% | 2:05:48 | 1:19:30 | ||
| Below Cutoff | 920 | 59% | 2:07:59 | 1:06:55 | ||
| Total | 1561 | 100% | 2:08:41 | 1:06:55 | ||
| Fastest City (by overall mean) | ||||||
| Madison, WI | 26 | 2% | 1:59:35 | 1:07:07 | ||
Average/Best Times By State (cutoff=20)
| State | # | % | Mean | Best | ||
|---|---|---|---|---|---|---|
| IL | 79 | 5% | 2:09:40 | 1:21:08 | ||
| MI | 74 | 5% | 2:12:54 | 1:16:21 | ||
| MN | 72 | 5% | 2:07:15 | 1:25:13 | ||
| WI | 1257 | 81% | 2:08:03 | 1:07:07 | ||
| Below Cutoff | 79 | 5% | 2:15:13 | 1:06:55 | ||
| Total | 1561 | 100% | 2:08:41 | 1:06:55 | ||
| Fastest State (by overall mean) | ||||||
| MN | 72 | 5% | 2:07:15 | 1:25:13 | ||
The above stats are based on 5/22/05 results from the Cellcom Green Bay Marathon results page.
posted by Ken at
5:20 PM
4 Comments:
I'm a Packers fan and want to do this marathon so bad! When they first announced that the marathon would go through the stadium, I put it on my list. They have reworked the course because you used to run through the stadium from end to end.
One day though.
By
Anonymous, at 6:11 PM
Do you know why they changed the course like this? Does the race finish at some park now where it's easier for them to have the post-race party?
By
Ken, at 7:14 AM
I have no idea...
The stadium used to be able mile 10-12 on the course and you just ran through it. So I don't know the change, although I would rather run a lap than just through it. You're in there longer!
By
Anonymous, at 7:26 AM
I think they may have changed the course because of the construction during the rebuilding of Lambeau Field. This year the half marathon and marathon will run right through the stadium. The course homepage (www.greenbaymarathon.com) details both courses, and Lambeau is at the very end before the finish line.
By
Anonymous, at 9:54 AM
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