Road Race Stats - Marathons & Other Running Races

Tuesday, May 17, 2005

Fifth Third River Bank Run 25K

DeReuck's Stats

The standout of last Saturday's (5/14/05) Firth Third River Bank Run was with the women. Colleen DeReuck, 41, broke the the world masters record with a time of 1:25:15. It was the second fastest 25K ever by an American woman of any age. It was the third fastest women's time in the event's 28-year history. It was over 1.5 minute faster than her nearest competitor (Albina Ivanova, 27 of Russia) who finished 1:26:53. It was over 4.5 minutes faster than her nearest Masters competitor (1:29:50). She won $4K for the US title, $4K for the Open title, and another $1.5K for the Masters title.

DeReuck mentions that she ran with a group of guys in the first half which resulted in a faster than planned first half. Her first half time was 42:33. But she was able to keep up the pace. Her second half time was only 8 seconds slower (42:41).

More on DeReuck at mlive.com.

Ochoro's Stats - One Week Recovery?

On the men's side, three Kenyans (Simon Wangai, Julius Kibet, and Wesley Ochoro) took the top three places with times of 1:13:27, 1:13:50, and 1:15:14. Ochoro had just ran the Indy Half-Marathon the previous Saturday. Is a week enough recovery time? I thought it would be interesting to compare the pace of Ochoro between the two races. His Indy Half-Marathon pace was 4:51/mile (finished first with 1:03:31). His River Bank pace was the same at 4:51/mile. It was somewhat cooler in Grand Rapids: 55°F and rainy vs 63°F with mostly cloudy skies in Indy. I would think the rain would tend to slow you down due to the wet shoes and slick roads. However, his competition in the River Bank probably helped improved his time. If he had another week or two of recovery, how much faster could he had run the race?

More on the winners at mlive.com and at RunningUSA.org.

Age-Graded Results

One thing a little odd to me about the stats is the Age-Graded percentage listed for DeReuck at the results page. It's listed at 96.4%. You would think this should be closer to 100% since she broke the world masters record. According to the Age-Graded definition, 100% should be approximately world record level. On the men's side, the 26 year-old winner, Simon Wangai, has a Age-Graded percentage of 97.5%. The second place winner, 23 year-old Julius Kibet, has a percentage of 97.0%. Seems like the DeReuck should have had a higher Age-Graded result.

I had been thinking that the Age-Grade results may be a good tool to use in comparing races. It could simplify comparing overall temperature effects or other conditions. So I'll start looking at Age-Graded results. For this race, I computed the mean overall Age-Graded percentage of 57.6%.

Fifth Third River Bank Run 25K Stats

Below are the full stats for the 5/14/05 race. One common trend continues and that is women in their twenties are the largest women's age group. Also, they outnumber their males counterparts 561 to 448.

Women Run Better in the Rain?

I also find it interesting how well the women did overall. Women's mean time was 2:27:47 vs the men's mean of 2:13:26. That's a ratio of 1.11. Compare that to the Indy Half-Marathon. Women's mean time was 2:43:38 vs. the men's mean of 2:15:56 (ratio of 1.20). Actually, both men and women had a faster mean time in the 25K vs the Half. It was cooler in the River Bank by about 8°F (63°F at Indy vs. 55°F at Grand Rapids. Perhaps the rain at the River Bank race kept away more of the recreational runners. Also, it should be mentioned that age demographics may have an important factor in these comparisons.

Total Runners by Times

under 2:002:00 to 2:152:15 to 2:30over 2:30
868 (21%)984 (23%)1148 (27%)1223 (29%)


Male Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.

Agesunder 2:002:00 to 2:152:15 to 2:30over 2:30
teens25 (38%)17 (26%)13 (20%)10 (15%)
twenties178 (40%)107 (24%)93 (21%)70 (16%)
thirties227 (33%)195 (28%)151 (22%)116 (17%)
forties223 (29%)221 (28%)201 (26%)137 (18%)
fifties93 (20%)110 (24%)133 (29%)120 (26%)
sixties7 (8%)15 (17%)27 (31%)37 (43%)
seventies0 (0%)3 (27%)3 (27%)5 (45%)
eighties+0 (0%)0 (0%)0 (0%)1 (100%)
unknown0 (0%)1 (33%)1 (33%)1 (33%)
Total753 (30%)669 (26%)622 (24%)497 (20%)


Female Runners by Times - Move mouse over cells to see median times. Top 3 times also shown in left columns.

Agesunder 2:002:00 to 2:152:15 to 2:30over 2:30
teens0 (0%)12 (21%)12 (21%)33 (58%)
twenties53 (9%)109 (19%)173 (31%)226 (40%)
thirties39 (7%)102 (19%)182 (34%)205 (39%)
forties20 (5%)72 (19%)128 (33%)167 (43%)
fifties3 (2%)18 (14%)29 (22%)83 (62%)
sixties0 (0%)2 (13%)2 (13%)11 (73%)
unknown0 (0%)0 (0%)0 (0%)1 (100%)
Total115 (7%)315 (19%)526 (31%)726 (43%)


Average/Best Times By Age Groups (Male and Female)

AgesNumberPercentMean TimeBest Time
teens1223%2:20:011:33:34
twenties100924%2:18:021:13:27
thirties121729%2:17:431:19:15
forties116928%2:18:191:17:12
fifties58914%2:23:111:34:45
sixties1012%2:30:551:42:09
seventies110%2:30:092:06:30
eighties+10%3:00:313:00:31
unknown40%2:26:062:14:18
Total4223100%2:19:091:13:27
Fastest Ages (by average)
thirties121729%2:17:431:19:15


Average/Best Times By Male Age Groups

AgesNumberPercentMean TimeBest Time
teens653%2:08:571:33:34
twenties44818%2:08:171:13:27
thirties68927%2:11:171:19:15
forties78231%2:13:041:17:12
fifties45618%2:19:321:34:45
sixties863%2:28:581:42:09
seventies110%2:30:092:06:30
eighties+10%3:00:313:00:31
unknown30%2:23:452:14:18
Total2541100%2:13:261:13:27
Fastest Ages (by average)
twenties44818%2:08:171:13:27


Average/Best Times By Female Age Groups

AgesNumberPercentMean TimeBest Time
teens573%2:32:402:04:10
twenties56133%2:25:491:26:53
thirties52831%2:26:071:28:29
forties38723%2:28:561:25:15
fifties1338%2:35:431:57:51
sixties151%2:42:052:04:08
unknown10%2:33:092:33:09
Total1682100%2:27:471:25:15
Fastest Ages (by average)
twenties56133%2:25:491:26:53


Average/Best Times By Cities (cutoff=40)

CityNumberPercentMean TimeBest Time
ADA, MI832%2:18:451:34:45
BELMONT, MI451%2:17:291:48:33
BYRON CENTER, MI571%2:21:241:45:48
CALEDONIA, MI471%2:14:121:27:52
GRAND HAVEN, MI621%2:20:301:39:10
GRAND RAPIDS, MI91222%2:21:141:24:28
GRANDVILLE, MI1103%2:19:361:32:13
HOLLAND, MI2536%2:17:301:31:19
HUDSONVILLE, MI1383%2:20:571:41:44
JENISON, MI1032%2:25:051:31:39
KALAMAZOO, MI672%2:17:141:43:14
KENTWOOD, MI902%2:18:241:32:14
LANSING, MI642%2:22:291:21:02
LOWELL, MI441%2:10:551:26:06
MUSKEGON, MI621%2:22:171:48:52
ROCKFORD, MI1784%2:19:391:36:32
SPRING LAKE, MI491%2:21:321:40:27
WYOMING, MI1183%2:20:181:24:00
ZEELAND, MI752%2:22:191:35:39
Below Cutoff166639%2:17:331:13:27
Total4223100%2:19:091:13:27
Fastest City (by average)
LOWELL, MI441%2:10:551:26:06


Average/Best Times By State (cutoff=10)

StateNumberPercentMean TimeBest Time
CO120%1:54:241:24:12
IL832%2:17:191:31:05
IN281%2:16:191:29:52
MI397494%2:19:431:15:27
OH180%2:08:291:33:40
WI130%2:06:161:21:43
Below Cutoff952%2:04:461:13:27
Total4223100%2:19:091:13:27
Fastest State (by average)
CO120%1:54:241:24:12


These stats are based on results from the Fifth Third River Bank Run: 05/14/2005 results page.

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4 Comments:

  • Ken,

    When comparing mean times (or median times) between event, you have to consider whether the events are "walker friendly." The Mini, for example, attracts a huge population of walkers. The course is kept open for anyone who can sustain an 18:00 min/mile pace from the time the last starter crosses the start line. I have no idea whether the River Bank 25k attracts many walkers or not.

    Example: A marathon with a cut-off time at 5:00:00 will almost always have faster mean and median times than an event with a cut-off time of 8:00:00.

    Also, you should consider field size when comparing times across races. When you have a "mega" field size like the Mini or certain big city marathons, the field tends to grow at the back, not the front.

    I believe the walker factor and the field size factor are probably more explanatory of faster times at the River Bank 25k than the weather factor.

    Just more fun stuff to think about...

    Jeff

    By Anonymous Jeff, at 7:29 PM  

  • Jeff, that is a good point about "walker friendly" races, field sizes, and cut-off times. When I started looking at temperature effects in previous posts, I included 100th place and 500th place times. I'm thinking these numbers might be less prone to the number of walkers.

    Your comment about field sizes reminds me of the LA Marathon which had lots of walkers. There were some finish times of over 12hrs. And they have one of the slowest average times of the major marathons.

    By Blogger Ken, at 5:30 AM  

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