Road Race Stats - Marathons & Other Running Races

Monday, November 07, 2005

2005 New York City Marathon Stats

I'm glad to see some excitement yesterday at the New York city Marathon. Here's a quote from the Runner's World summary of the race:
Tergat, the world-record holder in the marathon, outleaned Hendrick Ramaala at the finish line in the closest, most dramatic finish ever in a major marathon competition.

Paul Tergat finished with a time of 2:09:30. I also found some interesting facts about Tergat from SI.com:
The 36-year-old Tergat, one of 17 children, is the ninth straight African man to win in New York. Until Sunday, his only win in a major marathon was at the 2003 Berlin Marathon, where he set the marathon world record of 2:04:55.

The women's winner was Jelena Prokopcuka from Latvia who won the womens' race with a time of 2:24:41. This is her first major marathon win. She was fifth last year in the New York Marathon.

Tergat earned $125K in winnings and Prokopcuka earned a marathon-record of $160K.

The Stats

There were a total of 36872 finishers. 24812 (67%) were males, 12060 were females. Since there were so many runners, I've limited my stats to the top 2000 men and top 2000 women. Instead of pulling the race results from New York City Marathon website, I used the results at the Marathon Guide since the results are in an easier format for me.

For both the men and the women, the 20s age group was the fastest. However, most of the top 2000 runners for both genders were from the 30s age group.

I was surprised to see the number of marathon runners from outside the US. For the top 2000 men, 57% were from outside the US. The non-US nation with the most runners was Italy with 14%.

There weren't quite as many non-US women marathon runners. Out of the top 2000 women finishers, 33% were from outside the US. Like the men, Italy had the most with 5%.

Below are the detailed stats based on the results of the 2005 New York City Marathon results from the Marathon Guide.

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

Agesunder 2:302:30 to 2:452:45 to 3:00over 3:00
13-190 (0%)0 (0%)1 (20%)4 (80%)
20-2914 (4%)28 (9%)69 (21%)211 (66%)
30-3921 (3%)51 (6%)188 (24%)527 (67%)
40-494 (1%)27 (4%)117 (17%)538 (78%)
50-590 (0%)0 (0%)23 (12%)168 (88%)
60-690 (0%)0 (0%)1 (11%)8 (89%)
Total39 (2%)106 (5%)399 (20%)1456 (73%)


Average/Best Times By Male Age Groups

AgesNumberPercentMean TimeBest Time
13-1950%3:11:082:57:42
20-2932216%3:03:502:11:01
30-3978739%3:05:302:09:30
40-4968634%3:09:472:24:55
50-5919110%3:13:102:45:27
60-6990%3:17:072:56:45
Total2000100%3:07:302:09:30
Fastest Ages (by average)
20-2932216%3:03:502:11:01


Average/Best Male Times By State (cutoff=10)

StateNumberPercentMean TimeBest Time
CA553%3:05:082:09:56
CO201%3:02:022:25:32
CT241%3:06:102:42:41
GA121%3:09:422:39:27
IL241%3:09:492:40:18
MA442%3:07:232:39:35
MD141%3:08:162:50:17
MI131%3:05:092:17:14
NJ774%3:09:492:14:28
NY40020%3:06:402:18:58
OH111%3:05:042:45:27
PA342%3:08:582:50:49
VA151%3:10:112:47:39
WA131%3:09:132:33:25
outside US114157%3:07:472:09:30
Below Cutoff1035%3:07:032:11:24
Total2000100%3:07:302:09:30
Fastest State (by average)
CO201%3:02:022:25:32


Average/Best Male Times By Country (cutoff=10)

CountryNumberPercentMean TimeBest Time
AUS111%3:03:182:24:25
AUT211%3:11:302:52:46
BEL131%3:08:352:49:02
CAN472%3:07:222:34:49
DEN191%3:07:262:26:58
ESP513%3:05:412:20:40
FRA19810%3:08:432:29:42
GBR1095%3:09:172:13:29
GER1176%3:09:532:33:57
IRL161%3:09:382:33:13
ITA28814%3:07:352:11:33
JPN191%3:07:462:20:59
MEX181%3:07:192:37:18
NED432%3:14:222:38:37
NOR241%3:04:472:46:08
SUI372%3:05:092:11:44
SWE201%2:59:402:26:56
USA85143%3:07:082:09:56
Below Cutoff985%3:03:202:09:30
Total2000100%3:07:302:09:30
Fastest Country (by average)
SWE201%2:59:402:26:56


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

Agesunder 3:003:00 to 3:203:20 to 3:40over 3:40
13-190 (0%)0 (0%)1 (25%)3 (75%)
20-2916 (3%)41 (8%)142 (27%)329 (62%)
30-3917 (2%)56 (7%)198 (24%)565 (68%)
40-496 (1%)32 (6%)93 (18%)393 (75%)
50-590 (0%)6 (6%)21 (22%)70 (72%)
60-690 (0%)0 (0%)0 (0%)4 (100%)
70-790 (0%)0 (0%)0 (0%)2 (100%)
Total39 (2%)135 (7%)455 (23%)1366 (68%)


Average/Best Times By Female Age Groups

AgesNumberPercentMean TimeBest Time
13-1940%3:45:083:30:07
20-2952826%3:43:172:24:41
30-3983642%3:44:342:24:55
40-4952426%3:47:142:40:10
50-59975%3:46:583:08:42
60-6940%3:55:283:44:48
70-7920%3:49:003:46:18
Total1995100%3:45:042:24:41
Fastest Ages (by average)
20-2952826%3:43:172:24:41


Average/Best Female Times By State (cutoff=10)

StateNumberPercentMean TimeBest Time
AZ101%3:42:482:49:32
CA995%3:46:442:37:07
CO382%3:39:292:41:43
CT462%3:41:572:33:59
DC181%3:41:593:13:02
FL412%3:47:113:13:39
GA241%3:44:593:08:30
IL322%3:45:513:09:44
MA744%3:47:143:05:52
MD161%3:43:062:54:53
MI141%3:44:233:13:22
MN141%3:39:553:03:10
NC261%3:46:053:17:59
NJ1116%3:45:152:56:06
NY54527%3:46:072:40:10
OH151%3:45:013:20:50
OR121%3:42:573:19:58
PA342%3:45:282:52:17
TX352%3:47:543:24:46
VA161%3:48:193:17:35
WA191%3:43:453:05:16
outside US64832%3:44:342:24:41
Below Cutoff1085%3:42:282:49:06
Total1995100%3:45:042:24:41
Fastest State (by average)
CO382%3:39:292:41:43


Average/Best Female Times By Country (cutoff=10)

CountryNumberPercentMean TimeBest Time
AUS111%3:46:483:14:39
AUT121%3:47:513:13:57
CAN482%3:47:033:06:40
CHI101%3:42:213:09:16
FRA744%3:45:452:45:58
GBR894%3:46:232:46:47
GER563%3:49:063:13:44
IRL111%3:47:533:18:12
ITA915%3:43:582:27:15
JPN241%3:44:292:43:55
MEX271%3:49:022:33:19
NED613%3:48:322:28:28
NOR121%3:40:203:07:52
SUI181%3:47:223:09:21
SWE151%3:43:522:59:43
USA134167%3:45:222:33:59
Below Cutoff955%3:33:262:24:41
Total1995100%3:45:042:24:41
Fastest Country (by average)
NOR121%3:40:203:07:52

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