Key words
cardiorespiratory fitness - endurance - men - history - running - performance
Introduction
Among the road-based running races, marathon running is of high popularity. For example,
the number of overall finishers in marathon races held in the United States of America
was 507,600 in 2016, slightly lower than in 2015 with 509,000 finishers. A total of
1,100 marathons were held in both 2016 and 2015 (www.runningusa.org/marathon-report-2017).
Marathon running for men has been part of the Olympic Games since 1896 (www.sports-reference.com/olympics/summer/1896/ATH/mens-marathon.html). The oldest non-Olympic annual marathon race is the Boston Marathon, held continuously
since 1897 (www.baa.org/races/boston-marathon/boston-marathon-history.aspx). To date, we know a bit about trends in marathon running after 1976 [2], but we have no knowledge about trends in participation in marathon running since
the beginning of large city marathons such as the Boston Marathon or the New York
City Marathon. We know that the participation of age group marathoners in large city
marathons such as the New York City Marathon increased at the end of the last century
[8]
[12] and the number of women increased after 1972 when the first women were allowed to
officially run in large city marathons [10]
[19]. Moreover, master athletes of very high ages older than 75 years continue increasing
their participation and improving their performance [1].
Apart from participation, the trend in performance in marathon running is also of
interest. Current studies have mainly investigated performance trends in elite [13] and recreational age groups [27] competing in recent years or decades in large city marathons. However, we have no
knowledge about the performance trends in elite and recreational athletes since the
beginning of marathon running in 1897. It would be interesting to see both annual
data on men’s participation and men’s average race times. This would provide both
cultural and scientific information to historians and sports scientists alike.
A further aspect is the nationality of the marathoners. It is well known that East
African runners from Ethiopia and Kenya are the fastest in marathon running when the
fastest race times in marathon running [17] and the races of the World Marathon Majors [9] were analyzed. We have, however, no knowledge about the trends in participation
and performance of East African marathoners competing in large city marathons. Particularly,
we do not know for specifically when East African marathoners entered the large city
marathons. For the Boston Marathon, Kenya’s Ibrahim Hussein finished in 1988 one second
ahead of Tanzania’s Juma Ikangaa, and became the first African to win the Boston Marathon,
or any other major marathon (www.baa.org/races/boston-marathon/boston-marathon-history/boston-marathon-milestones.aspx).
The Boston Marathon is always held on Patriots’ Day, the third Monday of April. The
race is a point-to-point course from east to west with a big weather influence [13], where warmer temperatures and headwinds on the day of the race slow winning times
[15]. For a fast time in the Boston Marathon, the temperature should be below 8 °C [23]. When the runners have a tailwind, they get a ‘push’ for 42 km. This might explain
why Geoffrey Mutai from Kenya set in 2011 a new course record as well as a new world’s
best time of 2:03:02 h:min:sec and the top four men all finished under the old course
record. The wind was from WSW (West-South-West) with a speed of 15 mph (24.14 km/h)
with a maximal wind speed of 26 mph (41.84 km/h), and a maximal gust speed of 35 mph
(56.32 km/h).
The aim of the present study was, therefore, to investigate participation and performance
trends in male marathoners competing between 1897 and 2017 in the Boston Marathon
with the hypothesis that participation increased and performance improved in the last
120 years in this city marathon. Regarding nationality, we hypothesized that East
African runners would enter the race in the seventies and be the fastest from their
entry in the race to the present. Since race times in Boston Marathon are highly influenced
by environmental conditions (i. e. temperature, wind direction, and wind speed), we
also considered these characteristics in our analyses.
Methods
Ethical approval
This study was approved by the Institutional Review Board of Kanton St. Gallen, Switzerland,
with a waiver of the requirement for informed consent of the participants as the study
involved the analysis of publicly available data. The research was conducted ethically
according to international standards and as described by Harriss and colleagues [7].
Data sampling and data analysis
The Boston Marathon is the world’s oldest annual city marathon (www.baa.org/races/boston-marathon/boston-marathon-history.aspx). To compete in this race, athletes must meet time standards that correspond to age
and sex and that have changed over years (www.baa.org/races/boston-marathon/participant-information/qualifying.aspx).
Data for the 120 years of competition from 1897 to 2017 were obtained from the official
race website (www.baa.org/races/boston-marathon.aspx). Available information was name, surname, nationality of runners, sex and runners’
year of competition, and race times. We integrated this dataset with other information
on year 2005. For the year 2005, race results from the official race website were
incomplete, and we obtained them from the website www.marathonguide.com/results.
To identify observations from a single runner, we defined an id variable with name,
surname, nationality and period of competition, supposing that a single runner could
participate at most for 25 years. All male finishers in the history of the Boston
Marathon (n=368,940) were analyzed.
Since temperature and wind including direction seemed to have an influence on race
time in the Boston Marathon [13]
[15]
[23], we merged the database with additional information on the weather conditions during
the days of the race: average temperature, precipitation in mm, humidity level, wind
direction and wind speed. Weather data (i. e. temperatures, precipitation, humidity,
wind direction, wind speed) were obtained from different websites (http://w2.weather.gov/climate/local_data.php?wfo=box; www.baa.org/races/boston-marathon/boston-marathon-history/weather-conditions.aspx; http://findmymarathon.com/weather-detail.php?zname=Boston%20Marathon&year=; www.wunderground.com/history/airport/KBOS/2013/1/15/MonthlyHistory.html).
Data about temperature and precipitation were available for the entire 120 years.
Other information (i. e. humidity, wind direction and wind speed) was available from
1945 onward.
Statistical analysis
All data are presented as means±standard deviation for continuous variables and as
number N (%) for categorical variables. Calendar year was considered as both a discrete
value of a continuous variable and as an endpoint of a time-interval categorical variable.
In fact, to compare performance by period of time, we grouped each calendar year into
time-periods of 20 years. We used the following conventions: [year1, year2) denoted
interval from year1 (included) to year2 (excluded) and [year1, year2] denoted interval
from year1 (included) to year2 (included). Performance, or race time, was recorded
in the format “hours:minutes:seconds”. The acceptable type I error was set at p≤0.05.
All statistical analyses were carried out using statistical package R, R Core Team
(2016). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
For data visualization, we used the ggplot2 package. One-way analysis of variance
examined the effect of calendar year, grouped into time-periods, on race time, and
the effect of country, grouped into eight geographical areas (i. e. Kenya-Ethiopia,
rest of Africa, Asia, Canada, Central-South America, Europe, Oceania and USA). T-tests
were performed to compare the average race time between groups of weather conditions,
defined as dichotomous variables. Moreover, effect of calendar year on race time was
examined alone, in a uni-variable model, and together with country of origin and weather
conditions in a multi-variable model. We used a spline regression model, with a spline
smooth term in function of calendar year and a linear term in function of country,
temperature and precipitation. Temperature and precipitation were defined as dichotomous
variables 1/0 depending on, respectively, temperature below/above 8 °C and precipitation
(>0 mm/0 mm).
Since we have repeated measurements within runners, we performed a mixed model, with
random effects on intercept for each runner. We used Generalized Additive Mixed Models,
gamm4 in R, which are extensions of Generalized Additive Models, allowing for repeated
measurements and then for random effects. In a Generalized Additive Model, the response
variable depends linearly on unknown smooth functions of some predictor variables.
The uni-variable (1) and multi-variable (2) models were specified as follow:
-
Race Time (Y)~[Fixed effects (X)=S(YEAR, k)+[Random effects of intercept=Runners]
Race Time (Y)~[Fixed effects (X)=Country+Temperature≤8 °C+ Precipitation>0 mm + S(YEAR,
k) + [Random effects of intercept=Runners]
where S(YEAR, k) is a spline, changing with calendar year and with k basis dimension.
-
We performed different analyses and regression models for the following subgroups:
all runners, annual top hundred finishers, annual top ten finishers and annual winners.
In the multi-variable model we did not include, as predictors, other variables describing
weather conditions, because we did not have enough observations to study the whole
120-year period.
Results
Participation
Between 1897 and 2017, a total of 368,940 observations from 238,685 different finishers
were recorded in the race results. Therefore we had many observations per runner.
Overall, the average was 1.55 per runner, but in fact only 60,709 (25%) runners have
more than one record. These runners participated on average three times in the entire
period of observation. The maximum was 26, which means one runner participated annually
during the last 25 years. As shown in [Table 1], the number of finishers increased dramatically from the middle of the 1970s. Regarding
country of origin, USA and their territories had the largest participation, 306,131
observations (83% of 368,940) and athletes from Kenya and Ethiopia had the smallest
participation, 285 (0.08% of 368,940).
Table 1 Participation and performance (mean) overall, by time, country and weather conditions.
|
All finishers
|
Annual hundred fastest
|
Annual ten fastest
|
Winners
|
|
Period
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
|
Overall
|
368,940
|
|
03:38:21
|
00:41:46
|
|
7613
|
|
02:37:43
|
00:41:46
|
|
1205
|
|
02:28:59
|
00:16:57
|
|
121
|
|
02:22:05
|
00:11:57
|
|
|
(1897,1917)
|
355
|
0.10
|
02:53:15
|
00:19:08
|
<0.001
|
355
|
4.66
|
02:53:15
|
00:19:08
|
<0.001
|
197
|
16.35
|
02:48:09
|
00:19:15
|
<0.001
|
20
|
16.53
|
02:36:09
|
00:11:03
|
<0.001
|
|
(1917,1937)
|
703
|
0.19
|
02:58:42
|
00:18:46
|
|
703
|
9.23
|
02:58:42
|
00:18:46
|
|
198
|
16.43
|
02:40:02
|
00:09:57
|
|
20
|
16.53
|
02:31:06
|
00:06:53
|
|
|
(1937,1957)
|
932
|
0.25
|
03:00:58
|
00:21:32
|
|
932
|
12.24
|
03:00:58
|
00:21:32
|
|
200
|
16.60
|
02:37:00
|
00:08:52
|
|
20
|
16.53
|
02:27:51
|
00:05:39
|
|
|
(1957,1977)
|
4,580
|
1.24
|
02:56:19
|
00:19:54
|
|
1523
|
20.01
|
02:41:24
|
00:18:38
|
|
200
|
16.60
|
02:24:13
|
00:07:48
|
|
20
|
16.53
|
02:18:20
|
00:04:24
|
|
|
(1977,1997)
|
119,734
|
32.45
|
03:25:51
|
00:38:51
|
|
2000
|
26.27
|
02:23:39
|
00:06:35
|
|
200
|
16.60
|
02:13:19
|
00:03:34
|
|
20
|
16.53
|
02:09:57
|
00:01:58
|
|
|
(1997,2017)
|
242,636
|
65.77
|
03:45:38
|
00:41:29
|
|
2100
|
27.58
|
02:28:27
|
00:08:45
|
|
210
|
17.43
|
02:12:23
|
00:03:38
|
|
21
|
17.36
|
02:09:42
|
00:02:55
|
|
|
Country
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
|
Africa
|
411
|
0.11
|
03:36:12
|
00:54:39
|
<0.001
|
63
|
0.84
|
02:18:50
|
00:06:49
|
<0.001
|
16
|
1.35
|
02:11:47
|
00:03:32
|
<0.001
|
|
|
|
|
<0.001
|
|
Kenya-Ethiopia
|
285
|
0.08
|
02:18:20
|
00:12:24
|
|
260
|
3.49
|
02:15:06
|
00:05:53
|
|
137
|
11.57
|
02:11:41
|
00:03:14
|
|
25
|
20.83
|
02:09:20
|
00:02:11
|
|
|
Asia
|
10,220
|
2.78
|
03:45:19
|
00:47:15
|
|
279
|
3.74
|
02:26:13
|
00:09:13
|
|
63
|
5.32
|
02:22:41
|
00:10:24
|
|
11
|
9.17
|
02:19:02
|
00:07:00
|
|
|
Canada
|
25,098
|
6.84
|
03:30:30
|
00:32:11
|
|
575
|
7.71
|
02:43:13
|
00:18:15
|
|
113
|
9.54
|
02:34:31
|
00:11:36
|
|
15
|
12.50
|
02:30:48
|
00:06:56
|
|
|
Central-South America
|
4,962
|
1.35
|
03:36:04
|
00:44:25
|
|
224
|
3.00
|
02:24:34
|
00:10:50
|
|
51
|
4.31
|
02:18:28
|
00:08:44
|
|
2
|
1.67
|
02:25:19
|
00:09:17
|
|
|
Europe
|
18,415
|
5.02
|
03:46:42
|
00:47:00
|
|
433
|
5.80
|
02:24:29
|
00:08:13
|
|
105
|
8.87
|
02:21:07
|
00:09:00
|
|
16
|
13.33
|
02:21:14
|
00:07:34
|
|
|
Oceania
|
1,421
|
0.39
|
03:35:02
|
00:43:09
|
|
46
|
0.62
|
02:23:35
|
00:07:25
|
|
13
|
1.10
|
02:17:12
|
00:07:02
|
|
1
|
0.83
|
02:15:45
|
|
|
|
USA
|
306,131
|
83.43
|
03:38:18
|
00:41:38
|
|
5,580
|
74.80
|
02:40:29
|
00:20:24
|
|
686
|
57.94
|
02:35:07
|
00:17:40
|
|
50
|
41.67
|
02:27:01
|
00:12:54
|
|
|
missing
|
1,997
|
|
|
|
|
153
|
|
|
|
|
21
|
|
|
|
|
1
|
|
|
|
|
|
Weather conditions
|
|
Temperature
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
|
>8 C°
|
219,227
|
59.42
|
03:39:37
|
00:41:24
|
<0.001
|
4,675
|
61.41
|
02:39:20
|
00:20:54
|
<0.001
|
778
|
64.56
|
02:31:58
|
00:18:36
|
<0.001
|
78
|
64.46
|
02:24:12
|
00:13:01
|
0.003
|
|
≤8C°
|
149,713
|
40.58
|
03:36:29
|
00:42:14
|
|
2,938
|
38.59
|
02:35:08
|
00:18:31
|
|
427
|
35.44
|
02:23:32
|
00:11:36
|
|
43
|
35.54
|
02:18:15
|
00:08:36
|
|
|
Precipitation
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
|
0 mm
|
214,447
|
58.13
|
03:39:53
|
00:41:19
|
<0.001
|
4,330
|
56.95
|
02:39:45
|
00:21:06
|
<0.001
|
735
|
61.51
|
02:31:01
|
00:16:37
|
<0.001
|
74
|
61.67
|
02:23:31
|
00:11:58
|
0.034
|
|
>0 mm
|
154,483
|
41.87
|
03:36:12
|
00:42:18
|
|
3,273
|
43.05
|
02:34:51
|
00:18:06
|
|
460
|
38.49
|
02:24:23
|
00:13:35
|
|
46
|
38.33
|
02:19:03
|
00:10:31
|
|
|
missing
|
10
|
|
|
|
|
10
|
|
|
|
|
10
|
|
|
|
|
1
|
|
|
|
|
|
Wind
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
|
West
|
168,304
|
45.62
|
03:38:00
|
00:42:31
|
<0.001
|
2,902
|
47.09
|
02:30:37
|
00:14:42
|
<0.001
|
370
|
50.68
|
02:20:50
|
00:12:24
|
0.002
|
37
|
50.68
|
02:15:20
|
00:08:27
|
0.556
|
|
Other
|
199,186
|
53.99
|
03.38.56
|
00.41.06
|
|
3,261
|
52.91
|
02:34:42
|
00:18:21
|
|
360
|
49.32
|
02:18:28
|
00:08:15
|
|
36
|
49.32
|
02:14:19
|
00:06:00
|
|
|
missing
|
1,450
|
|
|
|
|
1,450
|
|
|
|
|
475
|
|
|
|
|
48
|
|
|
|
|
|
Wind speed
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
|
>16 (km/h)
|
182,701
|
42.59
|
03:38:46
|
00:42:21
|
<0.001
|
3,226
|
52.07
|
02:32:34
|
00:16:36
|
0.050
|
410
|
55.41
|
02:20:31
|
00:10:46
|
0.154
|
41
|
55.41
|
02:15:33
|
00:07:50
|
0.537
|
|
≤16 (km/h)
|
184,822
|
57.41
|
03:38:14
|
00:41:10
|
|
2,970
|
47.93
|
02:33:25
|
00:17:35
|
|
330
|
44.59
|
02:19:21
|
00:11:09
|
|
33
|
44.59
|
02:14:28
|
00:07:12
|
|
|
missing
|
1,417
|
|
|
|
|
1,417
|
|
|
|
|
465
|
|
|
|
|
47
|
|
|
|
|
|
Humidity level
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
N
|
%
|
Mean
|
SD
|
p
|
|
>70%
|
156,514
|
49.71
|
03:39:21
|
00:42:09
|
<0.001
|
2,316
|
37.58
|
02:31:27
|
00:15:54
|
<0.001
|
260
|
35.62
|
02:17:02
|
00:07:35
|
<0.001
|
26
|
35.62
|
02:13:21
|
00:05:55
|
0.167
|
|
≤70%
|
210,976
|
50.29
|
03:37:52
|
00:41:27
|
|
3,847
|
62.42
|
02:33:35
|
00:17:21
|
|
470
|
64.38
|
02:21:08
|
00:11:43
|
|
47
|
64.38
|
02:15:39
|
00:07:56
|
|
|
missing
|
1,450
|
|
|
|
|
1,450
|
|
|
|
|
475
|
|
|
|
|
48
|
|
|
|
|
P-values were computed from one way analysis of variance (for period and country)
and t-tests (for weather conditions).
Performance considering all finishers
Summary statistics
In [Table 1] we reported the summary statistics of the average performance overall, by time grouped
into classes, by country and by weather conditions. Overall, the average race time
was 03:38:21±00:41:46 h:min:s. Performance had a trend to decrease over time. In the
last 20 years (1997, 2017), race time, on average, was the slowest and in the first
period of observations (1897,1917) was the fastest. Exceptionally in period (1957,
1977), compared to the previous period, though an extremely high increase in participation,
race time decreased. In [Fig. 1], we plotted points showing the details of mean performance by calendar year. The
worst overall performance was in 2004 (04:16:16±00:46:53 h:min:s) and the best overall
performance in 1912 (02:29:31±00:04:27 h:min:s). It should be highlighted that there
were 526 observations before 1924, where the distance was not standardized at 42.195 km
(26 miles, 385 yards). In fact, the average performance in period (1897, 1924) was
02:51:17±00:18:13 h:min:s and significantly different (p<0.001) compared with the
average performance of 03:02:24±00:18:16 h:min:s in the rest of the period (1924,
1937).
Fig. 1 Performance of all men finishers, annual hundred fastest (top 100), annual ten fastest
(top ten) and winners from 1897 to 2017, by calendar year. Points are average of time
race. Lines are fitted curves.
Regarding the country of origin, athletes from Kenya and Ethiopia showed the fastest
performance (02:18:20±00:12:24 h:min:s) and athletes from Europe had the slowest performance
(03:46:42±00:47:00 h:min:s). We examined also the differences between single nationalities,
not grouped into geographical areas, which were in total 136. We show, in Appendix,
[Fig. 1S], the trend of average performance of the 49 most relevant nationalities, which had
60 or more observations each. We observed that athletes from Kenya and Ethiopia, differently
from other countries, had a decreasing trend in race time. Athletes from Kenya participated
for the first time in 1979 and athletes from Ethiopia in 1963.
Fig 1S Performance of all men finishers, for the most relevant nationalities, denoted with
IOC codes, from 1897 to 2017, by calendar year. Points are average of time race. Lines
are the linear trend.
Regarding weather conditions, we did not focus on details, as shown in [Table 1], but we found that temperatures≤8 °C, precipitation>0 mm, wind direction from the
west, wind speed≤16 km/h and humidity level≤70% significantly (p<0.001) improved performance.
Statistical model
In [Fig. 1] and [Fig. 2] we show the fitted curve, from the generalized additive mixed models, uni-variable
and multi-variable, described in the methods section and whose details are reported
in [Table 2]. In [Fig. 2] we do not show, graphically, the effect of precipitation, but only of country and
temperature. Overall, race time of all finishers increased across calendar years ([Fig. 1]) in line with the participation ([Table 1]).
Fig. 2 Performance of all men finishers, annual hundred fastest (top 100) and annual ten
fastest (top ten) from 1897 to 2017, by calendar year, country and air temperature
(below or above 8°C). Points are average of time race. Lines are fitted curves.
Table 2 Estimates, standard errors and p-values of fixed effects of mixed models of race
time for all finishers, annual hundred fastest (top 100) and annual ten fastest (top
ten).
|
Univariable model
|
All finishers n=368,940; runners=238,685
|
Top 100 n=7,613; runners=5,808
|
Top ten n=1,205; runners=820
|
|
Fixed Effects
|
Estimate
|
Std. Error
|
p
|
Estimate
|
Std. Error
|
p
|
Estimate
|
Std. Error
|
p
|
|
(Intercept)
|
0.15309
|
0,00005
|
<0,001
|
0.11027
|
0.00012
|
<0,001
|
0.10365
|
0.00017
|
<0,001
|
|
Smooth terms
|
edf=12.04
|
k=14
|
<0,001
|
edf=12.38
|
k=14
|
<0,001
|
edf=10.47
|
k=12
|
<0,001
|
|
S (year) 1
|
−0.03333
|
0.00840
|
|
−0.01255
|
0.00130
|
|
0.01637
|
0.00203
|
|
|
S (year) 2
|
0.05663
|
0.02436
|
|
0.06134
|
0.00784
|
|
0.07238
|
0.00616
|
|
|
S (year) 3
|
−0.00418
|
0.00967
|
|
0.00726
|
0.00107
|
|
0.02550
|
0.00149
|
|
|
S (year) 4
|
0.03521
|
0.01515
|
|
0.02340
|
0.00515
|
|
0.04696
|
0.00427
|
|
|
S (year) 5
|
−0.0164
|
0.00925
|
|
0.00316
|
0.00114
|
|
0.01967
|
0.00165
|
|
|
S (year) 6
|
0.03688
|
0.01471
|
|
0.03241
|
0.00515
|
|
0.03938
|
0.00428
|
|
|
S (year) 7
|
0.02887
|
0.00914
|
|
0.00824
|
0.00111
|
|
−0.01463
|
0.00186
|
|
|
S (year) 8
|
0.04856
|
0.01434
|
|
0.01958
|
0.00513
|
|
0.02887
|
0.00447
|
|
|
S (year) 9
|
−0.03928
|
0.00898
|
|
−0.00554
|
0.00118
|
|
0.00760
|
0.00202
|
|
|
S (year) 10
|
0.03683
|
0.01387
|
|
0.02282
|
0.00512
|
|
−0.10456
|
0.01032
|
|
|
S (year) 11
|
−0.03841
|
0.01006
|
|
−0.00996
|
0.00139
|
|
−0.04020
|
0.00447
|
|
|
S (year) 12
|
−0.13362
|
0.04660
|
|
−0.08430
|
0.01555
|
|
|
|
|
|
S (year) 13
|
−0.00483
|
0.00435
|
|
−0.02827
|
0.00465
|
|
|
|
|
|
Multivariable model
|
All finishers n=366,933; runners=236,806
|
Top 100 n=7,450; runners=5,658
|
Top ten n=1,174; runners=796
|
|
Fixed Effects
|
Estimate
|
Std. Error
|
p
|
Estimate
|
Std. Error
|
p
|
Estimate
|
Std. Error
|
p
|
|
(Intercept)
|
0.09661
|
0.00187
|
<0,001
|
0.10177
|
0.00064
|
<0,001
|
0.10433
|
0.00062
|
<0,001
|
|
Country=Africa
|
01:16:18
|
00:03:20
|
<0,001
|
00:04:00
|
00:01:53
|
0.034
|
−00:02:08
|
00:01:59
|
0.284
|
|
Country=Asia
|
01:28:02
|
00:02:43
|
<0,001
|
00:07:03
|
00:01:11
|
<0,001
|
−00:02:11
|
00:01:18
|
0.094
|
|
Country=Canada
|
01:12:13
|
00:02:42
|
<0,001
|
00:12:53
|
00:01:05
|
<0,001
|
−00:01:00
|
00:01:13
|
0.411
|
|
Country=Central-South America
|
01:17:06
|
00:02:45
|
<0,001
|
00:07:11
|
00:01:17
|
<0,001
|
−00:00:17
|
00:01:19
|
0.575
|
|
Country=Europe
|
01:26:54
|
00:02:42
|
<0,001
|
00:07:04
|
00:01:07
|
<0,001
|
−00:02:30
|
00:01:10
|
0.033
|
|
Country=Oceania
|
01:16:41
|
00:02:54
|
<0,001
|
00:09:29
|
00:02:04
|
<0,001
|
00:01:06
|
00:02:11
|
0.615
|
|
Country=USA
|
01:23:17
|
00:02:42
|
<0,001
|
00:15:37
|
00:00:56
|
<0,001
|
00:01:44
|
00:00:58
|
0.076
|
|
Temperature≤8° C
|
−00:03:35
|
00:00:07
|
<0,001
|
−00:03:26
|
00:00:17
|
<0,001
|
−00:03:57
|
00:00:28
|
<0,001
|
|
Precipitation>0 mm
|
00:00:06
|
00:00:07
|
0.426
|
−00:00:41
|
00:00:16
|
0.013
|
−00:00:58
|
00:00:27
|
0.031
|
|
Smooth terms
|
edf=11,52
|
k=13
|
<0,001
|
edf=10,76
|
k=12
|
<0,001
|
edf=10,52
|
k=12
|
<0,001
|
|
S (year) 1
|
−0.04989
|
0.00765
|
|
−0.01215
|
0.00103
|
|
0.00953
|
0.00187
|
|
|
S (year) 2
|
0.09068
|
0.02185
|
|
0.05219
|
0.00569
|
|
0.05762
|
0.00565
|
|
|
S (year) 3
|
−0.02290
|
0.00870
|
|
0.00755
|
0.00077
|
|
0.02000
|
0.00141
|
|
|
S (year) 4
|
0.05574
|
0.01398
|
|
0.01775
|
0.00391
|
|
0.03533
|
0.00390
|
|
|
S (year) 5
|
−0.03299
|
0.00822
|
|
0.00466
|
0.00085
|
|
0.01840
|
0.00156
|
|
|
S (year) 6
|
0.06252
|
0.01324
|
|
0.03273
|
0.00388
|
|
0.03327
|
0.00391
|
|
|
S (year) 7
|
−0.04538
|
0.00816
|
|
−0.00803
|
0.00087
|
|
0.01079
|
0.00173
|
|
|
S (year) 8
|
0.06611
|
0.01367
|
|
0.01903
|
0.00396
|
|
0.02160
|
0.00406
|
|
|
S (year) 9
|
−0.05493
|
0.00820
|
|
−0.00553
|
0.00106
|
|
0.00351
|
0.00190
|
|
|
S (year) 10
|
0.07368
|
0.01084
|
|
−0.07266
|
0.01095
|
|
−0.08149
|
0.00948
|
|
|
S (year) 11
|
−0.20473
|
0.04115
|
|
−0.02671
|
0.00341
|
|
−0.02648
|
0.00407
|
|
|
S (year) 12
|
−0.01112
|
0.00383
|
|
|
|
|
|
|
|
S=Spline. K is the basis dimension, edf are the estimated degree of freedom. Effects
on countries are reported with Kenya and Ethiopia being the reference group. Estimates
were left as numeric values, except estimates of the most important fixed effects,
in multi-variable model, that were reported in h:min:s for an immediate interpretation.
In [Table 2] we show that athletes from Kenya and Ethiopia were significantly (p<0.001) the fastest,
compared with every other country group. The estimates ranged from a minimum of 01:12:13 h:min:s,
which was the estimated difference among Canada and Kenya-Ethiopia, and a maximum
of 01:28:02 h:min:s, which was the estimated difference among Asia and Kenya-Ethiopia.
Temperature level≤8 °C significantly (p<0.001) improved performance of 00:03:35 h:min:s
, compared with temperature>8 °C. Absence or presence of precipitation was not significant
(p=0.426).
Performance considering the annual hundred fastest
Summary statistics
A large main effect of calendar year, grouped into classes, country and weather conditions
on race time was shown (p<0.001) in [Table 1]. In [Fig. 1], we plotted points showing the details of mean performance by calendar year. Performance
overall improved, but not monotonically, over time. On average, years (1977, 1997)
showed the best performances (02:23:39±00:06:35 h:min:s) and the fastest calendar
year was 1983 with 02:16:55±00:03:03 h:min:s. Instead, periods (1937, 1957) had the
worst performance 03:00:58±00:21:32, but 1927 was the slowest calendar year (03:32:54±00:22:47 h:min:s).
Regarding country of origin, athletes from Kenya and Ethiopia were the fastest, 02:15:06±00:05:53 h:min:s,
with a participation of n=260 (3.49%). Athletes from the USA, with the highest participation
of n=5580 (74.80%), were the slowest, 02:40:29 ± 00:20:24 h:min:s.
Regarding weather conditions, we did not focus on details, as shown in [Table 1], but we found that temperatures≤8 °C, precipitation>0 mm, wind direction from the
west significantly (p<0.001) improved performance. On the contrary, humidity level≤70%
had a worsening and significant (p<0.001) effect on performance, and also wind speed≤16 km/h
was not favorable (p=0.05).
Statistical model
In [Fig. 1] and [Fig. 2] we show the fitted curve, from the generalized additive mixed models, uni-variable
and multi-variable, described in the methods section and whose details are reported
in [Table 2]. We do not show, graphically, the effect of precipitation, but only of country and
temperature. We observed that race time of the hundred annual fastest finishers decreased
across calendar years ([Fig. 1]).
In [Table 2] we show that athletes from Kenya and Ethiopia were significantly the fastest, compared
with every other country group. The estimates ranged from a minimum of 00:04:00 h:min:s
(p=0.034), which was the estimated difference among the rest of Africa and Kenya-Ethiopia,
and a maximum of 00:15:37 h:min:s (p<0.001), which was the estimated difference between
USA and Kenya-Ethiopia. Temperature level≤8 °C significantly (p<0.001) improved performance
of 00:03:26 h:min:s, compared with temperature>8 °C. Absence of precipitation was
also significant (p=0.013) and improved performance of approximately 00:00:41 h:min:s.
Performance considering the annual ten fastest
Summary statistics
A large main effect (p<0.001) of calendar year, grouped into classes, country and
weather conditions on race time was shown in [Table 1]. In [Fig. 1], we plotted points showing the details of mean performance by calendar year. Performance
improved over time. On average, the last period (1997, 2017) had the best performances
(02:12:23±00:03:38 h:min:s) and the fastest calendar year was 2011 with 02:05:10±00:01:29 h:min:s.
Instead, the first period (1897, 1917) had the worst performance 02:48:09 ± 00:19:15 h:min:s
with 1897 the slowest calendar year (03:30:56 ± 0:28:56 h:min:s).
Regarding the country of origin, Kenya and Ethiopia, the fastest, 02:11:41±00:03:14 h:min:s,
was the second main relevant group in terms of participation, n=137 (11.57%) followed
by USA, n=686 (57.94%) which were the slowest, 02:35:07 ± 00:17:40 h:min:s.
We did not focus on details, as shown in [Table 1], but we found significantly better performances (p<0.001) on temperature≤8° C and
precipitation>0 mm. Wind from the west and also humidity level≤70% were significantly
not favorable (p=0.002 and p<0.001 respectively). Wind speed was not significant (p=0.154).
Statistical model
In [Fig. 1] and [Fig. 2] we show the fitted curve, from the generalized additive mixed models, uni-variable
and multi-variable, described in the methods section and whose details are reported
in [Table 2]. We observed that race time of the ten annual fastest finishers decreased across
calendar years ([Fig. 1]). From [Table 2], athletes from Kenya and Ethiopia seemed not to be the fastest. Significant differences
were found (see [Table 2]) among temperature levels below and above 8 °C (p<0.001, estimated difference=-00:03:57 h:min:s).
Presence of precipitation was also significant (p=0.031) and improved performance
by approximately 00:00:58 h:min:s.
Performance considering the annual winners
Summary statistics
A large main effect of calendar year, grouped into classes, on race time was shown
(p<0.001) in [Table 1]. In [Fig. 1], we plotted points showing the details of mean performance by calendar year. Performance
improved over time. The first period (1897, 1917) had the slowest race time 02:36:09±00:11:03 h:min:s
with 1897 being the slowest calendar year, 02:55:10 h:min:s. On average, the last
period (1997, 2017) had the fastest race time, 02:09:42 ± 00:02:55 h:min:s, with 2011
being the fastest calendar year (02:03:02 h:min:s).
In [Table 1], the effect of country on race time seemed significant but in fact it was not relevant,
since we did not have, for each country group, enough observations for a good inference.
Athletes from Kenya and Ethiopia (n=25, 20.83%) were the fastest with 02:09:20±00:02:11 h:min:s
and athletes from Canada (n=15, 12.50%) were the slowest with 02:30:48±00:06:56 h:min:s.
We found significantly better performances at temperatures below 8 °C (02:18:15±00:08:36 h:min:s
versus 02:24:12 ± 00:13:01 h:min:s, p=0.003), and precipitation>0 mm (02:19:03 ± 00:10:31 h:min:s
versus 02:23:31±00:11:58 h:min:s, p=0.034). Other weather conditions were not significant.
Statistical model
In [Fig. 1] we show the fitted curve, from the uni-variable generalized additive mixed model
described in the methods section but whose details were not reported in [Table 2]. Race time of the annual winners decreased across calendar years. Since we did not
have enough observations, we did not perform a multivariable analysis as we did for
all the finishers and annual top ten finishers.
Discussion
We investigated participation and performance trends in male runners competing in
the Boston Marathon since the first race held in 1897. We performed both descriptive
and inferential statistics. By analyzing all annual finishers, the annual hundred,
ten fastest and the annual winners, the main findings were (i) the number of finishers increased dramatically from the middle of 1970s, (ii) the fastest race times were achieved in the beginning of the race when all annual
finishers were considered, (iii) the fastest race times were achieved in the first two decades of the actual century
when the annual ten fastest and annual top runners were considered and (iv) Ethiopian and Kenyan runners were the fastest (v) air temperature (i. e. cold temperatures) and precipitation had a significant effect on race performance.
Performance considering all finishers
When all finishers were considered since the beginning of the race, the number of
finishers increased dramatically from the middle of 1970s. This increase in the seventies
is most likely due to the increase in age group runners [8]
[12] and the ‘running boom’ that hit the United States from the late 1970s onward [2]
[19]. For example, between 1968 and 1976 the number of marathons increased in the United
States of America by 300% [14]. We see in 1996 a very high number of finishers. In that year, the historic 100th event of the Boston Marathon attracted 38,708 entrants with 36,748 starters and had
35,868 official finishers, which stood as the largest field of finishers in the history
until 2004 (www.baa.org/races/boston-marathon/boston-marathon-history/boston-marathon-milestones.aspx). Although we examined here the trends in participation and performance for men in
the Boston Marathon, future studies also need to investigate the trends in participation
in women in large city marathons such as the New York City Marathon.
Furthermore, race times of all finishers increased across calendar years in line with
participation. This is most likely due to the increased variability on performance
introduced by the increased number of age group runners [8]
[12]. Although these age group runners improved their race times in the last decades,
they still run considerably slower compared to the elite marathoners. Therefore it
seems that the effect of increasing participation resulting with worsening race times
is greater than the improvement in performance with time passing and with improvement
conditions.
The first period of observation between 1897 and 1917 had the fastest race times,
with the overall best performance in 1912. During the last 20 years, the slowest race
times were found with the year 2004 having the overall slowest race time. This is
again most likely explained by the increase in participation of age group runners
in marathon races [8]
[12]. When running times and age of all 415,000 runners in the New York City Marathon
from 1983 to 1999 were examined, the number of master participants increased at a
greater rate than their younger counterparts [8].
Performance considering elite athletes (annual ten fastest and annual winners)
Differences in performance trends were, however, found for elite runners. When the
annual ten fastest were considered, the best performance were achieved in the last
20 years, whereas 2011 was the fastest year. However, the slowest race times were
found in the first year (1897) and the first period (1897, 1917) of the race. We also
observed that race times of the ten annual fastest finishers decreased across calendar
years. The same results were also obtained when the annual winners were considered.
In the beginning of the Boston Marathon, the early men had to run on muddy roads,
with bad shoes, bad nutrition, no fluids, and little training (personal communication
Amby Burfoot, winner Boston Marathon in 1968). Now, training and pre-race preparation
have changed and the elite East African runners are professional athletes. For elite
athletes, the trend of improvement is still going on. When running data from 150 years
of sprinting and distance running were analyzed, performance improved annually towards
an asymptotic limit [26].
Participation and performance of East African runners
An important finding was that runners from East Africa showed the smallest participation
but the fastest race times. It is well-known that East African runners from Ethiopia
and Kenya are the fastest in marathon running when taking into consideration the fastest
race times in marathon running from the world best list of the International Association
of Athletics Federations (IAAF) [17] and the races of the World Marathon Majors [9].
What is new and very astonishing is the finding that the average race times decreased
in Kenyan and Ethiopian runners since they entered the race in 1979 and 1963, respectively.
For all other countries, race times increased. This might be explained by the different
motivation in East-African runners compared to runners from other countries. It is
well-known that runners from Kenya [18] and Ethiopia [21] are motivated to run large city marathons in order to win prize money. However,
also superior physiological capacity is necessary in East-African runners to compete
faster than runners from another origin [11].
The influence of environmental conditions
We showed p-values and summary statistics of environmental conditions such as air
temperature, wind direction, wind speed, precipitation and humidity on race day. Race
times were faster at low temperatures (≤8 °C), with precipitation, humidity level≤70%,
wind from West and wind speed≤16 km/h, depending on whether all finishers, the annual
100 fastest, the annual 10 fastest or the annual winners were considered. However,
for the annual winners, wind from west, humidity and wind speed showed no influence
on race times.
The influence of weather on race times in marathon running is well-known [3]
[4]
[5]
[6]
[16]
[24]
[25]. It has been reported that warmer temperatures impair performance [4]
[15]
[16], especially in slower runners [3]
[5]
[6]
[16]. The influence of weather on race times has already been investigated for the Boston
Marathon [13]
[15]
[20]
[23]. However, it has been investigated only in limited samples such as the top 3 for
30 years [23], winning times for 1933-2004 [15] or the top 10 male and 10 female finishers from 2005 to 2014 [13].
Our study provides now, however, more and detailed data for the whole period 1897-2017
and for different performance levels of male runners. An interesting finding was that
temperature≤8 °C, precipitation, wind from west, wind speed≤16 km/h and humidity level
≤70% improved race times when considering all finishers. We can confirm previous findings
of Trapasso et al. [23]. These authors reported that record-breaking performances were characterized by
a wet bulb temperature of less than 7.8 °C and 100% sky cover where a light drizzle
was also conducive to better performance.
However, for the annual 10 fastest and the annual winners, wind speed showed no influence
on performance. A potential explanation for this finding could be the running speed
of the athletes. While the top runners compete at a running speed of ~20 km/h, they
might compensate for the influence of wind. And a potential explanation for the improved
performance a low temperatures and with precipitation could be ‘external cooling’
[22].
Our statistical model confirmed that air temperature (i. e. cold temperatures) significantly
improved performance for all finishers, the annual hundred and ten fastest. In addition,
precipitation significantly improved race performance for the annual hundred and ten
fastest.
Limitations, strength and practical application
The findings of the present study are limited by the specific characteristics of the
race in terms of participation and qualifying criteria. Thus, they should be generalized
with caution to other marathon races. Moreover, we did not have information about
age, and we could not exactly identify repeated measurements within runners although
we could reasonably suppose that two observations belonged to the same runner, if
they had in common both name, surname, country and participation, once a year, in
the same period of time.
A further limitation is that we must be aware that not all official finishers in the
early years were recorded when comparing the number of finishers with the number of
participants (www.bostonmarathonmediaguide.com/qualification/participation/). In fact it seems unusual that, considering all finishers, the year 1912 was the
fastest calendar year (02:29:31±00:04:27 h:min:s). According to the race director,
“The only results we have are those recorded by the athletics officials, who often
went home before the last finishers completed the course” (personal communication).
In later years, the number of finishers corresponds well with the number of participants.
We also have to mention that the Boston Marathon is the only large city marathon in
the world with qualifying times (www.baa.org/races/boston-marathon/participant-information/qualifying/history-of-qualifying-standards.aspx) and the qualifying standards could favor one sex and/or age group over the other.
Although qualification criteria exist, around 10,000 runners participate on sponsor
loyalties. Most likely, the number of this latter group of runners has increased most
over the year, while qualifiers remained stable over the years. It can be expected
that the qualified age-groupers will perform better than their free-entry counterparts;
however, as this information is unavailable, this speculative hypothesis cannot be
tested critically.
Unfortunately, we did not have the complete list of all runners belonging to push
rim wheelchair division starting in 1975. We attempted to exclude this category by
eliminating runners with race times shorter than the annual top record.
We also should be aware that in the first 27 years the distance was not standardized
at 42.195 km (26 miles, 385 yards). From 1897 to 1923, the race distance was ~37 km,
from 1924 to 1926 at ~40 km, since 1927 exactly 42.2 km, and since 1957 the official
distance of 42.195 km (www.baa.org/races/boston-marathon/boston-marathon-history.aspx). Therefore, results based on data before 1927 must be interpreted with caution.
Another bias could arise from incomplete information on the year 2005, which was integrated
with data coming from www.marathonguide.com/results. In fact, for this year, information
about nationality might not be exact or reliable, since it came from a different source.
Considering all observations, the large amount of data and the large number of runners
(clusters) made inferential statistics computationally challenging and obtaining a
model with a better fit was not feasible. In particular, [Fig. 1], the uni-variable model fit for all finishers looked weaker, compared to the other
runners groups, especially in the last 20 years. However, the fit in the multi-variable
model looked better ([Fig. 2]). Moreover, comparing performance by countries over the whole period of observation
was an easy way to identify different patterns in different countries ([Fig. 2]), but it could not be an accurate measure of country effect on performance, first
of all, because we compared countries that participated several years before Kenya
and Ethiopia, the reference group, entered for the first time. This fact caused the
extremely low fit of performance by Kenya and Ethiopia before the eighties, considering
all finishers, and it also might have affected the results in the multi-variable model
for the annual ten fastest, where it seemed that Kenya and Ethiopia were not the fastest.
In the environmental analysis, both temperature and participation were added as categorical
variables into the model, based on previous research. We did not show, but we verified,
that including these variables as continuous variables would not much improve the
model fit, though the effects of weather conditions should have been more accurate,
but this was beyond the main goals of this study and would be the subject of further
research.
So our results should be interpreted with caution due to the limited information available.
On the other hand, the strength of the study was that it analyzed trends in performance
and participation in one of the most popular marathon races worldwide, considering
all finishers (>half a million) through its history. Since marathon running continues
to increase its popularity, the findings are of great interest for coaches and trainers
working with marathon runners, as well as for scientists focusing on human performance.
Conclusions
In summary, participation increased dramatically in the Boston Marathon from the middle
of 1970s and this perhaps worsened performance because race times significantly increased
in time. However, when the annual ten fastest and the annual winners were considered,
performance improved over time and race times decreased. When the annual hundred fastest
are considered, performance improved with time but not monotonically. We found significant
differences among runners from East Africa and other countries for all finishers and
the annual hundred fastest. We also found significant effects of weather conditions
on performance. Race times were faster at low temperatures (≤8 °C), for all finishers,
the annual 100 and 10 fastest, and with precipitation, for the annual 100 and 10 fastest.
This would require further research. These findings need to be confirmed in other
large city marathons such as the New York City Marathon. Future studies also need
to investigate the influence of weather on different performance levels in both female
and male runners.
