Impact of Weather on Marathon Running Performance

Matthew R. Ely; Samuel N. Cheuvront; William O. Roberts; Scott J. Montain


Med Sci Sports Exerc. 2007;39(3):487-493. 

In This Article


The purpose of this study was to analyze running performance from multiple mass participation marathons to 1) quantify the impact of weather on performance, and 2) determine whether the weather effects are different by ability or gender. The principal finding of this study was that there is a progressive slowing of marathon performance as WBGT increases from 5 to 25°C. Our findings that modest changes in WBGT can impact marathon race performance agree with earlier studies [17,22]. It seems that this phenomenon holds true for men and women and that slower runners experience larger decrements in performance. Central to the acceptance of the primary findings in this study are the measurements and analysis of weather and performance.

Implicit in the evaluation of these findings is the use of WBGT as a meteorological index. WBGT was chosen because it is a singular index that incorporates ambient temperature, humidity, and solar load, which correspond to subjective perceptions around climatic temperature [21]. WBGT guidelines for distance running were developed by the National Collegiate Athletic Association. The guidelines state that distance races (> 16 km (10 miles)) should not be conducted when the WBGT exceeds 28°C (82.4°F) [1]. Although domestic and international marathons do not fall under the jurisdiction of a singular governing body, none of the marathons obtained in this study had WBGT exceeding 28°C. The WBGT race data were divided into quartiles and not into smaller sets because it would have decreased the power to detect meaningful changes in performance. Additionally, no single marathon appreciably influenced the outcome of the whole analysis because four of the seven marathons spanned all four quartiles, two spanned three quartiles, and one (Richmond) spanned only the two lowest WBGT quartiles.

To quantify the impact of WBGT on performance of top finishers, the average time of the top three finishers was sorted by WBGT. This approach revealed that the elite men were 1.7 ± 1.5, 2.5 ± 2.1, 3.3 ± 2.0, and 4.5 ± 2.3% (mean ± SD) slower than the course record as WBGT increased from Q1 to Q4, respectively (Fig. 1a). Whereas there seemed to be progressive slowing as WBGT increased, traditional statistics revealed that Q4 was slower than Q1 and Q2, and Q3 was slower than Q1. A common statistical confusion involves solely interpreting the importance of findings relative to rejection or acceptance of the null hypothesis. In this study, we employed both traditional analyses and nontraditional tests of statistical equivalence [7,18] as a means of better understanding the potential importance of the performance effect magnitude. Inferential confidence interval analysis [18] determined that the nonsignificant differences should not be considered equivalent and that an excess of 1% slowing between quartiles in all populations ( Table 2 ) should be considered of potential importance [11,18].

Unlike men, the average finishing times for top women finishers did not show a progressive slowing as a function of WBGT. In women, performance seemed to be preserved until Q3 conditions were achieved as times off course record were 3.2 ± 4.9, 3.2 ± 2.9, 3.8 ± 3.2, and 5.4 ± 4.1% for Q1-Q4, respectively (Fig. 1b). The lack of statistical difference between means, however, could have mainly been attributable to large variability in performance times in Q1 and Q4. Larger performance variability in women was also encountered by Hopkins and Hewson [11], who found that women had a higher coefficient of variation than men when examining repeat marathon performances (3.8 vs 2.5%). Another contributing factor may be the differences in depth of talent between men and women runners [5]. In this dataset, the mean difference in finishing time between the first- and third-place male and female finishers was 3.25 and 5.20 min, respectively. The potential importance of WBGT effects on performance for women were consistent with those for men because the maximal probable population differences were outside the 1% zone of indifference ( Table 2 ) using ICI analysis. There was a 0.7% difference between the performance decrements of elite men (2.8% between Q1 and Q4) and elite women (2.1%). Also, the average finishing time of elite women (2:36.55) is similar to that of the 25th-place finishers (2:31.17), and the similar magnitude of the performance decrements between the two populations suggests that gender does not measurably affect the impact of WBGT on performance (Fig. 2).

The data suggest that slower runners are affected more by a rising WBGT than faster runners. Regression analysis reveals that 25th-place runners slow approximately 1.1% between quartiles, whereas 50th-, 100th-, and 300th-place finishers slow by about 1.5, 1.8, and 3.2% with 5°C increases in WBGT ( Table 3 ). This slowing could be attributable to the fact that each population of runners is spending more time exposed to the environmental conditions. For example, the 300th-place runners spend approximately 50 more minutes (~38% more time) exposed to the elements than the elite men. Also, slower runners tend to run in closer proximity to other runners, which has been estimated to cause more than three times the physiological heat stress compared with running solo in identical weather conditions [4,6]. The increase in heat stress arises from a small net reduction of long-wave radiative heat losses (R + C), which amounts to an approximate 2°C increase in ambient temperature. More importantly, convective heat loss is reduced 50% as a result of entrainment of air [4,6]. Differences in fitness relative to physiological potential could also contribute to differences in performance times and ability to cope with increasing heat stress.

Equations from the regression analysis of performance and WBGT enabled the construction of a nomogram (Fig. 3), which should allow marathon runners (2:07-3:00 h) to estimate their potential performance decrement as a function of race-day weather (WBGT 5-25°C). In an effort to validate the accuracy of the nomogram, data from 291 participants who completed the 2005 and 2006 Boston Marathon were identified. Weather conditions differed by approximately 6°C WBGT between races, similar to part I quartiles (5°C WBGT). Consistent with the curves generated from regression analysis using the original population of runners, this independent dataset shows a consistent slowing in performance as WBGT increases. Like the nomogram, the magnitude of slowing was also greater for slower runners (Fig. 4). Because no data were available from part I to produce a nomogram curve for 7.3°CWBGT, a curve was generated by interpolating between 5 and 10° CWBGT. The actual slowing of performance associated with the 5.9°C WBGT increase in 2005 (13.2°C WBGT) compared with 7.3°C WBGT in 2006 fell precisely between expected values for 10 and 15°C WBGT from the nomogram for the fastest marathoners (133 ± 5 min). The performance decrements for the next two populations (150 ± 4, 165 ± 2) were slightly higher than expected (Fig. 5), which may be attributable to factors unrelated to WBGT. One important assumption made in comparing these two marathons is that runners' performances did not improve from 1 yr to the next. It seems unlikely that 86% of the runners would have improved that much in just 1 yr, especially given the natural variability in year-to-year performances for marathon runners [11]. Our interpretation is that the nomogram provides reasonably accurate estimates of changes in marathon finishing time produced by WBGT conditions. The practical application of the nomogram will allow athletes or sporting professionals to evaluate running performance and expected performance decrements as weather conditions vary from year to year.


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