DOI: 10.5281/zenodo.14249279
title: Environmental temperatures in the Australian and German professional football leagues.

description: This is environmental data for each match of the German Bundesliga (seasons 2014-21) and Australian A-League (seasons 2016-20). 

Environmental conditions in the form of temperature and WBGT were collated retrospectively for each match. Whereas temperature refers to the commonly known and easily accessible ambient air temperature, WBGT is a feels-like temperature adding the influence of relative humidity, wind, and solar radiation, for a more detailed interpretation of the observed heat stress. The use, advantages, and disadvantages of WBGT have been described extensively in previous research.1-3 Despite its widespread use, the black globe temperature (radiative heat gain) and natural wet-bulb temperature (evaporative heat loss) measurements are criticized as not representing human thermoregulation, thereby underestimating heat stress in many settings.1,4 It should also be mentioned, that WBGT is a heat stress index and is not validated for colder conditions. Therefore, to interpret the effects of colder environments on injury occurrence temperature was also used in our analyses. Although more modern and sophisticated thermal indexes  exist 4,5, WBGT remains widely used, especially in sports federation heat policies. Specifically, this index is also used in the heat policy introduced by FIFA, which recommends the use of drinking breaks at 32 °C WBGT6.

For Bundesliga matches, weather data was obtained from Meteostat.net.7 This is an open-source service, providing hourly meteorological data for any given coordinates. Data is obtained as a weighted interpolation depending on the distance and elevation difference from the four closest weather stations to a geological location. They provide the following data: temperature, relative humidity, dew point, wind speed, air pressure, total precipitation, and the current weather condition. Based on this, WBGT can be estimated in a variety of ways according to previous research.2 We used the estimation developed by Liljegren et al. (2008).3 This is validated and reliable in different environmental settings and is described as the best estimate for WBGT from different methods.8 The R code needed to implement these calculations has been provided and used in previous research.9 Wind speed was assumed to be a minimum of 1 m/s, as moving players generate airflow of at least equivalent to that. Solar radiation was estimated using the solar angle at the time and location of the match10. As Meteostat.net provides hourly data, two time points (the kick-off time and one hour later) were used per match and averaged. If the match did not start at a full hour, but at 15 or 30 minutes past the hour, the previous full hour was used as a starting point and the following hour as a second time point. For A-League matches, environmental conditions were provided by UBIMET.com.11 This commercial provider uses artificial intelligence and data input from multiple weather stations, radar, and satellite data, to estimate meteorological data at given ground locations. They provide temperature, relative humidity, solar radiation, and WBGT measurements for the starting times of the first and second half, which were then averaged to create one value per match. To validate the WBGT data based on Meteostat.net data, the WBGT estimation method used for the Bundesliga data was also performed with the A-League data. As internal validation, results were then compared to the WBGT reported from UBIMET.com. There was a very good linear association (correlation coefficient r = 0.93).

1.         Brocherie F, Millet G. Is the Wet-Bulb Globe Temperature (WBGT) Index Relevant for Exercise in the Heat? . Sports Med. 2015;45:1619-1621.

2.         Lemke B, Kjellstrom T. Calculating Workplace WBGT from Meteorological Data: A Tool for Climate Change Assessment. Ind Health. 2012;50:267-278.

3.         Liljegren J, Carhart RA, Lawday P, Tschopp S, Sharp R. Modelling the Wet Bulb Globe Temperature Using Standard Meteorological Measurements. J Occup Environ Hyg. 2008;5(10):645-655.

4.         Blazejczyk K, Epstein Y, Jendritzky G, Staiger H, Tinz B. Comparison of UTCI to selected thermal indicies. Int J Biometeorol. 2012;56:515-535. doi:https://doi.org/10.1007/s00484-011-0453-2

5.         Jendritzky G, de Dear R, Havenith G. UTCI - Why another thermal index? Int J Biometeorol. 2012;56:421-428. doi:https://doi.org/10.1007/s00484-011-0513-7

6.         Brown H, Chalmers S, Topham T, et al. Efficacy of the FIFA cooling break heat policy during an intermittent treadmill football simulation in hot conditions in trained males. Br J Sports Med. 2024;doi:10.1136/bjsports-2024-108131

7.         Meteostat.net. The Weather’s Record Keeper. https://meteostat.net/en/

8.         Patel T, Mullen SP, Santee WR. Comparison of Methods for Estimating Wet-Bulb Globe Temperature Index From Standard Meteorological Measurements. Military Medicine. 2013;178(8):926-933.

9.         HeatStress. Casanueva, A; 2019. https://zenodo.org/records/3264930

10.       Duffie J, Beckman W. Solar Engineering of Thermal Processes. 4th ed. John Wiley & Sons, Inc.; 2013.

11.       UBIMET GmbH. UBIMET WEATHER MATTERS. https://www.ubimet.com/en/