How do the climatological variables impact on match running performance in Spanish professional soccer players?

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José Carlos Ponce Bordón, David Lobo Triviño, Aldo A. Vasquez-Bonilla, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9171550/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Research has demonstrated that climate can affect the match running performance of soccer players. This study aimed to analyze the influence of various meteorological variables (i.e., air temperature, humidity, wind speed and rainfall) on the match running performance of Spanish professional soccer players. A total of 10,859 individual match observations from 515 soccer players competing in the First Spanish soccer league (i.e., LaLiga; n = 380 matches) over the 2023/24 season were collected. Match running performance was obtained using the Sportian Performance video tracking system in accordance with LaLiga. Air temperature, humidity, wind speed and rainfall were collected as climate variables. Linear Mixed Models showed that climate variables significantly impacted match running performance. Specifically, Temperature and Wind Speed significantly and negatively influenced on Total Distance (TD; p < .001), and significantly and positively on Decelerations (DEC; p < .05). Conversely, Humidity and Rain significantly and positively influenced on TD ( p < .001), and Very High-Speed Running ( p < .001). Also, the regression models revealed significant interaction effects of humidity, wind speed and rainfall on match running performance across temperature levels. Specifically, the influence of Humidity, Wind Speed and Rain on TD was most pronounced at high temperatures where was decreased, while Accelerations and DEC increased. This study contributes to our understanding of the influence of climate variables on match running performance in the First Spanish soccer league. These results should be considered when designing training programs and strategies for extreme environmental conditions. professional meteorological external load soccer thermal conditions Figures Figure 1 Figure 2 Figure 3 Introduction Professional football players compete throughout the year, and teams in the Spanish First Division (LaLiga) play from August to May. Consequently, matches are contested across different seasons, leading to substantial variations in average climatic conditions throughout the competition. In this context, it is well established that hot, humid, and windy environments can impair physical performance and increase transient fatigue, negatively affecting endurance in intermittent sports such as football and reducing running performance during matches (Özgünen et al. 2010 ; Nybo et al. 2021 ; Draper et al. 2023 ). Therefore, environmental stressors should be considered when planning preparation strategies and during match play. Hot environments have been shown to affect external load during official matches, leading to reductions in total distance (TD) covered (Özgünen et al. 2010 ). Data from the Bundesliga, J-League, and Turkish Super League indicates that higher “Wet Bulb Globe Temperature” (WBGT) values are associated with decreases in TD, high-speed running (HSR), and sprinting performance (Schwarz et al. 2025b ). Similarly, UEFA Champions League matches exhibited marked reductions in high-intensity physical efforts at temperatures above 21°C compared with matches played at 11–20°C (Pavlinovic et al. 2024 ). Although some studies report increased maximal speed in hotter conditions (Schwarz et al. 2025a ), this may reflect pacing strategies aimed at preserving capacity for high-intensity actions (Link and Weber 2017 ; Illmer and Daumann 2022 ). Despite FIFA guidelines recommending additional cooling breaks when WBGT exceeds 32°C (Gouttebarge et al. 2023 ), heat remains a significant performance stressor, with effects potentially varying across regions due to differences in heat acclimatization, ultimately influencing performance across the season. Humidity and wind have been less extensively studied in relation to match running performance in football. Higher relative humidity (> 60%) has been associated with reductions in high-intensity physical outputs, particularly when combined with elevated temperatures, as observed during the 2014 FIFA World Cup in Brazil (Chmura et al. 2017 ; Austin et al. 2021 ). Although the effects of wind speed on physical performance have not been directly controlled in football match studies, wind is known to influence high-speed running and may indirectly affect match performance (Illmer and Daumann 2022 ). Cold environments also negatively affect external load, especially high-intensity efforts. While moderate cold conditions (≤ 10°C) do not appear to substantially alter total or high-speed distance, temperatures below 0°C are associated with marked reductions in sprinting performance (Morgans et al. 2015 ). Furthermore, cold and rainy conditions can exacerbate thermal and metabolic stress, increasing energy expenditure and reducing physical performance, thereby potentially compromising player safety (Ito et al. 2015 , 2019 ). A recent systematic review by Draper et al. ( 2023 ) reported that meteorological factors reduce TD, HSR, and sprints, highlighting the need for standardized methodologies and integrated research approaches to support decision-making in sports. Environmental variables should not be examined in isolation; however, no study has yet simultaneously assessed temperature, humidity, wind speed, and rainfall in relation to external load during matches in the Spanish First Division. Existing evidence suggests that seasonal transitions, such as from winter to spring, may influence physical match demands (Tobías et al. 2021 ; de Korte et al. 2023 ). The present study is based on a very large sample of matches and additionally using official meteorological data and external load metrics collected via video-based tracking systems across all LaLiga stadiums. Therefore, the aims of this study were: (a) to analyze the influence of different atmospheric variables on match running performance in the Spanish professional soccer players, and (b) to examine the interaction between Humidity, Wind Speed and Rain on match running performance varies across different temperature levels. It was hypothesized that the combined effects of humidity, wind, and rain would differentially modulate running performance according to ambient temperature, with more negative effects under higher temperatures. Materials and methods Study design A retrospective and descriptive design was applied to analyze the influence of climate conditions on match running performance in Spanish professional soccer players. Match running performance data was collected by an optical tracking system ChyronHego (TRACAB, New York, US). Climatological variables on the days and times of the official matches were collected and provided by the official software of LaLiga (i.e., Sportian Performance, https://www.laliga.com/es-GB ) located into the stadiums where they took place. After the matches, spreadsheets were downloaded in Excel format. All stadiums were outdoors. The ambient temperatures at the beginning the start of the match and one hour after were collected in degrees Celsius (ºC), and the average temperature was used. The relative humidity during the matches was collected in percentage of humidity. Also, the wind speed during matches was also collected in kilometers per hour. Finally, whether or not it rained during the games was considered. Table 1 shows the descriptive statistics for temperature, wind speed and humidity variables. Table 1 Descriptive statistics of climate variables Temperature (ºC) Mean Minimum Maximum 18.29 -1 38 Humidity (%) 59.05 11 100 Wind Speed (km⋅h − 1 ) 12.69 1 42 Note. Cº = degrees Celsius; km⋅h − 1 = kilometres per hour. ***Insert Table 1 near here, please*** Sample A total of 380 matches from the 2023/2024 season of Spain's First Division were included in the analysis. This resulted in 10,859 individual match observations from 515 professional soccer players. All players who participated in matches (starters and non-starters) were included, except players who competed for less than 15 minutes during the matches, because it was observed that the average values obtained from these players were higher than the team average (Rampinini et al. 2021 ; García-Calvo et al. 2023 ). Goalkeepers were excluded from the analysis due to their unique role during matches. The data were provided by LaLiga™, which ensured that all participants were informed according to its protocols. To maintain confidentiality, all data were anonymized in accordance with the Declaration of Helsinki. The study received full approval from the Ethics Committee of the University of Extremadura, under the Vice-Rectorate of Research, Transfer, and Innovation – Delegation of the Bioethics and Biosafety Commission (Protocol number: 79//2025). Procedures and match running performance variables The optical tracking system TRACAB (ChyronHego VID, New York, NY, USA) was employed to gather match running performance data. This multi-camera system consists of eight 4K HDR cameras, supported by a positioning system (Tracab—ChyronHego VTS), which captures and analyzes the X and Y positions of each player from multiple perspectives, providing real-time two-dimensional tracking at a sampling frequency of 25 Hz. Furthermore, a custom report was generated using Sportian Performance (former Mediacoach) software (LaLiga, Madrid, Spain), which synchronized the tracking data with the video footage of each match. The validity and reliability of this system for the variables employed have been previously assessed (Felipe et al. 2019 ; Pons et al. 2019 , 2021 ) showing strong correlations ( r > 0.80) and high intraclass correlation coefficients ( r > 0.75) between the Mediacoach multi-camera tracking system and the Global Positioning System. Match running performance was divided into the following categories: total distance covered by soccer players in meters (TD); very high-speed running (VHSR, distance covered between 21–24 km·h − 1 ); sprinting speed running distance (Sprint, distance covered between 24–28 km·h − 1 ); high sprint distance (High Sprint, distance covered > 28 km·h − 1 ); total number of accelerations performed above 3 m·s − 2 (ACC, nº); and total numbers of decelerations performed above – 3 m·s − 2 (DEC, nº). Absolute values of match running performance variables were normalized to relative values per unit of time (i.e., m·min − 1 ) to account for possible differences in the total playing time of soccer players. All efforts that involved a minimum movement of one meter, maintained for at least one second, were recorded. Statistical analysis The statistical analysis was performed using RStudio (version 2026.01.0 + 392). Data processing, modelling, and graphical representations were performed within the RStudio environment to ensure reproducibility and transparency of the analysis. Considering the characteristics of the sample, organized hierarchically, nested in groups, and with a longitudinal structure, we considered that the best procedure to analyze the data is through linear mixed models (LMM). LMM have demonstrated their ability to cope with unbalanced and repeated-measures data (Heck and Thomas 2020 ). For instance, match running performance variables in matches are nested into players (i.e. each player has a record for every match they have participated in, and each match has observations of several players). Players, in turn, are also nested into different teams every season. Thus, the cross-classified multilevel models are suitable for data structures that are not purely hierarchical. Consequently, a general multilevel modelling strategy was applied where fixed and random effects in different steps were included by (Heck and Thomas 2020 ). Thus, LMM were applied to examine the impact of climate conditions on match running performance over the matches. Firstly, a two-level hierarchy was modeled for the analysis. Match running performance variables (i.e., distances covered at different speed thresholds and number of accelerations and decelerations) were included as dependent variables in the models, and climate variables (i.e., air temperature, humidity, wind speed and rain) were included as predictor variables of the match running performance. The soccer player variable was considered as the random effects in the analysis. Models were estimated using the lm function from the stats package. Model assumptions (linearity, homoscedasticity, and normality of residuals) were visually inspected using standard diagnostic procedures. Secondly, the interaction between temperature and other climate variables was included to explore how the effect of climate variables on match running performance varies across different temperature levels. To facilitate the interpretation of the interaction effects, model-based predictions were generated. Predicted values of each dependent variable were obtained across the observed range of climate variables, while Temperature was fixed at three representative values corresponding to the 10th (Low Temperature), 50th (Medium Temperature), and 90th (High Temperature) percentiles of its distribution. This approach allows for a continuous and statistically coherent interpretation of the interaction between the environmental variables. Predictions and associated 95% confidence intervals were derived using model-based inference and subsequently visualized. This strategy ensures that the displayed relationships reflect the fitted interaction model rather than separate regressions within arbitrarily defined subgroups. All figures were generated using ggplot2 , with predicted values and confidence intervals plotted to illustrate the conditional effect of climate variables at different temperature levels. All values were represented as coefficients and standard error (Coeff ± SE). Statistical significance was set at p < .05. Results Table 1 shows the descriptive statistics for climate variables. The mean value for temperature was 18.29 Cº, for humidity was 59.05 and for wind speed 12.69 km⋅h − 1 . Table 2 presents the outcomes of the LMM applied to examine the influence of climate variables on match running performance. The model demonstrates a statistically significant influence of climate variables on several match running performance variables. Specifically, Temperature significantly and negatively influenced on TD ( p < .001), VHSR ( p < .001), and Sprint ( p < .001); and significantly and positively on ACC ( p < .001) and DEC ( p < .001). Wind speed significantly and negatively influenced on TD ( p < .05); and significantly and positively on DEC ( p < .05). Conversely, Humidity significantly and positively influenced on TD ( p < .001), VHSR ( p < .001), and Sprint ( p < .001). Finally, Rain significantly and positively influenced on TD ( p < .05), VHSR ( p < .05); and significantly and negatively on ACC ( p < .001) and DEC ( p < .001). Table 2 Influence of climate variables on match running performance Variables TD (m⋅min − 1 ) VHSR (m⋅min − 1 ) Sprint (m⋅min − 1 ) High Sprint (m⋅min − 1 ) ACC (nº⋅min − 1 ) DEC (nº⋅min − 1 ) Fixed Effects Coeff SE Coeff SE Coeff SE Coeff SE Coeff SE Coeff SE Intercept 121.31 *** .44 4.64 *** .06 2.79 *** .05 .86 .03 22.52 *** .04 22.85 *** .04 Temperature (ºC) − .29 *** .01 − .03 *** .01 − .02 *** .01 − .00 .00 .01 *** .00 .01 *** .00 Humidity (%) .07 *** .00 .01 *** .00 .00 *** .00 .00 .00 .00 .00 .00 .00 Wind Speed (km⋅h − 1 ) − .02 * .01 .00 .00 − .00 .00 − .00 .00 .00 .00 .00 * .00 Rain .93 * .33 .11 * .05 .05 .04 − .02 .03 − .24 *** .05 − .22 *** .05 Note . Coeff = Coefficient; SE = Standard Error; m⋅min − 1 = meters per minute; ºC = degrees Celsius; km⋅h − 1 = kilometres per hour; TD = Total distance; VSHR = Very high-speed running; Sprint = Sprinting speed running distance; ACC = total number of accelerations; DEC = total number of decelerations; * p < .05; ** p < .01; *** p < .001. ***Insert Table 2 near here, please*** Figures 1 , 2 and 3 depict the interaction effect of Humidity, Wind speed and Rain on match running performance across temperature levels. The regression models showed significant effects of Humidity and Temperature (Fig. 1 ) interactions on match running performance variables. The influence of Humidity on TD, High Sprint, ACC and DEC was most pronounced at High temperatures where increased. The regression models also demonstrated significant effects of Wind speed and Temperature (Fig. 2 ) interactions on match running performance variables. The influence of Wind speed on TD, Sprint, High Sprint, ACC and DEC was most pronounced at High temperatures where decreased. Conversely, ACC and DEC increased under Low temperatures and windy conditions. Finally, the results of the linear regression models with the interaction between Rain and Temperature (Fig. 3 ) revealed that the effect of Rain on match running performance variables significantly varied with Temperature levels. The influence of Rain on TD, VHSR and Sprint was most pronounced at High temperatures where decreased. Conversely, ACC and DEC increased under High temperatures and rainy conditions. ***Insert Figs. 1 , 2 and 3 near here, please*** Discussion This study examined the impact of climate variables on the match running performance of Spanish professional soccer players. Previous research has shown that environment can significantly influence on athlete performance maintenance, but there is scarce published literature on the topic. Therefore, this the first attempt to examine the influence of the environment on match running performance in Spanish professional soccer players based on a very large sample of matches. The main findings showed that: i) the Temperature ranged from − 1 Cº to 38 Cº, and the Humidity ranged from 11 to 100%, reporting a huge thermal and humidity fluctuations in Spanish First Division official matches; ii) Temperature and Wind Speed significantly and negatively influenced on TD, and significantly and positively influenced DEC, while Humidity and Rain significantly and positively influenced on TD, and VHSR; iii) the interaction effects of climate variables and Temperature showed significant influence on match running performance, which was most pronounced at High temperatures. In Spain, official soccer games commonly occur between 2:00 p.m. and 10:00 p.m. Also, Spanish men´s soccer league has a long duration (from August to May), and matches occur in different seasons and regions of the country. These factors may result in variations in the average climate variables throughout the competition. Specifically, our results showed temperature fluctuations of 39 ºC, humidity variations of 89% and wind speed differences of 41 km⋅h − 1 . For instance, Brazilian national games had temperatures ranging from 14.1 Cº to 35.9 ºC with a relative humidity of 68% (Augusto et al. 2025 ). During French League 1, Temperatures ranged from 21 ºC (Carling et al. 2011 ), and in the German Bundesliga, recorded temperatures were in the range − 10 ºC to 28 ºC (Link and Weber 2017 ), reaching similar Temperature fluctuation than Spain. According to the current results, there was a wide range of environmental temperatures (-1 Cº to 38 Cº), so the physiological response to heat and cold can be heterogeneous among soccer players. Understanding the effect of variations in climate variables on the match running performance of Spanish soccer players is important (Draper et al. 2023 ). Different effects were observed concerning the impact of climate variables on match running performance. Specifically, Temperature negatively affected TD, VHSR and Sprint performance. Our results align with those of studies conducted under official match conditions that reported reductions in match running performance (Özgünen et al. 2010 ; Nassis et al. 2015 ). The most affected variable by Temperature was TD, similar to the results of a systematic review which analysed the effects of temperature on soccer (Draper et al. 2023 ). High temperatures have likely been associated with soccer players experiencing heat cramps and heat exhaustion, which results in a large reduction in TD (Mohr et al. 2010 ). On the other hand, high-intensity exercise in high temperatures could promote different bodily responses in professional soccer players that reduce performance (Augusto et al. 2025 ). The negative influence of Temperature on high-intensity exercise may be related to the increased internal temperature caused by heat which increases vascular and metabolic stress, the heart rate and decreases voluntary muscle contractions (McClelland et al. 2018 ; Périard et al. 2021 ; Cramer et al. 2022 ). Conversely, the positive impact of Temperature on ACC and DEC may have benefit by hot temperatures when these actions are performed only once. Improvements in single ACC and DEC may be due to better anaerobic system and muscle activity functioning (Augusto et al. 2025 ). However, in situations involving repeated efforts, such as soccer matches, the ability to perform ACC and DEC over time is impaired because the running intensity is adjusted over time in hot environments (Chodor et al. 2021 ; Chmura et al. 2022 ). Another possible explanation is the difficulty of reach high-intensity distances in a hot environment so, soccer players try to move forward accelerating (Cotteret et al. 2025 ). Previous research examining the impact of Humidity on soccer environments has observed inconclusive results. For instance, Özgünen et al. ( 2010 ) demonstrated that soccer players covered a significantly shorter TD in the second half of a match than in the first when the relative humidity was above 60%. Conversely, Hayes et al. ( 2014 ) claimed that air humidity between 33.1 and 78.3% does not significantly impact the numbers of repeat sprints performed by team sports players. Similar results were reported by Chmura et al. ( 2017 ) who showed that the best comfort zone for players to attain high levels of physical activity entails a relative humidity range below 60%. However, our results showed that Humidity positively impacted match running performance, specifically TD, VHSR and Sprint distance. These results suggest that higher humidity levels allow for greater match running performance of soccer players during official games. One possible explanation is that the best comfort zone for players to attain high levels of physical activity entails a relative humidity range below 60% (Konefał et al. 2014 ; Chmura et al. 2017 ), and the mean Humidity reported in Spanish matches was 59%. Therefore, it is necessary to understand the extent humidity to which Humidity positively or negatively impacts match running performance. However, unlike previous studies, our study did not include humidity levels or categories. Wind speed and Rain were included in the present study as new variables of weather conditions. The results showed that Wind speed negatively impacted TD, while Rain positively influenced TD and VHSR, but negatively influenced ACC and DEC. Although there is no previous literature on this topic, several factors could explain these results. First, the negative impact of Wind on TD could be attributed to the difficulty of playing in windy conditions. In these conditions, the direction of the ball can change during passes or crosses, causing players to cover less distance. Additionally, this difficulty could result in more game interruptions, impacting on effective playing time and decreasing the overall distance covered by soccer players (Altmann et al. 2023 ). Second, the positive influence of Rain on TD and VHSR could be explained by a reduction in ambient temperature, which favours maintaining match running performance during games (Chmura et al. 2017 ; Augusto et al. 2025 ). Playing in hot environments is linked to a drop in match running performance, but rain could decrease the ambient temperature. In these conditions, soccer players' performance would resemble that in cold environments, where their overall match running performance does not decrease (Carling et al. 2011 ). However, a decrease in muscle temperature in rainy conditions could affect the ability to produce dynamic, explosive contractions (Mohr et al. 2004 ). This could explain the decrease in ACC and DEC. Lastly, the interaction effects of Humidity, Wind speed and Rain on match running performance across temperature levels add an additional layer of complexity to our understanding of the influence of climate variables. Overall, the results indicated that the association between Humidity, Wind speed, and Rain and match running performance varied substantially across temperature levels. Specifically, the influence of Humidity on TD, High Sprint, ACC and DEC was most pronounced at High temperatures, where these variables were increased, probably due to the decrease in air temperature, which may reduce the drop of match running performance. Similar results were reported by (Chmura et al. 2017 ; Augusto et al. 2025 ), who found higher performance parameters when matches occurred in conditions of relative humidity below 60% and lower ambient temperatures. However, it is important to note that players’ physiological responses to high ambient temperatures and humidity are highly individualized (Özgünen et al. 2010 ; Arsac et al. 2013 ). Conversely, the influence of Wind speed and Rain on TD, VHSR, Sprint and High Sprint was most pronounced at High temperatures, resulting in decreased performance parameters. These environmental conditions may be the most uncomfortable for soccer players, since a hot environment with wind and rain would make it more difficult to play soccer fluently. Specifically, rainy conditions with high temperatures could be similar to high temperature and high humidity conditions reported by Chmura et al. ( 2017 ), which impaired player´s physical performance. Limitations and Future Directions To the best of our knowledge, this is the first study to analyse the impact of the environment on match running performance in the Spanish First soccer league under “real-world” conditions. However, some limitations should be acknowledged: i) although there are no big altitude differences in Spain, information about this aspect should be included in future studies; ii) further research should consider different age groups or tiers of soccer players because the environmental impact may differ depending on the type of person; iii) the athletes' dehydration, heart rate, thermal responses, and internal temperatures could be monitored in an attempt to determine the effects of different environments on individual anthropometric and physiological characteristics; iv) finally, although the temperature inside the stadiums was measured, a more complex method, such as the wet bulb globe temperature (WBGT), was not used. Therefore, further research should measure the temperature using this valid method. Conclusions Environmental factors are widely considered to affect players' match running performance in professional soccer. Firstly, this paper discusses the impact of climate variables on match running performance in the First Spanish professional soccer league, reporting a huge thermal and humidity fluctuation during official matches. Secondly, the data showed that Temperature and Wind Speed significantly and negatively influenced TD, and significantly and positively influenced DEC. Meanwhile, Humidity and Rain significantly and positively influenced TD, and VHSR. Finally, the results suggest that the interaction between climate variables and temperature plays a crucial role soccer players' match running performance. These results should be considered when designing training programs and strategies for extreme environmental conditions. Practical applications This study contributes to our understanding of the influence of climate variables on match running performance in the First Spanish soccer league. The results have several strengths such as the practical applicability. This allows strength and conditioning coaches and sports scientists to consider environmental factors when interpreting the context of a match. This information is also relevant to elite Spanish soccer and can help entities that organize matches carry out interventions to maintain player performance (Augusto et al. 2025). For instance, despite the abundance of broadcasting rights and competition in Spanish soccer, it would be interesting to change the time of official games to avoid high temperatures and extreme environments, which could decrease soccer players' match running performance. Additionally, performance seems to decrease with dehydration when soccer is played in high environmental temperatures. Therefore, fluid intake strategies should be a regular part of the physical preparation before and during games in elite soccer (Mohr et al. 2010). Finally, these findings can be also used to develop targeted interventions to help players adapt to matches in different environments. The goal of these training programs would be to achieve positive physiological effects without causing motor or health problems for players (Chmura et al. 2017). Declarations Ethics declaration The study received full approval from the Ethics Committee of the University of Extremadura, under the Vice-Rectorate of Research, Transfer, and Innovation – Delegation of the Bioethics and Biosafety Commission (Protocol number: 79//2025). Conflict of interest The authors declare no competing interests. Author contributions CRediT: JCPB : Data curation, Formal analysis, Software, Writing – review & editing; DLB : Conceptualization, Resources, Validation, Writing – original draft; AVB : Methodology, Validation, Visualization; JPT : Investigation, Methodology, Writing – original draft; RLC : Funding acquisition, Project administration; RR : Funding acquisition, Visualization; TGC : Investigation, Validation, Writing – review & editing Funding This work was supported by the European Regional Development Fund (ERDF), the Government of Extremadura (Department of Economy and Infrastructure), and LaLiga Research and Analysis Sections. David Lobo-Triviño was supported by a pre-doctoral fellowship (PD23068) awarded by the Junta de Extremadura and co-financed by the European Social Fund Plus (FSE+). José Carlos Ponce-Bordón was supported by a postdoctoral fellowship (PO24154_I) awarded by the Junta de Extremadura and co-financed by the European Social Fund Plus (FSE+). 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J Strength Cond Res 32:1662–1670. https://doi.org/10.1519/JSC.0000000000002027 Mohr M, Krustrup P, Nybo L, et al (2004) Muscle temperature and sprint performance during soccer matches – beneficial effect of re‐warm‐up at half‐time. Scand J Med Sci Sports 14:156–162. https://doi.org/10.1111/j.1600-0838.2004.00349.x Mohr M, Mujika I, Santisteban J, et al (2010) Examination of fatigue development in elite soccer in a hot environment: a multi‐experimental approach. Scand J Med Sci Sports 20:125–132. https://doi.org/10.1111/j.1600-0838.2010.01217.x Morgans R, Adams D, Mullen R, et al (2015) A comparison of physical and technical match performance of a team competing in the English Championship League and then the English Premier League following promotion. Int J Sports Sci Coach 10:543–549. https://doi.org/10.1260/1747-9541.10.2-3.543 Nassis GP, Brito J, Dvorak J, et al (2015) The association of environmental heat stress with performance: analysis of the 2014 FIFA World Cup Brazil. Br J Sports Med 49:609–613. https://doi.org/10.1136/bjsports-2014-094449 Nybo L, Flouris AD, Racinais S, Mohr M (2021) Football facing a future with global warming: Perspectives for players health and performance. Br J Sports Med 55:297–298. https://doi.org/10.1136/bjsports-2020-102193 Özgünen KT, Kurdak SS, Maughan RJ, et al (2010) Effect of hot environmental conditions on physical activity patterns and temperature response of football players. Scand J Med Sci Sports 20:140–147. https://doi.org/10.1111/j.1600-0838.2010.01219.x Pavlinovic V, Morgans R, Modric T (2024) Temperature-related variations in physical performance during elite soccer matches. Sports 12:341. https://doi.org/10.3390/sports12120341 Périard JD, Eijsvogels TMH, Daanen HAM (2021) Exercise under heat stress: Thermoregulation, hydration, performance implications, and mitigation strategies. Physiol Rev 101:1873–1979. https://doi.org/10.1152/physrev.00038.2020 Pons E, García-Calvo T, Cos F, et al (2021) Integrating video tracking and GPS to quantify accelerations and decelerations in elite soccer. Sci Rep 11:18531. https://doi.org/10.1038/s41598-021-97903-2 Pons E, García-Calvo T, Resta R, et al (2019) A comparison of a GPS device and a multi-camera video technology during official soccer matches: Agreement between systems. PLoS One 14:e0220729. https://doi.org/10.1371/journal.pone.0220729 Rampinini E, Martin M, Bosio A, et al (2021) Impact of COVID-19 lockdown on professional soccer players’ match physical activities. Science and Medicine in Football 5:44–52. https://doi.org/10.1080/24733938.2021.1995033 Schwarz E, Duffield R, Lu D, et al (2025a) Associations between injury occurrence and environmental temperatures in the Australian and German professional football leagues. Environmental Epidemiology 9:e364. https://doi.org/10.1097/EE9.0000000000000364 Schwarz E, Duffield R, Novak AR, et al (2025b) Associations between match running performance and environmental temperatures in 4 professional football leagues. Int J Sports Physiol Perform 20:109–119. https://doi.org/10.1123/ijspp.2024-0248 Tobías A, Casals M, Saez M, et al (2021) Impacts of ambient temperature and seasonal changes on sports injuries in Madrid, Spain: A time-series regression analysis. BMJ Open Sport Exerc Med 7:e001205. https://doi.org/10.1136/bmjsem-2021-001205 Supplementary Files Database.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 23 Mar, 2026 First submitted to journal 19 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9171550","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615490263,"identity":"cee42ff4-445e-4ce6-8d51-c4efe0c0bc93","order_by":0,"name":"José Carlos Ponce Bordón","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Carlos Ponce","lastName":"Bordón","suffix":""},{"id":615490264,"identity":"60ca1261-d40e-4782-953b-4aa6ab9bc854","order_by":1,"name":"David Lobo Triviño","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"Lobo","lastName":"Triviño","suffix":""},{"id":615490265,"identity":"4457bfa3-8654-4b14-9925-a9324391b571","order_by":2,"name":"Aldo A. Vasquez-Bonilla","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Aldo","middleName":"A.","lastName":"Vasquez-Bonilla","suffix":""},{"id":615490266,"identity":"b0d21228-cf1a-4446-bf65-180e6c14adc3","order_by":3,"name":"Jorge Polo Tejada","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACxgYGxgNAWo4kLQwHGBIYjEmzCaQlsYFo5cyzmw8c+PjDJn3D7eZjEgwVdUQ4bM6xhIMzEtJyN9w5lmzAcOYwEVpm5Bgc5kk4nLvhRo7hA8a2A0Rr+Z9ucCP/wwHGf8Q4DKLlQILBjRzGB4wNzERoAfslLdlw5o00Y4OEY0T4xXB288EHH2zs5PluJD+T+FBDhMMMZyDzEghrYGCQlyBG1SgYBaNgFIxsAABSgT/2jdUDiwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0006-3417-3585","institution":"Universidad de Extremadura Facultad de Ciencias del Deporte","correspondingAuthor":true,"prefix":"","firstName":"Jorge","middleName":"Polo","lastName":"Tejada","suffix":""},{"id":615490267,"identity":"785f2aef-4390-4436-a90a-7911551aaf94","order_by":4,"name":"Roberto López del Campo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"López del","lastName":"Campo","suffix":""},{"id":615490268,"identity":"5e77a447-7e27-4a3d-b9e5-200e25dc11ff","order_by":5,"name":"Ricardo Resta","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Resta","suffix":""},{"id":615490269,"identity":"8e3af38e-125a-43b2-942d-dec0e433d628","order_by":6,"name":"Tomás García Calvo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tomás","middleName":"García","lastName":"Calvo","suffix":""}],"badges":[],"createdAt":"2026-03-19 16:14:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9171550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9171550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106402285,"identity":"a10f036a-6ef0-4830-be03-628a57a51ff6","added_by":"auto","created_at":"2026-04-08 09:11:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":266410,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of Humidity on \u003cstrong\u003ea)\u003c/strong\u003e total distance (TD), \u003cstrong\u003eb)\u003c/strong\u003e very high-speed running (VHSR), \u003cstrong\u003ec)\u003c/strong\u003e sprinting speed running distance (Sprint), \u003cstrong\u003ed)\u003c/strong\u003e high sprint running distance (High sprint), \u003cstrong\u003ee)\u003c/strong\u003e number of accelerations (ACC); and \u003cstrong\u003ef)\u003c/strong\u003e number of decelerations (DEC) across temperature levels.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Match running performance is adjusted to reflect the real values if the match running performance was constant for the full duration of the match. Smooth fits using the \u003cem\u003elm\u003c/em\u003e (linear regression model) method are shown as well as 95% confidence intervals (Cleveland 1979).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9171550/v1/93c9db3607e369b9b72b9e12.png"},{"id":106197815,"identity":"308c78b7-5bfd-4184-b645-16d7815ea1fe","added_by":"auto","created_at":"2026-04-06 01:19:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":258552,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of Wind Speed on \u003cstrong\u003ea)\u003c/strong\u003e total distance (TD), \u003cstrong\u003eb)\u003c/strong\u003e very high-speed running (VHSR), \u003cstrong\u003ec)\u003c/strong\u003e sprinting speed running distance (Sprint), \u003cstrong\u003ed)\u003c/strong\u003e high sprint running distance (High sprint), \u003cstrong\u003ee)\u003c/strong\u003e number of accelerations (ACC); and \u003cstrong\u003ef)\u003c/strong\u003e number of decelerations (DEC) across temperature levels.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Match running performance is adjusted to reflect the real values if the match running performance was constant for the full duration of the match. Smooth fits using the \u003cem\u003elm\u003c/em\u003e (linear regression model) method are shown as well as 95% confidence intervals (Cleveland 1979).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9171550/v1/a624bc8fe20f198117da138d.png"},{"id":106402879,"identity":"119cace2-8455-4d18-b814-efad9439445e","added_by":"auto","created_at":"2026-04-08 09:13:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":225906,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction between Rain and Temperature on \u003cstrong\u003ea)\u003c/strong\u003e total distance (TD), \u003cstrong\u003eb)\u003c/strong\u003e very high-speed running (VHSR), \u003cstrong\u003ec)\u003c/strong\u003e sprinting speed running distance (Sprint), \u003cstrong\u003ed)\u003c/strong\u003e high sprint running distance (High sprint), \u003cstrong\u003ee)\u003c/strong\u003e number of accelerations (ACC); and \u003cstrong\u003ef)\u003c/strong\u003e number of decelerations (DEC).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Match running performance is adjusted to reflect the real values if the match running performance was constant for the full duration of the match. Smooth fits using the \u003cem\u003elm\u003c/em\u003e (linear regression model) method are shown as well as 95% confidence intervals (Cleveland 1979).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9171550/v1/07b2dc16dfb907fef249008c.png"},{"id":106405524,"identity":"1eed52fb-5229-4856-8faf-a8f5d98e46b7","added_by":"auto","created_at":"2026-04-08 09:27:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1363696,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9171550/v1/9f003897-33cd-4340-b154-243cdb33a86a.pdf"},{"id":106197818,"identity":"871ffc61-dcdd-4694-aff2-b238bc86eaf8","added_by":"auto","created_at":"2026-04-06 01:19:31","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1791814,"visible":true,"origin":"","legend":"","description":"","filename":"Database.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9171550/v1/cb5fb0730fb75ec1ff8a6b60.xlsx"}],"financialInterests":"","formattedTitle":"How do the climatological variables impact on match running performance in Spanish professional soccer players?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProfessional football players compete throughout the year, and teams in the Spanish First Division (LaLiga) play from August to May. Consequently, matches are contested across different seasons, leading to substantial variations in average climatic conditions throughout the competition. In this context, it is well established that hot, humid, and windy environments can impair physical performance and increase transient fatigue, negatively affecting endurance in intermittent sports such as football and reducing running performance during matches (\u0026Ouml;zg\u0026uuml;nen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nybo et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Draper et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, environmental stressors should be considered when planning preparation strategies and during match play.\u003c/p\u003e \u003cp\u003eHot environments have been shown to affect external load during official matches, leading to reductions in total distance (TD) covered (\u0026Ouml;zg\u0026uuml;nen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Data from the Bundesliga, J-League, and Turkish Super League indicates that higher \u0026ldquo;Wet Bulb Globe Temperature\u0026rdquo; (WBGT) values are associated with decreases in TD, high-speed running (HSR), and sprinting performance (Schwarz et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). Similarly, UEFA Champions League matches exhibited marked reductions in high-intensity physical efforts at temperatures above 21\u0026deg;C compared with matches played at 11\u0026ndash;20\u0026deg;C (Pavlinovic et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although some studies report increased maximal speed in hotter conditions (Schwarz et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e), this may reflect pacing strategies aimed at preserving capacity for high-intensity actions (Link and Weber \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Illmer and Daumann \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite FIFA guidelines recommending additional cooling breaks when WBGT exceeds 32\u0026deg;C (Gouttebarge et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), heat remains a significant performance stressor, with effects potentially varying across regions due to differences in heat acclimatization, ultimately influencing performance across the season.\u003c/p\u003e \u003cp\u003eHumidity and wind have been less extensively studied in relation to match running performance in football. Higher relative humidity (\u0026gt;\u0026thinsp;60%) has been associated with reductions in high-intensity physical outputs, particularly when combined with elevated temperatures, as observed during the 2014 FIFA World Cup in Brazil (Chmura et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Austin et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although the effects of wind speed on physical performance have not been directly controlled in football match studies, wind is known to influence high-speed running and may indirectly affect match performance (Illmer and Daumann \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Cold environments also negatively affect external load, especially high-intensity efforts. While moderate cold conditions (\u0026le;\u0026thinsp;10\u0026deg;C) do not appear to substantially alter total or high-speed distance, temperatures below 0\u0026deg;C are associated with marked reductions in sprinting performance (Morgans et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, cold and rainy conditions can exacerbate thermal and metabolic stress, increasing energy expenditure and reducing physical performance, thereby potentially compromising player safety (Ito et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA recent systematic review by Draper et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that meteorological factors reduce TD, HSR, and sprints, highlighting the need for standardized methodologies and integrated research approaches to support decision-making in sports. Environmental variables should not be examined in isolation; however, no study has yet simultaneously assessed temperature, humidity, wind speed, and rainfall in relation to external load during matches in the Spanish First Division. Existing evidence suggests that seasonal transitions, such as from winter to spring, may influence physical match demands (Tob\u0026iacute;as et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; de Korte et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The present study is based on a very large sample of matches and additionally using official meteorological data and external load metrics collected via video-based tracking systems across all LaLiga stadiums. Therefore, the aims of this study were: (a) to analyze the influence of different atmospheric variables on match running performance in the Spanish professional soccer players, and (b) to examine the interaction between Humidity, Wind Speed and Rain on match running performance varies across different temperature levels. It was hypothesized that the combined effects of humidity, wind, and rain would differentially modulate running performance according to ambient temperature, with more negative effects under higher temperatures.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA retrospective and descriptive design was applied to analyze the influence of climate conditions on match running performance in Spanish professional soccer players. Match running performance data was collected by an optical tracking system ChyronHego (TRACAB, New York, US). Climatological variables on the days and times of the official matches were collected and provided by the official software of LaLiga (i.e., Sportian Performance, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.laliga.com/es-GB\u003c/span\u003e\u003cspan address=\"https://www.laliga.com/es-GB\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) located into the stadiums where they took place. After the matches, spreadsheets were downloaded in Excel format. All stadiums were outdoors. The ambient temperatures at the beginning the start of the match and one hour after were collected in degrees Celsius (\u0026ordm;C), and the average temperature was used. The relative humidity during the matches was collected in percentage of humidity. Also, the wind speed during matches was also collected in kilometers per hour. Finally, whether or not it rained during the games was considered. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics for temperature, wind speed and humidity variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of climate variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTemperature (\u0026ordm;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.29\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind Speed (km\u0026sdot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote. C\u0026ordm; = degrees Celsius; km\u0026sdot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e = kilometres per hour.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e near here, please***\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample\u003c/h3\u003e\n\u003cp\u003eA total of 380 matches from the 2023/2024 season of Spain's First Division were included in the analysis. This resulted in 10,859 individual match observations from 515 professional soccer players. All players who participated in matches (starters and non-starters) were included, except players who competed for less than 15 minutes during the matches, because it was observed that the average values obtained from these players were higher than the team average (Rampinini et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Garc\u0026iacute;a-Calvo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Goalkeepers were excluded from the analysis due to their unique role during matches. The data were provided by LaLiga\u0026trade;, which ensured that all participants were informed according to its protocols. To maintain confidentiality, all data were anonymized in accordance with the Declaration of Helsinki. The study received full approval from the Ethics Committee of the University of Extremadura, under the Vice-Rectorate of Research, Transfer, and Innovation \u0026ndash; Delegation of the Bioethics and Biosafety Commission (Protocol number: 79//2025).\u003c/p\u003e\n\u003ch3\u003eProcedures and match running performance variables\u003c/h3\u003e\n\u003cp\u003eThe optical tracking system TRACAB (ChyronHego VID, New York, NY, USA) was employed to gather match running performance data. This multi-camera system consists of eight 4K HDR cameras, supported by a positioning system (Tracab\u0026mdash;ChyronHego VTS), which captures and analyzes the X and Y positions of each player from multiple perspectives, providing real-time two-dimensional tracking at a sampling frequency of 25 Hz. Furthermore, a custom report was generated using Sportian Performance (former Mediacoach) software (LaLiga, Madrid, Spain), which synchronized the tracking data with the video footage of each match. The validity and reliability of this system for the variables employed have been previously assessed (Felipe et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pons et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showing strong correlations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.80) and high intraclass correlation coefficients (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.75) between the Mediacoach multi-camera tracking system and the Global Positioning System. Match running performance was divided into the following categories: total distance covered by soccer players in meters (TD); very high-speed running (VHSR, distance covered between 21\u0026ndash;24 km\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); sprinting speed running distance (Sprint, distance covered between 24\u0026ndash;28 km\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); high sprint distance (High Sprint, distance covered\u0026thinsp;\u0026gt;\u0026thinsp;28 km\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); total number of accelerations performed above 3 m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (ACC, n\u0026ordm;); and total numbers of decelerations performed above \u0026ndash; 3 m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (DEC, n\u0026ordm;). Absolute values of match running performance variables were normalized to relative values per unit of time (i.e., m\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) to account for possible differences in the total playing time of soccer players. All efforts that involved a minimum movement of one meter, maintained for at least one second, were recorded.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed using RStudio (version 2026.01.0\u0026thinsp;+\u0026thinsp;392). Data processing, modelling, and graphical representations were performed within the RStudio environment to ensure reproducibility and transparency of the analysis.\u003c/p\u003e \u003cp\u003eConsidering the characteristics of the sample, organized hierarchically, nested in groups, and with a longitudinal structure, we considered that the best procedure to analyze the data is through linear mixed models (LMM). LMM have demonstrated their ability to cope with unbalanced and repeated-measures data (Heck and Thomas \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, match running performance variables in matches are nested into players (i.e. each player has a record for every match they have participated in, and each match has observations of several players). Players, in turn, are also nested into different teams every season. Thus, the cross-classified multilevel models are suitable for data structures that are not purely hierarchical. Consequently, a general multilevel modelling strategy was applied where fixed and random effects in different steps were included by (Heck and Thomas \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, LMM were applied to examine the impact of climate conditions on match running performance over the matches. Firstly, a two-level hierarchy was modeled for the analysis. Match running performance variables (i.e., distances covered at different speed thresholds and number of accelerations and decelerations) were included as dependent variables in the models, and climate variables (i.e., air temperature, humidity, wind speed and rain) were included as predictor variables of the match running performance. The soccer player variable was considered as the random effects in the analysis. Models were estimated using the \u003cem\u003elm\u003c/em\u003e function from the \u003cem\u003estats\u003c/em\u003e package. Model assumptions (linearity, homoscedasticity, and normality of residuals) were visually inspected using standard diagnostic procedures.\u003c/p\u003e \u003cp\u003eSecondly, the interaction between temperature and other climate variables was included to explore how the effect of climate variables on match running performance varies across different temperature levels. To facilitate the interpretation of the interaction effects, model-based predictions were generated. Predicted values of each dependent variable were obtained across the observed range of climate variables, while Temperature was fixed at three representative values corresponding to the 10th (Low Temperature), 50th (Medium Temperature), and 90th (High Temperature) percentiles of its distribution. This approach allows for a continuous and statistically coherent interpretation of the interaction between the environmental variables.\u003c/p\u003e \u003cp\u003ePredictions and associated 95% confidence intervals were derived using model-based inference and subsequently visualized. This strategy ensures that the displayed relationships reflect the fitted interaction model rather than separate regressions within arbitrarily defined subgroups.\u003c/p\u003e \u003cp\u003eAll figures were generated using \u003cem\u003eggplot2\u003c/em\u003e, with predicted values and confidence intervals plotted to illustrate the conditional effect of climate variables at different temperature levels. All values were represented as coefficients and standard error (Coeff\u0026thinsp;\u0026plusmn;\u0026thinsp;SE). Statistical significance was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; .05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics for climate variables. The mean value for temperature was 18.29 C\u0026ordm;, for humidity was 59.05 and for wind speed 12.69 km\u0026sdot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the outcomes of the LMM applied to examine the influence of climate variables on match running performance. The model demonstrates a statistically significant influence of climate variables on several match running performance variables. Specifically, Temperature significantly and negatively influenced on TD (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), VHSR (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and Sprint (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001); and significantly and positively on ACC (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and DEC (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Wind speed significantly and negatively influenced on TD (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05); and significantly and positively on DEC (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05). Conversely, Humidity significantly and positively influenced on TD (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), VHSR (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and Sprint (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Finally, Rain significantly and positively influenced on TD (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05), VHSR (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05); and significantly and negatively on ACC (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and DEC (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInfluence of climate variables on match running performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTD (m\u0026sdot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eVHSR (m\u0026sdot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSprint (m\u0026sdot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHigh Sprint (m\u0026sdot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eACC (n\u0026ordm;\u0026sdot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eDEC (n\u0026ordm;\u0026sdot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.79\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.52\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026ordm;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.01\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.01\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.01\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.00\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind Speed (km\u0026sdot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.00\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.93\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.11\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.24\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNote\u003c/em\u003e. Coeff\u0026thinsp;=\u0026thinsp;Coefficient; SE\u0026thinsp;=\u0026thinsp;Standard Error; m\u0026sdot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e = meters per minute; \u0026ordm;C\u0026thinsp;=\u0026thinsp;degrees Celsius; km\u0026sdot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e = kilometres per hour; TD\u0026thinsp;=\u0026thinsp;Total distance; VSHR\u0026thinsp;=\u0026thinsp;Very high-speed running; Sprint\u0026thinsp;=\u0026thinsp;Sprinting speed running distance; ACC\u0026thinsp;=\u0026thinsp;total number of accelerations; DEC\u0026thinsp;=\u0026thinsp;total number of decelerations; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .05; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e near here, please***\u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depict the interaction effect of Humidity, Wind speed and Rain on match running performance across temperature levels. The regression models showed significant effects of Humidity and Temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) interactions on match running performance variables. The influence of Humidity on TD, High Sprint, ACC and DEC was most pronounced at High temperatures where increased. The regression models also demonstrated significant effects of Wind speed and Temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) interactions on match running performance variables. The influence of Wind speed on TD, Sprint, High Sprint, ACC and DEC was most pronounced at High temperatures where decreased. Conversely, ACC and DEC increased under Low temperatures and windy conditions. Finally, the results of the linear regression models with the interaction between Rain and Temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed that the effect of Rain on match running performance variables significantly varied with Temperature levels. The influence of Rain on TD, VHSR and Sprint was most pronounced at High temperatures where decreased. Conversely, ACC and DEC increased under High temperatures and rainy conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e***Insert Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e near here, please***\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the impact of climate variables on the match running performance of Spanish professional soccer players. Previous research has shown that environment can significantly influence on athlete performance maintenance, but there is scarce published literature on the topic. Therefore, this the first attempt to examine the influence of the environment on match running performance in Spanish professional soccer players based on a very large sample of matches. The main findings showed that: i) the Temperature ranged from \u0026minus;\u0026thinsp;1 C\u0026ordm; to 38 C\u0026ordm;, and the Humidity ranged from 11 to 100%, reporting a huge thermal and humidity fluctuations in Spanish First Division official matches; ii) Temperature and Wind Speed significantly and negatively influenced on TD, and significantly and positively influenced DEC, while Humidity and Rain significantly and positively influenced on TD, and VHSR; iii) the interaction effects of climate variables and Temperature showed significant influence on match running performance, which was most pronounced at High temperatures.\u003c/p\u003e \u003cp\u003eIn Spain, official soccer games commonly occur between 2:00 p.m. and 10:00 p.m. Also, Spanish men\u0026acute;s soccer league has a long duration (from August to May), and matches occur in different seasons and regions of the country. These factors may result in variations in the average climate variables throughout the competition. Specifically, our results showed temperature fluctuations of 39 \u0026ordm;C, humidity variations of 89% and wind speed differences of 41 km\u0026sdot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. For instance, Brazilian national games had temperatures ranging from 14.1 C\u0026ordm; to 35.9 \u0026ordm;C with a relative humidity of 68% (Augusto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). During French League 1, Temperatures ranged from \u0026lt;\u0026thinsp;5 \u0026ordm;C to \u0026gt;\u0026thinsp;21 \u0026ordm;C (Carling et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and in the German Bundesliga, recorded temperatures were in the range\u0026thinsp;\u0026minus;\u0026thinsp;10 \u0026ordm;C to 28 \u0026ordm;C (Link and Weber \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), reaching similar Temperature fluctuation than Spain. According to the current results, there was a wide range of environmental temperatures (-1 C\u0026ordm; to 38 C\u0026ordm;), so the physiological response to heat and cold can be heterogeneous among soccer players. Understanding the effect of variations in climate variables on the match running performance of Spanish soccer players is important (Draper et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDifferent effects were observed concerning the impact of climate variables on match running performance. Specifically, Temperature negatively affected TD, VHSR and Sprint performance. Our results align with those of studies conducted under official match conditions that reported reductions in match running performance (\u0026Ouml;zg\u0026uuml;nen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nassis et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The most affected variable by Temperature was TD, similar to the results of a systematic review which analysed the effects of temperature on soccer (Draper et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). High temperatures have likely been associated with soccer players experiencing heat cramps and heat exhaustion, which results in a large reduction in TD (Mohr et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). On the other hand, high-intensity exercise in high temperatures could promote different bodily responses in professional soccer players that reduce performance (Augusto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The negative influence of Temperature on high-intensity exercise may be related to the increased internal temperature caused by heat which increases vascular and metabolic stress, the heart rate and decreases voluntary muscle contractions (McClelland et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; P\u0026eacute;riard et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cramer et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conversely, the positive impact of Temperature on ACC and DEC may have benefit by hot temperatures when these actions are performed only once. Improvements in single ACC and DEC may be due to better anaerobic system and muscle activity functioning (Augusto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, in situations involving repeated efforts, such as soccer matches, the ability to perform ACC and DEC over time is impaired because the running intensity is adjusted over time in hot environments (Chodor et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chmura et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another possible explanation is the difficulty of reach high-intensity distances in a hot environment so, soccer players try to move forward accelerating (Cotteret et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious research examining the impact of Humidity on soccer environments has observed inconclusive results. For instance, \u0026Ouml;zg\u0026uuml;nen et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) demonstrated that soccer players covered a significantly shorter TD in the second half of a match than in the first when the relative humidity was above 60%. Conversely, Hayes et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) claimed that air humidity between 33.1 and 78.3% does not significantly impact the numbers of repeat sprints performed by team sports players. Similar results were reported by Chmura et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) who showed that the best comfort zone for players to attain high levels of physical activity entails a relative humidity range below 60%. However, our results showed that Humidity positively impacted match running performance, specifically TD, VHSR and Sprint distance. These results suggest that higher humidity levels allow for greater match running performance of soccer players during official games. One possible explanation is that the best comfort zone for players to attain high levels of physical activity entails a relative humidity range below 60% (Konefał et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chmura et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the mean Humidity reported in Spanish matches was 59%. Therefore, it is necessary to understand the extent humidity to which Humidity positively or negatively impacts match running performance. However, unlike previous studies, our study did not include humidity levels or categories.\u003c/p\u003e \u003cp\u003eWind speed and Rain were included in the present study as new variables of weather conditions. The results showed that Wind speed negatively impacted TD, while Rain positively influenced TD and VHSR, but negatively influenced ACC and DEC. Although there is no previous literature on this topic, several factors could explain these results. First, the negative impact of Wind on TD could be attributed to the difficulty of playing in windy conditions. In these conditions, the direction of the ball can change during passes or crosses, causing players to cover less distance. Additionally, this difficulty could result in more game interruptions, impacting on effective playing time and decreasing the overall distance covered by soccer players (Altmann et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, the positive influence of Rain on TD and VHSR could be explained by a reduction in ambient temperature, which favours maintaining match running performance during games (Chmura et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Augusto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Playing in hot environments is linked to a drop in match running performance, but rain could decrease the ambient temperature. In these conditions, soccer players' performance would resemble that in cold environments, where their overall match running performance does not decrease (Carling et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, a decrease in muscle temperature in rainy conditions could affect the ability to produce dynamic, explosive contractions (Mohr et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This could explain the decrease in ACC and DEC.\u003c/p\u003e \u003cp\u003eLastly, the interaction effects of Humidity, Wind speed and Rain on match running performance across temperature levels add an additional layer of complexity to our understanding of the influence of climate variables. Overall, the results indicated that the association between Humidity, Wind speed, and Rain and match running performance varied substantially across temperature levels. Specifically, the influence of Humidity on TD, High Sprint, ACC and DEC was most pronounced at High temperatures, where these variables were increased, probably due to the decrease in air temperature, which may reduce the drop of match running performance. Similar results were reported by (Chmura et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Augusto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who found higher performance parameters when matches occurred in conditions of relative humidity below 60% and lower ambient temperatures. However, it is important to note that players\u0026rsquo; physiological responses to high ambient temperatures and humidity are highly individualized (\u0026Ouml;zg\u0026uuml;nen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Arsac et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Conversely, the influence of Wind speed and Rain on TD, VHSR, Sprint and High Sprint was most pronounced at High temperatures, resulting in decreased performance parameters. These environmental conditions may be the most uncomfortable for soccer players, since a hot environment with wind and rain would make it more difficult to play soccer fluently. Specifically, rainy conditions with high temperatures could be similar to high temperature and high humidity conditions reported by Chmura et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which impaired player\u0026acute;s physical performance.\u003c/p\u003e\n\u003ch3\u003eLimitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eTo the best of our knowledge, this is the first study to analyse the impact of the environment on match running performance in the Spanish First soccer league under \u0026ldquo;real-world\u0026rdquo; conditions. However, some limitations should be acknowledged: i) although there are no big altitude differences in Spain, information about this aspect should be included in future studies; ii) further research should consider different age groups or tiers of soccer players because the environmental impact may differ depending on the type of person; iii) the athletes' dehydration, heart rate, thermal responses, and internal temperatures could be monitored in an attempt to determine the effects of different environments on individual anthropometric and physiological characteristics; iv) finally, although the temperature inside the stadiums was measured, a more complex method, such as the wet bulb globe temperature (WBGT), was not used. Therefore, further research should measure the temperature using this valid method.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eEnvironmental factors are widely considered to affect players\u0026apos; match running performance in professional soccer. Firstly, this paper discusses the impact of climate variables on match running performance in the First Spanish professional soccer league, reporting a huge thermal and humidity fluctuation during official matches. Secondly, the data showed that Temperature and Wind Speed significantly and negatively influenced TD, and significantly and positively influenced DEC. Meanwhile, Humidity and Rain significantly and positively influenced TD, and VHSR. Finally, the results suggest that the interaction between climate variables and temperature plays a crucial role soccer players\u0026apos; match running performance. These results should be considered when designing training programs and strategies for extreme environmental conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical applications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study contributes to our understanding of the influence of climate variables on match running performance in the First Spanish soccer league. The results have several strengths such as the practical applicability. This allows strength and conditioning coaches and sports scientists to consider environmental factors when interpreting the context of a match. This information is also relevant to elite Spanish soccer and can help entities that organize matches carry out interventions to maintain player performance (Augusto et al. 2025). For instance, despite the abundance of broadcasting rights and competition in Spanish soccer, it would be interesting to change the time of official games to avoid high temperatures and extreme environments, which could decrease soccer players\u0026apos; match running performance. Additionally, performance seems to decrease with dehydration when soccer is played in high environmental temperatures. Therefore, fluid intake strategies should be a regular part of the physical preparation before and during games in elite soccer (Mohr et al. 2010). Finally, these findings can be also used to develop targeted interventions to help players adapt to matches in different environments. The goal of these training programs would be to achieve positive physiological effects without causing motor or health problems for players (Chmura et al. 2017).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received full approval from the Ethics Committee of the University of Extremadura, under the Vice-Rectorate of Research, Transfer, and Innovation – Delegation of the Bioethics and Biosafety Commission (Protocol number: 79//2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCRediT: \u003cstrong\u003eJCPB\u003c/strong\u003e: Data curation, Formal analysis, Software, Writing – review \u0026amp; editing; \u003cstrong\u003eDLB\u003c/strong\u003e: Conceptualization, Resources, Validation, Writing – original draft; \u003cstrong\u003eAVB\u003c/strong\u003e: Methodology, Validation, Visualization; \u003cstrong\u003eJPT\u003c/strong\u003e: Investigation, Methodology, Writing – original draft; \u0026nbsp;\u003cstrong\u003eRLC\u003c/strong\u003e: Funding acquisition, Project administration; \u003cstrong\u003eRR\u003c/strong\u003e: Funding acquisition, Visualization; \u003cstrong\u003eTGC\u003c/strong\u003e: Investigation, Validation, Writing – review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the European Regional Development Fund (ERDF), the Government of Extremadura (Department of Economy and Infrastructure), and LaLiga Research and Analysis Sections. David Lobo-Triviño was supported by a pre-doctoral fellowship (PD23068) awarded by the Junta de Extremadura and co-financed by the European Social Fund Plus (FSE+). José Carlos Ponce-Bordón was supported by a postdoctoral fellowship (PO24154_I) awarded by the Junta de Extremadura and co-financed by the European Social Fund Plus (FSE+).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author confirms that all data generated and analyzed during this study for inclusion in this article are available in Electronic Supplementary Material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAltmann S, Forcher L, Woll A, H\u0026auml;rtel S (2023) Effective playing time affects physical match performance in soccer: An analysis according to playing position. 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BMJ Open Sport Exerc Med 7:e001205. https://doi.org/10.1136/bmjsem-2021-001205\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"professional, meteorological, external load, soccer, thermal conditions","lastPublishedDoi":"10.21203/rs.3.rs-9171550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9171550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eResearch has demonstrated that climate can affect the match running performance of soccer players. This study aimed to analyze the influence of various meteorological variables (i.e., air temperature, humidity, wind speed and rainfall) on the match running performance of Spanish professional soccer players. A total of 10,859 individual match observations from 515 soccer players competing in the First Spanish soccer league (i.e., LaLiga; \u003cem\u003en\u003c/em\u003e = 380 matches) over the 2023/24 season were collected. Match running performance was obtained using the Sportian Performance video tracking system in accordance with LaLiga. Air temperature, humidity, wind speed and rainfall were collected as climate variables. Linear Mixed Models showed that climate variables significantly impacted match running performance. Specifically, Temperature and Wind Speed significantly and negatively influenced on Total Distance (TD; \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and significantly and positively on Decelerations (DEC; \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). Conversely, Humidity and Rain significantly and positively influenced on TD (\u003cem\u003ep\u003c/em\u003e\u0026lt; .001), and Very High-Speed Running (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Also, the regression models revealed significant interaction effects of humidity, wind speed and rainfall on match running performance across temperature levels. Specifically, the influence of Humidity, Wind Speed and Rain on TD was most pronounced at high temperatures where was decreased, while Accelerations and DEC increased. This study contributes to our understanding of the influence of climate variables on match running performance in the First Spanish soccer league. These results should be considered when designing training programs and strategies for extreme environmental conditions.\u003c/p\u003e","manuscriptTitle":"How do the climatological variables impact on match running performance in Spanish professional soccer players?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 01:19:27","doi":"10.21203/rs.3.rs-9171550/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-31T19:35:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T21:57:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Biometeorology","date":"2026-03-20T03:33:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5c4eee21-7adb-4a53-b5f9-57d38d9d714d","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T01:19:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 01:19:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9171550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9171550","identity":"rs-9171550","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}