###### Abstract  
The growing popularity of soccer has led to the prediction of match results becoming of interest to the research community. The aim of this research is to detect the effects of weather on the result of matches by implementing Random Forest, Support Vector Machine, K-Nearest Neighbors Algorithm, and Extremely Randomized Trees Classifier. The analysis was executed using the Spanish La Liga and Segunda division from the seasons 2013-2014 to 2017-2018 in combination with weather data. Two tasks were proposed as part of this study: the first was to find out whether the game will end in a draw, a win by the hosts or a victory by the guests, and the second was to determine whether the match will end in a draw or if one of the teams will win. The results show that, for the first task, Extremely Randomized Trees Classifier is a better method, with an accuracy of 65.9%, and, for the second task, Support Vector Machine yielded better results with an accuracy of 79.3%. Moreover, it is possible to predict whether the game will end in a draw or not with 0.85 AUC-ROC. Additionally, for comparative purposes, the analysis was also performed without weather data. s 2020 10 6750 10.3390/app10196750 18 August 2020 22 September 2020 10. 6750 10.3390/app10196750 18 August 2020 22 September 2020 2020 20. 10 6750 10.3390/app10196750 18 August 2020 22 September 2020 2020 20. 18 August 2020 2  
of each goal-scoring opportunity, instead of directly predicting the game result, and integration of these probabilities will give the final match result. The authors applied the following algorithms: Logistic Regression, Decision tree, RF, and a decision tree boosted with Ada-boost. The results showed that RF outperformed other techniques. Although women's soccer is not as popular as men's, it has still attracted the attention of researchers. One of these studies was carried out by [PERSON] et al. [9]. The authors applied a hybrid model based on RF and two ranking methods (Poisson ranking method and abilities based on bookmakers' odds). It must be mentioned that the results of such studies depend not only on the methods implemented but are also more and more dependent on the features included in the analysis. [PERSON] and [PERSON] [10] showed that the integration of data from Twitter could significantly improve the final results. In another study conducted by [PERSON] and [PERSON] [11], the authors utilized virtual data extracted from video games for soccer result prediction. Another important aspect is the weather conditions. These were the main subject of several studies and, although the main objective was not to predict the final result, some of them were focused on analyzing the impact of the weather on different features of the game, such as the distances run by players, injuries, successful passes, etc. For example, [PERSON] et al. [12] tried to analyze the occurrence of injuries by considering weather conditions and the playing surface. They applied nine classifiers, and the results showed that SVM outperformed other methods. [PERSON] et al. [13] attempted to correlate hot conditions with the physiological and physical status of the players. Two types of conditions were taken into account for analysis: temperate and warm ambient conditions. The results showed that warm weather conditions cause the following consequences: a reduction in the distance covered in the match and high intensity running, and an improvement in peak sprinting speed and successful passes. Similar research to this last study was carried out by [PERSON] et al. [14], who chose 64 games in the 2014 FIFA World Cup in Brazil in order to compare environmental conditions. The datasets consisted of Wet-bulb globe temperature parameters and relative humidity recorded one hour before the game. The environmental conditions were divided into high, moderate, and low stresses.