© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) Prediction of Football Games Results Roman Nestoruk 1 and Grzegorz Słowiński 2 1 Sollers Consulting Sp. z o.o., ul. Koszykowa 54, Warsaw 00-675, Poland 2 University of Technology and Economics, Engineering Department, ul. Jagiellońska 82f, Warsaw 03 -301, Poland Abstract For creation of 3 machine learning models, dataset of 50, 100 and 200 games are being used. All the models are built, using deep learning (DL) and machine technology (ML) technique with the goal to prove, that even ML algorithms can be used to predict football games result. The data set consists of different real games results, collected from the most recognizable tournaments, such as: English Premier League, Italian Seria A, German Bundesliga, Spanish La Liga and French League 1. The target values of the work are prediction of exact game score (Average accuracy obtained after the last wave of testing – 11.6%) and prediction of game result (Average accuracy obtained after the last wave of testing – 39%). Keywords machine learning, football games prediction, deep learning 1. Introduction Mainly, the regular person thinking that football is unpredictable and sometimes, analogical game, but we are living in the 21st century, where technologies have become one of the biggest parts of our lives. We are using virtual assistance, image and voice recognition, autopilots, we almost meet the era of self- driving cars. The brain of all these discoveries is Artificial Intelligence, with neural networks inside. We think these technologies are very helpful for achieving the main target of this work – proving that even football, where every match consists of thousands of different moments, can be predicted by Artificial Intelligence better than by benchmark. 2. Used Tools and technology As football statistic is not available in the format of data files, or API communication response, scraping algorithm is needed. To not enhance existing stack with extra languages, scrapping algorithm was written in Python and with use of Selenium Web Driver framework & BeautifulSoup4 library. For machine learning processes TensorFlow and keras frameworks has been used and CSV library for storing data. 3. Data for training and validation One of the most recognized kinds of statistics in football games are possession and shots, but for this algorithm, some more data are also useful: • Average game mark: Shows the performance of the team, during the season. • The average amount of goals, per game: Result of dividing the number of goals, scored by the look at team, by the number of played games. • Average possession: Average percentage of possession of the ball during the games. • Pass accuracy: Counting by diving number of all successfully completed passes, by the number of all passes of the team. • Shots per game: Anyone, who is connected to football knows, that goals are mainly the result of shots. • Average players mark from most possible starting line up: Shows the performance of every single player, during the season.