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I just published my first R package: {soccerAnimate}, which allows you to create 2D soccer animations using tracking data. Here I’m going to tell you some details about its functionalities and future additions. Main functionalities Currently all functions of the package are prepared to work only with the data format of the data provider Metrica Sports, due they made public a tracking dataset as example (here). Details about each available functions of soccerAnimate package is presented in the following paragraphs: »

In this article I am going to tell you what the passing networks are and how to create them, in addition to share with you some R codes that I prepared for that and some examples cases. If you don’t consider yourself a football analytics nerd and you have not seen yet the slides from the presentations “A look into Soccer Analytics using R” and “Soccer Analytics: A data revolution” I would suggest to check them in order to be familiar with the data types and terminology mentioned here. »

I just updated the R Shiny app which allows you to visualize the historical Men FIFA ranking interactively. Data (from August 1993 to April 2020) scraped from the FIFA official website. The app has a date range and country selectors. It is possible to visualize from 1 to 3 nations at the same time. Option to image download as PNG file. Link to the Github repo if you want to review the code and/or use the data by yourself. »

Along this article I am going to share some detail which could be useful if you want to fit your own xG model, in addition to its respective performance evaluation and results analysis. What is xG? The xG (eXpected Goals) is the main metric used into the Football Analytics field nowadays. In simple words, it is the probability (from 0 to 1) that a shot has to become in a goal. »

I couldn’t resist doing some data analysis for this new Soccer World Cup in Russia 2018. After searching online for a while and collecting some data I decided to focus on creating a prediction model for “expected goals for each team and each game”, allowing the estimation of probability to win, lose or draw. This way we can predict all games of the tournament. Now all details about the considered data, model fitting and results are presented. »