Relational Learning for Football-Related Predictions
نویسندگان
چکیده
Association football is becoming increasingly competitive and the financial stakes involved are causing football clubs and football leagues to become more professional. Over the past 25 years, club budgets have grown enormously due to ticket sale revenues, broadcasting revenues, merchandising, and prize money. Recently, player tracking systems were introduced and are producing overwhelming amounts of data which are being used by experts to analyze matches.
منابع مشابه
Jonathan Pines
Goals and Applications Our overall goal is to successfully predict the offensive play-calling decisions of college football coaches. We will attempt to make such predictions on the basis of data from past games and from previous plays in the current game. While there is extensive literature on machine learning in some other sports such as baseball, football has been relatively neglected; furthe...
متن کاملTransparent Machine Learning Algorithm Offers Useful Prediction Method for Natural Gas Density
Machine-learning algorithms aid predictions for complex systems with multiple influencing variables. However, many neural-network related algorithms behave as black boxes in terms of revealing how the prediction of each data record is performed. This drawback limits their ability to provide detailed insights concerning the workings of the underlying system, or to relate predictions to specific ...
متن کاملStatistics-free Sports Prediction
We use a simple machine learning model, logistically-weighted regularized linear least squares regression, in order to predict baseball, basketball, football, and hockey games. We do so using only the thirty-year record of which visiting teams played which home teams, on what date, and what the final score was. No real ”statistics” are used. The method works best in basketball, likely because i...
متن کاملStacked Graphical Learning
In reality there are many relational datasets in which both features of instances and the relationships among the instances are recorded, such as hyperlinked web pages, scientific literature with citations, and social networks. Collective classification has been widely used to classify a group of related instances simultaneously. Recently there have been several studies on statistical relationa...
متن کاملLearning and Inference for Information Extraction
Information extraction is a process that extracts limited semantic concepts from text documents and presents them in an organized way. Unlike several other natural language tasks, information extraction has a direct impact on end-user applications. Despite its importance, information extraction is still a difficult task due to the inherent complexity and ambiguity of human languages. Moreover, ...
متن کامل