Exploring polynomial classifier to predict match results in football championships

Abstract

a b s t r a c tFootball is the team sport that mostly attracts great mass audience. Because of the detailed information about all football matches of championships over almost a century, matches build a huge and valuable database to test prediction of matches results. The problem of modeling football data has become increasingly popular in the last years and learning machine have been used to predict football matches results in many studies. Our present work brings a new approach to predict matches results of championships. This approach investigates data of matches in order to predict the results, which are win, draw and defeat. The investigated groups were different type of combinations of two by two pairs, win-draw, windefeat and draw-defeat, of the possible matches results of each championship. In this study we employed the features obtained by scouts during a football match. The proposed system applies a polynomial algorithm to analyse and define matches results. Some machine-learning algorithms were compared with our approach, which includes experiments with information obtained from the football championships. The association between polynomial algorithm and machine learning techniques allowed a significant increase of the accuracy values. Our polynomial algorithm provided an accuracy superior to 96%, selecting the relevant features from the training and testing set.

Traffic characterization in a communications channel for monitoring and control in real-time systems

“…This classifier is based on Bayes' theorem and allows a general solution to the problem when the parameters are known [64] [65]. Given the classes 𝑤 1 , 𝑤 2 … , 𝑤 𝑀 assigned to M and known the pattern, which is represented by a characteristic of the vector x, the M conditional probability 𝑃(𝑤 𝑖 |𝑥), i = 1,2,...,M is formed.…”

Machine Learning Models Reveal Key Performance Metrics of Football Players to Win Matches in Qatar Stars League

“…The proposed model achieved an average accuracy of 69.5%. Martins et al used ML models to predict the outcome of football matches using dataset from the English Premier League (2014-2015), the Spanish La Liga (2014-2015), and the Brazilian League Championships (2010-2012) [21]. Different features were extracted for each league and then consolidated into one dataset.…”

A conditional fuzzy inference approach in forecasting

“…Vlastakis et al (2008) show that SVM performs better than a Poisson model when applied to the task of predicting European football match scores. Gomes et al (2016) and Martins et al (2017) also utilize SVMs to forecast the number of football corners, goals and outcomes of Premier League and several other championships respectively. Recently, Baboota and Kaur (2018) show that SVM with radial basis kernel is efficient in predicting the rank probability scores for the English Premier League using teams' past performance indicators as inputs.…”

“…Thus,Andersson et al (2009) apply this variable in their football betting models Oberstone (2011). andMartins et al (2017) apply shots on target as another proxy of the offensive capacity of a team. Most of the inputs in Table2are based on the performance of the home/away team during the last three games.…”