The future of football is bright with minds like that of Anthony Constantinou providing new ways of predicting match outcomes. Anthony Constantinou is an Assistant Professor in Machine Learning and Data Mining at Queen Mary University of London, as well as being Head of the Bayesian Artificial Intelligence Research Lab. His researching regarding Bayesian Networks (BNs) may one day be able to predict and solve problems revolving around sports, medicine, forensics, the UK housing market, and the UK financial market.
His scholarly article titled “Pi-Football: A Bayesian Network Model”, was published in 2012 but has since then started to gain traction. The study has been cited 90 times in total, with 26 of those citations occurring in 2018. Constantinou conducted a study regarding the 2010/2011 English Premier League.
Prior to the start of each match the results that Constantinou’s BNs had generated were posted online. By the end of the study, the conclusion was that the BN model was exceptionally accurate, which proves the usefulness of BNs. For the purpose of football matches, BNs can be used to essentially beat the bookies. For the larger implications, the usefulness of BNs is just beginning to come to light. See This Page for additional information.
Bayesian networks – also known as “belief networks” or “causal networks” – delve deep into probability theory and causation, using random variables to predict outcomes. Constantinou is at the forefront of this exciting and growing field of study. Scholarly articles written by Constantinou focus mainly on Association Football and predicting scores for the purpose of gambling.
According to Anthony Constantinou and Norman Fenton, Bayesian Networks are able to dive deeper into our everyday decision-making process, and help us understand why certain variables influence us and the world around us.