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Design and Implementation of a Predictive Model for Nigeria Local Football League
ADEBISI John, ALABI Damilola
Pages - 106 - 122     |    Revised - 25-07-2021     |    Published - 31-08-2021
Volume - 15   Issue - 4    |    Publication Date - August 2021  Table of Contents
Football, Sport, Machine-Learning, Poisson Distribution, Data Mining.
Sports prediction has become more interesting especially in the era of statistical information about the sport, players, teams and seasons are readily available. Sport analysts have opted out in their traditional ways of analyzing sport events and tends to leverage on the advantages of sports data; this enables more realistic analysis beyond sentiments. However, football game was considered in this research. Data from Nigerian Professional Football League (NPLF) was used to predict result based on different conditions such as home win, draw and away win of teams in the league. Machine Learning, k-Nearest Neighbor and mathematical Poisson distribution algorithm was hybridized using data mining tools together with Anaconda packages. The model accuracy was compared with other online bookmarkers, and it yielded 93.33% accuracy which will be helpful in making substantial profits in within the economy through the betting industries. This model is practically based on the home and away matches coupled with historical trends of goals scored and winning of previous matches, by implication, Nigerian football league will be more enhanced to catch up with their international counterparts and the players tends to get more feasibility from match result predictions for international participation and employment opportunities.
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Engineering/Computer, University of Namibia, Ongwediva - Namibia
Mr. ALABI Damilola
Engineering/Computer, University of Lagos, Lagos - Nigeria

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