The Evolution of Football Betting- A Machine Learning Approach to Match
  Outcome Forecasting and Bookmaker Odds Estimation
Purnachandra Mandadapu
This paper explores the significant history of professional football and the
betting industry, tracing its evolution from clandestine beginnings to a
lucrative multi-million-pound enterprise. Initiated by the legalization of
gambling in 1960 and complemented by advancements in football data gathering
pioneered by Thorold Charles Reep, the symbiotic relationship between these
sectors has propelled rapid growth and innovation. Over the past six decades,
both industries have undergone radical transformations, with data collection
methods evolving from rudimentary notetaking to sophisticated technologies such
as high-definition cameras and Artificial Intelligence (AI)-driven analytics.
Therefore, the primary aim of this study is to utilize Machine Learning (ML)
algorithms to forecast premier league football match outcomes. By analyzing
historical data and investigating the significance of various features, the
study seeks to identify the most effective predictive models and discern key
factors influencing match results. Additionally, the study aims to utilize
these forecasting to inform the establishment of bookmaker odds, providing
insights into the impact of different variables on match outcomes. By
highlighting the potential for informed decision-making in sports forecasting
and betting, this study opens up new avenues for research and practical
applications in the domain of sports analytics.