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In this work we study the problem of forecasting the final score of a football match before the game kicks off (pre-match) and show how the derived models can be used to make profit in an algorithmic trading (betting) strategy. It is mainly inspired from the author´s PhD thesis submitted to the university of Manchester in February 2016. The book consists of two main parts. The first part discusses the database and a new class of counting processes. The second part describes the football forecasting models. The data part discusses the details of the design, specification and data collection of a comprehensive database containing extensive information on match results and events, players´ skills and attributes and betting market prices. The database collected will be used to create a team based model built on top of an original count process derived in the first part. The second model is player based and makes heavy use of the data on the players´ attributes. The predictive value of both models are tested in an algorithmic betting strategy based on the Kelly criteria.

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Tarak´s background is in designing medium and high frequency algorithmic tradingstrategies for a world leading financial institution in Paris and Wall Street, New York.After obtaining his PhD in Statistics, Tarak became interested in developing models for both pre-match and in-play betting in football and implemented them in automatic strategies.

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