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In this thesis we study the problem of forecasting the nal score of a football match before the game kicks o (pre-match) and show how the derived models can be used to make pro t in an algorithmic trading (betting) strategy. The second part describes the football forecasting models. The database was created using state of the art web-scraping, text-processing and data-miming techniques. At the time of writing, we have collected data on all games played in the ve major European leagues since the 2009-2010 season and on more than 7000 players. The statistical modelling part discusses forecasting models based on a new generation of counting process with exible inter-arrival time distributions. The proposed algorithms are implemented in a contributed R package Countr available from the Comprehensive R Archive Network. One of these exible count distributions, the Weibull count distribution, was used to derive our rst forecasting model. Our second forecasting model uses the same statistical distribution but models the attack and defence strengths of each team at the players level rather than at a team level, as is systematically done in the literature. designer bags cyber monday

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