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Title: A journey across football modelling with application to algorithmic trading
Author: Kharrat, Tarak
ISNI:       0000 0004 6497 9510
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2016
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In this thesis 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. The thesis 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 was created using state of the art web-scraping, text-processing and data-mining techniques. At the time of writing, we have collected data on all games played in the five 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 flexible inter-arrival time distributions. Several different methods for fast computation of the associated probabilities are derived and compared. The proposed algorithms are implemented in a contributed R package Countr available from the Comprehensive R Archive Network. One of these flexible count distributions, the Weibull count distribution, was used to derive our first forecasting model. Its predictive ability is compared to the models previously suggested in the literature and tested in an algorithmic trading (betting) strategy. The model developed has been shown to perform rather well compared to its competitors. 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. For this model we make heavy use of the data on the players' attributes discussed in the data part of the thesis. Not only does this model turn out to have a higher predictive power but it also allows us to answer important questions about the 'nature of the game' such as the contribution of the full-backs to the attacking efforts or where would a new team finish in the Premier League.
Supervisor: Boshnakov, Georgi ; Donev, Alexander Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: statistical arbitrage ; Kelly betting ; Algorithmic Trading ; Football models ; Betting ; Weibull counting process