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Statistical Data Analytics Of Football For Beating The Odds

Airoldi, Luca (2014) Statistical Data Analytics Of Football For Beating The Odds. Diploma thesis, Dublin, National College of Ireland.

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Abstract

The aim of this project is to analyse the past 20 years of football data about the Italian Serie A league in order to gain an insight understanding of statistical facts and patterns for football outcome predictions for the sport betting industry.

There have been a number of research papers and books written in the past about football matches results in relation to the efficiency of fixed odd betting, exploring a number of techniques such as Fuzzy Models and Neural Networks (Rotshtein et al., 2005) Ranking Systems (Chang Wang & Martina Vandebroek, 2011) Bivariate Poisson Models (Siem Jan Koopman & Rutger Lit, 2012) Compound Approaches (Min et al., 2001) and Statistical Modelling (Dimitris
Karlis & Ioannis Ntzoufras, 1998).

The main inspirational resources for this project are:
- W.A.Hunter, (1996) Football Fortunes Results Forecasting. England: Gambling & Computing: Oldcastle Books.
- Joseph Buchdahl, (2003) Fixed Odds Sports Betting Statistical Forecasting and Risk Management. England: High Stakes Publishing.
- Jason Houlsby, (2013) A Guide to Betting on Football: England: Harry Haller Publishing.

The reason for this is that the books aforementioned proved to be particularly functional for the scope of this project, while at the same time proving to be a solid support and also an inspiration base for further investigation and analysis.

The approach adopted for this project is to build an easy and user-friendly tool that can be used by any punter interested in attempting to successfully place wagers on the outcome of the Italian Serie A League matches.

The tool selected for developing this project is Microsoft Excel®, the result is a spreadsheet named Sviluppo© (thereinafter in this document).

The reason for using Excel is that it is a widely used spreadsheet (so that whoever wants to test and experiment Sviluppo© does not require any specific software package) suitable for the analysis of data formatted as table; Excel has calculation and statistical analysis capability, can produce graphs and it has embedded VBA (Visual Basic For Application), which is an object oriented based programming language that comes as a standard with Microsoft Office, VBA
allows to program, automate and expand the basic functionalities of Excel.

Furthermore, Excel is suitable for handling large amounts of data once this is formatted into tables, this allows then further analysis easy to be implemented (Pivot Tables) it is also an excellent tool for building standard templates and models; finally Excel proved to be the perfect application for handling the source of data used in this project which is in .csv format and is freely available to download from: http://www.football-data.co.uk/italym.php.

The system evaluates 20 years of historical data for the Italian Serie A League from 1993/1994 to the current 2013/2014 seasons and elaborates a betting system strategy based on football results outcomes probability and combinations, while taking into account the odds provided by the selected Bookmaker.

The result is a method, which is suitable for playing football-betting pools tables (this is a gambling/football betting technique in which a bettor attempts to forecast the winners in a set of games, this type of betting is also known as “Accumulator”).

Item Type: Thesis (Diploma)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions: School of Computing > Higher Diploma in Science in Data Analytics
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 15 Dec 2014 13:56
Last Modified: 15 Dec 2014 13:57
URI: http://trap.ncirl.ie/id/eprint/1875

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