Statistical Data Analytics Of Football For. Beating The Odds

Transcription

1 Statistical Data Analytics Of Football For Beating The Odds by Luca Airoldi (X ) National College of Ireland, 2014 Higher Diploma in Science in Data Analytics (HDSDA) Luca Airoldi 2014 National College of Ireland Academic year 2013 /2014 Dissertation 1

2 Declaration I certify that this thesis, and the research to which it refers, are the product of my own work, and that any ideas or quotations from the work of other people, published or otherwise, are fully acknowledged in accordance with the standard referencing practices of the discipline. Luca Airoldi Tuesday, 27 May

3 Table of Contents Statistical Data Analytics Of Football For Beating The Odds... 1 Executive Summary Introduction Domain(s) Description Motivation Aims Main Problems being addressed / Goals/Scope Research Questions Solution Overview Structure Related Work (Background) System and Datasets Design and Architecture Implementation Requirements Graphical User Interface (GUI) Layout Datasets Testing and Evaluation System Testing Description Methods Results Conclusions Further development or research References Appendix Project Proposal Initial Requirement Specification

4 8.3 Management Progress Reports Literature Review

5 Executive Summary 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). 5

6 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: 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 ). Keywords: Statistics, Data Analytics, Probability, RPS (Ranking Probability Score), Italian Serie A League. 6

7 1 Introduction The aim of the project is to find and implement a winning method and strategy based on statistical facts from historical data in order to predict the outcome of the matches results of the Italian Serie A League. Those principles should also be suitable to be applied to any other football league, providing historical data is available to be elaborated and analysed. The study will focus on predicting correct match results only, i.e. win, draw or lose, in a fixed-odds betting environment. This means that real time results and odds changes or any other type of bet (i.e. number of goals scored in a match) are not contemplated, the focus is only on forecasting results ahead of the event to start, the reason is that this allows for odds calculation and potential outcomes with accuracy. This is important in terms of building a risk strategy. 1.1 Domain(s) Description Football gambling has become a huge industry World Wide, it is the World s most popular sport and it is by far the fast growing betting market, worth billions of Euros every year. BBC Sport Football [Online]. Available from: [Accessed 3rd of October 2013]. For as long as football remains the most popular sport in the World, so will be the practice of football betting, gambling and sport are two activities which are strongly linked together. In most cases punters place their wagers based on personal feelings, behaviours, knowledge of a particular league or team performance, the aim is to maximise the profit in case of win, however, this lack of a strategy for assessing the risk of the outcome event and minimize losses. On the other end a methodical approach to football betting has led to the development of statistical models for football analysis, in most cases focusing on the elaboration of team rankings or predictive probabilities of the matches 7

8 outcomes considered in the terms of home win, draw or away win (Alun Owen, 2010) This projects fits in this second category. 1.2 Motivation To predict football match results is a challenging task and it is of great interest for many, fans and punters. From a research point of view it is a fascinating problem due to its difficulty as the results of football matches depend on many different variables. Many factors influence the outcome of a football match such as the conduct of the referee, the fact that a team may have some of the key players out of the match due to injury or been disqualified, the home ground advantage, the geographical distance between the two teams home ground, the average attendance and how hostile is the crowd for the away team (historical rivalry between teams and supporters also have an impact on the game outcome) and finally the form of the two teams. These are the key variables to be taken into account when assessing the probability of outcome of a football match. For all of the above reasons football is a particularly unpredictable game, it is in fact impossible to predict with certainty the outcome of a football game even in the cases where the result seems to be apparently quite obvious. Because the implication of all these factors, even the most expert punter or a computer program has a limit to the number of results that can correctly be predicted. The evaluation of all these variables for a single match is extremely challenging and time consuming, having to do the same task for a number of matches will be impracticable. The strategy behind this project is to bet on accumulators, (this is a particular betting strategy/technique in which the winning from one bet goes on to the next 8

9 match and so forth until there are no matches left in the accumulator. This is a high risk way of gambling but if it is correct will produce great wins even with a small wager invested) for this reason such a deep analysis for each single match is not feasible, the assumption instead is that the odds provided by the bookmaker(s) will already take into account most of the above variables, therefore only a few other essential facts such as the historical performance of each team involved have to be considered. The analysis is to focus on the relationship between the odds offered by the bookmaker(s) and the data available: is there a similarity between the two? Are the historical results of the matches played between the two teams matching the actual probability assigned to the match by the bookmaker (in the form of quoting for each outcome the corresponding odds) for the home win, draw and away win? 1.3 Aims The strategy implemented in designing Sviluppo is based on this simple principle: given the odds offered by the selected bookmaker of choice, determine the corresponding probability of outcome for the correct result, match this probability against the historical probability, (gathered and elaborated from the historical datasets) finally evaluate the potential correct outcome and develop combinations of results based on these findings. Football match outcome prediction is such a challenging, fascinating and potentially rewarding (in case of the adoption of a proper system/strategy to correctly predict the results) study research that a vast literature and several books have been written on the subject, with many different approaches, theories, techniques and software implementation. To develop this project, a simplistic, but for this reason not necessary simple, approach to the problem, has been implemented, furthermore, the use of a commonly deployed software package such as MS Excel makes of Sviluppo quite a novelty in this field. 9

10 1.3.1 Main Problems being addressed / Goals/Scope Given so many theories, approaches, methodologies and computer applications already adopted in previous research to come up with a new idea and potential solution has proved to be a challenging task. The strategy is to implement a system easy to use and understand that is suitable to potentially maximise profit while reducing the risk involved in football betting. The application is rather practical then theoretical. The scope is to maximise potential profits playing accumulators, which is the most risky form of betting, but at the same time the most profitable. In order to reduce the level of exposure of losing a bet because of a single match failing to match the prediction, it is necessary to increase the chances to forecast the correct outcomes. For this reason Sviluppo makes use of permutations of the expected results so that the exposure to unpredicted outcomes is reduced, this is supported by the analysis of the historical data which has generated statistical facts to match against the odds proposed by the bookmakers Research Questions The basic assumption is that the odds provided by the bookmakers are in fact indicative of the actual probability of the outcome of a football match. The reason for this is that the odds are based and calculated using historical data, and assign a probability to a certain event to occur. Essentially the main question is: do the bookmakers attribute the percentages of their odds accurately? If so, then it should be sufficient to back the bookmaker s favourites to be profitable at betting. Obviously the answer to this is NO; otherwise the bookmakers would have been out of business by now. However, the bookmaker s odds and relative probabilities associated with that prove to be a good essential starting point ahead of football data analysis. 10

11 1.4 Solution Overview First of all it is essential to understand how the bookmakers calculate their odds. Bookmakers calculate the odds based on historical data facts, the odds are expressed either in a form of fraction (UK style) or decimals (EU style). A bookmaker can be defined as anyone offering odds on the outcome of an event; however, a more appropriate definition would describe a bookmaker as a person or a business that provide an odds market for one or more events, with prices available for all possible event outcomes, adjusted according to the demand of punters (Buchdal, 2003). The bookmakers then manage and adjust the probabilities of the potential outcomes with the purpose of making a profit; this is technically referred as overround. The bookmakers increase their profitability by encouraging the punters to place as many bets as possible, the profit is not only determined by their profit margin (overround), but also by the accuracy of their published odds which should, therefore, represent a good approximation of the true probabilities of any particular match without introducing biases (Constantinou, 2012). In (Dixon & Coles, 1997) the authors claimed that for a football forecast model to generate profit against bookmakers' odd, this will require a calculation of the probabilities that is satisfactorily more accurate than those obtained by the bookmaker published odds. Bookmakers make profit by adding a percentage to the actual probability of an outcome to happen, this percentage is called overround, and normally vary between 4% and 7% depending on the bookmaker strategy. The practical example below will help clarify this concept and how overround works. This shows the odds offered by a sample of bookmakers for the Euro 2014 Champions League final between Real Madrid and Atletico Madrid. 11

12 Figure 1: Overround sample table The table shows that none of the totals equal to 100% the reason for this is that the difference between the total and 100% represent the bookmaker overround or profit. In the sample above Ladbrokes apply the highest overround of 5.6% while BetVictor the lowest at 2.35%. The Max/Min row shows the highest odds compared with the lowest probability assigned by each bookmaker and the total overround applied which in this case is %. In some cases this total may result in being less that 100% if this situation occurs it is named negative overround. This situation present a chance for the punter to make a secure profit by betting simultaneously on the highest odds proposed by the bookmakers, it is called arbitrage opportunity. In a practical example if the total of the row Max/Min would have been 98% the punter would have secure a guaranteed 2% profit. The concept of arbitrage opportunity is directly linked to another fundamental concept related to gambling but that can be expanded also to other domains such as finance: inefficient market. An inefficient market is any large-scale gambling market (financial markets can also be considered gambling market) in which there is any betting procedure or technique that is suitable and consistent at generating a profit against the market. (Constantinou, 2012). In order to calculate the actual probability assigned to each match without taking into account the bookmaker overround, we can simply divide the total for each row representing a single bookmaker by the probability assigned to the result. In the case of BetVictor the home victory including the overround is 48.78% and the total is %. The actual probability is 48.78% / % = 47.66% which 12

13 is the actual probability assigned to the home victory of the event. Hence the bookmaker has applied a 1.12% profit on the outcome of the home win. The tables below shows the actual probabilities assigned without the overround and the related odds: Figure 2: Adjusted probabilities without overround Figure 3: Adjusted decimal odds without overround There are 3 methods to remove the overround from the bookmakers inflated odds the first two calculate a coefficient that need to be applied to the probability assigned to the match outcome before the overround is removed, the latter is a percentage calculated directly from the total probability of an event divided by the probability of each event: 1. 1 divided by (1/home odds + 1/draw odds + 1/away odds) à the result of this is a coefficient that needs to be multiplied by the probability assigned to each event before the overround is removed divided by (1 + overround) à also in this case the result is a coefficient that needs to be multiplied by the probability assigned to each event before the overround is removed 3. Let s call T po the total probability assigned to an event before the overround is removed and H po, D po, A po the probability that the outcome of the football match result is a home win, a draw or an away win before the overround is removed. Then the probability of each match without the overround is given respectively by H po /T po for the homes win, D po /T po for the draw and A po /T po for the away win. 13

14 Removing the overround normalized the inflated probabilities a bookmaker assign to the correct outcome of a result, this gives the punter a better picture of what the real odds of a football match are. 1.5 Structure The Dissertation is organised as follows: Chapter 1 introduces to the subject domain and provides a general overview and introduces some basic concepts about football betting, Chapter 2 is a summary of the most relevant works and state of the art solutions from previous studies on the subject domain, Chapter 3 deals with the datasets used for this project and the structure of the system, Chapter 4 demonstrates in practice how Sviluppo works using as sample 5 selected matches from the last match of the current 2013/2014 Serie A season, Chapter 5 provides a summary of the results and finally Chapter 6 look at further ideas and possible studies and developments for the future. 14

15 2 Related Work (Background) A bet is an agreement between two parties that one proved to be wrong while the other right, about the outcome of a specified event and will forfeit a stipulated payment (Buchdal, 2003). Sports, football in particular way is about setting arguments, which team is going to win the match, how many goals will be scored and so on; in a similar way betting is also prompt to setting arguments and this link is essentially the reason why betting is so common in sports. Because of the element of competition, which is intrinsic to sport, a speculation can be done on the outcome of a specific event. Forecasting the correct outcome of a football match is by no mean an exact science, major unpredicted, unforeseen upsets will always occur and it is exactly this high degree of uncertainty, which keeps the bookmakers in business. In (Dixon & Coles, 1997) the authors claimed that for a football forecast model to generate profit against bookmakers' odds without eliminating the in-built profit margin (the overround), this method would require to calculate and asses the probabilities more accurately than those obtained by published odds; while (Graham & Stott, 2008) advised that if such a work would have been particularly successful, it would not have been published. The way to eliminate the bookmakers overround effect and have a better understanding of the actual underlying probabilities assigned to an event is the basic idea behind Sviluppo. The rational idea behind what is defined as a fair betting market is that all the relevant knowledge is shared between the bookmakers and the punters, however, this is not the case, as bettors typically have access to far less information. (Buchdal, 2003). For these reason the betting market is considered an unfair market. 15

16 The punter, on the other hand, has the advantage that they can decide when and how to bet; it is essential to evaluate probabilities and have a value-based approach to betting. For success in forecasting the outcome of football matches is therefore essential to be the able to assess and weigh the probabilities, this can be achieved through the assembly of historical data and its interpretation. Having data on which to work is essential to perform any type of analysis, in general the larger the data set the higher the changes of achieving accurate results. Other previous studies have been focusing on the unfairness of the odds offered by the bookmakers, which essentially lead the punter to misjudge the real changes to correctly interpret the potential outcomes of a football match. In his research hypothesis (Constantinou, 2012) argues that in sport gambling market, with a particular emphasis on football, the odds published by the bookmakers are highly influenced towards maximising the profit (overround) and for this reason suffer from a degree of inaccuracy. In their research (Forrest & Simmons, 2008) established that the odds offered by bookmakers appear to be influenced by the number of fans of each club in a match, simply the bigger the club, the bigger the number of fans supporting the team, this conclusion has been drafted after observing that popular teams are offered more favourable odds on their bets. In their study (Graham and Stott, 2008) introduced two different types of forecasting models; the first based on historical football results, this model is similar to that of (Forrest et al., 2005), and the second one based on past bookmaking odds; with this method the Authors have attempted to compare the bookmakers predictions of various UK teams with the ratings generated by the football results based on the forecasting model. Their observations show that the bookmakers odds were rational and not significantly different to those generated 16

17 by the model; however, in some cases the logic behind producing the bookmaking odds shows partialities, which could not be explained. A number of studies have attempted to establish if there actually is a clear evidence of efficiency in the football betting market. Researchers have endeavoured to outclass bookmakers' odds by introducing their own forecast models, but the results proved to be poor. On the base of these findings, other significant studies have concluded that the betting market is efficient (Peel & Thomas, 1988; 1992; 1997; Vecer et al., 2009). This project is based on the assumption that the betting market is efficient, and is focused on the fixed odds football betting with the scope to predict the correct outcome of matches of the Italian Serie A League. 17

18 3 System and Datasets 3.1 Design and Architecture The results forecasting process as suggested by (W.A.Hunter, 1996) is explained in figure 4: Figure 4: The Result Forecasting Model 1) The Historical Data: the data sets used for this project comes from a specialized website from here all the historical data sets related to the Italian Serie A League have been downloaded in.csv format. The data sets cover 20 years of history from the season 1993/1994 to the current season 2013/ ) Gather Data: The historical data represents the raw material for the development of a forecasting system. The data sets have been imported into Excel using the text data import tool and plotted into 20 separate sheets. 3) Analyse Data: in order to analyse the data this needs first to be normalized by removing not relevant or missing datasets, from rows, columns and cells. A standard formatting has been applied to make the data more readable and easy to understand. For each season a Pivot Table summarises the performance of each team on home and away grounds. Using this information a table is produced ranking the position of each team at the end of each season. This table contains vital information about the performance of each team for every season split in total performance over the season, performance on home ground and performance away. Key Performance Indicators (KPI s) are the total points, the M.I. (this 18

19 stands for Media Inglese it is a kind of rating system that evaluate the performance of a team, it assigns 2 points for an away win, 0 points for an away draw, -1 for an away lost, while it assign 0 points for a home win, -1 for a home draw and -2 for a home lost), the total goal scored, the total goal conceived, the goal difference. Finally for each season the cumulative percentage of home wins, draws and away wins is calculated. 4) Prepare Strategy: the importance of value and the use of a value-based strategy on betting on football is something that most punters do not consider but is of extreme importance. It is essential instead to accurately calculate and evaluate the probabilities and only bet on prices that are greater than the evaluated probability (in practice skip matches that for example offer a 2/5 odd for a home win as there is no value in this bet). This is the only way that a bettor can be profitable betting on football in the long run. A good evaluation of probabilities has more value than a good prediction (Houlsby, 2013). For this project and in the use of Sviluppo the evaluation of the odds to find potentially value-based bets is part of this strategy. As principle the matches considered for analysis are only those where the odds for home wins, draws and away wins are quoted at least 1/1 (in some cases some exceptions may however be considered where for example one of the results is quoted slightly below the 1/1 threshold). 5) Forecast Results: similarly to the weather, football is a complex system in which many variables can influence the result of a match, for this reason it is difficult to predict with any degree of certainty (Houlsby, 2013). The forecasting method, which is the foundation in this project, is based on a technique called Rank Probability Score (RPS); Edward S. Epstein (Epstein, 1969) first introduced it in In his research paper Epstein makes use of stochastic dynamic prediction in order to attempt to correctly forecast weather conditions. To a certain extent this can be adapted and applied also to football forecast. In his research paper (Constantinou, 2012), mentions this type of technique while (Murphy, 1970) describes the 19

20 RPS method as an appropriate scoring rule for evaluating and measuring the probability forecast when there are more than two categories. The RPS formula is the following: Figure 5: the RPS formula (Constantinou, 2012) Where r is the number of categories or potential outcomes, and p J and e J are the cumulative distribution of forecast and observations. Because the RPS represent the difference between the cumulative distribution of forecast and observations, the lower the result from the RPS formula the higher the probability. 3.2 Implementation Failing to acknowledge that football outcomes are on an ordinal scaling rate may lead to an inaccurate forecast or poor and inadequate models (Constantinou, 2012). The vision adopted in implementing Sviluppo makes use of this principle. Sviluppo is an Excel based spreadsheet model that is made up by 48 sheets of which 42 are data sets and 6 are used for analysis, plus one extra sheet which is the raw data for the season 2013/2014. This sheet has been left in place because it was the only dynamic table (all the others stored historic data from the past seasons), which was automatically updating the data of the current season on a weekly basis when the matches were played. All the datasets are stored in Excel.csv format on the Website; in order to dynamically import the Excel.csv file, the data needs to be treated the same way as importing a text file. For this reason in Excel rather than using the standard tool to import data from the web it is necessary to use the import data from text and then in the 20

21 address bar paste the address of the file on the web server (in this specific case the address is Sviluppo is made up of the following sheets: Notes: contains a brief description of the abbreviations fund in the downloaded data sets and a counter for all the data sets in the workbook, totalling records. Combinations: this sheet performs some of the main tasks, it takes the data from the Picchetti sheet, which produce most of the analysis, thus develops the all the possible permutations (it is important to clarify that what in football betting is known as combinations of all the possible results of a pool of matches, in mathematics and statistics this is actually permutations because the order is important) for the selected matches using the predicted results outcomes using a VBA Macro (details of the code can be accessed directly from Sviluppo this can be viewed by pressing the keys ALT+F11 which opens the VBA editor in Excel, the VBA Module that stores the codes is unprotected so that it is open for viewing and editing). There are also a number of charts in the Combinations sheet, which shows the distributions of the most predicted home win, draws or away win from all the permutations produced. Another important feature of this sheet is that it produces a ranking based on the most probable combination outcome and plots it in a table that can be sorted in ordinal way accordingly. Using this table it is possible to make a selection of the most probable outcomes. Another feature of the betting strategy implemented with Sviluppo is to choose only 5 matches out of all the matches available in the Serie A League for a given date. These matches have to be selected based on the value of the potential outcome, therefore only the matches with the highest odds have to be considered. Calcolatore UK: this is a calculator for determining the potential outcomes of a bet using the UK style of odds, which is fractional. 21

22 Calcolatore EU: this is a calculator for determining the potential outcomes of a bet using the EU style of odds, which is decimal. Picchetti: this sheet performs together with the sheet Combinations most of the calculations and analysis, the only input that need to be done manually is to enter the names of the teams for each match selected and the relative odds; then Picchetti extracts the probability of the odds assigned by the bookmaker, removes the overround, produce the actual probability of the matches, performs the RPS analysis and prepare the data which will be then used in the sheet Combinations. Seasonal Trend: this sheet collects and summarizes the cumulative percentage of all the home win, draws and away wins of the past 20 years of the Serie A League up to the current season which just ended; the average is considered an empirical value to estimate the actual standard probability of a result. The chart in the sheet shows the trend and the variances over the years. To date the analysis indicate that 44.66% of the matches end in a home win, 27.85% in a draw and 23.63% in an away win. These details are currently used as empirical value to match against the probabilities offered for each match by the bookmakers. All of the other sheets: these are the actual normalized historical data sets imported and used for the analysis, each one includes a Pivot Table that summarizes the data for each season split in home and away games. All of the sheets that make up Sviluppo use conditional formatting and charts to visualise data trend and highlight variances to help in finding useful patterns for decision making. 3.3 Requirements The requirements specifications remained unchanged over the development of the project, for more details about the requirement specification refer to the document in appendix

23 3.4 Graphical User Interface (GUI) Layout When designing Sviluppo the basic idea has been inspired by the Occam s razor principle that states that there is absolutely no point in carrying out an analysis that is more complicated than it needs to be. And essentially this principle applies to the choice of statistical model just as strongly as to anything else: simplest is best. Sviluppo makes good use of the Occam s principle, based on few basic essential facts it builds the analysis and the strategy of the system. This facts can be summarise as: Football is a highly unpredictable event to correctly forecast, in order to minimize this risk and exposure Sviluppo makes use of permutations to increase the probability of win and reduce the exposure to capital lost. The odds that the bookmakers provide to bettors proved to be reasonably accurate over the long run with the exception of biases in some cases. Sviluppo uses the bookmakers odds as a starting point for the analysis and forecast of results. Availability of data, Sviluppo is backed by 20 years of data sets to support analysis and predictions. Furthermore Sviluppo make use of a very simple and intuitive Graphical User Interface (GUI) which requires few simple steps and data inputs to perform analysis and predictions. Below are some screen shots that illustrate some of the key features: The raw data set for the season 2013/2014 as it is when imported into excel 23

24 Figure 5: Portion of the raw data for the season 2013/2014 The same portion of data set as it looks like after data cleaning, normalization and formatting. Figure 6: portion of the normalized data set for the season 2013/2014 The two Pivot Tables generated from the normalized data set for the season 2013/2014 split in two parts for the home and away matches played Figure 7: the two Pivot Tables summarising the season 2013/

25 The table that summarizes the season 2013/2014 with the final rank position for each team, the essential data statistics for each team and the summary of the percentage of home wins, draws and away wins in the season. Figure 8: the summary table for the season 2013/2014 The table that summarizes the trend expressed as percentage of home wins, draws and away wins for the past 20 years of the Serie A League and the related chart showing the trend. Figure 8: the trend of home wins, draws and away wins of the Serie A League. The input cells from the sheet Picchetti where Team 1 is the home team, Team 2 is the away team and under the columns 1, X, 2 are the relative odds in fractional form need to be input. 25

26 Figure 9: the input cells in the Picchetti sheet The input cells in the Picchetti sheet after the data has been input, the conditional formatting applied to the cells in the rows under columns 1, X, 2 show in white in the mini pie charts the actual probability that the bookmaker assigns for the result outcome, at this state this probability is still comprehensive of the bookmaker overround, however, it provides at first glance a graphical representation of the probabilities assigned, where white is positive and black is negative. In the case of the first match between Genoa and Roma odds give apparently all the favours to Roma. Figure 10: the input data cells of the sheet Picchetti with data This is a broader view of the sheet Picchetti following some data input, highlighted with different colours are different part of the table, showing the team names, the odds expressed in form of fraction, the corresponding odds in decimal format and the bookmakers probabilities assigned to the outcome of the matches comprehensive of the overound. In the case of the example of the 26

27 match between Genoa and Roma the selected bookmaker offers odds that expressed in probability assign 28.57% to Genoa to win, 27.78% to a draw and 48.78% to Roma to win. Adding all of these percentages together gives %, which means that the bookmaker has applied a total overround of 5.13% on this match, and an average of 5.37% overround for all of the matches selected. Figure 11: the bookmaker overround These are the actual percentages of probability that the bookmaker assigns to the outcome of the match having removed the overround. The cells have been formatted so that the green indicates the higher percentage of probability and red the lowest with the different shades of green and red to indicate the strength of the probability. Figure 12: probabilities without overround This part of the Picchetti sheet shows at the top the empirical value of probabilities for home, draws and away wins of the outcome of the Serie A League over the past 20 year, below is the difference between the actual probabilities assigned to the match by the bookmaker and the empirical values. Again, some conditional formatting is applied to show in green where the 27

28 variance is low in yellow where it is medium and in red where it is high. If we look at the first row this is representing the values of the match between Genoa and Rome, according to the bookmakers Rome is favourite but at this stage of the analysis looks like the match is more likely to end up in a home win or draw. Figure 13: variances between empirical historical values and the bookmaker s probability. Finally we apply the RPS to determine which result is more likely to be accurate, out of three possibilities the system chooses in the order first the one with the lower coefficient, which means the higher probability to be accurate, then chooses the one with second lower coefficient and ignores the choice with the higher coefficient. The results are plotted is two separate columns and the system assigns the correspondent value 1 for a home victory, X for a draw and 2 for an away victory. The two columns show the results as first choice and a second choice. Again some conditional formatting is applied to highlight the choices. 28

29 Figure 14: the RPS table The system then plots the results from the RPS analysis into a table in the Combinations sheet. Figure 15: the results of the RPS analysis plotted in the Combinations sheet The Combinations sheet has two buttons that trigger two separate macros, the first button named Combinations performs all of the permutations from the table, the second button Clear Combinations clears the results. Figure 16: the Combinations and Clear Combinations macro buttons A portion view of the Combinations sheet with the tables where the permutations are plotted, the charts showing the rating distribution for each column, a counter on the left hand side that shows the total number of permutations produced out of the total possible available and the probability of winning assigned to each permutation and the cumulative. Each match has three possible outcomes, home win, draw and away win. With 5 matches selected the total possible number of permutations is 3^5 which equal to 243 possible permutations. Sviluppo eliminates from the three results the one that is less likely to occur leaving with two results out of three. The total number of permutations available on five matches with two results is 2^5 which equal to

30 Then the total probability to win the bet given that the results selected by Sviluppo are correct is 32/243 = 13.17%. Figure 17: a portion of the Combinations sheet with the permutations on the right hand side. 3.5 Datasets The datasets are stored on a specialized gambling/betting website The data sets are stored on the website in Excel.csv format and are free to download. The data sets for the Serie A Italian League are available from the season 1993/1994 up to the current season 2013/2014 however; the amount of information available is not consistent over the years. Specifically, the most recent seasons are richer in data, which includes for example the details of the odds offered by some of the major bookmakers, while the older data sets only contain data related to the matches. For the purpose of this project the interest is only focused on some key features, in the precisely: the date in which the match was played, the full time home goals, the full time away goals and the full time result; any other information relevant for the project has been calculated and determined using this data. After having assessed the type of information required, an Excel VBA macro has been implemented to automatically format all the tables containing the 20 years data in the same way and plot each data sets into tables in separate sheets in the Sviluppo system. 30

31 4 Testing and Evaluation The time for testing the system has been quite limited due to the fact that the project has been completed just before the end of the current Serie A League season; however, it has been tested on the very last round of matches. 4.1 System Testing The first trial to evaluate the system was given by the last round of matches of the Italian Serie A season 2013/2014. The matches selected were the following: Genoa Catania Chievo Fiorentina Udinese Roma Atalanta Inter Torino Sampdoria The reason for selecting these five matches is that they reflected the principle of value, which means that for every outcome of every match the odds offered by the bookmaker of choice were at least 1/1 or very close to this value. Figures 10 to 17 which illustrate how Sviluppo works are actually taken from this sample Description The odds offered by the bookmaker are reported in the table below: Genoa Roma 5/2 13/5 1/1 Catania Atalanta 10/9 5/2 22/9 Chievo Inter 11/4 12/5 1/1 Fiorentina Torino 13/7 13/5 11/8 Udinese Sampdoria 8/9 13/5 14/5 31

32 Which means that with the exception of the Udinese home win odds all the others are greater or equal to 1/1, this is the minimum threshold to identify what is defined as a value odd. Figure 11 shows the percentage of probability attributed by the bookmaker to the outcome of the matches results before and after the overround being removed Methods The logic on which Sviluppo is based evaluates the probabilities the bookmaker assigns to the outcome of each single match and compare it to the empirical value derived from the analysis of the datasets, this process is shown in figure 13. The system also shows the variances between the observed and the expected values of these probabilities and applies a conditional formatting accordingly in order to show where these variances are larger. It is interesting to notice how after this first step of the analysis is implemented the view of the potential outcomes changes from what is suggested by the bookmaker to what is actually the expected. Figure 18: variances between observed probabilities and expected probabilities. Figure 18 shows the variances between the observed and the expected probabilities; the model also calculates the difference between each probability (columns S, T and U) and also the mean (column X, this is defaulted to 33.33% because the chances of picking the right outcome out of three events is 1/3, one out of three). According to the bookmaker the most probable results are in the order 2, 1, 2, 2, 1. The variance shows X, X, X, X, 2. 32

33 The system then performs the Ranked Probability Score Analysis (RPS) as per illustrated in figure 14. It then applies a logical nested if formula to extract the most probable values according to the result of the RPS results (the lower the better). The results of this process are shown in figure 14 in the two columns First Choice and Second Choice. The Sheet Combinations then takes the values from these two columns and produces the permutations Results This first trial resulted to be successful, Sviluppo predicted correctly the results of the five matches first by eliminating the less probable results and then predicting the right matches results following the permutations performed in the Combinations sheet. The correct results for the selected matches: Genoa Roma 1 Catania Atalanta 1 Chievo Inter 1 Fiorentina Torino X Udinese Sampdoria X The correct results were predicted in column no. 24 of the Combinations sheet However, in the ranking system of the Combinations sheet this is only 5 th from the bottom. 33

34 This means that the matches results have to be considered highly unpredictable. The bookmaker predictions only got one correct result out of five (the Catania result). Placing 1 on an accumulator for the above bet would have paid using a UK fractional system and using an EU decimal system. Sviluppo develops 32 permutations in total which means to make sure to take full advantage from the solution produced by the system, the bettor have to be ready to place 32 stake (this if we place a minimum of 1 stake on each permutation). It is important therefore to evaluate the risk involved in placing such a bet. The advantage of the fixed odds bet market is that a bettor can estimate the potential return ahead of placing a wager, it is for this reason that Sviluppo provides two calculators (one for the fractional and one for the decimals) for evaluating potential returns and help the punter in making sound risk assessed decisions ahead of placing a bet. 34

35 5 Conclusions Sviluppo is a tool which has been thought and developed having in mind the punter. Being myself a bettor has made the process of requirements elicitation easy, what is needed is a system that in few simple steps can provide analysis of the selected matches and provide for a solution by suggesting a number of results to choose from. It is then up to the punter strategy to identify the outcomes that are potentially more likely to be correct (the system suggest anytime 32 different results) or simply decide to accept all the solutions suggested by the system. Using Sviluppo can only be of benefit to any gambler looking to save a considerable amount of time in searching for insights ahead of any football match planned to bet on. The system has been designed and developed for academic and recreational purpose only, the scope of the project is to suggest and provide incentive for further research on the subject while introducing an interesting and valid approach to football betting, not to propose a solution. 35

36 6 Further development or research Sviluppo is an on-going project, the system has to be tested over the time of an entire football season in order to draw some conclusions on its efficiency. The next step will be to implement a tracking sheet in the system to measure the performance between matches results forecast and the actual outcomes. If proving to be successful over the course of the forthcoming football season then the system could apply the same methodology also to other Leagues (provided availability of data to be analysed). A further step ahead could be to adapt the system also to international competitions such as the UEFA Champions League or the FIFA World Cup. 36

37 7 References [1] Alun, O. (2010) Dynamic Bayesian forecasting models of football match outcomes with estimation of the evolution variance parameter. Loughborough, England. Mathematics Education Centre. [2] BBC Sport Football (2013). Football betting - the global gambling industry worth billions [Online]. BBC Sport Football. Available from: [Accessed 3rd of October 2013]. [3] Joseph Buchdahl, (2003) Fixed Odds Sports Betting Statistical Forecasting and Risk Management. England: High Stakes Publishing. [4] Constantinou, A.C. (2012) Bayesian networks for prediction, risk assessment and decision making in an inefficient Association Football gambling market. London, England: Faculty of Science and Engineering, Department of Electronic Engineering and Computer Science, Queen Mary, University of London. [5] Dixon, M., and Coles, S. (1997). Modelling association football scores and inefficiencies in the football betting market. Applied Statistics, 46, [6] Epstein, E. (1969). A Scoring System for Probability Forecasts of Ranked Categories. Journal of Applied Meteorology, 8, [7] Forrest, D., Goddard, J., and Simmons, R. (2005). Odds-setters as forecasters: The case of English football. International Journal of Forecasting, 21, [8] Forrest, D., and Simmons, R. (2008). Sentiment in the betting market on Spanish football. Applied Economics, 40, [9] Graham, I., and Stott, H. (2008). Predicting bookmaker odds and efficiency for UK football. Applied Economics, 40, [10] Jason Houlsby, (2013) A Guide to Betting on Football: England: Harry Haller Publishing. 37

38 [11] W.A.Hunter, (1996) Football Fortunes Results Forecasting. England: Gambling & Computing: Oldcastle Books. [12] Karlis, D., Ntzoufras, I. (1998) Statistical Modelling For Soccer Games: The Greek League. Athens, Greece: Department of Statistics, Athens University of Economics and Business. [13] Koopman, S.J., Lit, R. (2012) A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League. Amsterdam, Holland: Faculty of Economics and Business Administration, VU University. [14], Min, B., Choe, C., McKay, R.I. (2001) A Compound Approach for Football Result Prediction. Seoul, Korea: School of Computer Science and Engineering, Seoul National University. [15] Murphy, A. (1970). The ranked probability score and the probability score: A comparison. Monthly Weather Review, 98, [16] Peel, D. A., and Thomas, D. A. (1989). Outcome uncertainty and the demand for football. Scottish Journal of Political Economy, 35, [17] Peel, D. A., and Thomas, D. A. (1992). The demand for football: some evidence on outcome uncertainty. Empirical Economics, 4, [18] Peel, D. A., and Thomas, D. A. (1997). Handicaps, outcome uncertainty and attendance demand. Applied Economic Letters, 4, [19] Rotshtein, A., Posner, M., and Rakytyanska, A. (2005). Football predictions based on a fuzzy model with genetic and neural tuning. Cybernetics and Systems Analysis, 41: 4, [20] Vecer, J., Kopriva, F., and Ichiba, T. (2009). Estimating the Effect of the Red Card in Soccer: When to Commit an Offense in Exchange for Preventing a Goal Opportunity. Journal of Quantitative Analysis in Sports, 5: Iss. 1, Article 8. 38

39 [21] Wang, C., Vandebroek, M. (2011). A model based ranking system for soccer teams. Leuven, Belgium: Faculty of Economics and Business and Leuven Statistics Research Center. 39

40 8 Appendix 8.1 Project Proposal Paper copy available with the printed copy of this dissertation, also available online as a Google Drive shared sp=sharing 8.2 Initial Requirement Specification Paper copy available with the printed copy of this dissertation, also available online as a Google Drive shared sp=sharing 8.3 Management Progress Reports Paper copy available with the printed copy of this dissertation, also available online as a Google Drive shared sp=sharing 8.4 Literature Review Paper copy available with the printed copy of this dissertation, also available online as a Google Drive shared sp=sharing 40