Results Of Football Matches Research Articles

Forecasting binary outcomes in soccer

Several studies deal with the development of advanced statistical methods for predicting football match results. These predictions are then used to construct profitable betting strategies. Even if the most popular bets are based on whether one expects that a team will win, lose, or draw in the next game, nowadays a variety of other outcomes are available for betting purposes. While some of these events are binary in nature (e.g. the red cards occurrence), others can be seen as binary outcomes. In this paper we propose a simple framework, based on score-driven models, able to obtain accurate forecasts for binary outcomes in soccer matches. To show the usefulness of the proposed statistical approach, two experiments to the English Premier League and to the Italian Serie A are provided for predicting red cards occurrence, Under/Over and Goal/No Goal events.

Forecasting football matches by predicting match statistics

This paper considers the use of observed and predicted match statistics as inputs to forecasts for the outcomes of football matches. It is shown that, were it possible to know the match statistics in advance, highly informative forecasts of the match outcome could be made. Whilst, in practice, match statistics are clearly never available prior to the match, this leads to a simple philosophy. If match statistics can be predicted pre-match, and if those predictions are accurate enough, it follows that informative match forecasts can be made. Two approaches to the prediction of match statistics are demonstrated: Generalised Attacking Performance (GAP) ratings and a set of ratings based on the Bivariate Poisson model which are named Bivariate Attacking (BA) ratings. It is shown that both approaches provide a suitable methodology for predicting match statistics in advance and that they are informative enough to provide information beyond that reflected in the odds. A long term and robust gambling profit is demonstrated when the forecasts are combined with two betting strategies.

Evaluating probabilistic forecasts of football matches: the case against the ranked probability score

Abstract A scoring rule is a function of a probabilistic forecast and a corresponding outcome used to evaluate forecast performance. There is some debate as to which scoring rules are most appropriate for evaluating forecasts of sporting events. This paper focuses on forecasts of the outcomes of football matches. The ranked probability score (RPS) is often recommended since it is ‘sensitive to distance’, that is it takes into account the ordering in the outcomes (a home win is ‘closer’ to a draw than it is to an away win). In this paper, this reasoning is disputed on the basis that it adds nothing in terms of the usual aims of using scoring rules. A local scoring rule is one that only takes the probability placed on the outcome into consideration. Two simulation experiments are carried out to compare the performance of the RPS, which is non-local and sensitive to distance, the Brier score, which is non-local and insensitive to distance, and the Ignorance score, which is local and insensitive to distance. The Ignorance score outperforms both the RPS and the Brier score, casting doubt on the value of non-locality and sensitivity to distance as properties of scoring rules in this context.

Modeling of Football Match Outcomes with Expected Goals Statistic

Aim. Our research examined the predictive capabilities of mathematical models that are solely based on the expected goal statistics obtained from a publicly available database.
 Method. We collected match and expected goals data for 310 matches from three European Leagues (Bundesliga, La Liga, and Serie A). We created three probabilistic models based on the expected goals statistic and compared them with two well-established probabilistic models using binomial deviance, squared error, and profitability in the betting market as evaluation metrics.
 Results. Our best model adjusted the expected goal statistics for homefield advantage and outperformed the two probabilistic models used for comparison. Two of our models were profitable under certain betting conditions.
 Limitations. Our models explored a simplistic integration of expected goals into a Poisson based probabilistic model and did not include other contributing factors such as a team’s defensive prowess. The number of games simulated was also limited due to the premature closure of the European Leagues due to the COVID-19 pandemic.
 Conclusions. The use of a probabilistic model based solely on expected goals score statistic can provide some meaningful insight into forecasting the outcome of a football match and can develop useful betting strategies.

Football sentiment and stock market returns: Evidence from a frontier market

This study examines the effect of investor sentiment on the Vietnamese stock market. Using the results of international football matches as exogenous shocks on investor sentiment, and information from two major stock exchanges, we document a link between investor sentiment and stock market returns. In particular, we find a decline in market returns on the subsequent trading days after game losses. Moreover, the effect of investor sentiment on stock market returns is more pronounced between 2015 and 2020 during which football fans exhibited a stronger affection and attention to their teams. The results further depict that the impact of investor sentiment on stock market returns is only significant for small-cap stocks, and when more important tournaments are played.

Social Pressure in Football Matches: An Event Study of "Remote Matches" In Japan

We examine the effect of social pressure on the outcomes of football matches by assessing those matches that did not have spectators as a result of the COVID-19 pandemic. From the results of 768 matches with 43 unattended matches in Japan's top two divisions for the 2020 season, we find significant evidence for referee bias due to social pressure by the home team's supporters. With spectators in the stadium, the number of fouls awarded to home teams decreases significantly by about 1.05. In addition, we find that the absolute number of spectators is more dominant in the cause of referee bias than the share of the home team's supporters in the stadium by estimating the model that considers the detailed audience cap amid the pandemic.

Heterogeneous ensembles of classifiers in predicting Bundesliga football results

Predicting sports results is a well-known problem, and it applies to the more predictable individual sports as well as to the less predictable team sports – e.g., football. In the vast amount of information and parameters that are associated with team sports, there are few solutions related to the comprehensive approach of knowledge discovery in databases (KDD). In this paper, we propose a new approach to the whole process of discovering knowledge from data to predict the result of Bundesliga football matches. The main goal of the paper is to improve the prediction of football match results using heterogeneous ensembles of classifiers. To achieve the goal, it is necessary to prepare a training set and to develop a learning model – in this case, we analyze whether the use of limited information allows for the correct prediction of the results of football matches. The use of the developed model based on heterogeneous ensembles in the prediction of Bundesliga match results allows for improved prediction compared to classical ensembles models and single classifiers. Additionally, the possibilities arising from the use of heterogeneous ensembles of classifiers allow to distinguish some of the analyzed cases. The proposed approach was tested on real-world data, the results of Bundesliga football matches analyzed from the 2010/2011 season to the 2019/2020 season. Experiments were performed on methods selected based on an in-depth literature review. Conducted experiments, supported by the proper statistical analysis, confirm the usefulness of the proposed approach in predicting Bundesliga football results.

Tržišne reakcije na rezultate fudbalske utakmice - uticaj mesta događaja i vrste takmičenja

This study seeks to investigate the stock market reaction to football match results in different venues and competition types, especially for three major football clubs: Manchester United, Juventus FC, and Borussia Dortmund. We use a parametric paired sample t-test. The findings show that investors take not only match results but also venues and competition types into consideration when making investment decisions. This study indicates the stock markets of football clubs are semi-strong efficient markets. Different market reaction to match results for each football club studied implies the importance of understanding the specific characteristics of those clubs in making investment decisions. The findings suggest the football club investors need to consider their clubs' competitive performance in different venues and competition types when making portfolio investment decisions. This study complemented the previous assumption on the importance of venues, and competition types match results in making investment decisions.

The sound of silence in association football: Home advantage and referee bias decrease in matches played without spectators

The home advantage and unconscious referee bias are two well-documented phenomena in professional sports, especially in association football. Among the various factors determining them, the crowd noise is considered as one of the most relevant; yet, the majority of previous studies could not isolate its contribution. The possibility to study the effects of crowd noise – or, better, of its absence – in an ecological context was given by the matches played behind closed doors due to the COVID-19 pandemic. The aim of the present study was to investigate whether home advantage and referee bias still occur (and to what extent) during matches played in absence of spectators. In particular, the focus was on the first and second divisions of the top four countries in the UEFA ranking, for a total of 841 matches behind closed doors. The hypothesis was that, if these phenomena are largely due to the effect of crowd noise, the absence of spectators should reduce their occurrence. Various parameters for each of the two phenomena were considered, and the analyses revealed a reduction of home advantage and the absence of referee bias. The results bring further support to the claim that, among all the factors contributing to home advantage and referee bias, crowd noise has a relevant role. Thus, spectators can significantly contribute to determine the dynamics and the outcomes of professional football matches.

Football Sentiment and Stock Market Returns: Evidence From a Frontier Market

This study examines the effect of investor sentiment on the Vietnamese stock market. Using the results of international football matches as exogenous shocks on investor sentiment and information on two major stock exchanges, we document a link between investor sentiment and stock market returns. In particular, we find a market decline on the subsequent trading days after game losses. Further, the effect of investor sentiment is more potent for small-cap stocks, during the period when the affection and attention of local fans are stronger, and when more important competitions are being played.

A deep learning framework for football match prediction

An efficient framework is developed by deep neural networks (DNNs) and artificial neural network (ANNs) for predicting the outcomes of football matches. A dataset is used with the rankings, team performances, all previous international football match results and so on. ANN and DNN are used to explore and process the sporting data to generate prediction value. Datasets are divided into sections for training, validating and testing. By using the proposed DNN architecture, corresponding model performed excellently on predicting the FIFA world cup 2018 matches. This model had predicted 63.3% matches accurately. However, this accuracy can be increased with proper datasets and more accurate information of the teams. The outcome of this hypothesis can be derived that deep learning may be used for successfully predicting the outcomes of football matches or any other sporting events. For more accurate performance of the prediction, prior and more information about each team, player and match is desirable.

Discrete Weibull regression for modelling football outcomes

We propose the use of the discrete Weibull distribution for modelling football match results, as an alternative to existing Poisson and generalised Poisson models. The number of goals scored by the two teams playing a football match are regarded as a pairwise observation and are modelled first through two independent discrete Weibull variables, and then through two dependent discrete Weibull variables, using a copula approach that accommodates non-null correlation. The parameters of the bivariate discrete Weibull distributions are assumed to depend on covariates such as the attack and defence abilities of the two teams and the 'home effect'. Several discrete Weibull regression models are proposed and then applied to the 2015-2016 Italian Serie A. Even if the interpretation of parameters is less immediate than in the case of bivariate Poisson models, nevertheless these models represent a suitable alternative, which can be applied also in other fields than sport data analysis.

Multifactorial analysis of factors influencing elite australian football match outcomes: a machine learning approach

Abstract In Australian football (AF), few studies have assessed combinations of pre- game factors and their relation to game outcomes (win/loss) in multivariable analyses. Further, previous research has mostly been confined to association-based linear approaches and post-game prediction, with limited assessment of predictive machine learning (ML) models in a pre-game setting. Therefore, our aim was to use ML techniques to predict game outcomes and produce a hierarchy of important (win/loss) variables. A total of 152 variables (79 absolute and 73 differentials) were used from the 2013–2018 Australian Football League (AFL) seasons. Various ML models were trained (cross-validation) on the 2013–2017 seasons with the–2018 season used as an independent test set. Model performance varied (66.5-73.3% test set accuracy), although the best model (glmnet – 73.3%) rivalled bookmaker predictions in the same period (70.9%). The glmnet model revealed measures of team quality (a player-based rating and a team-based) in their relative form as the most important variables for prediction. Models that contained in-built feature selection or could model non-linear relationships generally performed better. These findings show that AFL game outcomes can be predicted using ML methods and provide a hierarchy of predictors that maximize the chance of winning.

Modelling Match Outcome in Australian Football: Improved accuracy with large databases

Abstract Mathematical models that explain match outcome, based on the value of technical performance indicators (PIs), can be used to identify the most important aspects of technical performance in team field-sports. The purpose of this study was to evaluate several methodological opportunities, to enhance the accuracy of this type of modelling. Specifically, we evaluated the potential benefits of 1) modelling match outcome using an increased number of seasons and PIs compared with previous reports, 2) how to identify eras where technical performance characteristics were stable and 3) the application of a novel feature selection method. Ninety-one PIs across sixteen Australian Football (AF) League seasons were analysed. Change-point and Segmented Regression analyses were used to identify eras and they produced similar but non-identical outcomes. A feature selection ensemble method identified the most valuable 45 PIs for modelling. The use of a larger number of seasons for model development lead to improvement in the classification accuracy of the models, compared with previous studies (88.8 vs 78.9%). This study demonstrates the potential benefits of large databases when creating models of match outcome and the pitfalls of determining whether there are eras in a longitudinal database.

Effects of Football Match Results of Croatian National Team on Stock Returns: Evidence from Zagreb Stock Exchange

Abstract This paper observes short term effects of football match results by focusing on the Croatian national team and stock returns on the Zagreb Stock Exchange. Existing literature identifies psychological factors affecting investor’s sentiment around sporting events on different stock markets. There does not exist any study focusing on the Croatian stock market. Thus, this paper extensively observes such effects for the first time in the literature. Event study methodology, a usual approach of investigating such effects, is used on a sample of 60 stocks on the Zagreb Stock Exchange for the period from 2014 until the end of 2018. The results indicate no significant effects of winning or losing a football match, even when controlling for game being friendly, competitive tournament or qualification one; as well as after controlling for investor’s expectations based upon betting odds. This means that no profitable trading strategies could be obtained around the football match day on the Zagreb Stock Exchange in the observed period for the stocks investigated in this paper.

The relationship between match performance indicators and outcome in Australian Football

ObjectivesTo identify novel insights about performance in Australian Football (AF), by modelling the relationships between player actions and match outcomes. This study extends and improves on previous studies by utilising a wider range of performance indicators (PIs) and a longer time frame for the development of predictive models. DesignObservational. MethodsNinety-one team PIs from the 2001 to 2016 Australian Football League seasons were used as independent variables. The categorical Win–Loss and continuous Score Margin match outcome measures were used as dependent variables. Decision tree and Generalised Linear Models were created to describe the relationships between the values of the PIs and match outcome. ResultsDecision tree models predicted Win–Loss and Score Margin with up to 88.9% and 70.3% accuracy, respectively. The Generalised Linear Models predicted Score Margin to within 6.8 points (RMSE) and Win–Loss with up to 95.1% accuracy. The PIs that are most predictive of match outcome include; Turnovers Forced score, Inside 50s per shot, Metres Gained and Time in Possession, all in their relative (to opposition) form. The decision trees illustrate how combinations of the values of these PIs are associated with match outcome, and they indicate target values for these PIs. ConclusionsThis work used a wider range of PIs and more historical data than previous reports and consequently demonstrated higher prediction accuracies and additional insights about important indicators of performance. The methods used in this work can be implemented by other sport analysts to generate further insights that support the strategic decision-making processes of coaches.

Dolores: a model that predicts football match outcomes from all over the world

The paper describes Dolores, a model designed to predict football match outcomes in one country by observing football matches in multiple other countries. The model is a mixture of two methods: (a) dynamic ratings and (b) Hybrid Bayesian Networks. It was developed as part of the international special issue competition Machine Learning for Soccer. Unlike past academic literature which tends to focus on a single league or tournament, Dolores is trained with a single dataset that incorporates match outcomes, with missing data (as part of the challenge), from 52 football leagues from all over the world. The challenge involved using a single model to predict 206 future match outcomes from 26 different leagues, played from March 31 to April 9 in 2017. Dolores ranked 2nd in the competition with a predictive error 0.94% higher than the top and 116.78% lower than the bottom participants. The paper extends the assessment of the model in terms of profitability against published market odds. Given that the training dataset incorporates a number of challenges as part of the competition, the results suggest that the model generalised well over multiple leagues, divisions, and seasons. Furthermore, while detailed historical performance for each team helps to maximise predictive accuracy, Dolores provides empirical proof that a model can make a good prediction for a match outcome between teams x and y even when the prediction is derived from historical match data that neither x nor y participated in. While this agrees with past studies in football and other sports, this paper extends the empirical evidence to historical training data that does not just include match results from a single competition but contains results spanning different leagues and divisions from 35 different countries. This implies that we can still predict, for example, the outcome of English Premier League matches, based on training data from Japan, New Zealand, Mexico, South Africa, Russia, and other countries in addition to data from the English Premier league.

Comparing probabilistic predictive models applied to football

We propose two Bayesian multinomial-Dirichlet models to predict the final outcome of football (soccer) matches and compare them to three well-known models regarding their predictive power. All the models predicted the full-time results of 1710 matches of the first division of the Brazilian football championship and the comparison used three proper scoring rules, the proportion of errors and a calibration assessment. We also provide a goodness of fit measure. Our results show that multinomial-Dirichlet models are not only competitive with standard approaches, but they are also well calibrated and present reasonable goodness of fit.

Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver.

The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crime‐related Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and non‐game temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionary‐based mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theft‐from‐vehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns.

Beating the Odds - A State Space Model for predicting match results in the Australian Football League

This thesis investigates whether state space models have the potential to pre- dict the outcome of Australian Rules Football matches and can produce significant positive return over the bookmaker’s odds. The point of departure is a sample of 18 Australian football teams over the period 2012 to 2016. Modeling and predicting a football match is a challenging task, since the model should incorporate two di ↵ er- ent random processes. Firstly, the evolution of parameters, i.e. the team strengths change over time as it incorporates changes in team composition, coaching, train- ing ground, injuries etc. Secondly, the distribution of ranking data with these time-varying propensities changes stochastically over time. Given that we cannot observe all team-specific and location-specific factors, a dynamic state space model for team strengths is introduced. The team strengths are assumed to follow an order-one autoregressive process and are estimated using a recursive Kalman filter algorithm. Smoothed state estimates are applicable for ranking teams and predict- ing future outcomes of the matches. We show that beating the bookmaker’s odds is a challenging task indicating that the betting markets are e cient. Keywords : Sport Analytics; Kalman Filter Algorithm; State Space models; Predictive inference; Ranking; Australian Rules Football.