Football Betting Research Articles

Research on predicting football matches based on handicap data and BPNN

Football is one of the most influential sports in the world, and billions of people around the globe pay much attention to the football matches. With the growing popularity of football and the continuous development of the football betting industry, the prediction of the outcomes of football matches has become a hot topic in the commercial operations of sports especially footballs in recent years. It is also an important subject of academic research. In this paper, we develop a football match result prediction model based on the back propagation neural network. We take the German Bundesliga competitions as the research object in this paper. In addition to utilizing historical statistic data and team attributes from previous matches, we also incorporate a new dataset, known as handicap data, which refers to the odds data of the football matches, as the input layer of the BPNN (back propagation neural networks) for prediction. We also innovatively use varying numbers of hidden nodes, which greatly improves the prediction accuracy and stability of the model. Experimental results indicate that the average prediction accuracy of this football match prediction model is around 57.2%, with the highest prediction accuracy reaching 59.8% and the lowest prediction accuracy at 53.8%. The prediction model demonstrates relative stability, with no significant fluctuations in prediction accuracy.

Predicting Football Match Outcomes with Machine Learning Approaches

The increasing use of data-driven approaches has led to the development of models to predict football match outcomes. However, predicting match outcomes accurately remains a challenge due to the sport's inherent unpredictability. In this study, we have investigated the usage of different machine learning models in predicting the outcome of English Premier League matches. We assessed the performance of random forest, logistic regression, linear support vector classifier and extreme gradient boosting models for binary and multiclass classification. These models are trained with datasets obtained using different sampling techniques. The result showed that the models performed better when trained with dataset obtained using a balanced sampling technique for binary classification. Additionally, the models' predictions were evaluated by conducting simulation on football betting profits based on the 2022-2023 EPL season. The model achieved the highest accuracy is the binary class random forest, but the model provided the highest football betting profit is the binary class logistic regression.

Are Betting Markets Inefficient? Evidence From Simulations and Real Data

Previous literature leaves the impression that betting market inefficiencies are widespread. However, most studies rely upon limited data and ignore biases’ persistence. Our simulation-based analyses show (1) the impact of low sample sizes on the chance to detect markets that only appear to be efficient and (2) the frequency of observing inefficient periods within fully efficient markets. Afterwards, we (3) empirically analyze real-world football betting markets for 14 consecutive seasons. While inefficiencies occur in singular seasons, they are not persistent or systematic across leagues. Moreover, our simulation-based analyses suggest that statistically significant effects in single seasons are likely to be observed even under full market efficiency.

What drives biased odds in sports betting markets: Bettors’ irrationality and the role of bookmakers

In sports betting markets, bookmakers may take speculative exposures to the game outcomes when they can assess the probabilities better than others. We propose a simple model of fixed-odds betting markets in which bookmakers tilt their odds to maximize their own speculative profits. They do so by accommodating bettors’ irrationality, thereby reinforcing systematic biases in betting markets. In European football betting markets, the Favorite-Longshot Bias and the Hot-Hand Bias persist despite the increased competition. This is difficult to explain only by bettors’ irrationality but consistent with the predicted odds-setting behavior of bookmakers. We discuss the implications for financial market anomalies.

Chance or Ability? The Efficiency of the Football Betting Market Revisited

Chance or Ability? The Efficiency of the Football Betting Market Revisited

Impact of the “when the fun stops, stop” gambling message on online gambling behaviour: a randomised, online experimental study

Safer gambling messages are a common freedom-preserving method of protecting individuals from gambling-related harm. Yet, there is little independent and rigorous evidence assessing the effectiveness of safer gambling messages. In our study, we aimed to test the effect of the historically most commonly-used UK safer gambling message on concurrent gambling behaviour of people who gamble in the UK. In this study, three preregistered, incentivised, and randomised online experiments, testing the UK's "when the fun stops, stop" message, were carried out via the crowdsourcing platform Prolific. Adults based in the UK who had previously participated in the gambling activities relevant to each experiment were eligible to participate. Experiments 1 and 3 involved bets on real soccer events, and experiment 2 used a commercially available online roulette game. Safer gambling message presence was varied between participants in each experiment. In experiment 2, exposed participants could be shown either a yellow or a black-and-white version of the safer gambling message. Participants were provided with a monetary endowment with which they were allowed to bet. Any of this money not bet was afterwards paid to participants as a bonus, in addition to the payouts from any winning bets. In experiment 2 participants had the opportunity to re-wager any winnings from the roulette game. The primary outcome in experiment 1 was participants' decisions to accept (or reject) a series of football bets, which varied in their specificity (and payoffs), and the primary outcomes of experiments 2 and 3 were the proportion of available funds bet, which were defined as the total amount of money bet by a participant out of the total that could have been bet. Participants for all three experiments were recruited between May 17, 2019, and Oct 17, 2020. Of the 506 participants in experiment 1, 41·3% of available bets were made by the 254 participants in the gambling message condition, which was not significantly different (p=0·15, odds ratio 1·22 [95% CI 0·93 to 1·61]) to the 37·8% of available bets made by the 252 participants in the control condition. In experiment 2, the only credible difference between conditions was that the 501 participants in the condition with the yellow version of the gambling message bet 3·64% (95% Bayesian credibility interval 0·00% to 7·27%) more of available funds left over than the 499 participants in the control condition. There were no credible differences between the bets made by the 500 participants in the black-and-white gambling message condition and the other conditions. In experiment 3, there were no credible differences between the 502 participants in the gambling message condition and the 501 participants in the control condition, with the largest effect being a 5·87% (95% Bayesian credibility interval -1·44% to 13·20%) increase in the probability of betting everything in the gambling message condition. In our study, no evidence was found for a protective effect of the most common UK safer gambling message. Alternative interventions should be considered as part of an evidence-based public health approach to reducing gambling-related harm. University of Warwick, British Academy and Leverhume, Swiss National Science Foundation.

Impact of Football Betting on Study Habit of Senior Secondary School Students in Enugu State, Nigeria

The study aims at examining the impact of football betting on study habit among senior secondary school students in Enugu state. The essence for the study came as a result of decline in education sector due to poor study habit among secondary school students in Enugu State. The study was guided by three research questions. The study adopted qualitative and quantitative methods of data collection. From the population of 6,565 senior secondary school students in the 10 senior secondary schools in Enugu East Senatorial Zone of Enugu State, the sample of 180 students were selected through simple random sampling technique. The instrument used for data collection was a close-ended questionnaire with 15 items developed from four research questions. Data collected were analyzed using mean and standard deviation on SPSS version 26. Five recommendations were made among which is that Nigeria Lottery Commission should enforce and regulate strict monitoring towards the establishment of betting centers in correspondence to the laws stipulated.

Prediction of football match results with Machine Learning

Football is one of the most popular sports in the world, so the perception of the game and the prediction of results is of general interest to fans, coaches, media and gamblers. Although predicting football results is a very complex task, the football betting business has grown over time. The unpredictability of football results and the growing betting business justify the development of prediction models to support gamblers. In this article, we develop machine learning methods that take multiple statistics of previous matches and attributes of players from both teams as inputs to predict the outcome of football matches. Several prediction models were tested, with the experimental results showing encouraging performance in terms of the profit margin of football bets.

An Effective Approach for Exploiting the Inefficiencies of the Italian Football Betting Market

In this paper, the author analyses the main inefficiencies of the Italian Serie-A betting market: in particular, using previous findings on the favourite-long shot bias, the team scoring home advantage and the league winner scoring advantage, a strategy aimed to outperform the market will be presented. By testing the betting scheme over the past 19 seasons, it was possible to obtain an average return per bet of 9.31%, with 16 of the 19 years resulting in positive financial returns. In order to verify the computations, different betting odds databases were used. The results obtained are particularly significant for two reasons: firstly all computations were performed on market average coefficients, leaving on the table an additional 3-4% of profit, which could be obtained by using best coefficients among bookmakers and secondly compared to market benchmarks and other betting strategies, the net profit is considerably higher. In particular other three strategies were used as a benchmark: the first one uses the favourite-long shot bias, the second one the Home factor and the third one the league favourite team advantage. All these betting schemes performed more poorly, with the second best strategy scoring on average 6% worse. After analysing possible future improvements, in the final section the author describes how the findings of this paper may be applied to other betting markets, such as different football leagues, basketball, hockey and tennis.

New Entry and Strategic Group Emergence in the Soccer Betting Market

The arrival of online trading in European football betting markets has brought significant structural change to the sector in recent years, with the emergence of two strategic groups of bookmakers, characterised by distinctive operating behaviours. We examine the impact on market efficiency of interactions between these two groups, by comparing 51,000 individual odds offered by leading bookmakers at nine separate points on game outcomes, across 2,132 games in six European leagues. This longitudinal analysis reveals that interactions between these groups enable information to be transmitted from informed bettors to market prices, thereby increasing market efficiency.

Market efficiency and the Greek fixed-odds betting market

PurposeThe purpose of this study is to empirically examine the efficiency of Greek fixed-odds (offline) betting market as offered by OPAP for the period 2016–2019.Design/methodology/approachUsing a four-year data sample of OPAP's opening and closing odds for football matches from all over the world and applying linear probability and probit models, the market efficiency is examined and the existence of possible anomalies is investigated.FindingsThe main findings of research suggest that although the odds are dominated primarily by favorite-longshot bias and secondarily by draw bias, this mispricing cannot prove profitable. However, the opening odds, the margin levels and the market structure provide information that is not fully captured by the closing odds, giving bettors profit opportunities. Thus, findings show that the semi-strong market efficiency is questionable. Finally, competition reduces commissions leading to more efficient odds.Practical implicationsThe conclusions of this study are useful for football betting market and, particularly, for government authorities, bookmakers and bettors. Findings can be extended in future research to prediction tasks.Originality/valueTo the best of the authors’ knowledge, this is the first study about the Greek football betting market. The contribution to the literature lies on the one hand in the examination of a monopolistic land-based betting market, which is being squeezed and threatened by the more competitive online betting market, and on the other hand in the simultaneous examination of the opening and closing odds.

Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis

This paper studies efficient market hypothesis in prediction markets and the results are illustrated for the in-play football betting market using the quoted odds for the English Premier League. Our analysis is based on the martingale property, where the last quoted probability should be the best predictor of the outcome and all previous quotes should be statistically insignificant. We use regression analysis to test for the significance of the previous quotes in both the time setup and the spatial setup based on stopping times, when the quoted probabilities reach certain bounds. The main contribution of this paper is to show how a potentially different distributional opinion based on the violation of the market efficiency can be monetized by optimal trading, where the agent maximizes logarithmic utility function. In particular, the trader can realize a trading profit that corresponds to the likelihood ratio in the situation of one market maker and one market taker, or the Bayes factor in the situation of two or more market takers.

Neural Networks Predictive Modeling for Football Betting

The aim of this paper is to explore neural network-based modelling strategies for football betting. A neural network model and a challenger model based on a traditional econometric approach introduced by Dixon were estimated on data from five national leagues results including France, Spain, Italy, Germany, England) Our results show that the Neural network-based model has better predictive accuracy compared with the traditional econometric models. Betting strategies were implemented using prediction outputs generated with both econometric and neural networks models. The latter provides with a better return over investment. Nevertheless, both approaches lead to losses in the long run.

Does what happen in Vegas stay in Vegas? Football gambling and stock market activity

We examine whether lagged football betting payoffs result in changes in retail investing in lottery-like stocks. We show that lagged, low betting imbalances are associated with increases in retail stock participation, particularly for lottery-like stocks. This finding implies support for the “break-even” hypothesis that following negative sentiment and losses from football gambling, investors use lottery-like stocks to offset losses or break-even. This result holds for lottery-like stocks defined based on high idiosyncratic volatility and skewness as well as stocks that trade in over-the-counter (OTC) markets. Finally, we address whether the reverse relation exists, finding that only OTC market activity leads to increases in football betting activity but not football betting imbalances. Overall, our paper contributes to the literature investigating the relation between gambling sentiment and stock market activity.

Game of hope; game of addiction: rising football betting among Nigerian youths and its implications for peace, security and stability

ABSTRACT Football betting (FB) is on the rise in Nigeria especially among the youths. This study examines the nature of FB among Nigerian youths, drivers, as well as its peace and security implications. Findings show that youths involved in FB usually play online by themselves or register in abet company outlet. The paper also discovered that increase in the use of smart phones and availability of the Internet facilities enhance playing of FB; but, it is driven largely by widespread unemployment and poverty among the youths. Rising FB among youth in Nigeria is found to be directly correlated to increase in violence and criminal activities. In view of the increasing call for ban of football and sport betting generally in Nigeria, the paper recommends that rather than criminalizing football or sport betting in Nigeria, the government should address widespread unemployment and poverty which continue to propel youths into betting.

Analyzing football betting behavior using prospect theory

Analyzing football betting behavior using prospect theory

Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.

Holdover Bias in the College Football Betting Market

This article investigated the efficiency of the college football betting market for the following season’s first game for members of the final Associated Press Top 25 Poll. Market inefficiency was identified, with bets against these teams winning significantly more than half the time. Separate analysis of the top 10 and the remaining members of the Top 25 indicated that the inefficiency was due to the previous season’s top 10 being significantly overvalued in the betting market at the start of the next season. Bets against the top 10 teams won significantly more than the 52.4% necessary for profitability. This was especially true when they played non-Power 5 schools. Efficiency could not be rejected for the teams ranked 11 through 25. The study contributed to the literature on betting market efficiency by providing evidence that bettors could be backward-looking and over-rely on outdated information.

Efficiency of online football betting markets

This paper evaluates the efficiency of online betting markets for European (association) football leagues. The existing literature shows mixed empirical evidence regarding the degree to which betting markets are efficient. We propose a forecast-based approach for formally testing the efficiency of online betting markets. By considering the odds proposed by 41 bookmakers on 11 European major leagues over the last 11 years, we find evidence of differing degrees of efficiency among markets. We show that, if the best odds are selected across bookmakers, eight markets are efficient while three show inefficiencies that imply profit opportunities for bettors. In particular, our approach allows the estimation of the odds thresholds that could be used to set profitable betting strategies both ex post and ex ante.

Risk-Neutral Pricing and Hedging of In-Play Football Bets

ABSTRACTA risk-neutral valuation framework is developed for pricing and hedging in-play football bets based on modelling scores by independent Poisson processes with constant intensities. The Fundamental Theorems of Asset Pricing are applied to this set-up which enables us to derive novel arbitrage-free valuation formulæ for contracts currently traded in the market. We also describe how to calibrate the model to the market and how trades can be replicated and hedged.