Profitable Betting Strategies Research Articles

Close but no cigar: does winning the game moderate or amplify failing to cover the spread in future NFL games

ABSTRACT In this paper, I examine the joint role of the previous game’s outcome with and without the spread on wagers’ preferences and outcomes in the NFL gambling market. The results show that gamblers are more likely to wager on the home team if they covered the spread in their previous game, while they are less likely to bet on the home team if the visiting team won their last game (not considering the spread). Further results show that teams are more likely to cover the spread if they covered the spread but lost their previous game. Profitable betting strategies are then put forward based on this information.

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 results and exploiting betting markets: The case of “both teams to score”

The continuous growth of available football data presents unprecedented research opportunities for a better understanding of football dynamics. While many research works focus on predicting which team will win a match, other interesting questions, such as whether both teams will score in a game, are still unexplored and have gained momentum with the rise of betting markets. With this in mind, we investigate the following research questions in this paper: “How difficult is the ‘both teams to score’ (BTTS) prediction problem?”, “Are machine learning classifiers capable of predicting BTTS better than bookmakers?”, and “Are machine learning classifiers useful for devising profitable betting strategies in the BTTS market?”. We collected historical football data, extracted groups of features to represent the teams’ strengths, and fed these to state-of-the-art classification models. We performed a comprehensive set of experiments and showed that, although hard to predict, in some scenarios it is possible to outperform bookmakers, which are robust baselines per se. More importantly, in some cases it is possible to beat the market and devise profitable strategies based on machine learning algorithms. The results are encouraging and, besides shedding light on the problem, may provide novel insights for all kinds of football stakeholders.

Design and Implementation of a Simulated Betting Exchange

his thesis details the development and implementation of possibly the first agent-based simulated betting exchange, named BBE (Bristol Betting Exchange), whose design principals are laid out in [11]. I prove that one of the main bottlenecks of the design can be tackled through the utilisation of a GPUs parallel processing capabilities, and I provide a detailed break-down on the behaviour exhibited by six implementations of betting agents that adopt some naive strategies. It is hoped that this implementation of BBE could be picked up by academics in order to aid in the research of profitable betting strategies, something that has been traditionally very difficult to do without the existence of an agent based simulator. Many people are familiar with financial exchanges, but far less are aware of the existence of betting exchanges. As a result, the development of profitable betting strategies to deploy on these exchanges, much in the same way automated trading algorithms are deployed on financial exchanges, has gone mostly overlooked. Various tools exist for testing out trading algorithms in a simulated environment such as BSE [8], however there is no equivalent tool for testing betting strategies. Getting access to high resolution historical data from an exchange to back-test a betting strategy is not only costly, but also fails to capture the feedback loops that may form between bettors as a result of the bettor interacting with others in real time on the exchange. In addition, there only exists as much historical data as there has been events that have been bet on, which may not meet the volume of data required to train a Machine Learning model in the hopes of discovering a profitable betting strategy. BBE aims to address this lack of data by generating plausibly realistic synthetic data modelled from the mechanics of a real betting exchange. In my implementation, the parallel processing framework OpenCL is used to relieve one of the core bottlenecks of BBE’s design — the need to simulate many thousands of horse races at regular intervals to simulate the changing of bettor’s opinions during an in-play betting session. I test and compare the run-times from simulating the races sequentially on a CPU and in parallel on a GPU to discover that outsourcing the task of simulating many races on a GPU results in a speed-up of over 100 times. My implementation of BBE is intended to be a gift to academics, which is why it has been made open source. It is hoped that BBE can be used to train a Machine Learning Model from the simulations of many thousands of betting sessions. Making BBE run as efficient as possible was a key focus for me, since I wanted to make BBE more accessible to those who do not have access to powerful servers or super computers for the long periods of time that would be required to train a Machine Learning model on an unoptimized version of BBE.

Bookmakers’ mispricing of the disappeared home advantage in the German Bundesliga after the COVID-19 break

ABSTRACT The outbreak of COVID-19 in March 2020 led to a shutdown of economic activities in Europe. This included the sports sector since public gatherings were prohibited. The German Bundesliga was among the first sport leagues realizing a restart without spectators. Several recent studies suggest that the home advantage of teams eroded for the remaining matches. Our paper analyses the reaction by bookmakers to the disappearance of such home advantage. We show that bookmakers had problems to adjust the betting odds in accordance with the disappeared home advantage, opening opportunities for profitable betting strategies.

How do Bookmakers Interpret Running Performance of Teams in Previous Games? Evidence From the Football Bundesliga

This study examines how running performance (intensive runs, total distance covered) of football teams in previous games impacts betting markets as it relates to expected win probability. Theoretically, bookmakers could interpret team’s running performance as effort or fatigue, with sports science studies suggesting that distance covered reflects effort and intensive runs signal fatigue. Using data from the 2011/12-2018/19 seasons of the German Bundesliga, beta regression models reveal that bookmakers interpret team’s running performance in previous games contrary to physiological explanations in sports sciences. Tests of market efficiency incorporating these findings do not find a profitable betting strategy for bettors.

Betting Market Efficiency in the Presence of Unfamiliar Shocks: The Case of Ghost Games During the COVID-19 Pandemic

Betting markets have been frequently used as a natural laboratory to test the efficient market hypothesis and to obtain insights especially for financial markets. We add to this literature in analyzing the velocity and accuracy in which market expectations adapt to an exogenous shock: the introduction of soccer ghost games during the COVID-19 pandemic. We find that betting odds do not properly reflect the effect of ghost games regarding changes in home advantage. Furthermore, we present evidence for a slow to non-existing adaption process with respect to new match results, indicating a lack of semi-strong efficiency. Based on these findings, we also identify very simple but highly profitable betting strategies which underline our rejection of the efficient market hypothesis.

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.

Efficiency of Online Football Betting Markets

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

PARX model for football match predictions

AbstractWe propose an innovative approach to model and predict the outcome of football matches based on the Poisson autoregression with exogenous covariates (PARX) model recently proposed by Agosto, Cavaliere, Kristensen, and Rahbek (Journal of Empirical Finance, 2016, 38(B), 640–663). We show that this methodology is particularly suited to model the goal distribution of a football team and provides a good forecast performance that can be exploited to develop a profitable betting strategy. This paper improves the strand of literature on Poisson‐based models, by proposing a specification able to capture the main characteristics of goal distribution. The betting strategy is based on the idea that the odds proposed by the market do not reflect the true probability of the match because they may also incorporate the betting volumes or strategic price settings in order to exploit betters' biases. The out‐of‐sample performance of the PARX model is better than the reference approach by Dixon and Coles (Applied Statistics, 1997, 46(2), 265–280). We also evaluate our approach in a simple betting strategy, which is applied to English football Premier League data for the 2013–2014, 2014–2015, and 2015–2016 seasons. The results show that the return from the betting strategy is larger than 30% in most of the cases considered and may even exceed 100% if we consider an alternative strategy based on a predetermined threshold, which makes it possible to exploit the inefficiency of the betting market.

Profitable Robot Strategies in PariiMutuel Betting

We have collected odds and results from 7 474 horse races in Norway and Sweden for a period of approximately 1.5 years. Based on the odds from the win game, we construct a profitable betting strategy for the corresponding triple game. Given a 30% track take, the existence of a profitable strategy is surprising. A robot is typically needed to identify and exploit underrated bets. We argue that the existence of heterogeneous beliefs between players in the market might form a basis for profitable betting strategies. We did expect that bigger pools (more liquidity) would remove this anomaly. That is not the case. More players, and thereby bigger pools, increases the profitability of the system.

College football and the Vegas line: Deconstruction and arbitrage

We examine the Vegas line in college football games by employing two separate regression models to deconstruct the Vegas line and actual margin of victory for 4,590 unique contests from the 2005 through 2011 seasons. A comparison of these two models suggests which factors represent a true relationship with the margin of victory and which reflect bettor biases. An additional model of the margin of victory illustrates which factors the Vegas line systematically misrepresents. The authors find a number of factors inadequately priced in the Vegas line that help explain variation in the actual margin of victory. Using a holdout dataset comprised of the 2012 and 2013 seasons we identify the magnitude of any mispricing and opportunities for arbitrage. We exploit this mispricing to develop and evaluate profitable betting strategies. A strategy betting on the top 35% mispriced games yields 55% correct picks and a 2.7% APY. A second strategy in which only the top 8% of mispriced games are bet yields 59% correct picks and a 5.9% APY.

Replacement Referees and NFL Betting Markets

Are betting markets efficient? The 2012 labor dispute between the NFL and the referees is used as a quasi-experiment to assess whether the betting markets are able to achieve accurate “prices” in an uncertain environment. More points were scored and underdogs performed relatively better resulting in upsets and closer-than-expected games. Betting markets, though, were unable to anticipate or adjust to this systematic effect even though irregularities in gambling markets were reported before the beginning of the season. Not only were they inefficient, but profitable betting strategies can be identified.

Exploiting Week 2 Bias in the NFL Betting Markets

Past research has provided evidence of hindsight bias in the NFL wagering market. Too much emphasis is placed on a team’s performance in the previous season when placing bets on NFL games during the first week (Week 1) of the season. Building on this contribution, our study extends the examination of hindsight bias in NFL gaming markets by focusing on how bettors consider the information provided by Week 1 performance when betting in Week 2. Our findings suggest multiple market inefficiencies in the NFL gambling market in Week 2, providing the opportunity for profitable betting strategies.

Player absence and betting lines in the NBA

We examine the efficiency of betting lines in the NBA when players are absent. We show that the betting line tends to move away from the team with absences, particularly when a meaningful player is absent. We show that opening lines set by bookmakers have significant errors in games when a player is absent. No profitable betting strategy exists in wagering at the closing line, however, as biases are removed by either the sportsbook responding to new information or the sportsbook responding to the actions of bettors.

Application of a Bivariate Poisson Model in Devising a Profitable Betting Strategy of the Zimbabwe Premier Soccer League Match Results

The study seeks to construct a profitable betting strategy for soccer results by developing a bivariate Poisson model for the analysis and computation of probabilities for football match outcomes. The dependence coefficient is estimated from Monte Carlo simulation and the scoring intensities are estimated from a log-linear model. The hypothesis tests show that the home-ground effect exists for some, but not all teams in the Zimbabwe Premier Soccer League. The profitable betting rule is to place a bet on the outcome of a particular match when a model's probabilistic forecast suggests a sufficient edge over the bookmaker's implied probability.

Can Euromillions Lottery Be Profitable?

EuroMillions is a major pan-European lottery where the player chooses 5 main numbers out of 50 and 2 different lucky star numbers out of 11 (the latest rules from May 10, 2011). A standard ticket costs EUR 2.00. The lottery imposes a Jackpot Cap, the maximal amount that can be won by guessing all the winning numbers. After the draw on July 12, 2011, this Cap is set to EUR 190,000,000 and since January 12, 2012 this Cap is locked permanently so it will not increase in the near future. The Jackpot Cap before July 12, 2011 was EUR 185,000,000. Since the last significant change of rules on May 10, 2011 there were several notable Jackpot wins: EUR 190M won on August 10, 2012 (the current Jackpot Cap), EUR 185M won on July 12, 2011 (the previous Jackpot Cap), or EUR 162.257M won on September 13, 2011. The fact that the Jackpot can get to rather significant numbers raises a question whether it is possible to have a profitable betting strategy.

Replacement Referees and NFL Betting Markets

Are NFL betting markets efficient? The 2012 labor dispute between the NFL and the referees is used as a quasi-experiment to assess whether the betting markets are able to achieve accurate “prices” in an uncertain environment. While the total points scored and, hence, the accuracy of the totals market does not change much with the replacements, evidence is presented that underdogs performed relatively better resulting in upsets and closer-than-expected games. Betting markets, though, were unable to anticipate or adjust to this systematic effect. Not only were they inefficient, but profitable betting strategies can be identified.

A TOBIT REGRESSION MODEL OF FOOTBALL PERFORMANCE VARIABLES FOR A PROFITABLE FIXED ODDS BETTING STRATEGY

An analysis of in-match football team performances in European top flight matches has been undertaken to develop profitable betting strategies in the fixed odds market. Three profitable betting strategies for betting on the home win are derived using a novel application of the Tobit regression model. Net profits of in excess of 15% are obtained in unseen out-of-sample testing. The generation of these profits may indicate that general team performance information may not be correctly factored into the fixed odds offered by bookmakers.

The Impact of Visiting Team Travel on Game Outcome and Biases in NFL Betting Markets

Using data on regular season National Football League games from 1981-2004, this study examines the impact that travel has on game outcome and whether betting markets fully incorporate this information. A visiting team travelling west to east and crossing at least one time zone is shown to significantly increase the probability of the home team winning. This impact increases with distance, but at a decreasing rate. Evidence on whether betting markets fully account for this travel effect is mixed. While there is evidence that markets do not fully account for the impact of travel and that bettors underestimate the home team’s score whenever the visitor crosses a time zone, the model does not provide a profitable betting strategy out of sample. Thus, any bias is likely too small to profitably exploit.