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Using Monte Carlo modelling for betting – a great article by PinnacleSports.com, presenting deterministic, stochastic and dynamic modelling.
Using Monte Carlo simulation to calculate match importance: the case of English Premier League by Jiri Lahvicka. This paper presents a new method of calculating match importance (a common variable in sports attendance demand studies) using Monte Carlo simulation. Using betting odds and actual results of 12 seasons of English Premier League, it is shown that the presented method is based on realistic predictions of match results and season outcomes. The Monte Carlo method provides results closest to Jennett’s approach; however, it does not require ex-post information and can be used for any type of season outcome.
Monte Carlo Analysis and the Ups & Downs of Sports Investing – a highly insightful article by sports betting analytics website SportsInsights.com, including a short investment analysis, mystery market charts, parameters for the authors’ Monte Carlo Analysis of Sports Betting, bankroll behavior and a summary on how to use the simulation result tables.
How To Build A Monte Carlo Simulation? – Zach Slaton’s simulation of individual match results, using pivot tables to roll up match results and highlighting some other examples of how the same approach can be utilised in competition forecasting (e.g. Transfer Price Index Simulations of the English Premier League Season, MLS Eastwood Index and CONCACAF World Cup Qualification). Great post!
Bet Smarter With The Monte Carlo Simulation by Tzveta Iordanova on Investopedia, providing you with a pretty thorough overview of the basics.
Statistical Methodology for Profitable Sports Gambling – a doctoral thesis by Fabián Enrique Moya (2012), Simon Fraser University, Canada.
This project evaluates the performance of betting systems using as many real-life elements as possible. Starting with a gambling record of more than 600 bets that were actually placed at an online sports gambling website, a Monte Carlo simulation is carried out to compare different bet selection strategies and staking plans. The best performing system is identified and its performance is measured taking into account the actual constraints found in online sports gambling; finally, the results are measured with respect to a 40,000 customer database from the same bookmaker where the bets were placed. The results offer compelling evidence that a finely tuned sports betting system involving a solid selection process and optimized staking has the potential to produce large profits with a limited initial bankroll after a relatively short amount of time. Full PDF Download.
Great example (Sport-Prognose.de) – an interesting model for predicting the winning team of the 2002 FIFA World Championship. The model was built using Tecnomatix‘ discrete simulation packages, emPlant.
Testing the efficiency of an ‘in-play’ sports betting market: A Monte Carlo approach – Hugh Norton’s Honors Thesis, UQ Business School, The University of Queensland (2013) – registration required for full-text PDF download.
A risk analysis of optimized betting unit size – Through empirical analysis and Monte Carlo simulation, a proposed method for finding an optimal balance between risk and reward in sports betting is presented. The model assumes that the implied odds, as represented by the betting line, and the actual outcome odds of any given proposition are known. Risk, in this case, is not the risk associated with improperly assessing these odds. Rather, risk here is the risk of experiencing “gambler’s ruin”, a mathematical concept which states that, given a finite bankroll, a gambler playing against an opponent with an infinite bankroll, i.e. the house, will eventually lose his entire stake. The balance is finding a small enough betting size to minimize the risk of gambler’s ruin without making the bet size so small that the money that is won becomes insignificant.
Beating the bookie: A look at statistical models for prediction of football matches by Helge Langseth, Norwegian University of Science and Technology, Trondheim, Norway. In this paper, the authors look at statistical models for predicting the outcome of football matches in a league. Their aim is to find a statistical model which, based on the game-history so far in a season, can predict the outcome of next round’s matches. Many such models exist, but the authors are not aware of a thorough comparison of the models’ merits as betting models. In this paper they look at some classical models, extract their key ingredients, and use those as a basis to propose a new model. The different models are compared by simulating bets being made on matches in the English Premier League.