The Method

In order to calculate the odds that, statistically, will allow bookmakers to earn a desired commission, they need accurate models to estimate the true probability of each event. There are many different factors that can be incorporated into a model to predict the true probability of the outcome of a sports game, for instance: the results of recent games for the two teams, the home or away results for those teams, the number of points/goals scored and conceded by each team during recent games, player injuries. and even the expected weather condition.  If we consider the scope of these variables the task of developing accurate models to predict the outcome of thousands of games across sports leagues around the world becomes an extremely complex challenge. In recent years, however, teams of professional analysts have improved the outcomes of their prediction models with increasingly sophisticated statistical analysis and large amounts of data in a variety of forms.

To quantify the predictive power of bookmakers’ models we use a historical analysis of sports game outcomes.  With the historical data, our analysis shows that the consensus probability among bookmakers is a strong predictor of the underlying probability, or true probability, of an outcome.  Based on these results, we build our betting strategy on this evidence that bookmakers already possess highly accurate models to predict the results of sports outcomes.

A strategy intended to beat the bookmakers at predicting the outcome of sports games requires a more accurate model than the ones bookmakers have developed over many years of data collection and analysis.  Instead of trying to create such a model, we use the bookmakers’ own probability estimates of the outcomes to find mispricing opportunities.  More specifically, we identify opportunities where the odds offered are above their estimated fair value.  Sometimes bookmakers offer odds above fair value either to compete to attract clients or to maintain a balanced book to avoid getting overly exposed to risk.  This means that bookmakers might offer odds with a lower implied probability than the actual probability of a result. This is the key factor that we exploit in the Mapa Method +.

This deviation from the true probability or mispricing provides an edge.  We use the size of this edge to allocate the optimal percentage of our bankroll to the bets.  By having an accurate calculation of the true probability versus the given odds at a particular bookmaker, we can utilize this money management system to minimizes losses and maximizes gains for long-term bankroll growth.

In summary, we base our betting strategy on the assumption that odds published by bookmakers allow us to obtain a highly accurate estimate of the actual probability of the outcome of an event (by taking the inverse of the mean odds across bookmakers minus a calculated constant that allows for the bookmaker’s commission).  Thus, our betting strategy consists of placing bets whenever the odds offered by some bookmakers deviate from the average and were above fair value, i.e., when the expected payoff of placing the bet was positive.  Importantly, the task of identifying the odds that satisfy the threshold did not require a model with higher accuracy than the bookmakers’ models.