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Betting systems

For a long time in the history of sports betting, the use of systems was considered to be unsuitable and unlikely to bring success to punters.

Unlike casino games, the human-focused nature of sporting events led many to believe that adopting any type of betting system would be futile, as sports were simply too open and unpredictable.

However, in recent times there has been a surge in the sheer amount of statistical analysis taking place in professional sports all over the world. Every day, sports coaches, broadcasters, spectators, and medical teams are furiously gathering data on every aspect of sporting performance, and this is fuelling the development of athletes at all levels in all sports.

It is also fuelling a surge in the use of betting systems, as keen gamblers take advantage of the vastly increased amount of data available to them via the web and other sources.

The popularity of publications like Moneyball have increased awareness of the statistical analysis being carried out in top level sports, and has prompted more and more people to utilise this statistical information when forming betting strategies.

To get the most out of the increased information available to you as a gambler, it is important to get to grips with some simple statistical concepts that will help you to interpret data and improve your betting returns.


Linear regression


Two of the most commonly applied concepts that help to inform sports betting systems are linear and ordered logistic regression.

Linear regression has been used for many years to predict outcomes in the fields of medicine and economics, but it has proved to be useful when applied to betting, as it helps to determine the factors that are most likely to have an influence on the outcomes of sporting events.

To apply linear regression to a sport such as football, you would need to take data from the past and observe certain trends in it.

For example, you might want to place a bet on the number of points Everton will finish with in a Premier League season. To help you do this, you apply linear regression to data about Premier League teams, analysing certain factors, or 'predictors', that affected how many points the teams earned previously.

For instance, you could take club wage bill as your predictor. To predict how many points Everton will earn next season, you could put data into a liner regression equation and plot the correlation between the wage bill of each Premier League team and their points total. To increase the accuracy of the correlation, you could plot this graph over multiple seasons.

If you were then privy to information about an increase in Everton's wage bill, you could use that information to increase the potential accuracy of a football prediction of the points they stand to accumulate in the coming season.


The multiple regression system


Like linear regression, multiple regression is a statistical model that uses past events to help you predict the outcome of future events.

Applying multiple regression analysis to sports data has proved so effective that professions have been created around this practice.

To use football as an example once again, a multiple regression betting system would take several predictors into account when analysing a variable, rather than linear regression's single predictor.

For the bet on Everton's final points total, you might use multiple regression to analyse transfer expenditure, attendance, player age, and the squad's wage bill. Data on all of these predictors, along with previous seasons' points data, would be entered into an equation, and the relationship between the predictors and the variable would be evident from the results.

You might, for example, find that there is a h3 correlation between the wage bill and the points total of each season, but a much weaker correlation between Everton's match attendances and the points totals.

Regression models commonly identify factors like club finances, transfer expenditure and manager selection as h3 predictors of success.


Statistical anomalies


While regression analysis looks at large amounts of data and seeks to draw a correlation between various factors and outcomes in order to inform a betting system, there is another approach that seeks to read between the lines drawn by regression.

Statistical anomalies are deviations from the commonly observed trends, and it is possible to identify and utilise them through rigorous observation of statistics.

The best way to learn about the statistical anomalies of a particular sports team or individual competitor is to follow their progress over a long period of time. For this reason, loyal supporters of particular sports teams are often in a privileged position when it comes to betting on their team, as they will have observed the influential anomalies that regression models often fail to take into account.

For example, there might be a particular venue that a team consistently underperforms in. Regression analysis of that team's statistics might view the defeats in that particular venue as normal results, overlooking the possible psychological influence that the venue has on the team's performance.

A supporter of a team might recognise the team's failure to get positive results when they are missing a particular player through injury. Again, broad statistical analysis might not pick up on the effect of the player's absence, but the fan can utilise their 'inside knowledge' to form a betting system around statistical anomalies that have consistently proved influential in their experience.

Statistical anomalies like this can be very difficult to prove, and it is often easy to mistake coincidental events for a pattern that is influenced by a particular anomaly. But if an anomaly proves to lead to a certain outcome frequently and consistently over a long period of time, it can be a powerful tool to form part of a betting system.


Correlation Vs. Causation


One of the arguments against using statistical models to inform betting systems and strategies is that concepts such as regression analysis are only effective in establishing a correlation between predictors and variables, and they cannot prove causational links.

Using the correlations evidenced by statistical models to predict future outcomes in sporting events might improve the accuracy of bets placed over time, but is by no means a guarantee of success.