The digitalisation of the world of work and the emergence of the internet as a modern medium have allowed many new industries to develop in the online world since the late 1990s. One significant industry is online betting. This is also accompanied by the ever increasing professionalisation of sports betting. 

Through important knowledge of statistics, mathematics and economics, sustainably successful betting is not witchcraft, but very much hard work. If you want to develop a betting model, you should first focus on one betting market. Concentrating on a certain type of bet has the advantage that one can intuitively estimate betting odds and probabilities of occurrence relatively quickly. Also, increasing experience ensures that the random component (which undoubtedly exists in betting) can be better controlled and its effect thus reduced. In this article, we present a simple but effective model for under betting that the betting base itself tested and is now evaluating.

Example: Betting model for under bets

In this guide article, we show how a data-based betting model can be built and evaluated. If you follow the betting tips section diligently, you will have noticed that from time to time there are special under bets to read about, which are based less on gambling arguments and more on pure frequency distributions. In fact, it has proven useful to calculate the probabilities of Under bets using the relative frequencies of the teams. An example: on the match between ZSKA Moscow and Zenit St. Petersburg, there are 2.10 odds on the Over 2.5 and 1.80 odds on the Under 2.5. 

After 20 matchdays, Moscow have played Under 2.5 in 13 of 20 matches in the league, while for St. Petersburg this tip had come in 12 of 20 matches. The proportions of the two events of the respective teams can be determined quite simply by the quotients 13/20 = 0.65 and 12/20 = 0.60. The counter event of 37.5% (and an implied odds of 2.67) shows that the value in this example is in the Under 2.5, because the 1.80 odds on this tip are significantly higher than the 1.60.

The approach is intuitive and very simple, but in the course of our elaborations very effective – though mainly in unknown leagues. This chance is marginally lower (and intuitively finer) than the previously calculated chance and the new “fair” odds are 1.63 (previously 1.60). But even this percentage is still interesting with a view to the real betting odds.

Blind gambling should not be the case with this model either

It is always advantageous to include other information and facts in the bet. Are there any noteworthy injuries, suspensions or changes in form? How did the last duels in the league or the last direct duels between the two teams go? If there are no irregularities there, the tip can be safely played according to the betting model. But there are leagues and teams that simply fit the betting model much better. These must first be observed and analysed separately!


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