The equation is widely used in various fields that deal with probability theory. Economic production, medical diagnostics, meteorology, forensics are just some of the areas in which Bayes' theorem is applied. And this dependence, formulated by an English priest and mathematician who lived in the 18th century, is also applicable in sports betting.
What is this theorem about?
It calculates the probability of an event occurring after we are aware of other information that may be relevant. Mathematically the formula looks like this:
P (A | B) = P (A) * P (B | A) / P (B)
At first glance, that doesn't tell us anything, naturally. The letter P comes from Probability. The equation itself describes the probability of event A occurring, given that it depends on whether event / factor B will occur. P (A | B) is the conditional probability of event A occurring upon condition / event B happening. P ( B | A) is the conditional probability of event B given that A is true. It is important that both values are positive and non-zero.
How is it applied in sports?
We need to have a combination of factors that are interdependent. For example, this is a Zenit match, which wins 40% of its matches in the Russian championship. We know that 15 percent of Zenith meetings are played at temperatures below 10 degrees Celsius. We are also aware that Zenith has won in 20% of the cases where the weather is cold.
In this situation:
- P (A) = Zenith's probability of winning the Russian Championship match - 40% (0,4)
- P (B) = Probability of a Zenith match to be played at temperature below 10 degrees = 20%
- P (B | A) = Zenith's probability of winning the match at a temperature below 10 degrees - 15%.
If we only know that Zenith is about to play and that the weather will be cold, how do we calculate the probability of success of this team?
P (A | B) = P (A) * P (B | A) / P (B) = 40% * 15% / 20% = 30%.
Such an example enables us to evaluate the chances of success on the basis of various prerequisites that may not be obvious at first glance.
Of course, there are many other factors to consider - the presence or absence of a key player on which terrain the match is played, how the team acts in a busy schedule (matches every 3-4 days), or in a not so busy schedule. Often we are guided mainly by odds and statistics, but they do not provide all the answers. Sometimes using additional information can give us important guidelines to consider in order to make a successful prognosis.
Other factors can be involved - Sevilla wins 70% of their La Liga matches after playing in Europe during the week while winning 40% of their away matches in the same championship. If the problem is formulated correctly, we can calculate the likelihood that the team will win on a foreign terrain after playing in Europe. The same can be done with the presence or absence of an important player. However, the theorem is not very easy to comprehend and one has to check that the events in it are really dependent on each other so that we know whether to rely on the formula.
