In this piece I will be looking at probability statistics. Probability comes from the Greek word petros meaning “to measure”. In mathematics this is used to describe how unlikely a set of events are, like the lottery or probability that an over on one side of a coin is a certain number. It can also be used in probability when talking about averages and ranges. For instance, if we are looking at the probability of an over on a red team winning the football game, we can say that their chances of winning will be X and their probability of losing is Y. This can then be written as: the probability of (the probability of X) x = (the probability of Y) y
This shows us that we have a basic idea of probability statistics. There are many ways in which they can be calculated. For instance, we can look at the historical results for any particular game. We can compare the teams playing against each other to try to find out the probability of who will win. We can also take a look at the form factors which have influenced teams lately to try and work out how likely it is that a team will win.
Of course there are more sophisticated methods that allow us to take a statistical approach to the probability of a result. One such method is to model the probability of the event happening based on the set of probability data that came from the past. For example, if we want to work out how likely Manchester United will win the next Manchester United cup, then we can model the odds that the team has been given so that we can determine their chances of winning. The same can be done with football games in general, by taking the form factors into consideration and then working out the probability.
However, all this comes with one big caveat. That is that we need to know what kind of statistical data we are dealing with. This can be difficult if the sport in question is very unique because all previous records are normally forgotten after a certain amount of time. For instance, if someone was playing in the prime time slots then their chances of getting a result may well be radically different to someone that is only in the lower tier.
In this case we can consider something like the World Cup. Every four years there is the World Cup Competition. Each country sends three teams to compete for the prize money. When the draw was made the countries with the best qualification rates were usually given the draw results. By analysing this data we can see which countries have the best chances of going home.
So how do we use probability statistics to make football predictions? Well, the simplest way is to take the form factors and the probability data and work our way backwards. This can take a while because it is often difficult to work out what form the team is in. If you know the probable starting eleven but no statistical information on what they have done recently then the team cannot be expected to perform well against the form factors they have. xsst
However, there are ways around this problem. One way is to look at past results and form factors. We can then work the probability data into the formulae for each team. For instance, if we know that this team has a good record against a particular opponent then their chances of winning will rise against that team. Likewise, if we know that this team has suffered a lot of injuries recently then we can estimate that they have a poor chance of performing well against an opponent that has a better record or has more experienced players. xs ba mien
These probability statistics can also be used to analyse the performance of team members individually. For example, we can find out how well a team member is performing by looking at the team’s average position each time they are on the field. We can also work out the probability of a player performing a certain action using this data. All of these methods can be useful and should be used when making football predictions.