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A mathematical concept, Poisson distribution works by converting mean averages into a probability. If we say, for example, that the 🍌 football club we’re looking at scores an average of 1.7 goals in each of their games, the formula would give 🍌 us the following probabilities:

That in 15% of their games they score three times

567 goals divided by 380 home games = 🍌 1.492 goals per game

Using these figures, we can then calculate the amount of goals Team A is likely to score 🍌 by multiplying their attack strength by Team B’s defence figure and the average number of home goals overall in the 🍌 Premier League. That calculation looks like this:

Key among these is that Poisson distribution is a relatively basic predictive model, one 🍌 that doesn’t take into account the many factors that can affect the outcome of a game, be it football or 🍌 hockey. Situational influences like club circumstances, transfers, and so on are simply not recognised, though the reality is that each 🍌 of these can massively impact the real-world likelihood of a particular outcome. New managers, different players, morale… The list goes 🍌 on, but none of these is accounted for within the remit of such a method.

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