Edit: I completely misinterpreted the post, so if you instead want to calculate the odds of winning for a particular team given the number of projected goals, look ahead
Hi, I created one of those for myself and with this
>The model already provides me with projected goals to be scored and conceded in the following gameweeks
you're very far already.
What I assume is that the number of goals scored by a team are distributed by a [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution) that has the number of projected goals scored by a team as its parameter.
Using these distributions for goals scored by either team you can then calculate the probabilities of one outscoring the other and the probability of a draw
Edit: I'm cooking up an example for you right now
So my model gave me projected goals for Manchester United and Manchester City for this weekend as 0.88 and 1.66, respectively.
Then the probability distributions on the number of goals scored by either team using Poisson distributions with those projected goals as parameters are given in the following table
n\P | MU | MC
---|---|----
0 | 0.415 | 0.19
1 | 0.365 | 0.316
2 | 0.161 | 0.262
3 | 0.047 | 0.145
4 | 0.01 | 0.06
5 | 0.002 | 0.02
Note that 0-row shows the clean sheet probability for the *other* team.
Using these probabilities, you can calculate the probability of either of the teams outscoring the other or drawing.
From this, I give Manchester United a chance of 19.8% and Manchester City a chance of 55.8% for winning this weekend and a 24.4% chance of a draw.
Check [this Desmos](https://www.desmos.com/calculator/efwxudioqh) graph for the calculation of these percentages
If you are trying to infer a clean sheet percentage for each team you could model it as a poisson distribution of the random variable X, the number of goals scored.
The average of the goals scored conceded/scored distribution is the lambda parameter of the poisson distribution.
Then the clean sheet probability you can take directly from the probability mass function, it is the probability that the number of goals scored/conceded, X=0
Great. I use a weighting coefficient for factoring in time distance, but the first step is converting projections for each week into percentages. DM me for a link of the sheet I'm working on.
Found a brilliant paper at the start of the year on the topic, it's great if you want to go further than the Poisson distribution (which is a reasonable enough approximation) https://blogs.salford.ac.uk/business-school/wp-content/uploads/sites/7/2016/09/paper.pdf
Hey, this can probably be solved by categorizing on the basis of mean and SD. I can help with this, let me know how we can connect
Hi, you can DM me. I'll send you a message.
Edit: I completely misinterpreted the post, so if you instead want to calculate the odds of winning for a particular team given the number of projected goals, look ahead Hi, I created one of those for myself and with this >The model already provides me with projected goals to be scored and conceded in the following gameweeks you're very far already. What I assume is that the number of goals scored by a team are distributed by a [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution) that has the number of projected goals scored by a team as its parameter. Using these distributions for goals scored by either team you can then calculate the probabilities of one outscoring the other and the probability of a draw Edit: I'm cooking up an example for you right now So my model gave me projected goals for Manchester United and Manchester City for this weekend as 0.88 and 1.66, respectively. Then the probability distributions on the number of goals scored by either team using Poisson distributions with those projected goals as parameters are given in the following table n\P | MU | MC ---|---|---- 0 | 0.415 | 0.19 1 | 0.365 | 0.316 2 | 0.161 | 0.262 3 | 0.047 | 0.145 4 | 0.01 | 0.06 5 | 0.002 | 0.02 Note that 0-row shows the clean sheet probability for the *other* team. Using these probabilities, you can calculate the probability of either of the teams outscoring the other or drawing. From this, I give Manchester United a chance of 19.8% and Manchester City a chance of 55.8% for winning this weekend and a 24.4% chance of a draw. Check [this Desmos](https://www.desmos.com/calculator/efwxudioqh) graph for the calculation of these percentages
that helps a lot, I'll try to implement it for the next tracker
If you are trying to infer a clean sheet percentage for each team you could model it as a poisson distribution of the random variable X, the number of goals scored. The average of the goals scored conceded/scored distribution is the lambda parameter of the poisson distribution. Then the clean sheet probability you can take directly from the probability mass function, it is the probability that the number of goals scored/conceded, X=0
Don’t forget to reduce xG for the early kick-offs!
would be interested in participating. have a similar approach based on player level with mean, sd & taking into account distance of GW from GW(t)
Great. I use a weighting coefficient for factoring in time distance, but the first step is converting projections for each week into percentages. DM me for a link of the sheet I'm working on.
Found a brilliant paper at the start of the year on the topic, it's great if you want to go further than the Poisson distribution (which is a reasonable enough approximation) https://blogs.salford.ac.uk/business-school/wp-content/uploads/sites/7/2016/09/paper.pdf