In today’s part of our odds compiling series, we’ll finally take a look at zeroing in on a closer xG numbers for the upcoming Premier League match in GW 34 between Liverpool and Palace using our 5 season data.
In Part V we are going to look at
refining our xGoals calculation building league tables and evaluating teams over/under performance. With games coming thick and fast this past week, I will wait for a lull in fixtures (after GW 32) to write about improving our xGoal calculations using Attack and Defence ratings.
In this part of our tutorial, we’ll be using our RANK function again to automate everything so it updates after every game week. We’ll also be learning about some metrics commonly used in fanalytics.
So far in this series we have looked at a basic way of estimating xGoals, using a Poisson Distribution, and Bookmaker margin. In Part IV we are going to take a look at using previous results as a way of deriving odds.
Article originally posted on 30th March 2017 here
In Part I of the series we looked getting data and some basic formulas. Part II looked at xGoals in more detail and a basic method of finding the expected number of goals scored between Liverpool and Everton. This method is by no means the end point and there is still some improvements to be made, but we’re beginning from scratch and we’ll come back to improving the numbers later on.
In Part I we looked at gathering and cleaning data from football-data.co.uk, and building our first basic table. In this part we’ll discuss xGoals in a bit more detail and run through our first method for calculating odds, using the upcoming game between Liverpool and Everton.
Julian Brandt is a young German international winger who plays for Bayer Leverkusen. He is a 20 year old who has already played over 3,600 minutes of professional football in a top league, along with 700 minutes of European football. Brandt has represented Germany at every level from U15 to senior level and is European Golden Boy nominee this year. He is definitely a player to watch in the future. Continue reading Julian Brandt – Statistical Profiling
Expected goals (xG) is a tool to value the probability of a shot resulting in a goal. Each xG value is the chance of that shot being scored. For example: 0.156 xG = 15.6% likelihood of that shot being scored. By having a database of shots a xG model will work out how important each variable is in a shot being scored. The database takes into account a variety of variables of the shot which will be covered later.