This is a free preview of a paid episode. To hear more, visit www.cannonstats.comEver wondered what stats mean and when the right time was to use them was? Well, this is the podcast/video for you!In this bonus episode, I am joined by Clive Palmer to talk about advanced stats, and how they help us to understand the game. Learn how the stats work, what the limitations are, and what context is needed for the stats.Below is the very detailed notes and explanations I wrote to prepare for this podcast.At the very bottom for premium subscribers is the video recording that we made as well. (if you subscribe to the Arsenal Vision Podcast Patreon this should also be available to you on that channel)Expected Goals (xG):This is probably the advanced stat one that people are most familiar with but it is probably still a bit understood.I think the resistance to the idea behind rating chances has broken down. We all naturally have an intuitive sense of when a chance is high or low-quality. For example the Gabriel Jesus goal against Manchester United. We KNOW this is a clear chance and that there is a high probability of scoring here.But what is the estimate of scoring here?For this shot in my expected goals model, it is rated as 67% chance of being converted, which is going to be on the higher side for what chances will end up being rated. The largest factors for a shot’s rating is the distance from goal, the angle of the goal for the player to aim at, and if it was taken with feet, head or other.The information for this comes from event data that gives the time and location of every on-ball action on the field. From this, we can infer information about the chance and use that to compare it to how those factors of applied to shots with similar characteristics.This shot is from a fast break situation, which helps tell that the defense is not going to be set and it a more dangerous situation. The speed of this move is 7.3 yards per second making this a very direct attack with is another positive indicator that the defense has been unsuccessful in slowing the attack down.Jesus completes a dribble before he shoots this is a major positive for his chance as it is a clear indicator that he has opened up space for a shot.This is also given the “big chance” indicator by Opta and that is a bit of a cheat that helps that this is not pressured and a one-on-one.One of the major limits of doing this with the regular event data is that the model is blind to where the other players are and information needs to be inferred from other characteristics. We see this when he takes the shot:The model from the event data only knows this and has to guess as to everything else from the proceeding events and the characteristics of the shot:This is an obvious shortcoming and why when you see the xG for a shot, especially from ones that don’t have tracking elements (for example Statsbomb uses freeze frames to also show where all the players are in the frame for the events that they capture. The newer Opta data also uses something similar to get around this) it should come with pretty large error bars for the estimate of the “true” probability for the shot. With this shot we can make some decent assumptions, it’s a direct/fast break so minimal defenders back. Jesus made a dribble before he shot which probably means he isn’t under pressure when he shoots but it is still a bit of guesswork on to just how open it might be.When to Use xGYou will see xG used in a number of different places and ways. One of the more popular is after a match; I post a couple of different illustrations of the xG for a match that I think are helpful for describing the game.The first is the xG shot map, which plots all of the shots and goals for each team and the size of the dot corresponds to the xG value. This gives a nice picture of where the shots took place and how dangerous those shots were. There are a number of different ways that a team can accumulate the different xG values and seeing t