PLFantasy

Readers of this blog will likely be comfortable with the idea that one way in which we tend to mis-evaluate a player's performance to date is to focus too much on outcomes and not enough on process i.e. on goals and not shots. This problem, of course, is what stats like expected goals (xG) try to combat, as does simply looking at underlying stats rather than focusing on goals or assists. Another area for caution is to adjust for the opponents an individual player has faced. A player may well be good value for their 3 goals in 3 games based on their underlying stats, but if those all came in games against Crystal Palace, Bournemouth and Swansea then it doesn't necessarily mean they will enjoy future success when the opponents get tougher. This is implicitly factored into the player projections , which are based on individual opponents but there are holes in the model that can need to be recognized. For example, the model allocates a team's projected shots to individual p...

The new Tableau just launched and they've added treemaps, a form a visualisation I've been meaning to add to the site for some time. Now that Tableau carry this format, my existing data is already ready to go, so below is the first of likely many treemaps, here showing players' share of shots inside the box, split between games at home and away. Player share is given on a percentage basis based on the minutes they have played so you don't need to adjust for playing time. However, standard caveats on small samples should be noted, hence I've excluded all players with less than 900 total minutes played: Learn About Tableau

One of the measures that goes into the team model looks at how each team has performed against their opponents compared to how the league fared in that same fixture. So, if for example, Southampton at home are surrendering nine shots inside the box per game and Everton come in and rack up 12 SiB, they have 'overperformed' by 33%. Rather than look at the standard measure of form (i.e.goals) which focuses on the results rather than the process, we can look at this data and see if teams are playing consistently or trending in one direction or the other. The added benefit to this metric is that it is opponent adjustment so 'form' doesn't get blurred by simply having a run of easy or hard games. Although the model measures shots inside the box, outside the box and those on target, I have found SiB to be (a) predictive of future success (see graph below) and (b) generally consistent for a given team (unlike SoT which can be affected by crazy on-target rates like Steve ...