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...

With the proliferation of Opta and multiple news sources starting to dip their proverbial toes into the world of statistical analysis, casual fans have access to a greater depth of data than at any point before. Converting that data into useful information therefore comes more and more into focus and that’s why we need to review any proposed “advanced” metrics to ensure they remain relevant and as accurate as possible (all while acknowledging we are a long way from even touching the kind of analytics that are prevalent in other sports). Before we go on it should also be noted that any reference to terms like “advanced” should be taken lightly. By “advanced” I mean, slightly more useful than looking at the “goals scored” chart and assuming that the past explains the future. I am not a stats professor nor even a student and more complex models surely exist which might shave a point or two off the margin of error from the analysis in these electronic pages. However, I believe the output...

If you direct your attention to the navigation bar you will note that there is now a new link to the team plus-minus page. I've added a brief summary below but it's a pretty simple principle. One word of caution: the data is based solely on 2015-16 data so the sample sizes are ludicrously small. That said, these numbers (particularly with penalty box touches and total shots) do stabilise relatively quickly so it's worth at least having a glance at these numbers in the coming weeks if you are worried that your initial assessment of a team is a bit off (are Chelsea really struggling? Are Leicester for real?). Workings Plus-minus (+/-) is a very simple metric which simply tries to adjust stats to put them in a context of a team's opponents to date. For example, we might note that two teams have each registered five shots on goal per game and conclude they are of equal ability but if one team did it against Man City and Everton while the other did it against Norwich and...

Alright, enough is enough. While I'm concerned about delving into data too soon and reaching all sorts of ridiculous small-sample-driven conclusions, I'm equally conscious that people want to start making big decisions with their respective teams and thus it's time to launch the weekly rankings and forecasts (still with that small sample asterisk though). Before that, let's look at a new addition to the weekly forecasts. With most of the forecasts we do on this site, there are two distinct parts to the puzzle: What is the expected volume of an underlying event (normally we focus on shots, shots on target etc) What is the impact of those events on actual footballing events (i.e. goals, assists and clean sheets). With goals we've spent a reasonable amount of time talking about how we forecast shots and how we convert those expected totals into goals, but I've tended to neglect the defensive side of the game. This had led to the unfortunate position where t...

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

We looked at this similar data for team shots generated a few weeks back, but as a reminder, this below +/- data is calculated as below: If Arsenal have faced Stoke, Southampton and West Ham who had, on average, managed 8, 12 and 10 shots inside the box (SiB) but Arsenal surrendered 6, 14 and 9, we would record a +/- of -25%, 17% and -10%. We then just take a simple average to give us an overall rating of -6% for these games. Note that from a defensive perspective a minus number is good as it represents less  shots surrendered than the league average. By plotting this data against actual goals conceded per game (GPG), we might be able to identify teams whose results have fallen short of their underlying performance and thus might be poised for some improved results (or vice versa). The data has been split between home and away given that some teams present significant distinctions at home and away. The size of each plotted square represents the total SiB surrendered (at home/...

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 ...