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Whether you're lucky enough to still have your original wildcard stashed away in your back pocket or not, as we approach the second wildcard window, it's worthwhile putting together a transfer plan to best exploit the fixtures on offer. We have at least four transfers before then (GW16-19) followed by the important period right after we play the wildcard, so with that in mind, let's try and identify a few fixture streaks during which we might want to target specific players. For simplicity I've only looked at streaks of four gameweeks here. In reality we might want to dig deeper into streaks where you like three out of four games, or four out of six, but this is the starting point, for better or worse. Double gameweeks will also likely cause chaos at some point too, but without knowing where or when they'll strike, their impact is ignored for now. The below table shows the expected goal total over the four highlighted games above or (below) the team average. So...

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Learn About Tableau Given how early in the season we are, the model is still liable to throw up the odd outlier and so in these weekly posts I plan to address those, shall we say, unexpected results. In future weeks, the plan is to post the data as soon as possible after the final games' data is up and then you can raise questions/issues during the week, to be addressed on either the following Thursday or Friday. For this week, I'll just try and guess where the questions might lie: Keiren Westwood Sunderland have conceded at least two goals in six straight contests, yet the model thinks they'll do okay this week. What gives? Well, having conceded 7.3 shots inside the box at home, they're hardly a team without hope (that alone would be the 9th best  total of the teams playing this week). Add to that the fact that Newcastle have averaged 30% less SiB against their opponents than average, while only averaging 6.0 SiB on their travels, and you get a game where w...

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One of the issues of the new forecast model is deciding which kind of shots to use to forecast player success: all of them, only those in the box or only those on target. Those on target have the best correlation to goals scored, however, there is a slight concern with limiting ourselves to that data alone. Consider the below example: Robin van Persie: Appearances 10, Total Shots 55, 10 Shots On Target, 3 Goals If we only look at his shots on target we see that he is averaging just one a game, with 30% of them hitting the back of the net. The issue is that he has historically hit the target at a much better rate than 10/55 (18%), having a success rate more in the 44% range. We therefore need to adjust for the fact that we believe 24 of his next 55 shots (44%) will hit the target and thus his expected goals will be higher. We have some issue about how to generate this historic rate, especially with regards to what data to use, but for now I'm happy to look at such data for pl...

This week's preview piece is going to be in two pieces. The first piece will be the actual forecast for the week and a very high level explanation of what the numbers mean. Lower down you'll find the mechanics behind the forecast along with some identified weaknesses and some proposed questions as to where we go next with the model. First then, here is this week's forecast: Powered by Tableau xG Shots Expected goals based on shot data for both the highlighted team and their opponent to date xG Shots Regressed Expected goals based on shot data for both teams, this team regressed using league average shot conversion rates. The Nuts and Bolts This is the end result of a project I've been working on for a couple of weeks now, and it was discussed at length in an earlier piece . Now the data has actually come together though, I wanted to take the opportunity to run through an example so as to (a) help everyone understand where I'm coming from and (b) I ...

I am away this weekend so I'm not going to have time to write up how  the below numbers have been generated, but it's essentially the format prescribed in the recent goals and assist pieces, only limited to teams for now. I'm posting the data now so we will have a real test set of data from this weekend to work with, which will hopefully highlight a couple of areas where the model could be improved (and by that I mean where the process, rather than the result, is wrong. If Stoke hold United to no goals on 27 shots, the model isn't necessarily broken, it was just a bad day at the office for van Persie and co). xG Shots - expected goals scored based on the shot data they have registered and the shots given up by their opponents, regressed to a team average conversion rate (not a league average). xG Chances - expected goals scored based on the chances created by a given team and the chances surrendered by the opposition, regressed on a team average conversion rate...

I meant to post this a few days back but I was having some troubles with Tableau (my skill level not the software, which is great). Anyway, here are the forecasts goals per game for the first ten gameweeks of the season based purely on prior year data.  The calculation uses a combination of a team's own defense and the strength of the opposition to generate the expected goals scored/conceded. I haven't made any adjustments for transfers, manager changes etc as that is all too subjective and if I overlay my personal opinions on the relative strengths of the new arrivals then the data just becomes an extension of my own biases which are helpful to no one. If you believe that Tottenham will be much better with Vertonghen in defense then feel free to make the mental adjustments yourself. For the promoted teams I use a factor to translate their Championship data into a Premier League expectation. This is explained in more detail here . Note that for the GW1 double gameweeks, ...