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The revised strategy for this season is to post the weekly preview data as soon as possible, giving you (a) the chance to use it to help with any early transfer decisions and (b) to collate questions on why a given player is so low or high, to be answered on Fridays before the transfer deadline. Learn About Tableau

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...

As a quick introduction for those new to the blog, this piece runs every couple of weeks during the season and looks to shine a light on those "hot" players whose "form" looks unsustainable and those "cold" players who should enjoy success in the future if they keep playing the way they have to date. The below chart shows this week's subjects, with those on the left having outscored their underlying data and those on the right having outperformed their score. Before we start, as is becoming a tendency on this blog, I need to add a quick caveat as to exactly what we're saying here. First, we are not  saying that a player will somehow "get back" or "give back" their production to date or that bad luck will necessarily follow good luck. We are saying that players' (and teams') conversion rates should regress to the mean , seeing them earn points at a rate more in line with their underlying stats (which could be a good ...

Producing forecasts is a tricky business. Even with hindsight it is tough to predict the expected outcome of a given game (i.e. how shots transform into goals) and that problem increases exponentially when you also need to try and forecast the underlying data. Throw in uncertainty around how much players will play and the issue of small sample sizes and you have a recipe for some funky results over these early weeks of the season. First, to make sure the model isn't totally off track, let's look at how it performs retrospectively over the first seven weeks of the season (using actual  shot totals as inputs): Learn About Tableau Though we can see outliers in the above chart (especially at the top end of the market), the overall trend is promising and the r-squared of 57% for players with a risk factor of 2.5 or less is encouraging enough. That's not to say it's infallible, but it's a good start and for the majority of the extreme outliers we can point to sp...

Learn About Tableau Risk slider - this season each player is assigned a risk rating, based on their playing time and current injury status (as explained here ). There's no reason to definitely exclude all risky players, but you should consider their playing time before pulling the trigger with them. I've had a couple of questions about a couple of the 'odd' forecasts above, namely Loic Remy's lofty status and how Sturridge out ranks Suarez. I'll address those in more detail tomorrow but wanted to get the data out now.

Hopefully you've had a chance to look at the new player dashboard which has launched this week and can be found here or by following the link on the menu bar above. I'm sure that for the most part the data is self explanatory but I thought it might be useful to quickly run through the new features so you can get the most out of them. Let's start with the points section: 1. Here we simply see the players' actual points by week plotted against their expected total. One key to note here is that the expected number is based on their actual  shot data rather than the forecast number that will be given each gameweek starting with this one. Point being, the expected number shows how many points we'd expect a given player to score given all the other events observed from his performance. 2. This is a somewhat crude depiction of how each players' points total was earned. You can hover over each slice of pie for an explanation, namely: Appearance (less yellow a...

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...