PLFantasy

As we sit in the middle of another dull international week, many managers will be tempted to play (or have already played) that wildcard chip. With that in mind, I wanted to link to a few resources that I have posted over the past few weeks that might be useful Overall player projection  - projected points for the next 12 gameweeks Player share of key stats - the percentage of their team's SiB and CC that each player has accounted for Points vs Expected Points - a quick way to highlight players who have underperformed their underlying stats to date and could see increased fortunes in the future with improved conversion rates. Performance vs prior season - see how players have performed in this season's fixtures compared to the same fixture last season Team snapshot and projection - which teams are performing best and who do you need to secure coverage for? Defender selector - which defender gives the best combination of value and attacking threat within each ...

As we enter another drab international week, it presents a well-overdue chance to dig a little deeper into this year's model and look at some of the results which don't necessarily align with what the majority of people appear to be thinking. I don't want to get too bogged down in the merits of any particular assumption in the model, though obviously as we look back and see where the model missed reality, tweaks can and are made. I do however want to illustrate how individual forecast numbers are made up and why that leads to the situation, for example, where Alexis Sanchez has so-so forecast numbers despite leading all midfielders in points to date (by a distance). Regressing conversion rates The first and most obvious point to note is that Sanchez has a particularly high goals per shot on target ratio (50%) which based on history is unlikely to be sustained. The model uses a regressed conversion rate based on a combination of a player's history (where applicable)...

Learn About Tableau

Learn About Tableau

Learn About Tableau

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...