PLFantasy

We had an interesting comment from a reader this week regarding the visualization posted plotting actual points with expected points. My proposition was that the players whose xP trailed their actual points by a distance were likely undervalued by the market, and while we wouldn't suggest they will somehow "make up" those points left on the table to date, we would  expect their production to take an uptick assuming they continue to get chances and playing time at a relatively consistent rate. The reader had a different view: "When I look at this chart I don't see underperformers or overperformers all I see is players on form who are capitalising on their chances (Ramsey and Rooney) and players who are of such quality that they will always out perform the normal (Aguero and Yaya) . . . I believe that if you reconstructed this table after xmas with a start date of tomorrow then the same players would occupy the two sides." It's a fair proposition and...

As promised, it's time to take a step back from forecasting and projecting and look at how the model has performed since I rolled it out in Gameweek 8. There are three ways to look at this: Look at each player's weekly forecast score versus their actual weekly score Look at each player's aggregated forecast score versus their actual aggregated score Look at each player's average forecast score (per 90 minutes) versus their average actual score Each measure has it's advantages and disadvantages but I will throw the first option out as any model is simply not going to be accurate enough on a weekly basis enjoy great success. Of course, we can use its outputs to forecast the probability of different players' chance to succeed but even if it was perfect we'd still see massive fluctuations. So that leaves options two and three which each have some advantages: option two is the truest comparator of how the model performed over the period while option thre...

The purpose of this post is two-fold. First, I want to illustrate a point I've made before in graphical form and highlight a couple of players coming out of that analysis. Second, I want to give a little bit of narrative around regression to the mean and issue a word of caution as to where I'm seeing it misapplied fairly regularly in these stat-friendly times. From some quick research over the top forwards in the league, we see that, generally, they haven't really shown an ability to consistently convert shots on target (SoT) into goals above the league average rate . The league average hovers around the 33% mark for forwards and 28% mark for midfielders, with very few players able to exceed those totals each year (and even then that would be expected purely based on statistical variance). In short then, whether it seems intuitive or not, if a player's SoT are turning into goals at a very high rate, we must expect some  regression in the coming weeks. Now then, a q...

WARNING : This post is going to skew long  and won't contain any directly relevant fantasy advice to help you make your transfers this week. It might get a bit nerdy at times, though I'm far from classically trained in statistics so the concepts, if not always the terminology, should be accessible to all. I also won't reach any definitive conclusion as these models are a work in progress. Any input on anything not considered below or anywhere where you disagree with any conclusions can be posted in the comments or over at Shots on Target where you'll find a handy forum to discuss these issues. We're also only looking at forecasting goals here, assists will need a separate post. Tracking historic success The way I look at it, there are three distinct ways to track historic success: Historic classic stats (goals, assists etc) Historic fantasy points (similar to classic stats but accounting for all fantasy relevant events) Historic underlying stats (a much de...