PLFantasy

With 8 clean sheets and big performances from the likes of Spurs, United and to a degree Arsenal, there were points a plenty this week, though as we can see above, not all those performances were necessarily backed up by the underlying stats. A couple of names to focus on here: Alexis Sanchez The model liked the Arsenal man as the best performer of the week with 10 expected points on 8 shots (4 SiB) and 6 CC. The "three elite forward" strategy which was en vogue for all of 2 weeks has been devastated by injuries to Aguero, Morata and Lukaku and so managers are now looking back to elite midfielders to lead their team and Sanchez is well placed to potentially fill that role. His incredibly low ownership number of just 3%, plus his earlier price fall make him an incredible opportunity to turn a profit if (or should we say when) his actual points haul starts matching his xP. Arsenal's fixtures are good but not spectacular as they face City and Spurs in GW10 an...

The visualization below plots the expected fantasy points arising from expected goals versus those arising from expected assists. The idea here is that this is a quick snapshot of how a player has performed to date and where their points are coming from. It should be noted that xG and xA numbers used to generate the xP are based on shot, created chance and possession date for the 2017-18 season only but the conversion rates to convert those raw events into goals are regressed using team and league rates for both the current and prior seasons. I hope this eliminates some noise from the small sample sizes of the early season but it's still worth noting that this is a snapshot based on three games so should act as a data point for your transfer assessments but not an all encompassing answer.

The below chart is something I've posted before and am tweaking for the new website so I thought I should post it now and get your feedback while also hopefully providing a quick view to identifying some players to research further: Learn About Tableau

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...

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...