PLFantasy

Clean Sheet Rankings Learn About Tableau Attacking Rankings Learn About Tableau Individual Rankings Note : Individual rankings are based on an historical average data model only and no account is taken of expected playing time or rotation risk. Where a players is confirmed as injured or suspended I endeavor to remove them for the below listings but assumptions regarding rotation are not included in the below analysis. Learn About Tableau

The new Tableau just launched and they've added treemaps, a form a visualisation I've been meaning to add to the site for some time. Now that Tableau carry this format, my existing data is already ready to go, so below is the first of likely many treemaps, here showing players' share of shots inside the box, split between games at home and away. Player share is given on a percentage basis based on the minutes they have played so you don't need to adjust for playing time. However, standard caveats on small samples should be noted, hence I've excluded all players with less than 900 total minutes played: Learn About Tableau

You don't hear it so much these days - or at least I don't, having left the world of 24/7 Sky Sports News to one in which my weekly football dose is received in 90 minute spells with little or no nonsense chat - but anytime a mid table team would lose towards the end of the season, we always heard that they had one eye on their upcoming holiday and had mentally checked out by late March. If this is indeed the case then the fantasy impact would be huge as (a) we would obviously wish to avoid such players and (b) playing someone like Swansea would suddenly become a potentially easier fixture than facing a team battling to stay in the league. The below assessment is very  simplistic and should not be taken as an attempt to definitively answer this question. It's merely a first attempt to shed a little bit of light on this topic and see if there's something here to investigate. The analysis contains a number of limitations, not limited to: I have not adjusted f...

Clean Sheet Rankings Learn About Tableau Attacking Rankings Learn About Tableau Individual Rankings Learn About Tableau

A slight change of pace with this week's fanning the flames piece due to a couple of observations over the last few weeks and months. First, I felt that old graphic didn't really show enough information that might lead us to fan said flame (or, more likely douse those hyped up fires). Second, looking at the data on a single week basis was always going to lead to a large number of 'variances' due to the ridiculously small samples involved. I've therefore currently included three gameweeks of data, but have built the model to allow for anywhere between one and five to be included so this may again be tweaked in the future. Finally, this is a piece which should probably run every two or three weeks to avoid too much repetition, so this new format lends itself more to that time frame. First then, let's look at the new graphic and a couple of lines to illuminate the way the data is intended to be read: Powered by Tableau The G , xG , A , xA , P and xP ...

Aston Villa Guzan, Lowton, Clark, Vlaar, Baker, Weimann, Bannan, Westwood, Sylla, Agbonlahor, Benteke Subs: Given, N'Zogbia, Holman, Bowery, Dawkins, Bennett, Carruthers After a couple of promising outings, N'Zogbia once again found himself back on the bench which only serves to underline that unless your squad is very deep, he's a luxury that can't really be afforded at this stage of the season, even if Villa have been better in the second half of the season. The rest of this team is fairly predictable with Sylla the only odd name here, though obviously not one to cause too many fantasy waves despite a promising first full game. Bent's miserable season continues, though he looks like he could be fit in the next week or so, which mind dent any meagre value offered by Weimann or Agbonlahor. Liverpool Jones, Johnson, Carragher, Agger, Jose Enrique, Lucas, Gerrard, Downing, Suarez, Coutinho, Sturridge Subs: Gulacsi, Skrtel, Shelvey, Allen, Henderson, Sterling,...

Look guys, it's going to be carnage this week. I would genuinely take 20 points if offered right now, but given the lack of great options available, I don't believe it's a situation where splashing out on four points is going to really help. Even if you have a wildcard, selling out for this week is likely a bad idea as you don't want to be setup with no players from teams like Man Utd, Man City, Chelsea and Arsenal. In short then, just try and minimise your losses and target selling players who have an off week and  whose future prospects you aren't crazy about (Everton players spring to mind here given their upcoming fixtures). To address a point that will almost certainly come up if anyone links to this page, you'll notice that the all conquering Gareth Bale finds himself well down the rankings despite all the talent missing this week. I understand that this goes against your gut, but please remember that this model is based on statistical averages and is n...

Regular readers will know that one of the key drivers behind my team goal model (which ultimately backs up a lot of the individual player data too) is based on shot totals, split between those inside and outside the box. I like shots on target too, of course, but the reason I have opted for total shots in the model is for fear that over a short period, shots on target can be a bit misleading for individual players as, just as we wouldn't expect a player to score every time he hits the target, we equally wouldn't expect him to actually hit the target every time either. With that in mind, I want to draw your attention to some interesting shot data from this and last year and open up the floor to suggestions as to (a) what is going on and (b) how we should deal with it next year. You'll notice that a lot of the posts for these remaining few weeks are going to be a bit more 'blue sky' than normal, mainly because there's only so many times you can write up the sam...

One of the areas we need to work on over the summer is how shots on target are converted into goals, specifically the split between types of shot. We've observed that, on average, shots on target become goals at a league wide steady rate (30%-33% depending on position), but on an individual level things are more complicated. We consistently see players like Adel Taarabt show up as outliers in that they've taken a lot of shots, hit the target but yet not received the final reward. One of the obvious starting points is to look at the ratio of shots taken inside and outside the box, and this is something that is already baked into the current model. However, playing around with some data recently, I put together a graphic which hopefully lays out some of the questions that remain unanswered, and I hope to spark some debate to push this forward in the offseason. Powered by Tableau SiB Ratio - percentage of shots taken inside the box G/SoT - percentage of shots on targe...