PLFantasy

As the season ticks on, we're starting to get a bit of data to work with, albeit sometimes in small samples. This season I am not working with a relatively complex player model but am really interested in trying to "play the fixtures" as much as I can. A couple of sources of frustration, therefore, have been as below: 1. Sites which provide the difficulty of fixtures often seem a bit simplistic. For example, the Premier League site shows Wolves as an average "3-rated" opponent, both at home and away yet the reality is more complicated. While at home they have been very solid defensively, with an Expected Goals Conceded (xGC) of just 4.71 in five games, which ranks third best, yet on their travels they have surrendered 7.94 xGC, which is the third worst . Even more confusing is that most sites ( though not all ) don't distinguish between attacking and defensive fixtures, so facing Leeds at home is presented as an easy "2-rated" fixture when in reali...

If you are reading this then you almost certainly own Mohamed Salah. The Egyptian winger has been sensational for Liverpool this season and fantasy managers have responded by adding him to their teams by the thousand. At the time of writing is ownership is up to 51% and his value has increased by almost a million pounds since the game started. Today's piece is not about him though, but his colleague who hopes to operate on the other side of the field - Sadio Mane. A red card, injury and the form of Salah have pushed Mane out of many managers minds, but as teams begin to converge and differentiation becomes increasingly difficult, the Senegal star is a promising option. First, a quick word on Liverpool. I think many people might be hesitant to invest close to 20 million on two Liverpool players (assuming you already have Salah) but I might suggest that fear is misplaced. They've scored just two goals less than Man United, four more than Arsenal, five more than Chelsea and ten ...

The popularity of "expected goals" as a metric has exploded over the past year or so, with mainstream TV broadcasts now starting to dip their toes in the water of advanced analytics. One inevitable, if slightly unfortunate, consequence is that there are now multiple xG models, which could potentially disagree by a reasonable amount, which to those who need a bit more persuasion as to the merits of statistical analysis, might suggest a lack of accuracy. This has somewhat been the case in baseball with the two big "Wins Above Replacement" (WAR) metrics sometimes disagreeing by a relatively large amount, especially when it comes to valuing pitchers. There is sound methodology behind each metric, of course, but for those who aren't well versed in the intricacies of the debate, the differences can be distracting and serve as fuel for those who want to dismiss analytics and focus on old fashioned "eyeball tests" etc. I, of course, have my own model which p...

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 ...

Readers of this blog will likely be comfortable with the idea that one way in which we tend to mis-evaluate a player's performance to date is to focus too much on outcomes and not enough on process i.e. on goals and not shots. This problem, of course, is what stats like expected goals (xG) try to combat, as does simply looking at underlying stats rather than focusing on goals or assists. Another area for caution is to adjust for the opponents an individual player has faced. A player may well be good value for their 3 goals in 3 games based on their underlying stats, but if those all came in games against Crystal Palace, Bournemouth and Swansea then it doesn't necessarily mean they will enjoy future success when the opponents get tougher. This is implicitly factored into the player projections , which are based on individual opponents but there are holes in the model that can need to be recognized. For example, the model allocates a team's projected shots to individual p...

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.