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...

As noted in other recent posts, it can be hard to know what data to rely upon in the early gameweeks. We all know that shots inside the box or those on target tend to correlate well to goals, but with only a few of those events happening each week, it can be easy to overreact to unstable data . One data point I am interested in, is how well teams are doing compared to same fixtures from prior season. This data is still fraught with issues - teams might have changed personnel or have injuries or simply suffered a bad game - but we at least have a baseline to compare against which gives a little more context to what we're looking at. The below visualization shows the Goals (G), Expected Goals (xG), Shots Inside the Box (SiB), Created Chances (CC) and Successful Passes in the Final Third (P3rd) for each team's fixtures for 2018-19 (CY) compared to the same slate of games from the 2017-18 season (PY). We'll highlight a couple of the more interesting observations below the vi...

Gameweek 1 has been somewhat less dramatic than last season. With the new Friday kick off and a solid day of Saturday action under our belts I went to bed on August 12th just like any other Saturday night. A few hours later I was woken by my wife who was in labour - 7 weeks before our due date. A few more hours later our twin girls appeared and let's just say that questions around Gabriel Jesus's playing time or Paul Pogba's xG suddenly seemed somewhat trivial. Needless to say, the past year has meant I've had less time to dedicate to fantasy football, although the early starts on the weekend make watching the 7am ET games much easier. Over the offseason I have been debating how, if at all, this blog can continue to offer value. With the prevalence of Opta's xG, my own version of a similar (but more basic) model becomes somewhat irrelevant and my absence from England means that I will never be as up to date on team news as those who live in the city, read the loca...

However good a player projection system is, it will always have certain assumptions built in which require some judgement on behalf of the model's creator. This might include how much weight to put on recent games versus historic data or how much to regress team or player conversion rates back to a league or historic average. One such assumption I do not currently factor into my own model is which kind of games a particularly player performs well in. For example, if Harry Kane accounts for 30% of his teams shots inside the box and Spurs are forecast for 10 SiB then his forecast will be three SiB regardless of who the opponents are. The strength of those opponents is of course somewhat baked into how we get to the 10 SiB projection in the first place, but no attention is paid to whether Kane has tended to over or under perform expectations against weaker or stronger teams, or whether he's struggled against teams who deploy three centre backs. The data below takes the first ste...

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...

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.

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...

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...