Fantasy Premier League is a complex game. Just like its underlying reality, football. It’s growing more complex, too. Or, rather, the way we understand it is. We are witnessing an explosion of content, models, stats. A proliferation of information.

Ironically, this can make it harder for us to understand the game, as our overloaded brains struggle to sift through the data extravaganza. It may be possible for those at the upper echelons of FPL to filter all the nitty-gritty stats and watch hours of video. But for most of us, that’s not feasible.

So sometimes, we need to cut through complexity. That’s what this post is about. I’m not claiming to have some final, golden stat or The Model To Rule Them All (TM). And there are endless facets to the game that we could consider. But some data, some analyses, are more critical and useful than others for FPL purposes.

Below, I outline five bits of data, analyses and tools that, in my humble opinion, represent the essential information for your Fantasy Premier League pre-season planning needs.

In short: You need to know something about how players have performed previously. And something about how players are likely to perform going forward. And something about fixtures. And something about making choices. And something about adding your own personal touch.

So, without further ado:

1. Player Performances: Expected and actual stats

You’re probably already tired of hearing about Expected Goals, Expected Assists, Expected Goal Threat, Expected Whatever by now. They’re established, in some corners overused, bits of data.

Don’t despair. I’m not here to preach. But it is worthwhile repeating that expected stats offer one of the most comprehensive, easy-to-understand views of the game’s key indicators: goals, assists, clean sheets and the like. It’s a hugely valuable route to evaluating player performance. Especially when combined with data on actual performance.

In the gallery below, you’ll find one example of charts for expected vs. actual goals and assists in the 2018/19 Premier League. The visuals are also broken down by timespan (full season vs. last five games) and position (defenders, midfielders, forwards).

These overviews provides an intuitive look at expected and actual performances last season, and allows simple comparisons between the two. For instance, those to the left of the line overperformed relatively (e.g. Alexander-Arnold and Eriksen), while those to the right underperformed (e.g. Salah).

Interested in more? Check out detailed data across multiple leagues and seasons at understat, or more FPL-specific data at, e.g., Fix ($). Or check out StatsBomb’s excellent season preview articles using (expected) stats.

2. Player Forecasts: Points projections

Having historical performance data is all good, but how are players actually going to perform in the future? Prosaic analyses abound, but points projections offer a more concise and comparative tool for forecasting how players will do.

Inevitably, putting one singular number on the projected points of players masks the underlying variance and risk. But similar to expected stats, points projections offer a valuable, encompassing, tangible analysis.

FPL Review, for instance, offer an excellent suite of customisable FPL projections. They draw on expected stats and, importantly, bookie odds. No one is better than the bookmakers at player forecasts across the board. No, not even you. Why not take advantage of their wealth of knowledge?

Using projections enable you to do exactly that, and to test your own assumptions about player futures. For instance, which looks the best option of Salah and Mané for the first five gameweeks? And how might different combinations of 4.5m GK rotations look? (Note the tables shown below are only preliminary – the full suite of final projections will be made available before the season.)

Interested in more? A number of sites now offer FPL projections including Fix ($), foomni and Fantasy Overlord.

3. Schedule Hazards: Alternative fixture difficulty

‘Form or fixtures’ is an age-old question in sports analysis, the dichotomy of player quality and opponent difficulty. Both, however, are essential to making FPL plans.

Team schedules and opponent strength is key to your FPL analysis repertoire. It allows you to evaluate the short- and long-term outlook of players across the season, and plan for favourable stretches.

The Alternative Fixture Difficulty Ranking (aFDR) offers a bottom-up, data-driven view of FPL fixture difficulty. It is more nuanced and precise than the official FPL rankings, based on the actual FPL points allowed by opponents. This is arguably the most relevant measure to assess FPL schedule hazards. It also splits fixture difficulty by position group. That’s important: in 18/19, Newcastle were a great fixture for opposing defenders – they scored few goals – but a mediocre fixture for opposing forwards – they defended well. The aFDR reflects this. It also avoids awkwardly grouping fixtures in four or five clusters, but visualizes difficulty on a gradient scale (from red to green).

Using the aFDR we can, for instance, assess teams with the best season starts. From GW1-6, the best defensive schedules are: 1. EVE, 2. MCI, 3. LIV, 4. MUN, 5. CRY, 6. BHA. And the best attacking schedules are: 1. MCI, 2. EVE, 3. LIV, 4. WHU, 5. SOU, 6. SHU.

Interested in more? There are quite a few useful, nuanced, data-driven FPL fixture planners on offer, such as Tim Bayer’s Fixture Tracker, Premier Fantasy Tools, and Football Analysis. You can also generate your own aFDR analysis and fixture table using the fplscrapR package for R.

4. Evaluating Trade-offs: Optimization for FPL

One of the most difficult things in FPL is to weigh different options against each other. The complexity of choice is high. Is a 4.5m DEF/6.0m MID combo better than a 6.0m DEF/4.5m MID combo? And what does either choice mean for your FPL squad as a whole? Fortunately, there are tools to help us evaluate such trade-offs.

Optimization is one way to reduce the complexity of our options by modeling the many trade-offs and producing a succinct answer to ‘what is the best choice?’.

Optimization represents an underexplored analytic for FPL – and there is much room for improvement in current analyses – but there is some help out there. Data scientist Ben Torvaney used to run a weekly FPL squad optimiser. And FPL Review has integrated a selection of line-up optimizations in its toolbox for the upcoming season. I’ve also developed optimizer functions for R. These functions produce a single, optimal line-up or squad based on your projections of choice, the standard FPL rules, and any constraints you want to add (e.g. a particular formation, ‘must have’ players or a specific budget).

Using these tools can tell you the best options for your squad, for your Free Hits and your Wildcards, and allow you to play around with custom constraints. For instance, combining optimizing with projections we can evaluate the optimal GW1 formation across budget ranges, or the best possible GW1 squad, as shown below. (Note again that the analysis shown below are from preliminary projections.)

Interested in more? Check out FPL Review’s Team Planner or my Optimize_FPL repo. Or for a more qualitative take on decision-making, see e.g. WGTA’s ‘Psychology Corner‘ series.

5. The Eye Test

Finally, I would be remiss if I didn’t add a note about actually watching football (or at least relying on the inputs of those who do).

Hard data is great but part of the art of evaluating football – and the fun of it! – is, of course, to use your own eyes. There are few better feelings than when a self-assessed punt comes off.

Substantively, ‘the eye test’ also offers valuable qualitative context for the stats and data I’ve advocated here and that you’ll see elsewhere. It helps you guide your analysis towards important trends, e.g. spotting the upcoming rise or decline of a popular pick.

In this respect, multiple FPL hubs offer updates on pre-season performances, evaluations of new prospects, and so forth. If you’re here, you probably already know where to look.

That’s it. Thanks for reading!