READ THIS FIRST:

These predictions are produced with maximum vibes. These are lower quality than my Premier League predictions. You should treat with skepticism, adjust for your own beliefs, and use if useful.

These take significant time to produce (tournament editions especially). Everything is shared completely free for the benefit of others with no ads, no paywall, no sign ups.

If you want to show your appreciation, there are two ways you can pay it forward:

1. Help me for free by liking, sharing, or subscribing to my free project here where I am encouraging people to work on their decision skills; or

2. Consider making a donation to the most effective charities, under the guidance of Give Well.

Enjoy! 💜

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Here is a list of the current contents of this site which covers the group stage. I will do my best to keep this page updated as we progress into the knockouts (but no promises!).

Contents:

• Team Predictions (Goals and Clean Sheets).

• Match Predictions (Goals, Clean Sheets, Win %).

• Player Predictions (Goals, Assists, Goal Involvement).

Team Predictions

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Match Predictions

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Player Predictions

Notes: This is a selection of players. Some may be missing. Expect this to be volatile until tournament starts.

These take significant time to produce (tournament editions especially). Everything is shared completely free for the benefit of others with no ads, no paywall, no sign ups.

If you want to show your appreciation, there are two ways you can pay it forward:

1. Help me for free by liking, sharing, or subscribing to my free project here where I am encouraging people to work on their decision skills; or

2. Consider making a donation to the most effective charities, under the guidance of Give Well.

Enjoy! 💜

Congratulations on getting to the end of another season of this silly little game we play!

In case you missed it, I shared an article of my reflections on being one of the best FPL managers over the past few years and I released my general decision framework on my new project here.

If you appreciate any of the free content I have provided on this site over the past few years then I would be grateful if you could support the new project in any way you can.

One of the core ideas is that a decision is only “good” or “bad” relative to how likely it is to move you towards or away from your goals. Which means to even attempt to make a good decision, you actually have to think about your goals first.

I find it useful to distinguish between two kinds:

• Ultimate Goals: the end point e.g. top 10k, win your mini league, whatever it is for you.

• Enabling Goals: the smaller decisions and strategies that get you there.

A surprising amount of poor decision-making comes from confusing these two where people end up optimising for the wrong thing at the wrong time.

I see it constantly in FPL. Someone plays 80% of a season in a steady, sensible way, and then in one random game week they completely go rogue and go for a high-variance play that is entirely inconsistent with their actual goal.

Essentially, they let vibes and emotions allow the sub-decision (one gameweek, one chip, etc.) to get disconnected from the bigger picture. Then they just go back to playing steady again. Poor consistency indicates poor process.

The good news: we’re in GW38, so that particular mistake is mostly off the table now. Yay! (sorry if you've been making this mistake all season and you’re only just realising).

What this means practically is that the most useful thing you can do right now is reflect clearly on what you are actually trying to achieve e.g. what rank goal do you have? What’s your mini league status? etc. Once you know that, you can ask whether your GW38 strategy gives you the right distribution of outcomes to get there. For example, if you are 20 points off the top of your mini league and all you care about is winning that mini league, then the decision you make should be one you deem to be most likely to get that specifically.

For many people, answering your goals question is probably a better use of your time than looking at underlying stats, predictions, etc.

Final match predictions and player predictions are below.

Maybe see you for free World Cup Predictions!

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Full data page here.

Much love 💜

All Premier League data continues to update in a single page here.

Hello!

I hope you are all doing well. A rare email inbox appearance from me to quickly give some updates and answer a couple of common questions thrown my way recently.

Future Projects: I’ve been putting a lot of time into some exciting personal work lately. If you are interested in making better decisions, then it will be useful for you. It’s not ready to share yet, but I’ll share more when I can. When I do, your support would genuinely mean a lot.

World Cup Fantasy: Some of you may remember the dedicated pages I built for the World Cup 2022 and Euros 2024. There is an archived page (with randomised data) to show what they looked like.

100,000s of people used those pages, and a lot of people have asked if I’ll do this again for the World Cup this summer. I’m open to it, but only if there’s enough genuine interest. It would be entirely free with no restrictions (like everything else here) and cover the basic stuff (e.g. team goals, clean sheets, win %, and player production baselines).

Please vote in the poll below if you’d actually use it frequently (please try to avoid your bias for ‘free!’ and answer as honestly as possible).

FPL ID: Some keep asking what my FPL ID and rank is this season. My ID is 9325733, and my rank is currently 1453. Unfortunately, I am still getting lucky!

Vibe-coding: A few people have sent me links to other sites that are using the new powers of vibe-coding to essentially directly replicate what is on here. I appreciate you flagging it. Honestly, I don’t mind if people reuse or build on my stuff. Everything I put out is free, and I have always encouraged others to take it in their own direction. My general advice would be to use these new tools to be revolutionary. There is so much interesting novel stuff happening in football analytics now. Here are some examples: 1 and 2 (and fantasy too!).

wishing you all good health. 💜

elevenify

Use a browser for best experience.

This site is a free personal project shared publicly for the benefit of others. Apply your own judgments and use if useful.

Everything here is free and always will be. If it helps you, share it with others.

Or alternatively, consider making a donation to the most effective charities, under the guidance of Give Well. Let me know if you do!

This page features Premier League projections from my model. All values represent average expected outcomes to set realistic expectations of likely performance. Updated every gameweek within hours of the final fixture.

Contents:

• Team Ratings

• Simulated PL Table

• Matchweek Predictions

• Team Predictions:

• Player Predictions

• Fast Fantasy Model

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Team Ratings

Simulated PL Table

Matchweek Predictions

Team Predictions

To return next season!

Player Predictions

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Fast Fantasy Model

March 2026 update: Fast Fantasy will return for the 2026/27 season. As we enter the final stretch of the current season, blanks, doubles, and chip strategies become more important, and Fast Fantasy’s more generic approach becomes less appropriate. At this stage, everyone’s squad is different, and player importance is highly team-specific. Use the expanded player baselines above to guide your decisions. Good luck!

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This site is a free personal project. Everything here is free and always will be. If it helps you, share it with others.

Or alternatively, consider making a donation to the most effective charities, under the guidance of Give Well. Let me know if you do!

5 January 2026 Update: Rúben Amorim has been sacked by Manchester United. This article was written in early September. I have updated the model screenshots below with the ratings as of today. No other changes have been made.

The phrase “give the manager time” is firmly entrenched in football psyche. This is partly justified because deciding whether to sack a manager is difficult: the kind of poor performance that leads to dismissal considerations usually results from a mix of bad luck and bad coaching.

The saying also touches on the widely-held belief that the feedback loop on a managerial decision won’t be clear for a long time, so we should abstain and give managers time. I don’t think that belief helps us understand things. You should never believe a feedback loop is so long that you can learn nothing from it.

You don’t hire a manager and then receive no new information for years. We’ve had a steady stream of information throughout Amorim’s tenure. We can grade and evaluate Amorim’s appointment now (even if it is true that more will become clear in the future). That is what matters and what decisions should be based on… and every moment you choose to keep the status quo, e.g. retain a manager, you are making an active decision, even if it feels like passively doing nothing.

United Under Amorim

As a reminder, my model uses raw performance data to generate estimates of team strength. These strengths are updated after each match based on how wrong its predictions were, taking into account match performance and the strength of the opponent. In my view, this is the best way to separate bad play from bad luck. There is no other metric or trend that better calibrates beliefs based on the strength of the evidence.1

Two things can be true about United & Amorim:

1. United are an actual and deserved mid-table side. Fixing that in Amorim's short tenure is ~impossible.

2. Equally, there is little evidence that United have improved under Amorim.

Some may argue that playstyle is an alternative explanation for the lack of improved results, that the system will suddenly “click”, and results will turn around rapidly. I think this is unlikely2 and does not change the reality that Manchester United are no more likely to win a Premier League match today than when Amorim was appointed.

Decision Landscape

So things are bad, most would agree with that. How do INEOS decide whether to sack Amorim or give him more time?

It’s helpful to start with a ‘base rate’ for our expectations: what data points do we have that help us understand what usually happens in a situation like this?

We shouldn’t limit ourselves to just Amorim’s data when forming our prior expectations. When the question is whether Amorim has shown enough evidence during his tenure to be considered a Premier League-level manager, we also have data from 20+ other Premier League managers in the same period. My model has made nearly 1,000 predictions and received outcomes for all of those data points. That is a lot of information we can use to make decisions and help us address the long feedback loop problem.

These data points suggest that when a team hasn’t improved (at all) within the time Amorim has been given, it is unlikely to improve later.3 I think there are some fair ‘reference cases’: Nuno quickly improved Nottingham Forest, Iraola steadily improved Bournemouth, Glasner revamped Crystal Palace, and Thomas Frank consistently improved Brentford. 4 Slot even won the league in his first season with essentially the same squad. 5

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Recommendations

Primary Recommendation:

I think INEOS should dismiss Amorim now. I feel comfortable enough to predict Manchester United are not going to substantially improve under Amorim any time soon,6 nor do I see the long-term vision.

Good decision process is walking away when you ought to, not when you want to.

INEOS have invested heavily in this project. There are many signals that they should stop, but they may view Amorim as “their guy.” It was their first major decision, and now that they’ve committed to him, it may be hard to sack him. Classic sunk-cost fallacy.

The project is well underway, and there is now ample evidence that it isn’t working. Rather than focusing on the resources and reputation already invested in Amorim and the squad, it’s better to view each new investment in the project as further waste. Wasted investments are not just retrospective; they also impact future planning. Manchester United are already dealing with this issue, carrying a squad built by multiple managers with different tactical ideas.

Which, in itself, is an added benefit of sacking Amorim quickly. Manchester United actually have a fairly decent squad, and one thing most of the players have in common is (ironically?) that they don’t really know how to play well in Amorim’s system. Players are ultimately more important than the manager when it comes to on-field performance, so there’s a strong case to be made that the quickest and most reliable way for United to become a better team is by replacing Amorim with a manager who can better utilise the current squad.7 8

Secondary Recommendation:

My secondary recommendation would be to give Amorim more time with a clear deadline and measurable expectation for concluding the project is a failure. For example: until the end of the season and Manchester United’s model projected goal difference should improve from 0.01 to 0.15.9

I stress that the club should only continue if they have their own compelling evidence that the expected value of Amorim in the future is there. If you keep working with something that won’t make you a better football club, every minute spent persisting with an underperforming strategy represents an opportunity cost that could be redirected to a higher-probability success path. Focus on the future expected value of the decision.

Good decision-making process means separating what we knew at the time we made the decision from what has been revealed afterwards. I do not know enough about the original decision process to evaluate whether hiring Amorim in the first place was a good decision.10 However, the ‘feedback’ from that original hiring decision has been plain for all to see, and we are now in a good position to know enough about the current decision regarding Amorim’s future.

In a world of messy outcomes tangled up in luck and skill, I don’t really believe we are in a timeline where Amorim’s outcomes are determined by bad luck.

Sometimes, it is helpful to rephrase the question:

If INEOS were making the decision today, with the information now available, would they start the Amorim project?

Based on the evidence, probably not.

All model data is already updated for the next gameweek in a single page here.

I’ve been sharing my hobby of building team ratings models for several years now. Almost every week, I’m asked whether my model “accounts for XYZ”.

It is a good and fair question!

But what does it actually mean?

Considering and Accounting

One of the biggest ‘myths’ implicit in a lot of these interactions is that data-driven models are entirely objective. That there is a ‘correct’ way to do things.

Models will always, to a certain extent, reflect the beliefs of their creators. This is true mechanically. Models are shaped by countless approximations, assumptions, and decisions made during creation. These decisions are central to the core question of ‘what wins football matches?’ and venture into smaller questions such as:

• How to account for red cards?

• What about predicting/rewarding penalties?

• How much does ‘finishing skill’ matter?

• Do own goals ‘count’?

• What about manager influence?

• Do we count incorrect decisions (e.g. offside goal given)?

• How to account for new transfers?

These are just a handful of many examples. People often ask me directly for the "answer" to questions like these. To me, at least, these are very difficult questions. There is no clear answer, and dealing with them is (currently) more of an art than a science. Over time, this art can evolve into a science as we gain insights into which assumptions are flawed and refine our approach.

The question “Does the model account for this?” is often a loaded question that jumps towards an answer to the questions above. The question is almost always implying an expectation that ‘the concept’ should be accounted for in a specific positive or negative way. In essence, the question already has the approximations, assumptions, conclusions and beliefs of the questioner baked in.

I think it’s therefore important to take a step back from these implicit assumptions and first look at the concepts in a neutral way. For this, I like to use the term consider.1

So the flow looks like this:

Concept → Consideration → Accounting

There is a concept that might affect the model (e.g., player absences, penalties, etc.). I then consider how this might affect things and determine whether it warrants attention, from which I move on to the actual doing of accounting for it in the model.

However, this does not mean that every concept automatically leads to a specific positive or negative adjustment. The jump from consideration to accounting is where the ‘values’ part steps in. Whether a concept ends up being accounted for in a particular way depends on the individual’s philosophy and personal beliefs about how football works. Simply put, the model considers everything, but it doesn’t necessarily account for everything (either at all or in the way you might expect).

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Case Study I: Rodri’s Absence

Let’s take a concrete example: Rodri missing the season for Manchester City.

• People asked: “Does the model account for this?”

• What they really meant was: “Have you made Man City worse because he is injured?”

On the surface, it seems a straightforward and fair question: if a key player is missing, does the model reflect a drop in team strength?

However, the question itself is somewhat flawed and circular. By assuming Manchester City will be worse without Rodri, the question already has a conclusion baked in. People asking it are not really prepared to reckon with the full consequences of adjusting the team rating either way. The question I would ask back is ‘are you willing to accept a world where Man City are better without Rodri?’.

If the answer to that is no, which it often is, then any conversation about updating beliefs is dead on arrival. Logical consistency requires that we remain open to the possibility that outcomes could go either way. Teams adapt, change tactics or formations, and other players may step up, sometimes maintaining or even improving overall performance. If, in reality, Manchester City performs better without him, the logical conclusion would be that they might actually be stronger without Rodri, but that contradicts the questioner’s initial assumption.

Case Study II: Red Cards and Penalties

Another helpful example. I often receive comments on weekly team ratings updates that imply an update is incorrect because a red card or penalty was issued in a game. Penalties and red cards are difficult to predict, making them particularly challenging to incorporate into a model. It can be difficult to determine exactly how they should be considered and accounted for. Similar to the Rodri example, these events can create challenges with logical consistency (for example, what do you do if a team gets a red card and then performs better?).

A common approach is to simply dismiss red cards entirely, removing all game time after the event from the data, or to focus only on non-penalty metrics, essentially pretending that penalties never exist. Both of these approaches are the “considered but not accounted for” variety. Given the high degree of randomness in these events, you can see why this is appealing!

But ignoring actual events risks leaving the model unprepared for updating beliefs when there might be some signal, such as a team receiving red cards or penalties in consecutive games. At some point, what was considered “noise” may carry meaningful information. The key is to design a model that can sensibly consider and account for these rare events to some degree, while maintaining logical consistency and the ability to update predictions based on actual outcomes.

We want to avoid creating paradoxes.

What do you do if a team is performing better without their key player? What do you do if a team performs better after getting a red card? What do you do if a team loses a match but missed 3 penalties in the match?

If you don’t have fleshed out consideration to accounting flow response to these questions, then you’re probably creating some sort of paradox.

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Conclusion

So if these are ‘bad’ questions, what do good ones look like? As a general rule, I think we should start by highlighting the consideration whilst sharing more context about our personal beliefs on accounting for that. For example, today I was asked two similar questions:

• “Does this factor in Bruno’s positioning from this season or does it take data from last season.”

• “does this take into account bruno's new role as a cdm?”

For the reasons outlined in this post, I don’t really know how to engage with these questions. Yes, I am considering Bruno’s position, but that still doesn’t answer the accounting part. Perhaps a better question would be something like “What are your considerations around Bruno in your model? Those Bruno projections look higher than my expectations, as I think he will play CDM and get less production from that position”.

After doing this hobby for multiple years, I think I have almost definitely considered every concept at this point.

So if the question is simply, “Have you thought about this?”, then the answer is yes.

If the question is, “Am I accounting for this concept in the exact positive/negative way you think I should?”, then the answer is: I don’t know!

How concepts are ultimately accounted for in predicted outcomes depends on personal beliefs and philosophy. I try to be as transparent as I can and share all my outputs openly. That said, I can’t spend all my time explaining every nuance of my philosophy and approach behind this little hobby. If you’re desperate to find out exactly how something should be accounted for, the best way might be to experiment and build your own model :).

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All model data is already updated for the next gameweek in a single page here.

The start of the season is always tricky. We want to get excited and react, but deep down, we know the sample is far too small to support strong conclusions.

I jotted down some thoughts on why early-season analysis is so hard, and what helps me make sense of what’s happening in the Premier League.

I think we can broadly carve out two buckets of difficulty: the mathematical and psychological.

The Mathematical: A few games cannot reliably capture a team’s underlying strength. It is statistically negligible and tells us almost nothing about the season ahead (unless it’s combined with prior knowledge - spoilers for how we can do better!). Football outcomes are highly variable, influenced by chance, situational factors, and opponent-specific matchups. Extreme performances in the first few weeks almost always regress towards preseason expectations, and where they don’t, it is generally better to wait and be reinforced by subsequent evidence.

The Psychological: Human thinking doesn’t lend itself to useful reactions when dealing with the tiny sample of early-season games:

• Much of the information we process in daily life comes in small samples, so we tend to assume those samples reflect the larger population. This reflects a broader pattern: we often overestimate how consistent and coherent the world really is (e.g. expecting one match to mirror season-long performance).1

• We are also storytelling creatures. Building a coherent narrative from limited evidence feels persuasive, even when it isn’t warranted.

• Hindsight bias is powerful!! I sometimes get the impression that people have got bored with this concept as it has gained more awareness, but awareness doesn’t make it any less powerful or dangerous.2

• I get the sense that a limited sample amplifies any ‘availability’ bias we have. With less information to draw from, those results that stick in our minds can give an inflated sense of importance. Furthermore, the limited information might cause us to only build conclusions based on what is in front of us3 and not take the time to think about what evidence is missing from our analysis (e.g. injuries, fitness, training data, style-specific elements, etc.).4

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Here are some things that help me attempt to do a better job:

1. Anchor to your Priors and Base Rates: place early-season results within the context of some sensible pre-match expectations. Start with the most reliable long-run information, then update cautiously as new evidence arrives. In football, grounding judgments in long-term data or team strength models provides those priors, ensuring that predictions are stable and less distorted by short-term noise or recency bias.

2. Think in Probabilities: I think it is a good habit to get used to expressing reactions in probabilities rather than absolutes. For example, when a team performs well in a match and exceeds expectations, get used to saying ‘they are probably ~10% better than I thought’ rather than ‘wow, they are good now’. I also find that making a habit of probabilistic reasoning increases the subsequent chance of sensible revisions as more evidence accumulates.

3. Find Robustness Through and Across Metrics: we can obtain more ‘robust’ reactions through two sensible interpretations of metrics: (1) look for explanations through the good metrics available to you (e.g. xG over raw shots); and (2) by making sure the evidence for reaction aligns across multiple indicators/metrics (e.g. rather than just relying on a scoreline result for a belief that a team’s defence is getting worse, you could look across xG conceded, moving to a less possession heavy style, a change in goalkeeper, etc.).

4. Stress-Test Alternative Explanations: before revising expectations, come up with at least two other plausible explanations.

   Much love 💜

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This post shares screenshots from my latest model data, which I use to anticipate what might happen in the new season. Adjust for your own beliefs and use it if it’s helpful to you!

I don’t spam this data out via email every week because everything below is already in live-updating tables on a single page here. You can view at any time. For the best experience, use a browser (not the Substack app).

Team Ratings and Simulated Table

Here are my final team ratings for 2025/26.

A common reaction is disbelief about teams like United and Spurs, with many expecting a quick, dramatic turnaround. My model does incorporate off-season changes (including transfers), but their effect on ratings is smaller than market data. I put far more weight on on-field evidence before adjusting priors.

You may also be interested in reading my post here, which explores some of the differences with the market data.

Gameweek 1 Match Predictions

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6 Gameweek Horizon for Goals/Assists/Clean Sheets

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Player Predictions (Goals / Assists / Clean Sheets)

Note: (assuming each player starts, so adjust for your own beliefs, e.g., if you think Isak won’t start, you’d set him to 0 goals early on).

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Fast Fantasy

Fast Fantasy puts fantasy on “easy mode” for those short on time.

It’s not a predicted-points model. It’s a tool for idea generation. Pick likely starters, roll often, and adjust for your beliefs and chip strategy. Beyond a certain point, extra effort yields diminishing returns. Much of the heavy lifting happens in the sorting and filtering, which this model does for you.

I have made the decision to remove defensive contributions from these calculations because many people use them for non-FPL fantasy games. In any event, Defenders are less important than other positions, so rely on personal preference when selecting them.

If you are interested in being a better Fantasy manager, I highly recommend you read this Fantasy Frameworks article. For good fantasy results, this stuff is far more important than any of the data above.

Much love ✌️💜

All Premier League data is on one single page here.

With just 3 days to go until the Premier League kicks off, here’s the best guess we have right now of how the season might unfold.

Below is a visual comparison of my model’s predicted table versus Spreadex’s market predictions, as of August 12, 2025. My model will get a final update Friday, but no major changes are expected.

It’s worth reminding ourselves why market data is important when trying to predict the Premier League (with the usual caveat that all reference to market data on this website is solely for its utility in understanding the premier league, not for betting).

Spreadex markets pool the knowledge of many professional and informed participants and operate with minimal margin or hedging. This means their predicted points totals are carefully calibrated to be as accurate as possible. Plus, markets quickly absorb new information, making their predictions dynamic and up-to-date. All this adds up to a high predictive signal. For more on this, I recommend the video from Solio Analytics here.

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Let’s take a quickfire look at some differences that interested me:

Man City, Man United, Tottenham: these teams are prime examples of what I call the ‘trust me, bro’ effect often seen in market-based predictions over a summer. My model accounts for off-season changes like transfers, but I believe their impact on team ratings is much smaller than what the markets currently reflect. I care much more about seeing good on-field evidence to update my prior beliefs. There is very little on-field evidence to support the idea that (1) Manchester City can instantly return to challenging Liverpool and Arsenal so closely, or (2) that Manchester United and Tottenham can jump dramatically from their deserved 15th and 17th place finishes last season to 6th and 8th respectively.

Bournemouth and Crystal Palace: I was surprised to see the markets rank these teams this low. This represents the opposite side of the above effect. We have a full season of solid on-field evidence supporting Bournemouth’s and Palace’s strength. I don’t see enough off-field factors to justify such a steep hit to their inferred quality. Equally, I appreciate I may be too high on them.

One limitation is that Spreadex doesn’t provide team season total goals, so there’s no direct insight into attack versus defense strength or other specific reasons behind team performance projections. This lack of granularity makes it difficult to fully understand why shifts in team rankings occur. For example, a lower expected finish compared to last season might mean: (1) the team is perceived as weaker; or (2) it might simply reflect a stronger overall league which translates into a lower finish.

Brentford: lol don’t even get me started

The outlooks for Man City, Man United, Tottenham, Bournemouth, Crystal Palace, Brentford are the perfect cases of making sure you appropriately adjust for your own beliefs and philosophy.

Chelsea: Spreadex seems more optimistic about Chelsea closing the gap on the top three, while my model suggests any improvement will be more incremental than a big leap. This is amusing, as I was significantly more bullish on Chelsea than Spreadex during the Club World Cup! This also highlights an interesting observation: my model sees the league as more tightly bunched than Spreadex’s projections imply.

Here are some other predicted league table resources you may be interested in:

• Simon’s Premier League model here.

• Scott Willis frequently simulates the league with his model.

• The Transfer Flow predicted table and previews.

• Opta league table projections.

At this moment, these insights and resources offer the clearest and most reliable prediction of the Premier League season ahead.

If you missed it, I posted newsletter #01 of this season looking at somee player snapshot graphics (link here).

Much love ✌️💜

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All Premier League data is on one single page here.

I’ve been developing a way to clearly show how players may contribute to Team Ratings in a ‘fun’ and easy-to-digest format.

I’ve developed a ratings system that works for me. It’s built around three broad categories:

⚔️ Attack Rating: elements directly important for scoring goals (think about stuff like: goals, assists, threatening passes and runs).

🛡️ Defence Rating: elements directly important for preventing goals (think about stuff like: tackles, blocks, goal-line clearances).

⚖️ Support Rating: this is necessary to recognise all the other indirect contributions a player makes to support their team’s Attack and Defence Ratings.

These categories are chosen because they align with my belief that analysis, whether tactical, data-driven, or otherwise, should always connect directly to the actual effects on the likelihood of winning.

The ratings are scaled so the total equals 100, representing a player’s relative influence on Team Rating. They are not absolute values for better/worse comparisons.

For example, if two players from different teams both have an Attack Rating of 60, it means they contribute about the same relative importance to their own team’s attack. It doesn’t mean they are equally skilled or productive; it simply reflects that their impact on their team’s attack is similar.

To illustrate further, let’s walk through some examples:

Forwards

Benjamin Šeško and Viktor Gyökeres

As expected for players resembling a no. 9 striker, their ratings are heavily weighted towards Attack, with smaller contributions in Support and minimal in Defence.

Small differences hint at stylistic variations. For example, Šeško’s slightly higher Defence Rating aligns with what you might expect from a generational aerial talent.

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Midfielders

Jhon Arias and João Palhinha

I think Arias and Palhinha are an interesting comparison because both skew towards Support while contributing equally to their team’s Defence Rating.

This might feel odd for Palhinha, but under this ratings system, his work is less about directly stopping goals and more about keeping the team structure functioning (albeit with a defensive slant).

This is somewhat inferred also by the way we see Arias is much more likely to contribute to scoring goals compared to Palhinha.

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Defenders

Daniel Muñoz and Joachim Andersen

An interesting comparison because it really pulls out the differences you might see in defenders. Andersen maxes out on Defence and Support, reflecting his status as one of the league’s best passing centre-backs.

Muñoz, meanwhile, keeps a high Defence Rating while also being heavily involved in the final third (for a defender), a testament to his remarkable physical output. His p90 data from last season, available in the FotMob app, backs this up:

Bonus: Florian Wirtz

Sharing a last bonus one because it is interesting. There is a good chance this Attack Rating is not what you expected when thinking about Florian Wirtz. A couple of reasons why his rating might be more evenly distributed than we anticipated:

• My projection model suggests his role in that Liverpool team will lean slightly more towards creation than scoring.

• Wirtz does a lot to support the game generally. He touches the ball a lot and is happy to have it under pressure. He averages 64 passes attempted and 61 passes received per 90.

• Even after league adjustments, his defensive work should translate well enough. Last season, he ranked in the 97th percentile for StatsBomb’s possession-adjusted pressures.

That’s all for now. This concept is useful for me, and I hope it’s useful for others. If there’s interest, I can collect player suggestions and make another post in the same format.

Much love ✌️💜

NOTE: the data on this page been put together in response to popular demand! This is the first version of the new expanded 32-team club world cup format and there is limited data for many teams. Therefore, the data on this page is a healthy blend of vibes and analysis, combined for fun. They should not be expected to be as accurate as the Premier League predictions. I expect this page to be maintained throughout the whole tournament, but no guarantees!

Thanks for following along! If you are interested in more stuff like this, then check out my Premier League predictions and alternatively the work done by @SolioAnalytics.

Contents:

• Team Model Accuracy

• Next season plans

• Screenshots of Match Predictions and Top 10 Player Goal Involvement

Team Model Accuracy

Update: post-season some back-of-the-envelope analysis:

• The team predictions have managed a season mean average error (MAE) of 0.92 to Goals and 0.59 to xG.

• On MAE, this is slightly better than the Spreadex market predictions as frozen at FPL deadline.

• My model was closer to actual Goals 376 times compared to Spreadex 363.

• My model was closer to actual xG 383 times compared to Spreadex 356.

• There were 21 total draws (same predictions as market).

There is some interesting analysis to be done. I have an experimental ‘add-on’ to my model that adjusts for specific games to pick up on big ‘time-sensitive contextual’ events (e.g. injuries, rotation, etc.). I would like to backtest that if I get the time. The reality is I won’t get the time, but I hope all of this encourages people that they should still see building their own models as something worth doing.

Next season plans

I don’t expect to be playing fantasy again next season. However, I would be open to keeping elevenify predictions going, as the single best page for free Premier League predictions.

I’m exploring the best path forward to do this sustainably (e.g. less time commitment). Please reach out if you know anyone who wants to help with this.

Data Screenshots

As always, adjust for your own beliefs, overlaying whatever views you have about any final day of the season intangibles etc.

Have a good summer 💜

A list below of all decision-making blog posts in case any interest you (all short 3-minute reads). In addition to these, I am also considering repurposing notes into a kind of ‘52 decision tools’ list, where I share one framework each Monday morning as a decision technique to focus on applying that week - let me know if that interests you.

• is it more useful to feel shame or guilt?

• do trade-offs always exist?

• when to trust your gut

• can your make your own luck?

• how to detect a scam

• your personal risk appetite

• when to quit

Set of simulations for goal involvement:

Here is a collection of the 2-minute reads released on the decision-making blog so far:

On Deciding...

do trade-offs always exist?

I read a great article by Nicholas Gruen that explores how economics is often framed as the study of trade-offs (e.g. cost vs. quality). The idea that trade-offs are axiomatic is so central to economic thinking that it's rarely questioned. A consequence of assuming trade-offs are always necessary is that better solutions may be hidden…

Read more

a year ago · 4 likes · On Deciding...

First, thank you to everyone who checked out the new blog. No football stuff there, but the posts on personal risk appetite and making your own luck may interest anyone who plays fantasy. Upcoming general posts on trade-offs, scams, and feedback.

Second, unfortunately, the elevenify site will continue to have technical issues for the rest of the season. For your convenience, I have pasted below the following screenshots from offline working tables:

• GW35 Match Predictions

• A set of sims of the top 10 players for projected goal involvement for GW35

• An updated Fast Fantasy table for GW35-38

Apologies for the site issues - maybe Substack does not like my choice to make everything free! I am thinking about next season, and I invite people to reach out if they want to use my team ratings (or website!) for any cool projects. Many people have been using them for public projects/content that help everyone, and I am happy for that to continue. Enjoy the rest of the season!

On Deciding...

your personal risk appetite.

The purpose of this post is to clarify my thinking on determining risk appetite for any given decision…

Read more

a year ago · 8 likes · 1 comment · On Deciding...

elevenify site: You may have noticed the site’s currently a bit broken. Due to technical issues with Substack, the layout has reset, and player tables aren't updating properly. None of this is intentional. I probably can’t resolve this season. The model is still updating normally, and team-level data (goals, clean sheets, Win %) is available in the 6GW horizon table (GW33–38) here. Player data likely won’t return live this season.

New blog: I have created a new blog called “On Deciding…”. Most posts will be short, 200-500-word “mini-essays” written (for myself) to consolidate/clarify my learnings on decision-making topics. If that sounds interesting or you enjoyed Fantasy Frameworks, then you might like it. The first post, ‘when to quit’, is a good example of what to expect. No football stuff there, but I feel refining my decision quality improved my fantasy performance more than anything else.

GW34 Fantasy Data: I am on FH34 this week and given the site is not working, here is a list of the top options to choose from (xPts):

Goalkeepers:

• Pope (4.5), Alisson (4.3), Leno (4.3), Sanchez (4.3)

Defenders:

• Ait-Nouri (5.4), Trippier (5.0), Livramento (4.8), Burn (4.6), Van Dijk (4.4), Robinson (4.3), Cucurella (4.1)

Bonus: if you think Sessegnon starts RW and gets ~65 mins, then you’re looking at ~5.1. A Trent start and ~65 mins gets you ~4.7.

Midfielders:

• Salah (8.8), Palmer (6.6), Diaz (5.1), Kluivert (5.1), Semenyo (5.0), Mbeumo (4.8), Murphy (4.8), Mitoma (4.7)

Bonus: if Barnes / Gordon start, they are automatically the third-best midfielder here.

Forwards:

• Isak (7.4), Raul (5.8), Cunha (5.6), Welbeck (5.5), Evanilson (5.0)

If the deadline were right now, my XI would probably be: Alisson, Ait-Nouri, Trippier, Livramento, Salah (C), Palmer, Kluivert, Diaz, Isak, Raul, Cunha… GK choice, Sessegnon, and Diaz most likely possible changes.

Hope you all have a good weekend 💜

Extra love in advance to those of you who check out On Deciding… 💜

I’ve been thinking a lot about how our decisions usually start from a default.

The way we interface with the physical world generally has a consistent, long-term default, but our interface with the digital world changes constantly.

Sticking with the default settings on the tools we use feels comforting because there’s an assumption that the creators probably know best.

But there’s no reason to assume that the data we consume or the tools we rely on are set up to encourage the decisions we want.

I’m increasingly feeling that if I’m not designing my own decisions, then someone else is designing them for me.

Even if truly ‘neutral design’ is impossible, things could definitely be better.

Our subscriptions auto-renew, meeting invites assume 30-minute slots, boxes come pre-ticked, autoplay is enabled, and our search results are sorted by invisible priorities.

I’m trying to develop the habit of pausing with every new tool or dataset to ask: “Is this default actually right for me?”

Having the wrong default settings for your purposes can have a butterfly effect on the entire output and send you down the wrong decision path.

It feels like a good idea to try and reclaim some agency by investing more time up front in the settings of the tools we use.

I expect this will also have a causal effect on producing better outcomes over the long term.

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Fantasy games are about managing resources under constraints.

Finding the ratio between that resource allocation and expected return is the formula for success.

If you want to do this particularly well, try to locate a high output from investment in assets that diverge from crowd consensus. Taking a risk by ‘chasing differentials’ every week is the basic version of this. A more advanced version might be obtaining those same assets through strategic means at times when others cannot (e.g. patient utilisation of transfers/wildcards).

A good way to conceptualise your risk appetite is by the amount of investment you allocate to strategies (players or even chip usage) that diverge from the market. This is a useful definition in a fantasy context because it ties risk-taking to your distribution of outcomes. You understand allocating resources outside of the most-owned assets increases your chances of both benefit and harm.

The above is good fantasy play and is generally known to most people. We just aren’t very good at executing it. Indeed, most players lean too hard into the above, to the point that their play worsens because of it. Risk-seeking to their detriment. Harm-seeking.

So if all the above is what we know about good play - what is unknown? I feel like there is a lot more to learn. I am quite interested in working out what questions to ask, so I can learn what I need to know.

Expected minutes and chip plans seem to garner attention as a source of edge. I think there is a seductive mask to granularity in forecasting these. It isn’t obvious to me that the level of granularity has a strong causal link to good fantasy decisions. Or, at least not to the extent that the outcome is a net positive for fantasy decisions, considering the negatives being drunk on this granularity can bring. I don’t think we handle time horizons well in a fantasy context, nor do we adequately appreciate the switching costs that make many decisions almost irreversible.

How do we deal with that? My best guess is that the answer lies in something we might think of as robustness. I am never fully sure what that looks like, but in my head, I generally associate it with principles that look like (1) selecting assets that fare well no matter what the future holds; or (2) exploring a little less than you might think.

Optimisation models are probably going to develop better approaches to the above - but they probably won’t be helpful for us using them if we are not asking the right questions.

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elevenify.com is now entirely free.

Read on if you wish to find out why, otherwise enjoy the Premier League page which now has all the site data in a single page ‘hub’.

I've disabled paid features and issued pro-rated refunds for all subscribers.1

If you were a paid subscriber - thank you for your support.

This has always been a hobby. Paid subscriptions were just a way to justify extra requests I did not have time for (e.g. Euro 2024 project, PL player stuff, etc.). Paywalling is the only option to support creators on Substack, but I never liked it.

I’d love a setup where I can share everything freely and sustainably, allowing me to focus on what excites me: learning how to make better decisions.

I will take some time to evaluate how I can sustainably achieve this.

For now, all content will remain available to everyone and will continue updating immediately after each gameweek, just as it has all season.

Long-term, I’m exploring the best path forward. I may move to a platform with more flexibility for hosting new features (e.g., self-hosting on Ghost). If you know someone who could help with that, please reach out!

If you have any ideas on all of the above then please reach out. I want this to work for everyone.

much love

elevenify 💜✌️

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