Fantasy Frameworks
Tools for making better decisions.
Introduction
I've been obsessed with decision-making for as long as I can remember. I am not an ‘expert’, but I consider myself a ‘decision science enthusiast’. I spent over five years at university researching decision-making, and now my day job revolves around helping people navigate uncertainty in business and compliance. I am enthralled by trying to understand what conditions lead to the best outcomes and designing strategies to improve decision success. Six years ago, this obsession naturally led me to one of the most fascinating games of making decisions amid uncertainty: Fantasy Premier League.
This experiment has gone quite well. At the time of writing, I have the best average rank over the six years I have played. Of course, a lot of that is luck, but perhaps there’s some signal buried in there. People often assume that any signal is linked to being an ‘analytics’ manager and the quality of data I use. Indeed, I was an early adopter of FPL Review and elevenify.com is popular mainly because I share my data and models. Naturally, people seek out the best projections and tools, but you might be surprised to hear that I don’t think having the best data is the most important factor in improving at fantasy games (excellent comment to devalue my website!).
The idea that models ‘play fantasy for you’ is completely wrong. Even with perfect data, you are still the one making decisions. You can’t remove the human element from the process, and having the best tools doesn’t automatically make you a logical, rational decision-maker. Sometimes, it does the opposite. There will always be aspects of the game that humans can navigate better than machines. Data is important, but what is more important is a proper marriage between the human and the data.
Fantasy will always be a game of incomplete information and luck. But that doesn’t mean we should be nihilistic about success. In the long run, fantasy is a game of skill. The best way to think about your actions is that they don’t guarantee a specific outcome, but they shape your distribution of outcomes. So, your goal is to find strategies that help you make the best possible decision given uncertainty. Refining your decision quality will improve your fantasy performance more than anything else. I believe techniques like those in this article would give you an advantage over others even if you only had access to worse data than they did. This article comes from the general notes I’ve kept over the years on improving my process, now expanded with specific applications to Fantasy Premier League.
My fantasy decision-making process generally starts with: Do I have a rule or heuristic I can apply? If I do, I use that as my foundation and build from there. This article contains 23+ frameworks, each with an application to fantasy decision-making. It will serve as a dedicated resource that I’ll continue to refine over time. Please message me or comment if you see any fantasy-specific examples when reading through this, and I will add them to the article.
It’s important to recognize that there is no reason to believe that fantasy games or the tools we use to help us are set up to encourage good decision-making by default. The hope is that these frameworks can act as a counterweight, creating a better system and a better environment for making strong decisions. I firmly believe that these skills can be trained and developed like muscles. Your goal is to implement and practice them as often as possible, and over time, you will get better. I truly believe that implementing these ideas correctly will improve your fantasy management more than anything else.
Switching Cost
Framework: The higher the switching cost, the more reluctant you should be to explore and try new options.
Fantasy application: In fantasy, the cost of switching between players is generally high because your resources are limited: one free transfer per week, hits required beyond that, and only two wildcards per season.
Beyond these tangible costs, there are also intangibles to consider:
Football is inherently unpredictable, so you don’t get immediate, clear feedback on whether a switch was good or bad.
Players are dynamic so there’s always a risk that both the player you bring in and the one you move out shift in unexpected ways, especially early in the season.
If these intangibles lead you to make a mistake, you often end up paying the tangible cost twice: once to bring a player in and again to transfer them out. The cost might be even greater if it involves expensive players or a shift in team structure.
From my experience, a conservative approach (exploring less than you might feel tempted to) is a solid rule of thumb. You want the moves you made 2–4 weeks ago to support the decisions you’re making now. Some might find this playstyle "boring," but I encourage you to see it differently: rolling transfers (exploring less) gives you more flexibility and freedom to make better decisions when they matter most.
Resources and related frameworks: (1) Free Rolls Framework; (2) Upstream Problem Solving Framework.
Understand The Meta
Framework: Try to understand the wider context of how games are played.
Fantasy application: If you understand the fundamental philosophies of a game, you can adapt your play to capitalize on opportunities. It’s not just about updating your assumptions about players and teams, you should also be reassessing the broader landscape (meta) as it evolves. The game has a fixed set of rules, but what is important within the game rules can shift over time. This applies both to the game of football itself and to the game of fantasy football:
In football, the meta could shift in a way that increases the average goals per game across the league, making clean sheets less common. In this scenario, you might feel dissatisfied with your defensive options (especially the expensive ones) and look for alternatives. Recognizing this meta-shift and adjusting your strategy accordingly might lead you to view defenders as less valuable. Instead of simply trying to find better defensive options, you might decide to allocate fewer resources to defence altogether, opting to play three at the back more often. You can make similar analogies for shifts in football rules that make (1) penalties more likely (penalty takers become more valuable), or (2) red cards become more common (certain players e.g., defenders might become less valuable). These are examples of how understanding broader meta-trends in football can help you make better decisions in a fantasy context.
In fantasy, it’s crucial to remember that it’s a game with a specific set of rules. While everyone is aware of the rules (e.g., forwards earn 4 points per goal, midfielders earn 5, etc.), few take the time to truly understand what those rules mean in practice. The Fast Fantasy Model I released serves as a useful heuristic, showing that, on average, defenders and goalkeepers score fewer points than other positions. As a result, they tend to be less important, and in most cases, your resources (e.g., transfers, budget) are better spent elsewhere.
Resources and related frameworks: (1) Fast Fantasy Model; (2) the rules page of the game you are playing; (3) any articles or statistical resources about football more generally.
Blinding
Framework: Limit conscious or unconscious bias by concealing key information.
Fantasy application: I structure my data with an extra page that removes team and player names to eliminate biases and preconceptions. This approach is especially useful for sensitivity analysis, as that process tends to highlight players with minutes risks and surfaces options I might not have otherwise considered. I don’t think this should be a core technique (as you still need to be engaged with the reality of your potential decisions) but I think it is a good check and balance you should be using infrequently. I typically check in this way first thing after a gameweek and then once or twice throughout the week.
Resources and related frameworks: search for blinding (or masking) in clinical trials.
Upstream Problem Solving
Framework: Solving problems before they happen rather than reacting to problems.
Fantasy application: The earlier Switching Cost framework emphasized that fantasy resources are valuable and limited. If you’re constantly using them just to fix problems rather than preparing for the future, it’s usually a sign of poor play. If this sounds like a habit in your game, you need to address the root cause. Bad habits creating these kinds of problems might include:
Buying injury-prone players.
Making unnecessary “luxury” transfers.
Bringing in players with minutes risks.
Constantly chasing the “best” player each week.
Eliminating these kinds of habits will help you manage your team more efficiently and reduce the need for reactive moves.
Resources and related frameworks: (1) Upstream by Dan Heath; (2) Switching Cost Framework.
Wisdom of the Crowd
Framework: Collect multiple different independent data points and average them together.
Fantasy application: try to gather multiple data sources to inform your decisions. For example, you could aggregate: (1) starting lineup predictions; (2) team predictions; and (3) player projections.
A unique method to improve decision-making is leveraging your past self. If you document your decisions over time, you can refer back to them, creating a kind of "wisdom of the crowd" effect from your own historical thinking. Since opinions constantly evolve, reviewing past decisions can provide valuable perspective.
That said, always consider the quality of your data sources. For instance, averaging elevenify goal predictions with market-based forecasts is likely a solid process. However, adding mainstream pundit predictions might dilute accuracy rather than enhance it.
You don’t need to do this for every decision, but for major ones, it can be a powerful tool.
Resources for further exploration: (1) The Wisdom of Crowds By James Surowiecki; (2) Make Predictions Framework.
Kill Criteria
Framework: Determine criteria for changing your mind (e.g. what specific information do you need to find out and by what specific time before you can finalise your decisions).
Fantasy application: This framework is an excellent way to know when to cut your losses. Here are a few examples:
Choosing a chip strategy often requires waiting for more information over an extended period. While there’s always the possibility that a better opportunity arises later, delaying too long can cost you points. (For more on this, see the Over-Patience Framework.) To avoid indecision, be clear on what specific information you need (such as cup fixture results that determine double and blank gameweeks) before committing to a strategy.
If you’re unsure about a player (e.g., minutes risk, penalty duties), define in advance what evidence will confirm or refute your belief. For example, if they get benched without any clear explanation or miss out on a penalty they were expected to take, that could be a signal to move on.
To avoid rushed decisions on your week-to-week transfers, determine what you need to see before making a move, such as waiting for a midweek fixture or holding off until a press conference for injury updates.
Resources and related frameworks: (1) Quit by Annie Duke; (2) Over-patience Framework.
Personal Policies
Framework: Establish a hard rule with yourself to counteract unwanted behaviour.
Fantasy application: This framework was inspired by something I heard Adam Grant discuss on a podcast about how establishing personal policies can improve workplace decision-making. I think this concept is useful in the context of this article as a kind of beginner framework. By now, you’ve probably realized that applying all these frameworks consistently is hard. It’s a constant challenge. As a first step, it may help to identify a specific struggle or flaw in your play and create a personal policy to address it. For example:
If you tend to make knee-jerk transfers, set a policy to never move early in the week.
If you struggle with price-change FOMO, create a rule that you must list other valid reasons before making a transfer.
If you constantly chase differential captains, consider a policy of always captaining your most expensive player.
Similar to Effort Allocation Framework principles, these policies might sometimes prevent you from making the absolute best decision at a given moment. However, over the long run, they will help you avoid costly mistakes while you improve other areas of your game. Once your decision-making improves, you can gradually move beyond rigid rules and make more nuanced choices.
Resources and related frameworks: (1) Rethinking the Workplace with Adam Grant; (2) Effort Allocation Framework.
Befriend Your Resources
Framework: Fully understand the resources that you are using.
Fantasy application: Understanding your resources is crucial for making informed decisions and avoiding mistakes caused by incorrect assumptions.
Metrics like expected goals (xG) and expected assists (xA) require context. You should account for factors like sample size, playing time, and finishing ability to avoid misjudging a player’s true potential.
Projection models are a result of their inputs and settings. It is helpful to take a step back and think about the transparent and hidden aspects of any given tool. For example, you might be able to see things like minutes and penalty share but you might be unable to see positional assumptions. Understanding how these models work helps you evaluate their reliability and make better-informed choices. A key part of this process is reading the FAQs or documentation that accompany the tools you use. These often explain how the models are built, their assumptions, limitations, and what the different parameters mean. You can’t expect good results from a tool if you don’t understand how to use it properly.
Resources and related frameworks: any FAQs or manuals accompanying your resources.
Scales of Likelihood
Framework: Learn to express your beliefs in scales of likelihood.
Fantasy application: Cromwell’s rule advises against assigning absolute probabilities of 0 or 1 unless you are absolutely certain of an outcome. I think this is a useful principle to keep in mind. Humans aren’t naturally wired to think probabilistically, so developing that skill will give you a significant edge. The goal is to train yourself to see the world as a probability distribution rather than a series of binary outcomes.
This struggle with probability is especially apparent at the margins. In fantasy, player starting likelihoods and penalty-taker probabilities are good examples. Many people treat a 10% chance of a player taking a penalty as effectively zero, which is a mistake. Every possibility, no matter how small, should factor into your decision-making (for instance, see here a meme I made about Flekken’s expected assists months before he got an assist doing the exact free-kick routine I modelled).
A helpful next step is to express percentages as likelihoods. A 90%, 99%, or 99.9% chance of winning all feel like "almost certain" outcomes, but flipping the perspective clarifies the difference:
A 90% win probability means a 1-in-10 chance of failure.
A 99% probability means 1-in-100.
A 99.9% probability means 1-in-1,000.
Similarly, instead of just seeing a clean sheet probability of 30%, reframe it as: “If this game were played ten times, this team would keep a clean sheet in three of them.” This mental shift encourages more critical and rational thinking about probabilities, helping you make clearer, more informed decisions.
Resources and related frameworks: (1) Thinking in Bets by Annie Duke; (2) Bayesian Thinking Framework.
Over-patience
Framework: Waiting too long to make a decision and favouring optionality can cause you to miss opportunities that outweigh the benefits of options.
Fantasy application: I believe this framework represents the biggest weakness in my own play. I generally consider myself a patient, analytics-driven manager, and I think that type of manager often overvalues optionality, which can lead to leaking a lot of points in the process.
To counteract this, I’ve started focusing more on when to "lock in" a strategy, similar to the philosophy behind the Kill Criteria Framework. This is especially relevant for chip strategies, which I now approach as a kind of optimal stopping problem. I’m not saying they are optimal stopping problems, but I find the thought experiment useful in the context of thinking about patience.
Resources and related frameworks: Kill Criteria Framework.
Planning Fallacy
Framework: Don’t get too optimistic that your future plans will materialise.
Fantasy application: We are generally too optimistic about the likelihood our future plans will materialise. Be careful about committing too rigidly to a plan that extends too far into the future. Small shifts in the football landscape can create butterfly effects that alter the optimal decisions day by day.
Always consider what could go wrong (injuries, red cards, unexpected team changes, new key information) and ensure you have a viable backup plan. If a single event can completely derail your multi-week strategy, then your planning wasn’t robust enough. A practical way to test this is by stress-testing your plan: create a hypothetical scenario where something goes wrong every week and evaluate how you would respond. This exercise helps you build more flexible, resilient strategies that can adapt to changing circumstances.
Generally, I consider future plans to be 'very ‘speculative’ a lot earlier than you might imagine. Even just 2-3 weeks ahead is still significantly abstract. Always take a step back and remember that you are only ever actually making a move in the current gameweek - the process will start fresh next gameweek.
Resources and related frameworks: (1) Wiki; (2) PwC Article; (3) general reading on optimism bias.
Drunk on Models
Framework: Not doing whatever your model tells you.
Fantasy application: This one is particularly meta because, in a way, many other frameworks serve as antidotes to being drunk on models. It’s crucial to remember that models are, first and foremost, tools for idea generation, not absolute truth.
Always take a step back and sanity-check their suggestions. Pay close attention to the assumptions and simplifications being made, as well as any outside factors or intangibles that the model might be missing.
Ultimately, take responsibility for your own decision-making. Models can guide you, but they shouldn’t own your choices.
Resources and related frameworks: (1) Befriend Your Resources Framework; (2) consider the model-specific elements of every other Fantasy Framework.
Teamwork
Framework: You make better decisions and improve at an activity when participating in a group.
Fantasy application: Find a trusted person or small group to discuss your decisions with. Having the right decision group can improve your process in ways you simply can’t achieve alone. A good group can challenge your thinking, play proper devil’s advocate, correct errors, and share valuable insights. For this to work effectively, a few key requirements:
Everyone in the group should be there for good-faith collaboration, genuinely engaged in the decision-making process and share the same objective. Including you! Good-faith means actually thinking through responses, not just giving surface-level advice like "just roll." This is why smaller groups tend to be more effective as there’s more accountability and depth of discussion.
Be mindful of groupthink and confirmation bias. A good decision group should challenge your ideas, not just reinforce what you already believe. Make sure there’s space for differing opinions and real debate.
Resources and related frameworks: your friends!
Make Predictions
Framework: Make predictions, document them, and evaluate accuracy.
Fantasy application: This might seem obvious (after all, playing fantasy is already about making predictions) but you need to be more explicit about it. Put yourself out there. Put it on the line.
Who is the penalty taker?
Who will start this week?
When will that fixture be rearranged?
Write them down, track them, and evaluate your accuracy. To build a real feedback loop, you need actual data on what you're getting right and wrong. The goal is to identify your strengths and weaknesses so you can improve.
Make sure to focus on relatively predictable things (you won’t learn much from trying to guess a random red card or penalty event) but you can refine your ability to assess things like team selections and set-piece takers. Fantasy football is inherently difficult because it’s a noisy, unpredictable game. Even the best managers are often wrong. The key is to learn from being wrong and gradually refine your process.
Resources and related frameworks: (1) Wisdom of the Crowd Framework; (2) FS The Knowledge Project Ep. #68; (3) Alliance for Decision Education Decision Journal Article.
Free Rolls
Framework: Look for decisions with only upsides and no downsides.
Fantasy application: True free rolls are rare in fantasy (mostly due to the high switching cost) but you should always be looking for small, free wins with no risk. This ties into the concept of decision stacking: making low-impact, easy-to-reverse decisions that help inform your high-impact, harder-to-reverse decisions. A classic example of decision-stacking in life is renting before buying a home or dating before marriage.
In fantasy, the goal is to identify small, low/no-risk choices that make your big decisions easier and more effective. By training yourself to find these free advantages, you make your overall decision-making process smoother and more robust. Here are some fantasy-specific examples:
If two players have similar projected points, but one has a 10% penalty share and the other has a 90% penalty share, it might be better to bet on the 10% player. If the assumption about penalty takers is wrong, there's more potential upside in your favour.
Choosing a player who fits multiple potential chip strategies (e.g., someone who covers a blank gameweek but also has a strong bench boost week).
When you are forced into a move (e.g. replacing an injured/suspended player), check if you can gain other small wins, like picking someone who also helps navigate an upcoming blank or double gameweek.
Doing proper due diligence on your ‘dead spots’ (e.g. choosing the 4m defender/goalkeeper with the most likelihood to play over a long horizon).
Resources and related frameworks: (1) Freerolls paper; (2) Switching Cost Framework.
Path Exclusion
Framework: Think about what alternatives are foregone (or made impossible) when making your choices.
Fantasy application: In fantasy, the focus is often on the players you want, but it’s just as important to consider the opportunities you give up by making certain choices. Every decision comes with an opportunity cost, and recognizing these trade-offs can help you make better long-term decisions. Some fantasy examples are:
Choosing a third player from a team means you lose the flexibility to bring in another option from that team later.
Playing a chip at one point in the season means you can’t use it elsewhere, and it may limit how effectively you use other chips (e.g. the Assistant Manager chip locks out three full weeks, or using your final Wildcard early can make it harder to set up a Bench Boost later).
Allocating your budget to specific players or positions can make it harder for you to rearrange your team. For example, choosing a 4.5m striker dead-spot may allow you to put funds elsewhere but if you need to get a striker back into that position you are forced to use two transfers.
Any given expensive premium player generally means you forego other premiums and this could have knock-on effects (e.g. missing out on the best captaincy options).
Resources and related frameworks: reading generally on opportunity cost is relevant here.
Hyperbolic Discounting
Framework: People prefer outcomes that come sooner.
Fantasy application: This cognitive bias gets to the core of fantasy play in evaluating transfer options. Generally, people are too focused on what a player can provide them within the next single game week and it is better to look at the longer horizon of average.
Some analytics tools address this by using decay (a method of valuing the near future more than the distant future). This is done by applying a discount to predicted points further into the future (e.g., reducing their value by 10% per week). The logic is simple: the further ahead we plan, the more uncertainty we introduce (injuries, suspensions, tactical shifts, etc.). Let us take two players and imagine they have equal decay applied:
Player A: GW1: 5 points , GW2: 4.6 points , GW3: 4 points
Player B: GW1: 4 points , GW2: 4.5 points , GW3: 7 points
Total points over three weeks favour Player B, but due to hyperbolic discounting, many people will still prefer Player A because the good outcomes arrive sooner.
If you're considering the longer horizon and applying appropriate decay, be mindful that your own mental biases might further exaggerate the preference for immediate returns even when it has already been dealt with by decay. Recognizing this bias is all about making sure you have a logically consistent approach to the near and future horizon.
Resources and related frameworks: (1) Wiki.
Loss Aversion
Framework: People prefer avoiding losses compared to equivalent gains.
Fantasy application: This well-known bias relates to the human tendency to feel losses more strongly than gains. In fantasy, this can manifest in several ways:
You may feel pressured to buy a player simply because you don’t own them, fearing that you're missing out. In reality, you often have near-equal ways to get similar points, either from players already in your squad or through a different set of moves. It’s important to remember: you can’t own everyone.
The temptation to buy or sell a player just because their price is about to rise or fall can lead to rash decisions. While price changes matter, they shouldn’t dictate your transfers, often, waiting for more information (injuries, team news, etc.) is the better play.
Being biased against selling a player because you are too invested in the value you have built up in them even when there are better options and it is time to move on.
Resources and related frameworks: (1) Wiki.
Mere Exposure Effect
Framework: Preference for things because you’re familiar with it
Fantasy application: Although fantasy resets each season, certain ideas and concepts carry over and sometimes, that’s a problem. Your decision-making can be compromised in several ways:
You may place too much trust in players you’ve had success with in previous seasons, forming an emotional attachment. This is where the Blinding Framework can help and also make sure you’re truly updating your priors using Bayesian Thinking rather than relying on outdated perceptions.
Just because a chip strategy worked for you before (e.g., always wildcarding before a bench boost) doesn’t mean it’s the best approach every season. Be mindful of tunnel vision, and evaluate fresh options rather than defaulting to past habits.
If you’ve had good or bad experiences with a well-known approach (e.g., "rotating goalkeepers", bench boosting in a double), you may develop an emotional bias for or against it, rather than assessing it objectively based on the current season’s conditions.
Resources and related frameworks: (1) Blinding Framework; (2) Bayesian Thinking Framework; (3) Wiki.
Bayesian Thinking
Framework: Place great weight on your existing assumptions and update with new evidence as the season unfolds without completely discarding its existing assumptions.
Fantasy application: This update process involves adjusting existing assumptions based on how well new evidence aligns with or contradicts them. Crucially, the degree of adjustment should be proportional to the strength of the evidence: strong, consistent data warrants a significant update, while weaker or conflicting information should lead to only minor changes. In my opinion, this approach is one of the best ways to calibrate beliefs appropriately. It ensures that you’re neither overreacting to small sample sizes nor ignoring meaningful trends, leading to more balanced and rational decision-making.
In fantasy, I think we often overreact to new information. If a player starts five matches in a row but is then benched once, many tend to downgrade their expected minutes too much without considering the broader contextual pattern. Applying Bayesian thinking correctly across different scenarios is crucial: for example, a player starting five games before being benched once isn't the same as a player taking five penalties but missing the next. While the patterns appear similar, each requires a unique adjustment (e.g., is penalty-taking more stable than starting matches?). Put more generally - you should try to avoid overreacting to low-variance events but willing to update more quickly with high-variance events.
Resources and related frameworks: (1) Everything is Predictable by Tom Chivers; (2) Julia Galef’s videos referencing Bayes.
Effort Allocation
Framework: Allocate your decision-making efforts to those decisions that truly matter.
Fantasy application: We’re only human, with limited resources (time, energy, intelligence, etc.). Your decision-making suffers when you're tired, overwhelmed, or out of the loop. That’s why it's crucial to focus your attention on big decisions and streamline the ones that matter less.
For most fantasy decisions there is barely any correlation between effort expended and good outcomes. In fantasy, much of the heavy lifting happens in the sorting and filtering stage. That’s the primary value of my Fast Fantasy Model, it quickly highlights the best options so you don’t waste time on unnecessary decisions. Even if choosing between the final few options feels difficult, the reality is that the hard work is already done, you’re simply picking between good choices. At that point, you should probably just make a decision and move on.
If you frequently feel overwhelmed, consider setting some rules (similar to the Personal Policies Framework) to lighten your cognitive load. For example, always pick 4.5m goalkeepers and ignore pricier options. While this might not guarantee the perfect choice in every situation, it will likely improve your overall decision-making process by reducing fatigue, leading to better results in the long run.
Resources and related frameworks: (1) Fast Fantasy Model; (2) FS Blog Decision Matrix.
Bug-fix Your Decisions
Framework: Examine your wins and go beyond just identifying mistakes.
Fantasy application: Try this thought experiment: do you analyze a good outcome as deeply as you do a bad one? Most people instinctively dissect their mistakes, but they rarely scrutinize their successes. Don’t stop analyzing just because a decision worked out, go further and ask whether there was an even better choice you could have made.
Many of the frameworks in this article focus on good processes and on distinguishing between a good and bad decision. But don’t mistake identifying the mistake as the ultimate goal. You need to go beyond recognizing the error and ask: What should I have done instead? Sometimes, this process will even reveal that what you thought was a mistake wasn’t one at all.
Your fantasy evaluation should be so objective that an outsider wouldn’t be able to tell whether you had a good or bad gameweek. The goal isn’t just to celebrate wins but to refine your decision-making, even if that means realizing a so-called "good" decision wasn’t actually optimal. This requires being comfortable with the pain of turning subjective wins into potential losses. It’s not easy, but it’s necessary for long-term improvement.
Resources and related frameworks: general reading on outcome bias.
Heuristics for Taking a Hit
Framework: A checklist of heuristics to help me decide whether or not a hit is worth it.
Fantasy application: In the context of uncertainty as to whether a hit will be ‘mathematically’ worth it, the hope here is that thinking and acting by the below principles should help produce our best estimate of an optimal hit - or at the very least translate into more worthwhile hits. It is worth noting that, although this is framed in the context of taking a hit, all can be applied equally to transfers more generally.
Captaincy: Does the hit allow me to gain the optimum captain for a GW(s)?
XMins/Injury: Does the hit allow me to remove a player who has poor xMins? (injury, lack of fitness, rotation risk, transfer risk, suspension, manager comments, media stories, etc.).
Team Value: Does the hit allow me to significantly increase my team value? Your team value can generally be mapped to an increase in points. As a general rule of thumb, this can be anywhere between ~0.3 to 0.5pts per gameweek per £1m.
Excess of Options: having a significant number more moves of significantly higher value than you have free transfers is generally a good indicator you should consider taking a hit.
Chip Strategy / DGW/Blank: Does the hit allow me to follow an optimal chip strategy or allow me to have better coverage in a DGW/Blank?
Future/Extra Transfers: Does the hit allow me to bring a transfer forward which I will almost definitely have to make? (If your team is in such a bad way or you are a victim of poor planning then sometimes it might be worth taking a hit to bring a transfer forward that you know you will want to make anyway - this is especially true if doing so allows you to hit a better fixture with that player). Another consideration here is that if you know you will be priced out of a future move that you will likely make then you could be considered to be buying yourself ‘2 free transfers’ with a hit.
Resources and related frameworks: Thinking About Hits
Summary Checklist:
Below is a quick summary checklist that can be used as a reference for improving your play:
Switching Cost: Can you justify this resource use?
Understand the Meta: Have you considered how the broader landscape affects your decision?
Blinding: Are you ensuring all options are considered?
Upstream Problem Solving: Are you preparing for the future or just firefighting?
Wisdom of the Crowd: Can you crowdsource aspects of this decision?
Kill Criteria: Have you set clear criteria for changing your mind?
Personal Policies: Does this decision break a rule you should follow?
Befriend Your Resources: Do you fully understand the resources supporting your decision?
Scales of Likelihood: Are you thinking clearly about probabilities?
Over-patience: Are you waiting too long when you should act now?
Planning Fallacy: Are you too rigidly committed to a long-term plan?
Drunk on Models: Are you sanity-checking your model’s suggestions?
Teamwork: Should you discuss your process with someone else?
Make Predictions: Are you documenting and reviewing your decisions?
Free Rolls: Is there a risk-free win available?
Path Exclusion: What alternatives does this decision eliminate?
Hyperbolic Discounting: Are you balancing short-term and long-term gains?
Loss Aversion: Are emotional losses clouding your judgment?
Mere Exposure Effect: Do you prefer this just because it's familiar?
Bayesian Thinking: Are you updating your beliefs appropriately?
Effort Allocation: Are you using your decision-making energy wisely?
Bug-fix Your Decisions: Are you analyzing all decisions, even the wins?
Heuristics for Taking a Hit: Consider captaincy, injuries, team value, chip strategy, and future transfers.