How to Choose Your Captain (this week and every week)

Applying Fantasy Frameworks.

Last week, I released Fantasy Frameworks, a dedicated resource of fantasy decision-making frameworks. I believe that if you practice these frameworks consistently, they will improve your fantasy management more than anything else.

This week is a perfect time to apply some. At the time of writing, there are six captaincy options with just a 5% difference in predicted points. How do you decide who to pick?

You may notice a slight shift in my focus. While my models and data will continue to passively update weekly, any active posts will focus more on decision-making. I don’t want you to feel like the ‘right’ answer is somewhere out there already produced for you in models/data, instead, I want to empower you to become better decision-makers.

I won’t run this site forever, but before I stop, I’m committed to helping you become a better and more responsible fantasy manager.

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This post will be most helpful if you’ve already read Fantasy Frameworks. This post takes a small selection of those frameworks and gives you an idea of how you might apply them to making your decision. So I recommend checking that out if you haven’t.

Blinding

One of the Fantasy Frameworks I discussed is Blinding, a method to reduce bias by hiding key information. Here are the anonymized predicted points for the top six captaincy options this week:

• A: 6.5

• B: 6.3

• C: 6.2

• D: 6.2

• E: 6.2

• F: 6.2

Pause for a moment and reflect. Think about how you feel about that spread of points and what your beliefs are, for example:

• Do you feel like 6.5 is sufficiently far ahead of the other options?

• Are C, D, E, and F equal? (They are all 6.2 so the answer should be yes).

Take note of your beliefs before moving on. The next section will include the same information but with the player names. It is very likely some thoughts might sway, for example:

• confirmation bias might make the 6.5 feel further ahead than your current belief

• a preference or dislike for certain players might make C - F start to feel less equal even though they are the same

When the options are this close, I think these biases are more likely to affect your decisions. That’s not inherently wrong, but you should recognize them and ensure you’re adjusting through a coherent process, rather than pure intuition.

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Baselines

Here are the top 6 players for predicted points from my model for this week:

• Trossard: 6.5

• Odegaard: 6.3

• Semenyo: 6.2

• Isak: 6.2

• Salah: 6.2

• Palmer: 6.2

In terms of making our decision, we can take the above predictions as a kind of ‘base rate’ - our best guess starting point as to what we can expect from these players. The idea is that we will then forecast away from these base rates if we have a good reason to.

This principle would also apply if you were interested in other predictions such as predicted goals.

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Switching Cost

Fantasy Frameworks emphasizes that switching between players comes at a cost because your resources are limited. With the upcoming blank and a chaotic schedule, these costs are higher than usual.

Because of this, I don’t recommend making a transfer just for captaincy this week. This is also made easier by the options being so close so the gain would be minimal. From a captaincy decision-making perspective, you can immediately rule out any players you don’t already own.

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Wisdom of the Crowd

Fantasy Frameworks suggests using multiple data sources to make better decisions. In this case, we can compare my projections with the FPL Review Free Model and take an average:

• Player: elevenify | Review | average

• Trossard: 6.5 | 6.2 | 6.35

• Odegaard: 6.3 | 6.5 | 6.4

• Semenyo: 6.2 | 5.1 | 5.65

• Isak: 6.2 | 5.7 | 5.95

• Salah: 6.2 | 5.5 | 5.85

• Palmer: 6.2 | 5.4 | 5.8

Averaging these models reduces the gap between the best options which might seem like it has made the decision harder. However, having multiple perspectives may increase your confidence in choosing the top option, even when the differences are small.

Reviewing market forecasts for players and matches can be another useful way to gather crowd-sourced insights that may justify adjusting your base rates.

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Practical Example

I’ve selected a few Fantasy Frameworks to show how they can be practically applied, though many others could also apply to your captaincy decision. Here’s a quick walkthrough of my process using my GW26 team (1FT, 0.2m ITB):

• Switching Cost: As noted earlier, I won’t make a transfer solely for captaincy. Additionally, none of the "Heuristics for Taking a Hit" apply. This allows me to eliminate Odegaard, Trossard, and Semenyo from consideration because I do not own them.

• Wisdom of the Crowd: After averaging multiple projections, I’m left with these three options: Isak (5.95), Salah (5.85), Palmer (5.80).

• Personal Policies: I have a Personal Policy that I will not chase ‘differential’ captains and should select from the restricted pool of top options. This confirms my choice is between Isak, Salah, and Palmer.

At this point, the frameworks have helped me narrow down my decision to three valid choices. Since the differences in their predicted points fall within a reasonable margin, there is no clear ‘correct’ pick and any of the three is a justifiable option.

If you want to go deeper, you could apply other frameworks. For example:

• Following the Befriend Your Resources framework might lead to the thought that models like elevenify may take time to adjust. You might reason that Nottingham Forest is slightly undervalued, which could lead you to discount Isak a bit.

• Another Befriend Your Resources theme might be adjusting your evaluation of Trossard, as you know we don’t have a good sample of him in his current role in the Arsenal team. You might be concerned that models are overestimating his contribution.

Finally, it is also sensible to finish your decision based on your risk appetite. For instance, because I have a low appetite for risk, I would likely captain Salah, expecting him to be the most owned option.

Summary shortcut process:

1. Get a starting point (e.g. xPts, predicted goals, etc.).

2. Apply any frameworks that might affect your decision.

3. Adjust if you have **good** reason.

4. Adjust for your risk appetite.

5. Choose the top option.

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I hope this walkthrough gave you a clear, practical view of how to apply Fantasy Frameworks to your decision-making process.

If you found this useful and would like to see more, please consider sharing this post and letting me know. I always appreciate your feedback. 💜 ✌️