FPL Captain Choice with Power BI: A Story-Structured Report

FPL Captain Choice with Power BI: A Story-Structured Report

If you’ve been following my series on data-driven storytelling, particularly the last post on structuring reports with a beginning, middle, and end, you’ll know I’ve been building toward something practical.

So now it’s time to apply that framework to my own FPL report.

Because if the structure works, it should work where the stakes are obvious: picking the right captain each week in your fantasy premier league team.

Turning My FPL Report Into a Story: Beginning, Middle, End

Your FPL captain choice is the most important decision you make each gameweek. This post applies the beginning–middle–end report framework to my Fantasy Premier League Power BI report and shows how to design it to deliver a clear recommendation.

Power BI report structure should follow stories

In the last post, I argued that a Power BI report should follow the same structure as a story:

Not because it sounds nice. Because it reduces cognitive load and improves decisions. So let’s apply that properly. Not in theory. In Fantasy Premier League.

The beginning: why should I care?

In FPL, there is one decision each week that matters more than any other:

Who is my captain?

Get it right and you double the points of your highest-performing player. So you get the score of your best player twice! If you make the right selection.

Get it wrong and you can drop thousands of places overnight.

This isn’t just another metric. It’s the highest leverage decision in the game. So the beginning of the report must frame that explicitly.

Not:

• “Gameweek Overview”
• “Player Metrics”

But:

Who should I captain this week?

That is the context. That is the scope. That is the decision. Everything that follows exists to reduce uncertainty around that one question.

If the opening page of the report doesn’t make that obvious within seconds, it’s not doing its job. As you can see from the screen shot below of the report page aptly named Beginning – Which player should I captain this week  the data suggested based on estimated points next week that the captain should be Cole Palmer or Nico Reily with a bit of Power BI Co-pilot for a bit of fun too.

The middle: what’s happening and why?

Once the decision is framed, we move into the middle.

This is where most dashboards live. But in a story structure, this part has a job: to build confidence in the decision.

In FPL terms, that means analysing:

• high form players
• high projected points
• strong historical scorers
• good value players
• position-based comparisons

But here’s the discipline: We only include what influences the captaincy or transfer decision. Not everything.

1) High form players

Form is one of the clearest short-term signals in FPL. If a player has scored well in the last 3–5 gameweeks, that’s meaningful momentum. So the report should clearly surface:

• Top  players by form
• With context (position, price, minutes played)

Not buried in a table with rows and rows of data. It should be highlighted. Because form directly influences captain confidence.

2) High projected points (EP_Next)

Projected points matter because captaincy is a forward-looking decision, you have heard it all before, “Past performance does not mean future returns”. Your report should clearly show:

• Top projected points overall
• And top projected points by position

This is where the story narrows. We’re not asking “Who is interesting?” We’re asking:

Who is most likely to deliver points this week?

So our middle section, page one of two aptly named Middle – Top Players by Form EP and Position looks like this

3) High scoring but cheap – Good ROI for the business people out there

Transfers matter too. Let’s not forget that. The middle section should also surface:

• players with high total points
• relative to their cost
• with strong recent form

That’s how you identify value.

A £5.5m defender averaging 6 points per game might be a better transfer than a £7.5m underperforming midfielder.

The report should help answer:

• If I need a transfer this week, where is the value?
• Which positions offer upside?

This builds the case. This builds the confidence.

The end: what do we do next?

Now comes the part most dashboards miss. The ending. The implication. The recommendation.

Based on:

• form
• projected points
• value
• position comparisons

The report must land on:

This is the captain.

And optionally:

These are the top alternatives by position.

So one of the previous post in this series made Brent Ozar’s newsletter, it was the one called Data Overload Is Killing Decision-Making  and he added comment in his newsletter about my post that said “Gethyn Ellis says Data overload is killing decision-making, and I’ll add this one of the reasons people are leaning harder on AI to distill stuff” So I did, for my ending I got Power BI Copilot describe based on my data the best team this week. Here it is if you want it for Friday’s deadline

This doesn’t remove nuance. It removes ambiguity. It gives the manager clarity.

Why this works

This structure works because it aligns with how the brain processes decisions.

Beginning: Why should I care? → Captaincy decision.

Middle: What’s happening and why? → Form, projections, value.

End: What do we do next? → Captain selection + alternatives.

That’s not creative writing. It’s cognitive alignment.

What this means for business analytics

Now zoom out. Replace:

“Who should I captain?”

With:

• Which supplier should we renegotiate with?
• Which product should we prioritise?
• Which region should we invest in?

The structure is identical. Most business dashboards stop in the middle. The FPL example makes it obvious because the stakes are visible and immediate. If I publish an FPL dashboard that never tells me who to captain, it’s useless. The same should be true in business.

The real test

If someone opens your Power BI report and asks:

“So what should we do?”

Then your story hasn’t finished. In FPL, that costs you rank.

In business, it costs you clarity, speed, and alignment. That’s why structure matters.

Want to apply this beyond FPL?

Fantasy Premier League makes the stakes obvious.

Get the captain wrong and you drop rank. Get the decision wrong in business and you lose time, money, and momentum. The structure is the same.

Inside the Data Accelerator, we work with teams to move from reporting to decision support — starting with the decision, structuring reports with intent, and making the implication explicit.

If you’re serious about turning your Power BI reports into decision tools, not just dashboards, that’s exactly what we focus on.

In the next post, we’ll look at something even more uncomfortable: you are not the hero of the report — the audience is.

Related: Power BI Report Structure: Beginning, Middle, End

Example: Decision-Driven Analytics in Practice: A Fantasy Football Example