Moneyball Fantasy Premier League Power BI: FPL Decisions Based on Data from My Madrid Talk

Moneyball in Madrid: Weekend FPL Data, Results and Analysis

I spent the weekend in Madrid giving my talk “Moneyball: Building a Killer Fantasy Football Team with Power BI.” It was a brilliant event, and although my session was in English, the turnout was fantastic. A huge thank you to everyone who came, engaged with the content, and stayed afterward to ask questions. Events like this remind me why I love combining data, football and teaching.

There is one question that stands out above the rest. Joaquín González Galdo’s LinkedIn profile asked:

“What are the key metrics you use when making decisions at FPL?”

It’s a deceptively simple question, but a powerful one. Choose a Bachelor metrics to determine your decision-making process is something every FPL analyst and manager struggles with. This deserves its own full blog post, and it’s questions like those I’ll be bringing to the next episode of the FPL Power BI Show with Ben Ferry and Justin Bird. There’s a lot to unpack, from expected points to form trends, fixture difficulties and effective possession. Watch this space for a deeper dive.

Power BI Files and API Scripts Now Available

During the session, I promised to provide my Power BI files, along with the PowerShell script I used to connect to the FPL API and retrieve individual players’ game week data. I’ve now uploaded everything to my GitHub hub repo, and you can access it here:

Feel free to explore, clone, modify and test models. The Power BI report shows how to create a star schema model for FPL, how to perform gameweek level analysis, and how to apply metrics and calculations that help drive decision making. The PowerShell connector script is lightweight yet effective, making it a useful starting point for anyone looking to expand their FPL-powered data pipeline.

When Your Internet Fails and You Panic-Transfer Your Guard

Ironically, just as I was demonstrating the value of data-driven decisions, the Wi-Fi in the venue failed—leaving me behind
unable to update my own team live during the talk. Classic.

Once I was back to a stable connection, the first step I took was on target: Roef is out, Henderson is in.

Henderson’s point estimate come in around 7compared to Roefs in 2.7making this trade-off easy within my evaluation framework. Even small improvements in the goalkeeper position can change the weekly variance significantly, so this is a no-brainer.

Semenyo vs Grealish – A Tale of Two Bad Performances

I’m still unsure whether to move to Semenyo or not. The projected numbers and points clearly indicate that he should go—but selling him would mean losing value to the team. Value preservation is a key element of long-term strategy, so for now it is here to stay.

Grealish, however, hasn’t made much of an effort either, and unlike Semenyo, I’m not too emotionally invested in keeping him. His form was declining, and Everton were erratic.

So I switched: Grealish → Harvey Barnes (Newcastle United)

Barnes is sharp, has strong underlying metrics, and a very favorable run of games. There was also some interesting chatter off the pitch about the possibility of him switching international allegiance to Scotland after the World Cup qualifiers, nothing short of a burst of motivation to keep his form on the rise.

Woltemade Is Out – But Who’s In?

The next step is outbound shipping Nick Woltemade. West Ham’s Callum Wilson was in top form, and although the match against Liverpool looked challenging on paper, FDR did not paint a picture of how poor Liverpool’s defense was. There’s a part of me that thinks Wilson could really pull it off. He’s clinical, confident and loves the highlight reel.

The alternatives I am considering are Igor Thiago from Brentford. He’s less injury prone than Wilson, he’s playing well, and his game schedule is much better. His underlying numbers put him in the “decent shot” category. I may still pivot before the deadline.

I choose Thiago.

When Gut Instincts Creep Back

One thing I admitted during this session: over the past few weeks, I let my instincts start to override my framework. With Manchester City’s game being tough, I tried to be smart and avoid captaining Haaland. It hasn’t paid off yet. This week, the data actually shows that Harvey Barnes could be a worthy captaincy choice. His point estimate places him squarely in the conversation. But there is one metric that trumps all: effective ownership. Haaland remains great. If he pulls out—and he will—going without him as captain would be a major disaster.

So, while Barnes is a legitimate choice in this model, I’ve done the sensible thing: Haaland is back as captain. Here is my final lineup

A good reminder that data-driven decision making is a discipline. It requires consistency.

Final Thoughts

Madrid is amazing. Great city, great people, great conversations, and a great reminder of how global the FPL and Power BI communities are. I’ll be writing more about key metrics, decision frameworks, and how to build your own analytics workflow for Fantasy Premier League.

Until then, enjoy the files, enjoy the data, and good luck for the upcoming game week.

Useful Links

Exploring Microsoft Fabric Through Fantasy Premier League Data

The Consequences of Doing Nothing: How Ignoring Data Strategy Drains SMB Growth

How to Win in the Fantasy Premier League Using Data Analysis and Power BI