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Best FPL Team so Far This 2025/2026 Season: Using Power BI to Analyse Points, Form, and Player PerformanceOne of the best things about Fantasy Premier League (FPL) is that it feels analytical, at least it is to me. I know for many folk most decisions are still driven by gut instinct, social media hype, or last week’s highlights. Over the weekend, I was listening to a football radio show (names withheld to protect the guilty) where the presenters were debating their players of the season so far.It sparked a simple question:

What happens if we let FPL data decide?

Rather than opinions, narratives, or club bias, I decided to use my Power BI FPL model to answer two very specific questions:

1. Who are the best-performing players this season so far, based purely on points?
2. Who are the players in form right now, based on recent performance?

The results were interesting, and in one case, mildly controversial.

Ranking Players by Total FPL Points

The first step was straightforward. I already have a Power BI semantic model with:

• A Detailed Player Data fact table (match-level FPL data)
• Dimension tables for Player, Club, and Position

To rank players objectively, I added a DAX measure that calculates a dense rank across all players based on total FPL points.

Player Rank Measure (Season to Date)

Player Rank in Club = RANKX( ALL ( 'Player Data (Dim)'[Player Name] ), CALCULATE( [Total Player Points] ), , DESC, DENSE )

This measure ignores any filters on individual players and ranks everyone globally by points scored so far this season.

Building the “Best Team So Far” in Power BI

With the ranking measure in place, I created four table visuals in Power BI, one for each position:

• Goalkeepers
• Defenders
• Midfielders
• Forwards

Each visual was filtered by position and sorted by Player Rank. From there, I selected the top performers to assemble a 15-man FPL squad.

To be clear:

• This team does not consider FPL budget constraints
• It sticks to the FPL squad requirements
• It is purely performance-driven

The Best FPL Players So Far (By Points)

Goalkeepers

• Robin Roefs – Sunderland
• Jordan Pickford – Everton

Defenders

• Gabriel – Arsenal
• Marc Guéhi – Crystal Palace
• Trevoh Chalobah – Chelsea
• Jurriën Timber – Arsenal
• James Tarkowski – Everton

Midfielders

• Declan Rice – Arsenal
• Antoine Semenyo – Bournemouth
• Bruno Guimarães – Newcastle
• Bruno Fernandes – Manchester United
• Morgan Rogers – Aston Villa

Forwards

• Erling Haaland – Manchester City
• Thiago – Brentford
• Jarrod Bowen – West Ham

Two observations stand out immediately:

1. This squad would leave you around £17m over budget
2. There isn’t a single Liverpool player in the list

Data can be uncomfortable like that.

The Problem with Total Points

Season-long points are useful, but they have a major weakness: recency bias works both ways.

• A player who started the season hot but faded still ranks highly
• A player returning from injury or hitting form late can be under-represented

To solve that, I introduced a form-based approach.

Measuring Player Form (Last 30 Days)

Instead of looking at the entire season, I created a measure that calculates average points over the last 30 days, based on actual kickoff times.

Player Form Measure

Form = VAR TodayDate = MAX('Detailed Player Data (Fact)'[Kickoff_Time]) VAR StartDate = TodayDate - 30 RETURN CALCULATE( AVERAGE('Detailed Player Data (Fact)'[Total Points]), 'Detailed Player Data (Fact)'[Kickoff_Time] >= StartDate && 'Detailed Player Data (Fact)'[Kickoff_Time] <= TodayDate )

This dynamically adjusts as new matches are played, ensuring that form always reflects current performance, not historical reputation.

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Ranking Players by Form

With form calculated, I applied the same ranking logic as before.

Player Rank by Form

Player Rank Form = VAR ThisPlayerForm = [Form] RETURN RANKX( ALL ( 'Player Data (Dim)'[Player Name] ), CALCULATE( [Form] ), , DESC, DENSE )

Now I can instantly answer questions like:

• Which defenders are actually delivering right now?
• Are premium midfielders justifying their price recently?
• Is a forward on a hot streak or living off one big haul?

This is where Power BI really shines: switching between season consistency and short-term momentum without rebuilding anything.

Why This Matters for FPL Strategy

Using both views together gives you a much stronger decision framework:

• Total points highlight reliable, season-long performers
• Form identifies momentum, rotation risk, and short-term opportunity

If you’re planning transfers for the second half of the season, form-based rankings are often the difference between climbing the mini-league and standing still.

More importantly, this approach removes emotion from decision-making. No hype. No narratives. Just data.

From FPL to Real Business Decisions

What I’ve described here isn’t really about Fantasy Premier League.

It’s about:

• Clear metrics
• Trusted models
• Decision-making backed by data

The same principles apply whether you’re picking an FPL captain or making multi-million-pound business decisions from dashboards.

Want This Level of Clarity in Your Organisation?

If your reporting feels slow, inconsistent, or hard to trust, that’s exactly the problem my Data Platform & Analytics Accelerator is designed to solve.

It helps organisations:

• Build reliable Power BI and Microsoft Fabric foundations
• Define consistent metrics that people actually trust
• Move from dashboard noise to decision clarity

Book a call today to discuss the Accelerator and see how it could work for your organisation:

Book an appointment to talk about the Data Platform Accelerator

Because whether it’s FPL or the boardroom, better data always wins.

Useful Links

Why Data Platforms Like Microsoft Fabric Don’t Fix Broken Data Culture

Why Reporting Slows Down as Organisations Grow

Xander’s First Season – A Proud Dad’s Reflection