Best FPL Players of the 2025/26 Season So Far: Power BI Points and Form Analysis

Best FPL Teams So Far 2025/2026 Season: Using Power BI to Analyze Points, Form and Player Performance One of the best things about the Fantasy Premier League (FPL) is the teams felt analytical, at least for me. I know for many people, most decisions are still driven by instinct, social media hype, or last week’s highlights. Over the weekend, I listened to a football radio show (name withheld to protect the guilty) where the presenters debated their opinions. best player of the season so far.This prompts a simple question:

What happens if we let FPL data decide?

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

1. Who’s that the best performing player of the season so farjust based on points?
2. Who’s that player in his best performance at the momentbased on recent performance?

The results are interesting, and in one case, somewhat controversial.

Ranking Players by Total FPL Points

The first step is very easy. I already have a Power BI semantic model with:

• A Detailed Player Data fact table (match level FPL data)
• Dimension table for Player, ClubAnd Position

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

Player Rating Measurement (Season to Date)

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

This step bypasses any filters on individual players and ranks everyone globally based on points scored so far this season.

Building the “Best Team So Far” in Power BI

Given the ranking measures, I created four table visuals in Power BI, one for each position:

• Goalkeeper
• Defender
• Midfielder
• Forward

Each visual is filtered by position and sorted by Player Ratings. From there, I select the best players to collect a The FPL squad consists of 15 people.

For more details:

• this team No consider FPL’s budget constraints
• It complies with the FPL squad requirements
• This is purely performance driven

Best FPL Player So Far (By Points)

Goalkeeper

• Robin Roefs – Sunderland
• Jordan Pickford – Everton

Defender

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

Midfielder

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

Forward

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

Two observations stand out at once:

1. This army will leave you behind about £17 million over budget
2. There isn’t any Liverpool’s only player in the list

Data can be uncomfortable like that.

Total Points Issue

Season-long points are useful, but they have major drawbacks: Recency bias works both ways.

• A player who started the season hot but faded is still ranked highly
• A player returning from injury or reaching his best late may be underrepresented

To overcome this, I introduced a form-based approach.

Measuring Player Form (Last 30 Days)

Rather than looking at the entire season, I created a measure that does the math average points for the last 30 daysbased on actual kickoff time.

Player Shape Size

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
)

It dynamically adjusts as new matches are played, ensuring that its shape always reflects current performancenot historical reputation.

---

Ranking Players by Form

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

Player Rankings by Form

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

Now I can directly answer questions like:

• Which human rights defenders are actually successful? Now?
• Do premium quarterbacks justify their prices these days?
• Is a striker in top form or living off big results?

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

Why This Matters to FPL Strategy

Using both views together gives you a stronger decision framework:

• Total points highlighting players who are reliable and stick around throughout the season
• Shaping identify momentum, rotation risk and short-term opportunities

If you’re planning transfers for the second half of the season, performance-based rankings are often the difference between climbing the mini-leagues and staying put.

More importantly, this approach removes emotion from decision making. No sensation. There is no narrative. Data only.

From FPL to Real Business Decisions

What I’m explaining here isn’t really about the Fantasy Premier League.

It’s about:

• Delete metrics
• Trusted model
• Decision making is supported by data

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

Want This Level of Clarity in Your Organization?

If your reporting feels slow, inconsistent, or difficult to believe, that’s the problem Data Platform & Analytics Accelerator designed to be solved.

This helps organizations:

• Build a reliable Power BI and Microsoft Fabric foundation
• Determine 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 can benefit your organization:

Make 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 Aren’t Fixing a Broken Data Culture

Why Reporting Slows As Organizations Grow

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