3- Analysis & Visualization with Power BI

In this stage, I designed multiple dashboard sections in Power BI, each focusing on a different aspect of match and player performance. The goal was to move beyond raw statistics and build meaningful visualizations that tell a story about individual and team contributions.

Overview Dashboard

This section provides a high-level snapshot of the dataset:

• Attendance per Player – shows how many matches each player attended.

• Unique Player Count – number of different players who participated.

• Match Count – total matches played.

• Earliest Match Date – when the first match was played.

• Highest & Lowest Scoring Matches – dates of the matches with the most and fewest goals.

• Match Location Distribution – a pie chart showing the percentage of games played at different venues.

• Goal Distribution Over Time – a line chart visualizing how goals were scored historically.

Points & Performance Dashboard

This section introduces a custom points table using a non-standard scoring rule:

• Win = +1 point, Draw = 0 points, Loss = -1 point.

• Players were ranked based on their accumulated points.

• A separate ranking highlighted players by their Unbeaten Percentage ((Wins + Draws) / Total Matches).

• Another visualization ranked players by their Unbeaten Count (total matches without a loss).

• Finally, I designed a “Most Losses Ranking” table, where players with the highest loss count appeared at the top, providing insight into negative trends.

Player Historical Performance Dashboard

This section tracks player statistics over time and provides individual career summaries:

• Performance Trend Line – each player’s performance visualized chronologically.

• Matches Played – total appearances for each player.

• Unbeaten Percentage – percentage of matches without a defeat.

• Unbeaten Count – total matches without a loss.

• Points Table Score – player’s current points using the custom scoring system.

• First & Last Match Dates – timeline of player participation.

• Lowest-Scoring Match Date – when the player was involved in the least goal-heavy match.

• Highest-Scoring Match Date – when the player participated in the most goal-heavy match.

Captaincy Analysis Dashboard

This section focuses exclusively on leadership impact:

• Captaincy Count – number of times each player served as captain.

• Captaincy Distribution Pie Chart – percentage breakdown of captaincies across all players.

• Win Percentage as Captain – players ranked by their win ratio while serving as captain.

• Unbeaten Percentage as Captain – highlighting which captains led their teams to the most stable results.

Key DAX Measures and Logic Used

To power these insights, I developed several custom measures in DAX, including but not limited to:

• Result Calculation (Win/Draw/Loss):

   

  Win Count = CALCULATE(COUNTROWS('FootballData'), 'FootballData'[Result] = "Win")
Draw Count = CALCULATE(COUNTROWS('FootballData'), 'FootballData'[Result] = "Draw")
Loss Count = CALCULATE(COUNTROWS('FootballData'), 'FootballData'[Result] = "Loss")


• Custom Points Table:

   

  Points = [Win Count] * 1 + [Draw Count] * 0 + [Loss Count] * -1


• Unbeaten Percentage:

   

  Unbeaten % = DIVIDE([Win Count] + [Draw Count], [Total Matches], 0)


• Captaincy Unbeaten %:

   

  Captain Unbeaten % = DIVIDE([Captain Wins] + [Captain Draws], [Captaincy Count], 0)


• Date Calculations: Used MINX, MAXX, and CALCULATE to dynamically return first/last match dates, as well as dates of highest- and lowest-scoring matches.

Outcomes

Through these dashboards, the analysis revealed:

• • Clear comparisons between players in terms of consistency, reliability, and leadership.

  • Unique insights such as which captains had the strongest unbeaten records, which players accumulated the most points, and who suffered the most losses.

  • Chronological visualizations that allowed tracking performance trends over time.

You can access the Power BI file from the GitHub logo below