Fifa Player Performance Market Value

By shrijeetverma13 · April 22, 2026

This dataset contains comprehensive information on 2,800 professional football players, including their personal details, performance statistics, ratings,…

The analysis successfully identified the 10 players with the highest market values in the dataset. A bar chart and data table have been generated showing each player's market value (in millions of euros), overall rating, potential rating, position, and club.

The analysis reveals that player market values peak at age 27, with an average market value of €102.56M. A bar chart with a trend line has been generated showing how average market value changes across all age groups, with a green dashed line marking the peak at age 27.

The analysis identified 87 high-rated players (overall rating 80+) who have 0-1 contract years left and are classified as high transfer risk. A bar chart and data tables have been generated showing the top players ranked by overall rating, color-coded by market value.

The analysis identified the top 20 players under 21 years old with the largest gap between their potential rating and current overall rating. A bar chart has been generated showing these players ranked by their potential gap, with color intensity indicating the size of the gap. Data tables are also available with detailed breakdowns including each player's age, position, club, current overall rating, and potential rating.

The analysis produced a box plot visualization comparing injury-prone and non-injury-prone players across three key metrics: market value (in millions €), matches played, and minutes played. The chart clearly shows the distribution differences between the two groups side by side for each metric.

The analysis produced two visualizations and data tables showing how average market value differs across all 9 player positions. A bar chart ranks positions by their average market value in million euros, while a box plot reveals the full distribution and spread of values within each position — highlighting outliers and variability. The tables provide supporting statistics including average, median, and player counts per position.

The analysis produced a dual-axis chart and data table comparing all 7 clubs by their total and average market values. The bar chart shows each club's total squad value in millions of euros, while the red line tracks the average player value per club — making it easy to spot which clubs invest heavily overall versus which have the highest-value individual players.

The analysis identified forwards and midfielders with the best goals-per-90 minute ratios among players with at least 500 minutes of playing time. Two data tables were generated showing the top performers ranked by their scoring efficiency, broken down by role (forwards vs. midfielders).

The analysis identified 178 potential transfer bargains — players with a high potential rating (≥90) but a low market value (≤€45.4M). Two visualizations were generated to help explore these findings: a scatter plot highlighting bargain players versus others across the full dataset, and a bar chart showcasing the top 20 bargain players ranked by potential rating with their market values color-coded.

The analysis reveals a surprisingly weak relationship between overall rating and market value in this dataset. Two visualizations were generated: a scatter plot showing all players with a trendline, and a bar chart highlighting the top 10 players who deviate most from expected values. The correlation coefficient of just 0.013 indicates that overall rating alone is almost no predictor of market value — other factors play a much larger role in determining a player's market worth.

The analysis identifies which clubs get the most combined goals and assists relative to their total squad market value. A bar chart has been generated showing the top 20 most efficient clubs, and data tables provide the detailed breakdown for each club.

The analysis reveals that the overall squad average age is 28.0 years, with a median of 28, spanning from 17-year-old youngsters to 39-year-old veterans. Two visualizations were generated: a histogram showing the full age distribution across all players, and a horizontal bar chart ranking clubs from youngest to oldest average squad age. PSG fields the youngest squad on average (27.4 years), followed closely by Juventus (27.5) and Bayern Munich (27.8). On the more experienced end, Liverpool and Manchester City share the highest average age at 28.4 years. Overall, the squads are fairly tightly clustered — the difference between the youngest and oldest average squad is just about 1 year, suggesting a relatively balanced age profile across top clubs.

Two visualizations were generated to explore which players have the highest goal contributions (goals + assists) and how this metric relates to market value. The first chart shows a stacked bar chart of the top 20 players by combined goals and assists, breaking down each player's individual goal and assist counts. The second chart is a scatter plot showing the relationship between goal contributions and market value across all players, color-coded by position.

The analysis successfully produced two interactive visualizations and supporting data tables revealing which nationalities produce the highest-rated players and how broadly they spread across clubs and positions. The bar chart compares average overall and potential ratings for the top 15 nationalities (each with at least 10 players), while the scatter plot maps each nationality's club spread versus position coverage, with bubble size representing player count and color indicating average rating.

The analysis successfully identified which clubs face the greatest player departure risk. Two visualizations were generated: a stacked bar chart showing the full transfer risk breakdown (High/Medium/Low) for the top 15 clubs, and a color-coded bar chart highlighting each club's percentage of high-risk players. Multiple data tables were also produced with detailed breakdowns by club.

Contract years left has virtually no correlation with market value across all transfer risk levels. The overall correlation is just 0.0116, meaning how many years a player has left on their contract tells us almost nothing about their market value. Two visualizations were generated: a scatter plot showing individual players colored by risk level, and a grouped bar chart comparing average market values across contract years and risk groups.