Age, performance, and attacking output

How Does Age Affect Soccer Player Performance?

Across Europe's top leagues, we compare the age curve of attacking production to show how experience, role, and survival bias shape the numbers behind elite players.

Explore the Data

Age vs. Goal Contributions

Total observations analyzed: 2,783 players.

Figure 1: Scatterplot of player age versus goal contributions per 90 minutes across Europe’s top five leagues, with a LOESS smooth trend line.

Interpreting the Pattern

The scatterplot above displays all 2,783 player records from the 2025–2026 season. The LOESS smoothing curve indicates a modest peak in average goal contributions in the mid-to-late twenties, followed by a slight decline into the thirties.

Important note on correlation vs. causation: This graphic shows an observational association, not a causal relationship. Age itself does not determine a player’s goal contribution rate. Factors such as tactical position, team strength, survivor bias (only elite older players remain in top leagues), and role specialization heavily influence these numbers.

Project Overview

In professional soccer, age and output shape recruitment, squad planning, and player valuation. This report tracks attacking production across the Premier League, La Liga, Serie A, Bundesliga, and Ligue 1 during the 2025-2026 season.

We compare players using goal contributions per 90 minutes, which adjusts goals and assists for playing time.

RATE MODEL / PER 90

\[ ext{Goal Contributions per 90} = \frac{\text{Goals} + \text{Assists}}{\text{90s Played}} \]

GOALS + ASSISTS / 90 MINUTES PLAYED

Players and Goal Contributions

Goal contributions are defined as goals plus assists. Use the controls below to search by player name, narrow the age range, and filter by team, league, or position. The default sort is highest total goal contributions.

0 matching players
Player Age Team League Position Goals Assists Goal Contributions Goals / 90 Assists / 90 Goal Contributions / 90
Harry Kane 32 Bayern Munich Bundesliga FW / MF 36 5 41 1.36 0.19 1.55
Erling Haaland 25 Manchester City eng Premier League FW 27 8 35 0.82 0.24 1.07
Michael Olise 23 Bayern Munich Bundesliga MF 15 19 34 0.58 0.74 1.32
Bruno Fernandes 30 Manchester Utd eng Premier League MF 9 21 30 0.26 0.62 0.88
Kylian Mbappé 26 Real Madrid La Liga FW 25 5 30 0.87 0.17 1.04
Luis Díaz 28 Bayern Munich Bundesliga MF 15 14 29 0.55 0.51 1.07
Lamine Yamal 18 Barcelona La Liga MF / FW 16 11 27 0.64 0.44 1.08
Deniz Undav 29 Stuttgart Bundesliga FW / MF 19 7 26 0.76 0.28 1.04
Esteban Lepaul 25 Rennes Ligue 1 FW 20 5 25 0.72 0.18 0.90
Vedat Muriqi 31 Mallorca La Liga FW 23 1 24 0.66 0.03 0.69
Federico Dimarco 27 Inter Serie A MF 7 16 23 0.23 0.53 0.77
Lautaro Martínez 27 Inter Serie A FW 17 6 23 0.71 0.25 0.96
Igor Thiago 24 Brentford eng Premier League FW 22 1 23 0.60 0.03 0.63
Mason Greenwood 23 Marseille Ligue 1 MF 16 7 23 0.58 0.26 0.84
Christoph Baumgartner 25 RB Leipzig Bundesliga MF 13 8 21 0.42 0.26 0.68
Vinicius Júnior 25 Real Madrid La Liga FW / MF 16 5 21 0.51 0.16 0.67
Andrej Kramarić 34 Hoffenheim Bundesliga MF / FW 14 6 20 0.57 0.24 0.82
Jarrod Bowen 28 West Ham United eng Premier League MF / FW 9 11 20 0.24 0.29 0.53
João Pedro 23 Chelsea eng Premier League FW / MF 15 5 20 0.51 0.17 0.68
Yan Diomandé 18 RB Leipzig Bundesliga FW / MF 12 8 20 0.44 0.29 0.73
Patrik Schick 29 Leverkusen Bundesliga FW 16 3 19 0.72 0.14 0.86
Ollie Watkins 29 Aston Villa eng Premier League FW 16 3 19 0.51 0.10 0.60
Mikel Oyarzabal 28 Real Sociedad La Liga FW 15 4 19 0.50 0.13 0.63
Marcus Thuram 27 Inter Serie A FW 13 6 19 0.62 0.28 0.90
Morgan Gibbs-White 25 Nottingham Forest eng Premier League MF 15 4 19 0.44 0.12 0.55

Data Source

Data was retrieved from Kaggle: Football Players Statistics (2025–2026) by Hubert Sidorowicz, sourced from FBref.