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Football Insights from FIFA Data: What Comes and Goes with Age

Essential football lessons anyone can learn using data from the popular Electronic Arts game series. Part #1: What comes and goes with age.

I, like many of you, have played the EA FIFA game series for more than two decades. So when I looked for data for a new project and saw the open FIFA dataset, I was immediately intrigued. It has full player ratings spanning almost a decade, granting a great opportunity to learn about the development of football players over time. With market value and additional metadata on top of players’ attributes, this dataset may produce fascinating insights into players’ development, transfers, and price dynamics.

This mini-series will focus on what football insights we can squeeze out of the EA FIFA dataset alone. We will start from the basics, exercising some descriptive statistics to get a grasp on the domain. Gradually, we will tackle more complex (and useful) tasks that require heavier mathematical tools.


The data for this project is essentially a mesh of three different datasets I found on Kaggle: the FIFA 21 Dataset link (stats from FIFA 15–21), the FIFA 22 dataset, and the FIFA 23 dataset. Combined, these datasets cover 45,630 players (by ID) and 1,017 clubs and teams over the years 2015–2023.

We will use the following information: (1) Player metadata: age, position, etc; (2) Player attributes: overall and potential ratings; six main attributes (pace, shooting, passing, dribbling, defending, physic); and 40 skills ratings (crossing, jumping, free-kick accuracy, and more); (3) Player value: price, wage, and contract end-date.

Addressing major limitations and biases: Firstly, the selection of leagues distorts the true worldwide population of players. Secondly, players’ ratings and values may be subject to being over- or underestimated due to human evaluator bias, overall hype, sponsored entities, etc. To mitigate these understandable concerns, we won’t address the absolute values of the attributes, but rather self-ratios and their trends over time. Additional details regarding bias will be elaborated on later.

What comes and goes with age

Naturally, all people, including athletes, experience degradation in some physical aspects as they age, such as pace, acceleration, or stamina. However, footballers like Karim Benzema and Virgil van Dijk prove that years of experience and seasons of practice can benefit players, producing late bloomers. What is it that players gain with time? What might be lost or damaged? And how are these opposing forces aggregated into their overall performance and value? With the FIFA dataset in hand, let’s find out.

This first chapter will cover three main topics: the evolution of on- and off-ball skills, careers development and the first step in understanding players’ potential.


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