This is the story of the appearances.csv file—a relational goldmine that turns abstract match results into tangible human performance. Before we dive into queries, we must understand the granularity. In the jfjelstul/worldcup model, appearances.csv is a fact table linking players to matches. It contains approximately 4,000+ rows (depending on the latest update), covering every World Cup from 1930 to 2022.
In the ecosystem of sports data science, few repositories are as meticulously maintained or as democratically accessible as Joshua Fjelstul’s jfjelstul/worldcup database. While the goals.csv file gets the glory and the matches.csv file provides the narrative spine, there is one table that captures the raw, human cost of the World Cup: appearances.csv .
# Pseudocode for Python (Pandas) avg_sub_time = df[df['substitute_out'].notnull()].groupby('year')['substitute_out'].mean() In the 1980s, the average sub happened in the 75th minute. By 2022, it’s the 58th minute. This table empirically proves the tactical revolution: managers now treat the bench as a weapon, not a lifeboat. 4. The Anomaly Detection: Own Goals and Disciplinary Records Because appearances.csv includes own_goals and red_cards at the player-match level, you can ask bizarre, wonderful questions.
For the analyst, this file is a playground of temporal logic. For the fan, it is a reminder that every minute on that pitch is a dataset of one. Load the CSV. Run the join. Ask who really worked the hardest. The answer is waiting in the rows of appearances.csv .
Calculate the average minute of the first substitution per decade.
my mission is to capture your special moments & turn them into timeless memories, all at an affordable price.
Designed by Ced