Data-csv Appearances | Jfjelstul Worldcup

At first glance, it is merely a log of who played when. But look closer. This table is the structural engineering of football history. It tells you not just who won, but who endured. It captures the 89th-minute substitutions, the yellow card accumulation, the captains who played every second of extra time, and the reserves who never saw the pitch.

Calculate the average minute of the first substitution per decade. jfjelstul worldcup data-csv appearances

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 . At first glance, it is merely a log of who played when

import pandas as pd appearances = pd.read_csv('https://raw.githubusercontent.com/jfjelstul/worldcup/master/data-csv/appearances.csv') goals = pd.read_csv('https://raw.githubusercontent.com/jfjelstul/worldcup/master/data-csv/goals.csv') Filter for substitutes (game_started = FALSE) subs = appearances[appearances['game_started'] == False] Merge with goals to count goals by sub appearances sub_goals = goals.merge(subs, on=['match_id', 'player_id']) sub_goals_count = sub_goals.groupby('player_name_x').size().reset_index(name='goals') sub_goals_count.sort_values('goals', ascending=False).head(10) It tells you not just who won, but who endured

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.

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 .