Because of its high "unit of analysis" resolution, the Fjelstul World Cup Database is frequently used for:
The chart will show massive spikes at 45' (halftime tactical changes), 60' (the classic "fresh legs" moment), and 75' (the desperation sub). Post-2020, you will see a new spike at 80' due to the five-sub rule allowing late-game management.
Using the substitute_in minute, you can create a histogram of when players enter a match. jfjelstul worldcup data-csv appearances
: Predicting match outcomes or player performance based on historical appearance patterns.
Ensure the data is clean and organized. This might involve handling missing values, standardizing names (of players, teams, etc.), and structuring the data appropriately for analysis. Because of its high "unit of analysis" resolution,
Find players who scored an own goal and a red card in the same match.
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 . : Predicting match outcomes or player performance based
Before we dive into queries, we must understand the granularity. In the jfjelstul/worldcup model, appearances.csv is a linking players to matches. It contains approximately 4,000+ rows (depending on the latest update), covering every World Cup from 1930 to 2022.
Start by summarizing the data (counts, means, standard deviations), then visualize it to identify trends, outliers, or interesting patterns.
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)
: The file contains 21 distinct variables, ranging from basic player and team identifiers to specific match contexts.