Exploring the Existence and Influence of Crowd Atmosphere on Match Outcomes
In football, the idea of “home advantage” is widely accepted but not always well understood. Fans and commentators often talk about intimidating stadiums, familiar surroundings, or home supporters acting as a “twelfth man.” But beneath these narratives lies an important analytical question: does playing at home actually change match outcomes in a measurable and consistent way? And if home advantage does exist, does it affect all Premier League teams equally, or do some clubs benefit far more than others?
The English Premier League (EPL) provides an ideal setting to investigate these questions. It is one of the world’s most competitive leagues, with detailed match records spanning multiple decades. This allows us to examine long-term trends and to compare performance across seasons, teams, and match conditions.
Our project uses match data from 1993–94 through 2023–24, covering thousands of games across 31 seasons. We compute home and away win rates for every season and analyze goal-scoring patterns to evaluate whether home teams consistently outperform away teams. Beyond league-level trends, we also examine whether individual clubs show similar levels of home advantage, or whether team-specific factors lead to large variations.
Across the EPL era, we find clear evidence that home advantage exists: home teams win more often and score more goals than away teams in nearly every season. However, we also observe that the strength of home advantage is not constant. It varies across time and differs substantially between clubs. Some teams display a strong and consistent home edge, while others show relatively small differences between home and away performance.
Understanding these patterns matters because it reveals how environmental, psychological, and structural factors shape competitive outcomes. Home advantage is not just a narrative—it is a measurable feature of the league whose magnitude shifts depending on team identity, context, and league era.
This project investigates the existence and characteristics of home advantage in the English Premier League. Our analysis focuses on two guiding questions:
This question establishes the foundation of our study by examining long-term differences in win rates and goal-scoring between home and away teams across more than 30 seasons.
While league-wide averages show a clear home edge, this question explores deeper variation. By evaluating home and away performance for each club, we assess whether home advantage is universal or heavily team-specific.
Together, these questions allow us to measure the existence of home advantage at the league level and to understand how consistently (or inconsistently) this advantage appears across teams, seasons, and contexts.
Our analysis draws from three datasets that together cover Premier League seasons from 1993–94 through 2023–24. All files are stored locally in the data_raw/ directory as required by the course structure. Below, we describe each dataset, its origin, how it was collected, and potential limitations. Complete data dictionaries are included in the Appendix.
Pre-processed source:
Kaggle — English Premier League Results
https://www.kaggle.com/datasets/irkaal/english-premier-league-results
Original data provenance:
- Xscores: https://www.xscores.com/soccer/england/premier-league
- BBC Sport: https://www.bbc.com/sport/football/premier-league
- ESPN Soccer Scoreboard: https://www.espn.com/soccer/scoreboard/_/league/ENG.1
Description and collection details:
This dataset compiles nearly thirty seasons of Premier League match results. It was created by web-scraping match reports and score feeds from major sports reporting platforms, which draw from official Premier League statistical services. The dataset includes full-time and half-time scores, home/away team identifiers, match results, referee assignments, and various in-match statistics.
Because the file is pre-processed, the Kaggle contributor standardized variable names, removed inconsistencies, and formatted the data into a tidy match-level structure usable for long-term seasonal analysis.
Potential missing data or biases:
Early-season records may lack metadata such as referee names or shot totals. Inconsistencies may reflect differences in record-keeping practices over time, especially before digital archiving was standardized. However, full-time scores and match outcomes — the variables central to our analysis — are consistently available.
Pre-processed source:
Kaggle — Premier League 2022–2023
https://www.kaggle.com/datasets/evangower/premier-league-2022-2023
Original provenance:
Football-Data.co.uk
https://www.football-data.co.uk/englandm.php
Description and collection details:
This dataset provides match-level statistics for the full 2022–23 season. Football-Data.co.uk maintains weekly updates by scraping official Premier League match reports, incorporating bookmaker statistics, and manually correcting formatting issues. The Kaggle uploader reorganized the file into a tidy CSV format compatible with the long-term historical dataset.
Potential missing data or biases:
The dataset is complete for all 380 matches. Variables related to betting markets may differ from purely league-generated statistics but do not affect the scoring or outcome variables used in this study.
Pre-processed source:
Kaggle — English Premier League Matches 2023–2024 Season
https://www.kaggle.com/datasets/mertbayraktar/english-premier-league-matches-20232024-season
Original provenance:
FBref (Sports Reference LLC)
https://fbref.com/en/
Description and collection details:
This dataset includes match-by-match details for the ongoing 2023–24 season. It was collected by scraping FBref, a statistical service that maintains verified feeds from Premier League data partnerships. Unlike the historical match-level datasets, this file is team-centric — each match appears twice (once per team perspective). We filtered by Venue == "Home" or Venue == "Away" when computing home and away statistics.
Potential missing data or biases:
Because the season was active during collection, updates occur as new matches are played. Some advanced metrics (e.g., expected goals) rely on model-based estimates rather than direct observation.
Data dictionaries for all three datasets appear in the Appendix.
To evaluate whether home advantage exists in the English Premier League, we began by examining long-term trends in home and away win rates across every season from 1993–94 through 2023–24. This first figure provides a high-level view of league-wide performance patterns over three decades and serves as the foundation for the rest of our analysis.
Home_and_Away_Win_Rate_Table %>%
pivot_longer(cols = c(home_win_rate, away_win_rate),
names_to = "Type", values_to = "Rate") %>%
ggplot(aes(x = Season, y = Rate, group = Type, color = Type)) +
geom_line(linewidth = 1.3) +
geom_point(size = 3) +
scale_color_manual(
values = c("home_win_rate" = "#0077B6", # Deep blue
"away_win_rate" = "#D62828") # Muted red
) +
labs(
title = "Home vs Away Win Rate by Season",
subtitle = "English Premier League, 1993–2024",
x = "Season",
y = "Win Rate",
color = "Match Type"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
plot.subtitle = element_text(size = 13, hjust = 0.5, color = "gray40"),
axis.text.x = element_text(angle = 65, hjust = 1, vjust = 1, size = 10),
axis.title = element_text(face = "bold"),
legend.position = "top",
legend.title = element_text(face = "bold"),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
plot.background = element_rect(fill = "white", color = NA)
)
The pattern in this chart shows a clear and persistent gap between home and away performance: in nearly every season, home teams win more frequently than away teams, typically by a margin of 10–20 percentage points. This long-run consistency provides strong evidence that home advantage is a stable and measurable feature of Premier League competition.
While individual seasons show modest fluctuations, the overall trend is remarkably stable. Home teams consistently outperform away teams, suggesting that structural factors—such as pitch familiarity, reduced travel burden, and the psychological boost of playing in a supportive environment—play a sustained role in shaping match outcomes.
The one feature that stands out more than any other: the 2020–21 season, when home and away win rates nearly converge. This is the only instance in more than three decades where the traditional home-field edge effectively disappears. What makes this collapse so striking is that it occurred during the COVID-19 pandemic, when all Premier League matches were played without fans. This rare natural experiment strongly supports the idea that the crowd is a core driver of home advantage.
However, this chart only compares levels of home and away win rates. To better understand how strong the advantage is in each season, and how it changes over time, we calculate a more focused metric: net home advantage, defined as the difference between home and away win rates (Home − Away).
This transformation allows us to isolate the size of the advantage in each season and see more clearly when the gap widens, narrows, or behaves unusually. The next visualization shows this pattern over time.