McGheeFinal
2025-02-17
Abstract - REVISE
The 2018 FIFA World Cup saw Croatia’s Luka Modric awarded the prestigious Golden Ball, recognizing him as the tournament’s most valuable player. However, traditional statistical metrics such as goals and assists have led some to question the legitimacy of his selection. Soccer analytics has long relied on basic statistics like possession percentages, pass completions, and shots on goal, often overlooking spatial analytics that provide deeper insights into player performance. This paper employs advanced spatial analytics and data visualization techniques using open-source, location-based datasets to assess Modric ’s contributions compared to other key midfielders, including Kevin De Bruyne, Paul Pogba, Antoine Griezmann, and Ivan Rakitic . Through methodologies such as heatmaps, passing networks, spatial scatter plots, and spatial outputs of expected threat (xT) models, this study analyzes Modric ’s positional movements, passing distribution, offensive and defensive contributions, and overall impact. Utilizing R Studio and spatial analytics, this paper uncovers hidden trends in player performance. Anticipated results suggest that spatial statistics will substantiate Modric ’s influence on Croatia’s deep tournament run, reinforcing his case for the Golden Ball. By integrating spatial data analytics into soccer evaluation, this study provides a framework for assessing player contributions beyond conventional metrics through a spatial lens.
Introduction - REVISE
In the 2018 World Cup, the “Golden Ball” MVP award was given to Croatia’s Luka Modric. He was awarded this award due to his exceptional performances that led Croatia to their firstever World Cup final. While his performances were widely known to be crucial in Croatia’s success, several soccer fanatics use generic statistics to argue that Luka Modric was not deserving of the award. For example, France’s Antoine Griezmann had more goals and assists to goals in the tournament. The sport of soccer primarily utilizes basic statistical metrics through charts and numbers when discussing or comparing soccer players. Metrics such as the percentage of possession of time controlling the ball, total number of passes, total number of shots on goal are used widely. However, spatial statistics are hardly used outside of player positional heat maps. The usage of spatial analytics in relation to performances in soccer matches is growing, however, mainly due to the accessibility of open-source, location-based data that can be plotted in statistical computing software programs such as R Studio. By utilizing spatial analytics using this software, hidden trends can be found to validate or disprove several statements. This paper will utilize unconventional spatial analysis and data visualization techniques to compare Luka Modric to other top players of the 2018 World Cup to analyze whether he was deserving of the Golden Ball Award. In this report, Luka Modric’s seven games will be analyzed and compared to other key players, notably Belgium’s Kevin De Bruyne, France’s Paul Pogba and Antoine Griezmann, and his fellow Croatian teammate Ivan Rakitic . Unconventional metrics that utilize location-based data, including the distribution of passes, tackles, position, and more will be analyzed in chart and map-based visualizations, highlighting key discoveries. By consolidating this unique view of soccer data, fans will be able to make a more informed decision on if Luka Modric was deserving of his 2018 Golden Ball award.
Data
Soccer analytics and location-based datasets are gaining interest in both the proprietary and open-source worlds. As mentioned in the paper titled, “A public data set of spatiotemporal match events in soccer competitions”, several large open collections of soccer-logs are available to the public. These datasets contain spatio-temporal events that occurred during each match of prominent soccer competitions, including the world cup (Pappalardo, Cinita, Rossi, et al.). Soccer-logs describe match events, each containing information about its type (pass, shot, foul, tackle, etc.), a time-stamp, the player(s), the position on the field and additional information (e.g., pass accuracy) (Pappalardo, Cinita, Rossi, et al.). This data can be plotted in software such as R Studio for visualization.
Open-source data can be found here: https://figshare.com/collections/Soccer_match_event_dataset/4415000/2
Original data source, including open source and proprietary data, can be found here: https://wyscout.com/
Methodology
Challenges/Limitations
The spatial data accessible is presented in a csv format, with each action in a soccer match noted as a record in the dataset, containing fields such as a time-stamp, start x, start y, end x, end y, and the players involved. This data needed processed in R using tidyverse to filter by player, team, or specific match. Furthermore, the data can be processed to create specific columns as shown here:
Analysis & Methods
After processing of the data, the ggplot packages, including ggsoccer and ggforce, can be used to map the actions on a visual soccer pitch, using the following coordinates to ensure the data will be consistent throughout all soccer games.
METRICS USED - REFINE
Libraries Used
library(BasketballAnalyzeR)
library(dplyr)
library(gridExtra)
library(cowplot)
library(ggplot2)
library(gridExtra)
library(kableExtra)
library(magick)
library(tidyverse)
library(here)
library(janitor)
library(ggplot2)
library(ggforce)
library(ggsoccer)
library(scales)
library(knitr)
library(sf)
library(spdep)Results
Top Midfielders Player Comparison
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# Load the data
data <- read.csv("importantplayers_n.csv", fileEncoding = "latin1")
#Select the players you want to see. Choose 6 players for better visual results.
selected_players <- subset(data,
Player=="Luka Modric" |
Player=="Antoine Griezmann" |
Player=="Kevin De Bruyne" |
Player=="Ivan Rakitic" |
Player=="N'Golo Kante" |
Player=="Axel Witsel" )
# Select relevant columns
Sel <- selected_players %>%
select(Player, GCA, SCA, Cmp, PrgP, Succ, PrgC, Tkl_Int) %>%
mutate(across(where(is.numeric), as.numeric)) # Ensure numeric columns remain numeric# Convert radar chart to a grob
g2_grob <- ggplotGrob(g2)
g_base <- ggplot_build(g2)$plot # Extract base layers of g2
# Create a blank plot to use as a legend-like panel
#annotation_plot <- ggplot() +
#theme_classic() + # Remove gridlines and axis
#annotation_custom(descr) # Add the description
#g_final <- plot_grid(g2, annotation_plot, ncol = 2, rel_widths = c(3, 1)) # Adjust widths
g_base
#g_finalCode Description Table:
#create a table with descriptions for the stats we chose
descriptions <- data.frame(
"Category"=colnames(Sel),
"Description"=c("Player",
"Goal Creating Actions",
"Shot Creating Actions",
"Passes Completed",
"Progressive Passes",
"Successful Take-On Dribbles",
"Progressive Carries",
"Tackles & Interceptions"))
# Remove "Player" row
descriptions <- descriptions[-1, ] # Removes the first row
descr <- tableGrob(print(descriptions))## Category Description
## 2 GCA Goal Creating Actions
## 3 SCA Shot Creating Actions
## 4 Cmp Passes Completed
## 5 PrgP Progressive Passes
## 6 Succ Successful Take-On Dribbles
## 7 PrgC Progressive Carries
## 8 Tkl_Int Tackles & InterceptionsPart 1: Passing
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Progressive Pass (Explained)
A progressive pass is a pass that moves the ball forward towards the oppositions goal. In the example below, Luka Modric passes the ball to his teammate in the opponent’s 18 yard box.
Spatial Autocorrelation: Performing Moran’s I test on Modric Passing Locations
Explain what Moran I test is.
I tested whether the number of starting passes per location by Luka Modric is spatially clustered.
# Read the dataset
croatia_data <- read.csv("CroatiaTeamData.csv")
# Filter data to only player_id 8287 (Luka Modric)
croatia_data_filter <- croatia_data %>%
filter(player_id == 8287 & event_name == "Pass")
# Count number of passes at each location
croatia_data_aggregated <- croatia_data_filter %>%
group_by(start_x, start_y) %>%
summarise(pass_count = n())
# Convert to sf object
croatia_sf_pass <- st_as_sf(croatia_data_aggregated, coords = c("start_x", "start_y"), crs = 4326)
# Create neighbors
nb <- dnearneigh(st_coordinates(croatia_sf_pass), 0, 30)
lw <- nb2listw(nb, style = "W", zero.policy = TRUE)
# Perform Moran's I test on pass count
moran_test_count <- moran.test(croatia_data_aggregated$pass_count, lw, zero.policy = TRUE)The key results show that:
##
## Moran I test under randomisation
##
## data: croatia_data_aggregated$pass_count
## weights: lw
##
## Moran I statistic standard deviate = 2.5981, p-value = 0.004687
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 9.465372e-03 -2.309469e-03 2.053928e-05Interpretation of the data
Since p < 0.05, we reject the null hypothesis (which assumes passes are randomly distributed). This means that certain areas of the field see more passing activity.
This result makes sense when we look at the position that Luka Modric typically plays. The Moran I test shows us that Modric tens to make plasses from certain zones. When we visualize this data on a soccer pitch, it becomes clear that Modric does pass more in certain locations.
# Define pitch dimensions
pitch_length <- 100
pitch_width <- 100
grid_size <- 10
penalty_box_length <- 16
penalty_box_width <- 62
six_yard_box_length <- 6
six_yard_box_width <- 26
goal_width <- 12
# Create grid bins
df <- croatia_data_filter %>%
mutate(grid_x = floor(start_x / grid_size),
grid_y = floor(start_y / grid_size))
# Aggregate counts per grid cell
heatmap_data <- df %>%
group_by(grid_x, grid_y) %>%
summarise(event_count = n())
# Calculate the average start_x and start_y
average_start_x <- mean(croatia_data_filter$start_x, na.rm = TRUE)
average_start_y <- mean(croatia_data_filter$start_y, na.rm = TRUE)
# Create a dataframe for the average start position point
average_point <- data.frame(
x = average_start_x,
y = average_start_y,
label = "Avg Start Position"
)
# Start with the heatmap in the background
heatmap_plot <- ggplot() +
geom_tile(data = heatmap_data, aes(x = grid_x * grid_size + grid_size / 2,
y = grid_y * grid_size + grid_size / 2,
fill = event_count), color = "white") +
scale_fill_gradient(low = "yellow", high = "red", labels = scales::label_number(accuracy = 1)) +
labs(title = "Heatmap Luka Modric Starting Passes",
subtitle = "Darker regions represent more passes performed per grid",
fill = "Pass Count") +
theme_classic() +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()) +
# Full pitch outline
geom_rect(aes(xmin = 0, xmax = pitch_length, ymin = 0, ymax = pitch_width),
color = "grey", fill = NA) +
# Grid lines (10x10)
geom_segment(data = data.frame(x = seq(0, pitch_length, by = grid_size)),
aes(x = x, xend = x, y = 0, yend = pitch_width),
color = "lightgrey", linetype = "dotted") +
geom_segment(data = data.frame(y = seq(0, pitch_width, by = grid_size)),
aes(x = 0, xend = pitch_length, y = y, yend = y),
color = "lightgrey", linetype = "dotted") +
# Penalty box (left side)
geom_rect(aes(xmin = 0, xmax = penalty_box_length,
ymin = (pitch_width - penalty_box_width) / 2,
ymax = (pitch_width + penalty_box_width) / 2),
color = "grey35", fill = NA) +
# Penalty box (right side)
geom_rect(aes(xmin = pitch_length - penalty_box_length, xmax = pitch_length,
ymin = (pitch_width - penalty_box_width) / 2,
ymax = (pitch_width + penalty_box_width) / 2),
color = "grey35", fill = NA) +
# Six-yard box (left side)
geom_rect(aes(xmin = 0, xmax = six_yard_box_length,
ymin = (pitch_width - six_yard_box_width) / 2,
ymax = (pitch_width + six_yard_box_width) / 2),
color = "grey35", fill = NA) +
# Six-yard box (right side)
geom_rect(aes(xmin = pitch_length - six_yard_box_length, xmax = pitch_length,
ymin = (pitch_width - six_yard_box_width) / 2,
ymax = (pitch_width + six_yard_box_width) / 2),
color = "grey35", fill = NA) +
# Goals (left side)
geom_segment(aes(x = 0, xend = 0,
y = (pitch_width - goal_width) / 2,
yend = (pitch_width + goal_width) / 2),
color = "black", size = 3) +
# Goals (right side)
geom_segment(aes(x = pitch_length, xend = pitch_length,
y = (pitch_width - goal_width) / 2,
yend = (pitch_width + goal_width) / 2),
color = "black", size = 3) +
# Center circle
geom_circle(aes(x0 = pitch_length / 2, y0 = pitch_width / 2, r = 9.15),
color = "grey35", inherit.aes = FALSE) +
# Center line
geom_segment(aes(x = pitch_length / 2, xend = pitch_length / 2,
y = 0, yend = pitch_width),
color = "grey35") +
# Attack direction annotation
annotate("segment", x = 45, xend = 80, y = 110, yend = 110,
arrow = arrow(length = unit(0.3, "inches")), color = "black", size = 1.2) +
annotate("text", x = 60, y = 120, label = "Attacking Direction", size = 5, fontface = "bold") +
# Add the average start_x and start_y as a black point
geom_point(aes(x = average_start_x, y = average_start_y), color = "white",, fill = "black", size = 7, shape = 21, stroke =1.5) +
# Add text annotation above the average point
geom_text(data = average_point, aes(x = x, y = y + 6, label = "Average Pass Start Location"),
color = "black", size = 3, hjust = 0.5) +
coord_fixed()
# Show the final plot
print(heatmap_plot)
When comparing the heatmap and average starting pass location to the starting positional lineup, we can see that Modric tends to pass around the location he is assigned. This highlights his exceptional positional awareness and ability to follow tactical plans throughout a soccer match.
Part 2: Dribbling
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Progressive Carry (Explained)
A progressive carry is a action where a player dribbles (retains possession while moving the ball) forward towards the oppositions goal. In the example below, Luka Modric dribbles past an opponent towards the opposition goal.
Part 3: Defending
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Tackle/Interception (Explained)
A tackle/interception are defensive actions in soccer where the player stops the opposition from dribbling, passing, or shooting the ball. In the example below, Luka Modric intercepts the ball as he stopped the pass of his opponent.
Luka Modric vs N’Golo Kanté Defensive Contributions
# Load the dataset
croatiadata <- read_csv(here("CroatiaTeamData.csv"))
# Filter Luka Modric's events (Duel + Others on the ball)
modric <- croatiadata %>%
filter(player_id == 8287 & (event_name == "Duel" | event_name == "Others on the ball"))
# Define pitch dimensions
pitch_length <- 100
pitch_width <- 100
penalty_box_length <- 16
penalty_box_width <- 62
six_yard_box_length <- 6
six_yard_box_width <- 26
goal_width <- 12
# Define 18-zone grid (3 vertical x 6 horizontal)
num_vertical <- 3
num_horizontal <- 6
grid_x_size <- pitch_length / num_horizontal
grid_y_size <- pitch_width / num_vertical
# Assign events to the 18-zone grid
df <- modric %>%
mutate(grid_x = floor(start_x / grid_x_size),
grid_y = floor(start_y / grid_y_size))
# Aggregate total contributions per grid cell
heatmap_data <- df %>%
group_by(grid_x, grid_y) %>%
summarise(event_count = n(), .groups = "drop")
# Generate the heatmap with contribution counts in each zone
modric_heatmap_plot <- ggplot() +
geom_tile(data = heatmap_data, aes(x = grid_x * grid_x_size + grid_x_size / 2,
y = grid_y * grid_y_size + grid_y_size / 2,
fill = event_count), color = "white") +
scale_fill_gradient(low = "yellow", high = "red", labels = scales::label_number(accuracy = 1)) +
labs(title = "Luka Modric Defensive Contributions",
subtitle = "Darker regions indicate more contributions",
fill = "Contributions") +
theme_classic() +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()) +
# Full pitch outline
geom_rect(aes(xmin = 0, xmax = pitch_length, ymin = 0, ymax = pitch_width),
color = "grey", fill = NA) +
# Add 18 equal-area grid lines (3x6)
geom_segment(data = data.frame(x = seq(0, pitch_length, by = grid_x_size)),
aes(x = x, xend = x, y = 0, yend = pitch_width),
color = "grey", linetype = "dashed") +
geom_segment(data = data.frame(y = seq(0, pitch_width, by = grid_y_size)),
aes(x = 0, xend = pitch_length, y = y, yend = y),
color = "grey", linetype = "dashed") +
# Add event count labels in each zone
geom_text(data = heatmap_data, aes(x = grid_x * grid_x_size + grid_x_size / 2,
y = grid_y * grid_y_size + grid_y_size / 2,
label = event_count), size = 5, fontface = "bold") +
# Add penalty boxes
geom_rect(aes(xmin = 0, xmax = penalty_box_length,
ymin = (pitch_width - penalty_box_width) / 2,
ymax = (pitch_width + penalty_box_width) / 2),
color = "grey35", fill = NA) +
geom_rect(aes(xmin = pitch_length - penalty_box_length, xmax = pitch_length,
ymin = (pitch_width - penalty_box_width) / 2,
ymax = (pitch_width + penalty_box_width) / 2),
color = "grey35", fill = NA) +
# Add six-yard boxes
geom_rect(aes(xmin = 0, xmax = six_yard_box_length,
ymin = (pitch_width - six_yard_box_width) / 2,
ymax = (pitch_width + six_yard_box_width) / 2),
color = "grey35", fill = NA) +
geom_rect(aes(xmin = pitch_length - six_yard_box_length, xmax = pitch_length,
ymin = (pitch_width - six_yard_box_width) / 2,
ymax = (pitch_width + six_yard_box_width) / 2),
color = "grey35", fill = NA) +
# Add goals
geom_segment(aes(x = 0, xend = 0,
y = (pitch_width - goal_width) / 2,
yend = (pitch_width + goal_width) / 2),
color = "black", size = 3) +
geom_segment(aes(x = pitch_length, xend = pitch_length,
y = (pitch_width - goal_width) / 2,
yend = (pitch_width + goal_width) / 2),
color = "black", size = 3) +
# Add center circle and line
geom_circle(aes(x0 = pitch_length / 2, y0 = pitch_width / 2, r = 9.15),
color = "grey35", inherit.aes = FALSE) +
geom_segment(aes(x = pitch_length / 2, xend = pitch_length / 2,
y = 0, yend = pitch_width),
color = "grey35") +
# Attack direction annotation
annotate("segment", x = 45, xend = 80, y = 110, yend = 110,
arrow = arrow(length = unit(0.3, "inches")), color = "black", size = 1.2) +
annotate("text", x = 60, y = 120, label = "Attacking Direction", size = 5, fontface = "bold") +
coord_fixed()# Load the dataset
francedata <- read_csv(here("francedata.csv"))
kante <- read_csv(here("kante.csv"))
# Filter N'Golo Kante's events (Duel + Others on the ball)
#kante <- francedata %>%
#filter(player_id == 31528 & (event_name == "Duel" | event_name == "Others on the ball"))
#write.csv(kante, "kante.csv")
# Define pitch dimensions
pitch_length <- 100
pitch_width <- 100
penalty_box_length <- 16
penalty_box_width <- 62
six_yard_box_length <- 6
six_yard_box_width <- 26
goal_width <- 12
# Define 18-zone grid (3 vertical x 6 horizontal)
num_vertical <- 3
num_horizontal <- 6
grid_x_size <- pitch_length / num_horizontal
grid_y_size <- pitch_width / num_vertical
# Assign events to the 18-zone grid
df <- kante %>%
mutate(grid_x = floor(start_x / grid_x_size),
grid_y = floor(start_y / grid_y_size))
# Aggregate total contributions per grid cell
heatmap_data <- df %>%
group_by(grid_x, grid_y) %>%
summarise(event_count = n(), .groups = "drop")
# Generate the heatmap with contribution counts in each zone
kante_heatmap_plot <- ggplot() +
geom_tile(data = heatmap_data, aes(x = grid_x * grid_x_size + grid_x_size / 2,
y = grid_y * grid_y_size + grid_y_size / 2,
fill = event_count), color = "white") +
scale_fill_gradient(low = "yellow", high = "red", labels = scales::label_number(accuracy = 1)) +
labs(title = "N'Golo Kante Defensive Contributions",
subtitle = "Darker regions indicate more contributions",
fill = "Contributions") +
theme_classic() +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank()) +
# Full pitch outline
geom_rect(aes(xmin = 0, xmax = pitch_length, ymin = 0, ymax = pitch_width),
color = "grey", fill = NA) +
# Add 18 equal-area grid lines (3x6)
geom_segment(data = data.frame(x = seq(0, pitch_length, by = grid_x_size)),
aes(x = x, xend = x, y = 0, yend = pitch_width),
color = "grey", linetype = "dashed") +
geom_segment(data = data.frame(y = seq(0, pitch_width, by = grid_y_size)),
aes(x = 0, xend = pitch_length, y = y, yend = y),
color = "grey", linetype = "dashed") +
# Add event count labels in each zone
geom_text(data = heatmap_data, aes(x = grid_x * grid_x_size + grid_x_size / 2,
y = grid_y * grid_y_size + grid_y_size / 2,
label = event_count), size = 5, fontface = "bold") +
# Add penalty boxes
geom_rect(aes(xmin = 0, xmax = penalty_box_length,
ymin = (pitch_width - penalty_box_width) / 2,
ymax = (pitch_width + penalty_box_width) / 2),
color = "grey35", fill = NA) +
geom_rect(aes(xmin = pitch_length - penalty_box_length, xmax = pitch_length,
ymin = (pitch_width - penalty_box_width) / 2,
ymax = (pitch_width + penalty_box_width) / 2),
color = "grey35", fill = NA) +
# Add six-yard boxes
geom_rect(aes(xmin = 0, xmax = six_yard_box_length,
ymin = (pitch_width - six_yard_box_width) / 2,
ymax = (pitch_width + six_yard_box_width) / 2),
color = "grey35", fill = NA) +
geom_rect(aes(xmin = pitch_length - six_yard_box_length, xmax = pitch_length,
ymin = (pitch_width - six_yard_box_width) / 2,
ymax = (pitch_width + six_yard_box_width) / 2),
color = "grey35", fill = NA) +
# Add goals
geom_segment(aes(x = 0, xend = 0,
y = (pitch_width - goal_width) / 2,
yend = (pitch_width + goal_width) / 2),
color = "black", size = 3) +
geom_segment(aes(x = pitch_length, xend = pitch_length,
y = (pitch_width - goal_width) / 2,
yend = (pitch_width + goal_width) / 2),
color = "black", size = 3) +
# Add center circle and line
geom_circle(aes(x0 = pitch_length / 2, y0 = pitch_width / 2, r = 9.15),
color = "grey35", inherit.aes = FALSE) +
geom_segment(aes(x = pitch_length / 2, xend = pitch_length / 2,
y = 0, yend = pitch_width),
color = "grey35") +
# Attack direction annotation
annotate("segment", x = 45, xend = 80, y = 110, yend = 110,
arrow = arrow(length = unit(0.3, "inches")), color = "black", size = 1.2) +
annotate("text", x = 60, y = 120, label = "Attacking Direction", size = 5, fontface = "bold") +
coord_fixed()pl_list <- list()
pl1 <- modric_heatmap_plot
pl2 <- kante_heatmap_plot
pl_list[[1]] <- list(pl1, "Modric Defensive Actions")
pl_list[[2]] <- list(pl2, "Kante Defensive Actions")Modric vs Kante Defensive Actions Plotted
Modric Defensive Actions
Kante Defensive Actions
Part 4: Goal Creating Actions
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Type 1: Goal (Explained)
A goal is scored when a player kicks the ball into the opponent’s goal. In the example below, Luka Modric shoots and scores against Argentina in the World Cup.
