Goalkeepers and their heights

import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns

df_raw = pd.read_csv("../../data_sources/Players2024.csv")

A glimpse at the dataset

We’ll begin just by taking a glimpse at the dataset:

df_raw.head(10)

	name	birth_date	height_cm	positions	nationality	age	club
0	James Milner	1986-01-04	175.0	Midfield	England	38	Brighton and Hove Albion Football Club
1	Anastasios Tsokanis	1991-05-02	176.0	Midfield	Greece	33	Volou Neos Podosferikos Syllogos
2	Jonas Hofmann	1992-07-14	176.0	Midfield	Germany	32	Bayer 04 Leverkusen Fußball
3	Pepe Reina	1982-08-31	188.0	Goalkeeper	Spain	42	Calcio Como
4	Lionel Carole	1991-04-12	180.0	Defender	France	33	Kayserispor Kulübü
5	Ludovic Butelle	1983-04-03	188.0	Goalkeeper	France	41	Stade de Reims
6	Daley Blind	1990-03-09	180.0	Defender	Netherlands	34	Girona Fútbol Club S. A. D.
7	Craig Gordon	1982-12-31	193.0	Goalkeeper	Scotland	41	Heart of Midlothian Football Club
8	Dimitrios Sotiriou	1987-09-13	185.0	Goalkeeper	Greece	37	Omilos Filathlon Irakliou FC
9	Alessio Cragno	1994-06-28	184.0	Goalkeeper	Italy	30	Associazione Calcio Monza

Cleaning the data

The data had a few issues, as the following plot shows:

sns.catplot(df_raw, x = "positions", y = "height_cm")

It looks like some of the players’ positions and heights were recorded incorrectly. To clean, let’s remove the “Missing” positions and ensure that heights are reasonable:

df = df_raw.copy()

# Remove missing position
df = df[df["positions"] != "Missing"]

# Ensure reasonable heights
df = df[df["height_cm"] > 100]

To confirm, let’s plot the outliers in a different colour

# Identify outliers
outliers = pd.concat([df_raw,df]).drop_duplicates(keep = False)

sns.catplot(df, x = "positions", y = "height_cm")
sns.stripplot(outliers, x = "positions", y = "height_cm", color = "r")

Visualising the players’ heights

After cleaning the data we can now analyse the players’ heights to see if there’s differences between positions. A box plot can show the distribution of heights:

sns.catplot(data = df, x = "positions", y = "height_cm", kind = "box", order = ["Goalkeeper", "Defender", "Midfield", "Attack"])
plt.xlabel("Position")
plt.ylabel("Height (cm)")
plt.savefig("tb.png")
plt.show()

It looks like goalkeepers are taller than the rest!

Let’s through the age variable into the mix, to see if players’ heights allow them to compete longer.

px.scatter(data_frame = df, x = "age", y = "height_cm", color = "positions",
           facet_col = "positions", facet_col_wrap = 2, hover_name = "name",
           hover_data = "nationality", labels = {"height_cm": "Height (cm)",
                                                 "positions": "Position"})

It doesn’t look like there’s a relationship between heights and ages, but clearly it affects their position!

Global spread

We haven’t looked at the nationality column yet. Let’s draw up a map using plotly to see where the players come from.

# Change country names to match plotly reference
df["nationality"] = df["nationality"].replace(["England", "Türkiye", "Cote d'Ivoire", 
                                               "Northern Ireland", "Wales"], 
                                               ["United Kingdom", "Turkey", "Ivory Coast",
                                                "United Kingdom", "United Kingdom"])

# Make the count
countries = df.value_counts("nationality")

# Make the plot
px.choropleth(locations = countries.index, locationmode = "country names", color = countries,
              labels = {"locations": "Country", "color": "# of players"})

Looks like most players are from Europe. Pan and zoom to see the finer details.