This page was generated from doc/examples/ClubElo - Evolution of current top teams.ipynb.
You can download the notebook,

[2]:

import soccerdata as sd

[3]:

%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

sns.set_context("notebook")
sns.set_style("whitegrid")

Evolution of top team’s Elo ratings¶

How did the current top 5 teams in the world develop over time?

[4]:

elo = sd.ClubElo()
current_elo = elo.read_by_date()
current_elo.head()

/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccerdata/soccerdata/clubelo.py:18: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  return pd.read_csv(

[4]:

	rank	country	level	elo	from	to	league
team
Man City	1.0	ENG	1	2079.984619	2023-06-11	2023-08-11	ENG-Premier League
Liverpool	2.0	ENG	1	1949.932861	2023-06-11	2023-08-13	ENG-Premier League
Bayern	3.0	GER	1	1937.012451	2023-05-28	2023-08-18	GER-Bundesliga
Arsenal	4.0	ENG	1	1928.216187	2023-06-11	2023-08-12	ENG-Premier League
Real Madrid	5.0	ESP	1	1907.589233	2023-06-08	2023-08-12	ESP-La Liga

[5]:

num_teams = 5
smoothing = 100
elo_top_development = pd.concat(
    [elo.read_team_history(team)['elo'].rolling(smoothing).mean()
     for team in current_elo.reset_index()['team'][:num_teams]
    ],
    axis=1)
elo_top_development.columns = current_elo.reset_index()['team'][:num_teams]
elo_top_development.fillna(method='ffill')

fig = plt.figure(figsize=(16, 10))
ax1 = fig.add_subplot(111, ylabel='ELO rolling avg.', xlabel='Date')
elo_top_development.plot(ax=ax1)
ax1.legend(loc='upper left', frameon=False, bbox_to_anchor=(0, 1.05), ncol=num_teams)
sns.despine();

/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccerdata/soccerdata/clubelo.py:18: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  return pd.read_csv(
/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccerdata/soccerdata/clubelo.py:18: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  return pd.read_csv(
/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccerdata/soccerdata/clubelo.py:18: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  return pd.read_csv(
/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccerdata/soccerdata/clubelo.py:18: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  return pd.read_csv(
/cw/dtaijupiter/NoCsBack/dtai/pieterr/Projects/soccerdata/soccerdata/clubelo.py:18: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  return pd.read_csv(

../_images/examples_ClubElo_-_Evolution_of_current_top_teams_5_1.png