Udacity Nanodegree Project1: Investigate a Dataset - [European Soccer Database] - 09/10 Season

Name : Oke Oladunsi

Hi, if you need to skip the technical facts kindly click here for a summary of facts and findings carried out during the scope of study.

Table of Contents

• Introduction
• Questions
• Data Wrangling
• Exploratory Data Analysis
• Conclusions
• Limitations
• References

Introduction

Dataset Description

Tip: This soccer database comes from Kaggle which is well suited for data analysis and machine learning. It contains data for soccer matches, players, and teams from 11 European countries from 2008 to 2016.

• The database is stored in a SQLite database.which consist of:
• +25,000 matches
• +10,000 players
• 11 European Countries with their lead championship
• Teams' attributes* sourced from EA Sports' FIFA video game series, including the weekly updates
• Seasons 2008 to 2016
• Players attributes* sourced from EA Sports' FIFA video game series, including the weekly updates
• Betting odds from up to 10 providers
• Detailed match events (goal types, possession, corner, cross, fouls, cards etc…)

Question(s) for Analysis

During my investigation i would be trying to give answers to the following questions:

Tip: What are the top 20 performing teams (goals scored) in european top 5 (five) leagues for the 2009/2010 season

Tip: Are there any correlations between goals scored or conceeded and games won per team

Tip: Are there significance relationship between the amount of home games won and the the team position at the end of the season

these and more questions will i be investigating during this entire process

Data Wrangling

Tip: In this section of the report, you will load in the data, check for cleanliness, and then trim and clean your dataset for analysis. Make sure that you document your steps carefully and justify your cleaning decisions.

General Properties

Data Cleaning (N:B for this project i have downloaded the database into my pc and i will be accessing using pandas sql module provided by pandas.)

through out this project the following packages are going to be employed:

• pandas
• sqlite3
• numpy
• matplotlib

conda install pandas,sqlite3,numpy, matplotlib

# importing necessary packages

# Load your data and print out a few lines. Perform operations to inspect data
#   types and look for instances of missing or possibly errant data.

import pandas as pd
import sqlite3 as sq
import numpy as np
import matplotlib.pyplot as plt

# Standard plotly imports
import chart_studio.plotly.plotly as ply
import plotly.graph_objs as go
import chart_studio.plotly.plotly.iplot as iplot

from plotly.offline import iplot, init_notebook_mode# Using plotly + cufflinks in offline mode
import cufflinks
cufflinks.go_offline(connected=True)
init_notebook_mode(connected=True)

# working with matplotlib inline
%matplotlib inline  

from chart_studio.plotly.plotly import iplot

Loading data into pandas dataframe

# creating a SQL connection to the db
con = sq.connect('database.sqlite')
cur = con.cursor()

# exracting db info pandas
df_european_countries = pd.read_sql_query("SELECT * FROM country", con)
df_european_fc_players = pd.read_sql_query("SELECT * FROM player", con)
df_european_fc_players_attr = pd.read_sql_query("SELECT * FROM player_attributes", con)
df_european_fc_team_attr = pd.read_sql_query("SELECT * FROM team_attributes", con)
df_european_league = pd.read_sql_query("SELECT * FROM league", con)
df_european_teams = pd.read_sql_query("SELECT * FROM team", con)
df_european_matches = pd.read_sql_query("SELECT * FROM match", con)

df_european_countries

	id	name
0	1	Belgium
1	1729	England
2	4769	France
3	7809	Germany
4	10257	Italy
5	13274	Netherlands
6	15722	Poland
7	17642	Portugal
8	19694	Scotland
9	21518	Spain
10	24558	Switzerland

print(df_european_countries.describe())

                 id
count     11.000000
mean   12452.090909
std     8215.308472
min        1.000000
25%     6289.000000
50%    13274.000000
75%    18668.000000
max    24558.000000

Total players playing the europe during this period can be found running the cell below

print(df_european_fc_players.info())
df_european_fc_players.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11060 entries, 0 to 11059
Data columns (total 7 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   id                  11060 non-null  int64  
 1   player_api_id       11060 non-null  int64  
 2   player_name         11060 non-null  object 
 3   player_fifa_api_id  11060 non-null  int64  
 4   birthday            11060 non-null  object 
 5   height              11060 non-null  float64
 6   weight              11060 non-null  int64  
dtypes: float64(1), int64(4), object(2)
memory usage: 605.0+ KB
None

	id	player_api_id	player_name	player_fifa_api_id	birthday	height	weight
0	1	505942	Aaron Appindangoye	218353	1992-02-29 00:00:00	182.88	187
1	2	155782	Aaron Cresswell	189615	1989-12-15 00:00:00	170.18	146
2	3	162549	Aaron Doran	186170	1991-05-13 00:00:00	170.18	163
3	4	30572	Aaron Galindo	140161	1982-05-08 00:00:00	182.88	198
4	5	23780	Aaron Hughes	17725	1979-11-08 00:00:00	182.88	154

df_european_fc_players.duplicated().sum()

0

print(df_european_fc_players_attr.info())
df_european_fc_players_attr.head(2)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183978 entries, 0 to 183977
Data columns (total 42 columns):
 #   Column               Non-Null Count   Dtype  
---  ------               --------------   -----  
 0   id                   183978 non-null  int64  
 1   player_fifa_api_id   183978 non-null  int64  
 2   player_api_id        183978 non-null  int64  
 3   date                 183978 non-null  object 
 4   overall_rating       183142 non-null  float64
 5   potential            183142 non-null  float64
 6   preferred_foot       183142 non-null  object 
 7   attacking_work_rate  180748 non-null  object 
 8   defensive_work_rate  183142 non-null  object 
 9   crossing             183142 non-null  float64
 10  finishing            183142 non-null  float64
 11  heading_accuracy     183142 non-null  float64
 12  short_passing        183142 non-null  float64
 13  volleys              181265 non-null  float64
 14  dribbling            183142 non-null  float64
 15  curve                181265 non-null  float64
 16  free_kick_accuracy   183142 non-null  float64
 17  long_passing         183142 non-null  float64
 18  ball_control         183142 non-null  float64
 19  acceleration         183142 non-null  float64
 20  sprint_speed         183142 non-null  float64
 21  agility              181265 non-null  float64
 22  reactions            183142 non-null  float64
 23  balance              181265 non-null  float64
 24  shot_power           183142 non-null  float64
 25  jumping              181265 non-null  float64
 26  stamina              183142 non-null  float64
 27  strength             183142 non-null  float64
 28  long_shots           183142 non-null  float64
 29  aggression           183142 non-null  float64
 30  interceptions        183142 non-null  float64
 31  positioning          183142 non-null  float64
 32  vision               181265 non-null  float64
 33  penalties            183142 non-null  float64
 34  marking              183142 non-null  float64
 35  standing_tackle      183142 non-null  float64
 36  sliding_tackle       181265 non-null  float64
 37  gk_diving            183142 non-null  float64
 38  gk_handling          183142 non-null  float64
 39  gk_kicking           183142 non-null  float64
 40  gk_positioning       183142 non-null  float64
 41  gk_reflexes          183142 non-null  float64
dtypes: float64(35), int64(3), object(4)
memory usage: 59.0+ MB
None

	id	player_fifa_api_id	player_api_id	date	overall_rating	potential	preferred_foot	attacking_work_rate	defensive_work_rate	crossing	...	vision	penalties	marking	standing_tackle	sliding_tackle	gk_diving	gk_handling	gk_kicking	gk_positioning	gk_reflexes
0	1	218353	505942	2016-02-18 00:00:00	67.0	71.0	right	medium	medium	49.0	...	54.0	48.0	65.0	69.0	69.0	6.0	11.0	10.0	8.0	8.0
1	2	218353	505942	2015-11-19 00:00:00	67.0	71.0	right	medium	medium	49.0	...	54.0	48.0	65.0	69.0	69.0	6.0	11.0	10.0	8.0	8.0

2 rows × 42 columns

print(df_european_teams.info())
df_european_teams.head(2)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 299 entries, 0 to 298
Data columns (total 5 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   id                299 non-null    int64  
 1   team_api_id       299 non-null    int64  
 2   team_fifa_api_id  288 non-null    float64
 3   team_long_name    299 non-null    object 
 4   team_short_name   299 non-null    object 
dtypes: float64(1), int64(2), object(2)
memory usage: 11.8+ KB
None

	id	team_api_id	team_fifa_api_id	team_long_name	team_short_name
0	1	9987	673.0	KRC Genk	GEN
1	2	9993	675.0	Beerschot AC	BAC

Teams attributes can be found running the cell below.

N:B the only column without complete info is buildUpPlayDribbling

df_european_fc_team_attr.isnull().sum()

id                                  0
team_fifa_api_id                    0
team_api_id                         0
date                                0
buildUpPlaySpeed                    0
buildUpPlaySpeedClass               0
buildUpPlayDribbling              969
buildUpPlayDribblingClass           0
buildUpPlayPassing                  0
buildUpPlayPassingClass             0
buildUpPlayPositioningClass         0
chanceCreationPassing               0
chanceCreationPassingClass          0
chanceCreationCrossing              0
chanceCreationCrossingClass         0
chanceCreationShooting              0
chanceCreationShootingClass         0
chanceCreationPositioningClass      0
defencePressure                     0
defencePressureClass                0
defenceAggression                   0
defenceAggressionClass              0
defenceTeamWidth                    0
defenceTeamWidthClass               0
defenceDefenderLineClass            0
dtype: int64

print(df_european_fc_team_attr.info())
df_european_fc_team_attr.head(2)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1458 entries, 0 to 1457
Data columns (total 25 columns):
 #   Column                          Non-Null Count  Dtype  
---  ------                          --------------  -----  
 0   id                              1458 non-null   int64  
 1   team_fifa_api_id                1458 non-null   int64  
 2   team_api_id                     1458 non-null   int64  
 3   date                            1458 non-null   object 
 4   buildUpPlaySpeed                1458 non-null   int64  
 5   buildUpPlaySpeedClass           1458 non-null   object 
 6   buildUpPlayDribbling            489 non-null    float64
 7   buildUpPlayDribblingClass       1458 non-null   object 
 8   buildUpPlayPassing              1458 non-null   int64  
 9   buildUpPlayPassingClass         1458 non-null   object 
 10  buildUpPlayPositioningClass     1458 non-null   object 
 11  chanceCreationPassing           1458 non-null   int64  
 12  chanceCreationPassingClass      1458 non-null   object 
 13  chanceCreationCrossing          1458 non-null   int64  
 14  chanceCreationCrossingClass     1458 non-null   object 
 15  chanceCreationShooting          1458 non-null   int64  
 16  chanceCreationShootingClass     1458 non-null   object 
 17  chanceCreationPositioningClass  1458 non-null   object 
 18  defencePressure                 1458 non-null   int64  
 19  defencePressureClass            1458 non-null   object 
 20  defenceAggression               1458 non-null   int64  
 21  defenceAggressionClass          1458 non-null   object 
 22  defenceTeamWidth                1458 non-null   int64  
 23  defenceTeamWidthClass           1458 non-null   object 
 24  defenceDefenderLineClass        1458 non-null   object 
dtypes: float64(1), int64(11), object(13)
memory usage: 284.9+ KB
None

	id	team_fifa_api_id	team_api_id	date	buildUpPlaySpeed	buildUpPlaySpeedClass	buildUpPlayDribbling	buildUpPlayDribblingClass	buildUpPlayPassing	buildUpPlayPassingClass	...	chanceCreationShooting	chanceCreationShootingClass	chanceCreationPositioningClass	defencePressure	defencePressureClass	defenceAggression	defenceAggressionClass	defenceTeamWidth	defenceTeamWidthClass	defenceDefenderLineClass
0	1	434	9930	2010-02-22 00:00:00	60	Balanced	NaN	Little	50	Mixed	...	55	Normal	Organised	50	Medium	55	Press	45	Normal	Cover
1	2	434	9930	2014-09-19 00:00:00	52	Balanced	48.0	Normal	56	Mixed	...	64	Normal	Organised	47	Medium	44	Press	54	Normal	Cover

2 rows × 25 columns

The european countries and their respective leagues can be found running the cell below

df_european_league

	id	country_id	name
0	1	1	Belgium Jupiler League
1	1729	1729	England Premier League
2	4769	4769	France Ligue 1
3	7809	7809	Germany 1. Bundesliga
4	10257	10257	Italy Serie A
5	13274	13274	Netherlands Eredivisie
6	15722	15722	Poland Ekstraklasa
7	17642	17642	Portugal Liga ZON Sagres
8	19694	19694	Scotland Premier League
9	21518	21518	Spain LIGA BBVA
10	24558	24558	Switzerland Super League

Total matches in the database can be found running the cell below

df_european_matches.shape

(25979, 115)

Total seasons in the database can be found running the cell below

for i in df_european_matches.season.unique():
    print(i)

2008/2009
2009/2010
2010/2011
2011/2012
2012/2013
2013/2014
2014/2015
2015/2016

def getting_league_and_season(leag_id, season):
    """
    This function returns a string of the league name and the season as a single word

    WHERE:

    league_name : this extracts the league name from the european league. i.e (Belgium Jupiler League --> belgium_jupiler_league)
    season_name : this extracts the season tags and replace '/' to '_' i.e (2009/2010 --> 2009_2010)

    RETURNS
    a single string in the form belgium_jupiler_league_2009_2010
    """
    league_name = df_european_league.query("id == {}".format(leag_id))["name"].to_string().replace(" ","_")[5:]
    season_name = season.replace("/","_")
    if league_name[0] == "_":
        return league_name[1:]+"_"+season_name
    return league_name+"_"+season_name

def get_season_stats(fa_id,season):
    """ 
    This function generates valuable stats of season for the league_id given and also generated a csv file for the storage

    definintion of variable names

    temp: this is a container for every team in a particular league 
    home_win : this give the total amount of games won by each team in a season in their home stadium
    away_win : this give the total amount of games won by each team in a season in an opponent stadium
    away_draw : this give the total amount of games drew by each team in a season in an opponent stadium
    home_draw : this give the total amount of games drew by each team in a season in their home stadium
    home_loss : this give the total amount of games loss by each team in a season in their home stadium
    away_loss : this give the total amount of games loss by each team in a season in an opponent stadium

    goals stats------>

    home_goals_scored : this give the total amount of goals scored by each team in a season in their home stadium
    away_goals_scored : this give the total amount of goals scored by each team in a season in an opponent stadium
    home_goals_conceded : this give the total amount of goals conceded by each team in a season in their home stadium
    away_goals_conceded : this give the total amount of goals conceded by each team in a season in an opponent stadium

    definition of summary stats ---->
    GP : Total amount of games pLayed in a season
    HW : Total Games won in their home statium
    AW : Total Games won in an opponent stadium
    HD : Total Games drew in their home statium
    AD : Total Games drew in an opponent stadium
    HL : Total Games loss in their home statium
    AL : Total Games loss in an opponent stadium
    W  : Sum total of all games won in a season
    L  : Sum total of the games loss in a season
    D  :  Sum total of the games drew in a season

    P  : Total points the team got in a season were:
        3 points for each game won
        1 point for each game drew
        0 point for each game loss 

    F  : Sum total of the goals scored in a season
    A :  Sum total of the goals conceded in a season
    +/- : Differences between F and A i.e (+/- = F-A)

    """
    # All the matches each league in season from 2009/2010 - 2015/2015

    season_matches = df_european_matches.query('league_id == {} and season == "{}"'.format(fa_id,season))
    df_season = pd.DataFrame(season_matches.home_team_api_id.unique())
    df_season.columns = ["team_id"] 
    # temporary container for club names
    temp = []
    home_win = []
    away_win = []
    away_draw = []
    home_draw = []
    home_loss = []
    away_loss = []
    home_goals_scored = []
    away_goals_scored = []
    home_goals_conceded = []
    away_goals_conceded = []
    for team in season_matches.home_team_api_id.unique():
        temp.append(df_european_teams.query('team_api_id == {}'.format(team)).team_long_name)
        home_games = season_matches.query('home_team_api_id == {}'.format(team))
        away_games = season_matches.query('away_team_api_id == {}'.format(team))
        home_win.append(season_matches.query('home_team_goal > away_team_goal and home_team_api_id == {}'.format(team)).shape[0])
        away_win.append(season_matches.query('away_team_goal > home_team_goal and away_team_api_id == {}'.format(team)).shape[0])
        home_draw.append(season_matches.query('home_team_goal == away_team_goal and home_team_api_id == {}'.format(team)).shape[0])
        away_draw.append(season_matches.query('away_team_goal == home_team_goal and away_team_api_id == {}'.format(team)).shape[0])
        home_loss.append(season_matches.query('home_team_goal < away_team_goal and home_team_api_id == {}'.format(team)).shape[0])
        away_loss.append(season_matches.query('away_team_goal < home_team_goal and away_team_api_id == {}'.format(team)).shape[0])
        ########################################Goals###########################################################################################
        home_goals_scored.append(season_matches.query('home_team_api_id == {}'.format(team)).home_team_goal.sum())
        away_goals_scored.append(season_matches.query('away_team_api_id == {}'.format(team)).away_team_goal.sum())
        home_goals_conceded.append(season_matches.query('home_team_api_id == {}'.format(team)).away_team_goal.sum())
        away_goals_conceded.append(season_matches.query('away_team_api_id == {}'.format(team)).home_team_goal.sum())

    #### Stats###########
    df_season["team_name"] = pd.DataFrame(np.array(temp))
    df_season["GP"] = len(home_games+away_games)
    df_season["HW"] = pd.DataFrame(np.array(home_win))
    df_season["AW"] = pd.DataFrame(np.array(away_win))
    df_season["HD"] = pd.DataFrame(np.array(home_draw))
    df_season["AD"] = pd.DataFrame(np.array(away_draw))
    df_season["HL"] = pd.DataFrame(np.array(home_loss))
    df_season["AL"] = pd.DataFrame(np.array(away_loss))
    df_season["W"] = df_season["HW"] + df_season["AW"]
    df_season["L"] = df_season["HL"] + df_season["AL"]
    df_season["D"] = df_season["HD"] + df_season["AD"]
    df_season["P"] = df_season["W"] *3 + df_season["D"] *1 + df_season["L"]*0
    df_season["F"] = pd.DataFrame(np.array(home_goals_scored)) + pd.DataFrame(np.array(away_goals_scored))
    df_season["A"] = pd.DataFrame(np.array(home_goals_conceded)) + pd.DataFrame(np.array(away_goals_conceded))
    df_season["+/-"] = df_season["F"] - df_season["A"]
    df_season = df_season.sort_values(by=['P','+/-'], ascending=False)
    
    ######### setting file name and creating csv file
    name = getting_league_and_season(fa_id,season).lower()
    df_season.to_csv(name+".csv", index=False)
    

#creating csv file for each season
for i in df_european_matches.league_id.unique():
    for j in df_european_matches.season.unique():
        get_season_stats(i,j)

con.close()

Exploratory Data Analysis

Tip: At this point I have generated several csv's file for the standings for each league which is for 8 seasons. So therefore, from the generated csv file

Research Question 1 (What are the top 20 teams with the most goals in the 2009-2010 season in europe top five leagues)

spain_liga_league_09_10 = pd.read_csv("spain_liga_bbva_2009_2010.csv")
english_premier_league_09_10 = pd.read_csv("england_premier_league_2009_2010.csv")
france_ligue_1_09_10 = pd.read_csv("france_ligue_1_2009_2010.csv")
germany_1_bundesliga_09_10 = pd.read_csv("germany_1._bundesliga_2009_2010.csv")
italy_seria_a_09_10 = pd.read_csv("italy_serie_a_2009_2010.csv")

I will continue analysi on this later, so for now i will turn it to markdown cell

spain_liga_league_10_11 = pd.read_csv("spain_liga_bbva_2010_2011.csv") english_premier_league_10_11 = pd.read_csv("england_premier_league_2010_2011.csv") france_ligue_1_10_11 = pd.read_csv("france_ligue_1_2010_2011.csv") germany_1_bundesliga_10_11 = pd.read_csv("germany_1._bundesliga_2010_2011.csv") italy_seria_a_10_11 = pd.read_csv("italy_serie_a_2010_2011.csv")

I will continue analysi on this later, so for now i will have turn it to markdown cell that i might meet submition deadline

Tip: I wanted to investigate how teams in the top five leagues performed over the course of 5 seasons, but now i will only do with one season.

spain_liga_league_10_11 = pd.read_csv("spain_liga_bbva_2010_2011.csv") english_premier_league_10_11 = pd.read_csv("england_premier_league_2010_2011.csv") france_ligue_1_10_11 = pd.read_csv("france_ligue_1_2010_2011.csv") germany_1_bundesliga_10_11 = pd.read_csv("germany_1._bundesliga_2010_2011.csv") italy_seria_a_10_11 = pd.read_csv("italy_serie_a_2010_2011.csv")

spain_liga_league_11_12 = pd.read_csv("spain_liga_bbva_2011_2012.csv") english_premier_league_11_12 = pd.read_csv("england_premier_league_2011_2012.csv") france_ligue_1_11_12 = pd.read_csv("france_ligue_1_2011_2012.csv") germany_1_bundesliga_11_12 = pd.read_csv("germany_1._bundesliga_2011_2012.csv") italy_seria_a_11_12 = pd.read_csv("italy_serie_a_2011_2012.csv")

spain_liga_league_12_13 = pd.read_csv("spain_liga_bbva_2012_2013.csv") english_premier_league_12_13 = pd.read_csv("england_premier_league_2012_2013.csv") france_ligue_1_12_13 = pd.read_csv("france_ligue_1_2012_2013.csv") germany_1_bundesliga_12_13 = pd.read_csv("germany_1._bundesliga_2012_2013.csv") italy_seria_a_12_13 = pd.read_csv("italy_serie_a_2012_2013.csv")

seasons_09_10 = spain_liga_league_09_10
for league in [english_premier_league_09_10,france_ligue_1_09_10,germany_1_bundesliga_09_10,italy_seria_a_09_10]:
    seasons_09_10 = seasons_09_10.append(league)

# this is a standing of how each team in europe top five leagues performed
season_09_10 = seasons_09_10.reset_index()

# extracting only the top 20 teams with the most goals
top_20_team_with_most_goals_09_10 = season_09_10.sort_values(by=["F"], ascending=False)[:21]

# extracting only the top 20 teams that conceeded most goals
top_20_team_conceeding_most_goals_09_10 = season_09_10.sort_values(by=["A"], ascending=False)[:21]


# extracting only the top 20 teams with most  home wins
top_20_team_with_most_homeWin_09_10 = season_09_10.sort_values(by=["HW"], ascending=False)[:21]

# extracting only the top 20 teams with most away wins
top_20_team_with_most_awayWin_09_10 = season_09_10.sort_values(by=["AW"], ascending=False)[:21]

# extracting only the top 20 teams with most wins
top_20_team_with_most_Wins_09_10 = season_09_10.sort_values(by=["W"], ascending=False)[:21]


# extracting only the top 20 teams with most  home loss
top_20_team_with_most_homeloss_09_10 = season_09_10.sort_values(by=["HL"], ascending=False)[:21]

# extracting only the top 20 teams with most away loss
top_20_team_with_most_awayloss_09_10 = season_09_10.sort_values(by=["AL"], ascending=False)[:21]

# extracting only the top 20 teams with most loss
top_20_team_with_most_loss_09_10 = season_09_10.sort_values(by=["L"], ascending=False)[:21]

Top 20 clubs in all of europe top five(5) leagues for the 2009/2010 season can be found running the cell below

• What are the top 20 performing teams (goals scored) in european top 5 (five) leagues for the 2009/2010 season
• Does goals score relate to if a team will be in the top 4 team in their respective leagues.
• What are the top 20 performing teams (goals scored) in european top 5 (five) leagues for the 2009/2010 season </a>

top_20_team_with_most_goals_09_10

	index	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
20	0	8455	Chelsea	38	17	10	1	4	1	5	27	6	5	86	103	32	71
1	1	8633	Real Madrid CF	38	18	13	0	3	1	3	31	4	3	96	102	35	67
0	0	8634	FC Barcelona	38	18	13	1	5	0	1	31	1	6	99	98	24	74
21	1	10260	Manchester United	38	16	11	1	3	2	5	27	7	4	85	86	28	58
22	2	9825	Arsenal	38	15	8	2	4	2	7	23	9	6	75	83	41	42
78	0	8636	Inter	38	15	9	4	6	0	4	24	4	10	82	75	34	41
24	4	8456	Manchester City	38	12	6	4	9	3	4	18	7	13	67	73	45	28
43	3	8639	LOSC Lille	38	14	7	3	4	2	8	21	10	7	70	72	40	32
60	0	9823	FC Bayern Munich	34	12	8	4	6	1	3	20	4	10	70	72	31	41
62	2	8697	SV Werder Bremen	34	8	9	6	4	3	4	17	7	10	61	71	40	31
40	0	8592	Olympique de Marseille	38	14	9	3	6	2	4	23	6	9	78	69	36	33
79	1	8686	Roma	38	15	9	1	7	3	3	24	6	8	80	68	41	27
23	3	8586	Tottenham Hotspur	38	14	7	2	5	3	7	21	10	7	70	67	41	26
3	3	8302	Sevilla FC	38	10	9	6	0	3	10	19	13	6	63	65	49	16
63	3	8178	Bayer 04 Leverkusen	34	11	4	5	9	1	4	15	5	14	59	65	38	27
67	7	8721	VfL Wolfsburg	34	7	7	2	6	8	4	14	12	8	50	64	58	6
41	1	9748	Olympique Lyonnais	38	12	8	4	8	3	3	20	6	12	72	64	38	26
26	6	8650	Liverpool	38	13	5	3	6	3	8	18	11	9	63	61	35	26
80	2	8564	Milan	38	12	8	5	5	2	6	20	8	10	70	60	39	21
27	7	8668	Everton	38	11	5	6	7	2	7	16	9	13	61	60	49	11
82	4	8540	Palermo	38	13	5	6	5	0	9	18	9	11	65	59	47	12

top_20_team_with_most_goals_09_10.py(x="team_name",y="F",kind="bar", title="Top 20 teams with most goals in europe 09/10")
plt.legend(["Teams"])
plt.xlabel("Teams")
plt.ylabel("Amount of goals scored")
plt.show()

top_teams_with_most_goals_scored_less_goals_conceeded_09_10 = top_20_team_with_most_goals_09_10[["F","A","team_name"]].set_index("team_name")


top_teams_with_most_goals_scored_less_goals_conceeded_09_10.iplot(kind='bar', xTitle='teams',
                  yTitle='goals scored/conceeded', title='Top 20 teams in europe (Compare goals scored vs goals conceded in europe 09/10')

top_20_team_with_most_goals_09_10.plot(x="team_name",y="F",kind="bar", title="Top 20 teams with most goals in europe 09/10")
plt.legend(["Teams"])
plt.xlabel("Teams")
plt.ylabel("Amount of goals scored")
plt.show()

top_20_team_with_most_goals_09_10.plot(x="team_name",y="F",kind="hist", title="Top 20 teams with most goals in europe 09/10")
plt.ylabel("Team position")
plt.xlabel("Amount of goals scored")
plt.show()

top_20_team_with_most_goals_09_10['F'].iplot(kind='hist', xTitle='goals scored',
                  yTitle='Team position', title='Top 20 teams with most goals in europe 09/10')

top_20_team_with_most_goals_09_10['F'].iplot(kind='scatter', xTitle='goals scored',
                  yTitle='Team position', title='Top 20 teams with most goals in europe 09/10')

top_20_team_with_most_goals_09_10.plot(x="F",y="index",kind="scatter",title="Goals scored against team position 09/10 season")

plt.ylabel("Team position")
plt.xlabel("Amount of goals scored")
plt.show()

top_20_team_with_most_goals_09_10['A'].iplot(kind='hist', xTitle='goals conceeded',
                  yTitle='Team position', title='Top 20 teams with most goals in europe 09/10')

top_20_team_with_most_goals_09_10.plot(x="W",y="A",kind="hist", title="Top 20 teams with most goals in europe 09/10")
plt.ylabel("Team position")
plt.xlabel("goals conceeded")
plt.show()

Answers to the questions asked above

• The team with the most goals in europe for the 2009-2010 season is Chelsea FC
• we can deduce that if a team can score between 60-100 goals such a team will be among the top four best teams in their leagues.
• we can deduce that if a team can conceed goals less than 55 goals in the season, such a team will be among the top four best teams in their leagues.
• Another thing we can clearly see is that teams that conceed between 23-32 are outwrite winners of their league or becomes second runner

• from the scatter plot before the recent histogram we can see that there is about 85% chance that a team would will qualify for champions leagues if they scored more goals between (59-100 goals) taking amples from the 20 teams with most goals.

Research Question 2 ( Are there any correlations between goals scored and games won per team)

top_20_team_with_most_goals_09_10.plot(x="F",y="W",kind="scatter",title="A graph of Goals scored against games won 09/10")

plt.xlabel("goals scored")
plt.ylabel("games won")
plt.show()
print("The graph above shows that there is a possitive correlation between amount of goal scored and games won which is : {}%".format(round(top_20_team_with_most_goals_09_10["F"].corr(top_20_team_with_most_goals_09_10["W"])*100, 2)))

The graph above shows that there is a possitive correlation between amount of goal scored and games won which is : 84.85%

# extracting only the top 20 teams with the most goals
top_20_team_with_least_goals_09_10 = season_09_10.sort_values(by=["F"], ascending=True)[:21]

top_20_team_with_least_goals_09_10.plot(x="F",y="W",kind="scatter",title="A graph of Goals scored against games won 09/10")

plt.xlabel("goals scored")
plt.ylabel("games won")
plt.show()
print("The graph above shows that there is a possitive correlation between amount of goal scored and games won which is : {}%".format(round(top_20_team_with_least_goals_09_10["F"].corr(top_20_team_with_least_goals_09_10["W"])*100, 2)))

The graph above shows that there is a possitive correlation between amount of goal scored and games won which is : 11.38%

least_performing_teams_with_least_goals_scored_more_goals_conceeded_09_10 = top_20_team_with_least_goals_09_10[["F","A","team_name"]].set_index("team_name")


least_performing_teams_with_least_goals_scored_more_goals_conceeded_09_10.iplot(kind='bar', xTitle='teams',
                  yTitle='goals scored/conceeded', title='the least performing teams in europe top five leagues for 09/10 season (Compare goals scored vs goals conceded')

top_20_team_with_least_goals_09_10

	index	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
97	19	8537	Livorno	38	5	2	5	3	9	14	7	23	8	29	27	61	-34
56	16	9853	AS Saint-Étienne	38	5	5	7	3	7	11	10	18	10	40	27	45	-18
55	15	9874	FC Sochaux-Montbéliard	38	8	3	1	7	10	9	11	19	8	41	28	52	-24
10	10	8558	RCD Espanyol	38	9	2	6	5	4	12	11	16	11	44	29	46	-17
58	18	4170	US Boulogne Cote D'Opale	38	4	3	5	5	10	11	7	21	10	31	31	62	-31
59	19	9855	Grenoble Foot 38	38	4	1	5	3	10	15	5	25	8	23	31	61	-30
34	14	8602	Wolverhampton Wanderers	38	5	4	6	5	8	10	9	18	11	38	32	56	-24
75	15	8165	1. FC Nürnberg	34	5	3	4	3	8	11	8	19	7	31	32	58	-26
72	12	8722	1. FC Köln	34	3	6	6	5	8	6	9	14	11	38	33	42	-9
76	16	9911	VfL Bochum	34	2	4	6	4	9	9	6	18	10	28	33	64	-31
39	19	8462	Portsmouth	38	5	2	3	4	11	13	7	24	7	28	34	66	-32
38	18	8667	Hull City	38	6	0	6	6	7	13	6	20	12	30	34	75	-41
77	17	8177	Hertha BSC Berlin	34	1	4	6	3	10	10	5	20	9	24	34	56	-22
30	10	10194	Stoke City	38	7	4	6	8	6	7	11	13	14	47	34	48	-14
9	9	9783	RC Deportivo de La Coruña	38	9	4	4	4	6	11	13	17	8	47	35	49	-14
73	13	8358	SC Freiburg	34	5	4	4	4	8	9	9	17	8	35	35	59	-24
68	8	9905	1. FSV Mainz 05	34	9	3	6	5	2	9	12	11	11	47	36	42	-6
53	13	9941	Toulouse FC	38	8	4	4	7	7	8	12	15	11	47	36	36	0
14	14	9869	Real Sporting de Gijón	38	7	2	7	6	5	11	9	16	13	40	36	51	-15
57	17	8682	Le Mans FC	38	6	2	6	2	7	15	8	22	8	32	36	59	-23
95	17	8524	Atalanta	38	7	2	5	3	7	14	9	21	8	35	37	53	-16

Answers to the questions asked above

• we can clearly see that teams with 55-100+ goals won atleast 15-30+ games
• we also see that teams that score goals between 25-40+ goals wins 5-13 games (thus, this implies that the fewer the goals a team score the the chances of winning a match)

top_20_team_conceeding_most_goals_09_10

	index	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
37	17	8191	Burnley	38	7	1	5	1	7	17	8	24	6	30	42	82	-40
35	15	8528	Wigan Athletic	38	6	3	7	2	6	14	9	20	9	36	37	79	-42
38	18	8667	Hull City	38	6	0	6	6	7	13	6	20	12	30	34	75	-41
18	18	9867	CD Tenerife	38	8	1	6	3	5	15	9	20	9	36	40	74	-34
96	18	8551	Siena	38	4	3	7	3	8	13	7	21	10	31	40	67	-27
33	13	8559	Bolton Wanderers	38	6	4	6	3	7	12	10	19	9	39	42	67	-25
74	14	9904	Hannover 96	34	5	4	4	2	8	11	9	19	6	33	43	67	-24
39	19	8462	Portsmouth	38	5	2	3	4	11	13	7	24	7	28	34	66	-32
36	16	8654	West Ham United	38	7	1	5	6	7	12	8	19	11	35	47	66	-19
19	19	9868	Xerez Club Deportivo	38	6	2	4	6	9	11	8	20	10	34	38	66	-28
76	16	9911	VfL Bochum	34	2	4	6	4	9	9	6	18	10	28	33	64	-31
13	13	8394	Real Zaragoza	38	7	3	6	5	6	11	10	17	11	41	46	64	-18
17	17	10281	Real Valladolid	38	4	3	8	7	7	9	7	16	15	36	37	62	-25
58	18	4170	US Boulogne Cote D'Opale	38	4	3	5	5	10	11	7	21	10	31	31	62	-31
59	19	9855	Grenoble Foot 38	38	4	1	5	3	10	15	5	25	8	23	31	61	-30
97	19	8537	Livorno	38	5	2	5	3	9	14	7	23	8	29	27	61	-34
8	8	9906	Atlético Madrid	38	11	2	3	5	5	12	13	17	8	47	57	61	-4
86	8	10233	Genoa	38	11	3	5	4	3	12	14	15	9	51	57	61	-4
71	11	9788	Borussia Mönchengladbach	34	8	2	5	4	4	11	10	15	9	39	43	60	-17
92	14	8600	Udinese	38	10	1	5	6	4	12	11	16	11	44	54	59	-5
73	13	8358	SC Freiburg	34	5	4	4	4	8	9	9	17	8	35	35	59	-24

teams_with_most_goals_conceeded_09_10 = top_20_team_conceeding_most_goals_09_10[["A","team_name"]].set_index("team_name")


teams_with_most_goals_conceeded_09_10.iplot(kind='bar', xTitle='teams',
                  yTitle='goals scored/conceeded', title='A chart of Top 20 teams in europe top five leagues for 09/10 season that conceeded the most goals (Compare goals scored vs goals conceded')

top_20_team_conceeding_most_goals_09_10.plot(x="A",y="W",kind="scatter", title="Top 20 teams with most conceded goals in europe 09-10 season")

plt.xlabel("goals conceeded")
plt.ylabel("games won")
plt.show()

print("The graph above shows that there is a negative correlation of : {}%".format(round(top_20_team_conceeding_most_goals_09_10["A"].corr(top_20_team_conceeding_most_goals_09_10["W"])*100, 2)))

The graph above shows that there is a negative correlation of : -20.6%

• this histgram shows clearly that the more a team conceeds the less the chance the team wins a match

english_premier_league_09_10.sort_values(by=["L"])

	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
0	8455	Chelsea	38	17	10	1	4	1	5	27	6	5	86	103	32	71
1	10260	Manchester United	38	16	11	1	3	2	5	27	7	4	85	86	28	58
4	8456	Manchester City	38	12	6	4	9	3	4	18	7	13	67	73	45	28
5	10252	Aston Villa	38	8	9	8	5	3	5	17	8	13	64	52	39	13
2	9825	Arsenal	38	15	8	2	4	2	7	23	9	6	75	83	41	42
7	8668	Everton	38	11	5	6	7	2	7	16	9	13	61	60	49	11
3	8586	Tottenham Hotspur	38	14	7	2	5	3	7	21	10	7	70	67	41	26
6	8650	Liverpool	38	13	5	3	6	3	8	18	11	9	63	61	35	26
10	10194	Stoke City	38	7	4	6	8	6	7	11	13	14	47	34	48	-14
9	8655	Blackburn Rovers	38	10	3	6	5	3	11	13	14	11	50	41	55	-14
8	8658	Birmingham City	38	8	5	9	2	2	12	13	14	11	50	38	47	-9
11	9879	Fulham	38	11	1	3	7	5	11	12	16	10	46	39	46	-7
12	8472	Sunderland	38	9	2	7	4	3	13	11	16	11	44	48	56	-8
14	8602	Wolverhampton Wanderers	38	5	4	6	5	8	10	9	18	11	38	32	56	-24
13	8559	Bolton Wanderers	38	6	4	6	3	7	12	10	19	9	39	42	67	-25
16	8654	West Ham United	38	7	1	5	6	7	12	8	19	11	35	47	66	-19
15	8528	Wigan Athletic	38	6	3	7	2	6	14	9	20	9	36	37	79	-42
18	8667	Hull City	38	6	0	6	6	7	13	6	20	12	30	34	75	-41
17	8191	Burnley	38	7	1	5	1	7	17	8	24	6	30	42	82	-40
19	8462	Portsmouth	38	5	2	3	4	11	13	7	24	7	28	34	66	-32

spain_liga_league_09_10

	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
0	8634	FC Barcelona	38	18	13	1	5	0	1	31	1	6	99	98	24	74
1	8633	Real Madrid CF	38	18	13	0	3	1	3	31	4	3	96	102	35	67
2	10267	Valencia CF	38	13	8	5	3	1	8	21	9	8	71	59	40	19
3	8302	Sevilla FC	38	10	9	6	0	3	10	19	13	6	63	65	49	16
4	8661	RCD Mallorca	38	15	3	1	7	3	9	18	12	8	62	59	44	15
5	8305	Getafe CF	38	11	6	4	3	4	10	17	14	7	58	58	48	10
6	10205	Villarreal CF	38	13	3	3	5	3	11	16	14	8	56	58	57	1
7	8315	Athletic Club de Bilbao	38	11	4	4	5	4	10	15	14	9	54	50	53	-3
8	9906	Atlético Madrid	38	11	2	3	5	5	12	13	17	8	47	57	61	-4
9	9783	RC Deportivo de La Coruña	38	9	4	4	4	6	11	13	17	8	47	35	49	-14
10	8558	RCD Espanyol	38	9	2	6	5	4	12	11	16	11	44	29	46	-17
11	8371	CA Osasuna	38	7	4	8	2	4	13	11	17	10	43	37	46	-9
12	9865	UD Almería	38	8	2	6	6	5	11	10	16	12	42	43	55	-12
13	8394	Real Zaragoza	38	7	3	6	5	6	11	10	17	11	41	46	64	-18
14	9869	Real Sporting de Gijón	38	7	2	7	6	5	11	9	16	13	40	36	51	-15
15	8696	Racing Santander	38	4	5	5	7	10	7	9	17	12	39	42	59	-17
16	9864	Málaga CF	38	5	2	8	8	6	9	7	15	16	37	42	48	-6
17	10281	Real Valladolid	38	4	3	8	7	7	9	7	16	15	36	37	62	-25
18	9867	CD Tenerife	38	8	1	6	3	5	15	9	20	9	36	40	74	-34
19	9868	Xerez Club Deportivo	38	6	2	4	6	9	11	8	20	10	34	38	66	-28

france_ligue_1_09_10

	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
0	8592	Olympique de Marseille	38	14	9	3	6	2	4	23	6	9	78	69	36	33
1	9748	Olympique Lyonnais	38	12	8	4	8	3	3	20	6	12	72	64	38	26
2	8583	AJ Auxerre	38	11	9	5	6	3	4	20	7	11	71	42	29	13
3	8639	LOSC Lille	38	14	7	3	4	2	8	21	10	7	70	72	40	32
4	10249	Montpellier Hérault SC	38	12	8	4	5	3	6	20	9	9	69	50	40	10
5	9827	Girondins de Bordeaux	38	12	7	4	3	3	9	19	12	7	64	58	40	18
6	8689	FC Lorient	38	10	6	7	3	2	10	16	12	10	58	54	42	12
7	9829	AS Monaco	38	11	4	5	5	3	10	15	13	10	55	39	45	-6
8	9851	Stade Rennais FC	38	10	4	4	7	5	8	14	13	11	53	52	41	11
9	9873	Valenciennes FC	38	7	7	7	3	5	9	14	14	10	52	50	50	0
10	8588	RC Lens	38	9	3	7	5	3	11	12	14	12	48	40	44	-4
11	8481	AS Nancy-Lorraine	38	6	7	4	5	9	7	13	16	9	48	46	53	-7
12	9847	Paris Saint-Germain	38	9	3	5	6	5	10	12	15	11	47	50	46	4
13	9941	Toulouse FC	38	8	4	4	7	7	8	12	15	11	47	36	36	0
14	9831	OGC Nice	38	7	4	7	4	5	11	11	16	11	44	41	57	-16
15	9874	FC Sochaux-Montbéliard	38	8	3	1	7	10	9	11	19	8	41	28	52	-24
16	9853	AS Saint-Étienne	38	5	5	7	3	7	11	10	18	10	40	27	45	-18
17	8682	Le Mans FC	38	6	2	6	2	7	15	8	22	8	32	36	59	-23
18	4170	US Boulogne Cote D'Opale	38	4	3	5	5	10	11	7	21	10	31	31	62	-31
19	9855	Grenoble Foot 38	38	4	1	5	3	10	15	5	25	8	23	31	61	-30

• This shows that the worst defensive team in all of europe is Burnley who is joint 1st with portsmouth as team with most losses in epl.
• Also the tread occurs in la liga where CD Tenerife and Xerez Club Deportivo appeared and both relegated
  • Also the tread occurs in the france ligue 1 where US Boulogne Cote D'Opale and Grenoble Foot 38 appeared and both relegated ##### Hence N:B both teams relegated

top_20_team_conceeding_most_goals_09_10.plot(x="A",y="L",kind="scatter", title="A chart of goals conceded against matches loss 09-10 season",)
plt.xlabel("goals conceeded")
plt.ylabel("games loss")
plt.show()

top_20_team_conceeding_most_goals_09_10.plot(x="A",y="L",kind="scatter")
plt.xlabel("goals conceeded")
plt.ylabel("games Loss")
plt.show()
print("The graph above shows that there is a possitive correlation between amount of goal conceeded and games Loss which is : {}%".format(round(top_20_team_conceeding_most_goals_09_10["A"].corr(top_20_team_conceeding_most_goals_09_10["index"])*100, 2)))

The graph above shows that there is a possitive correlation between amount of goal conceeded and games Loss which is : 31.65%

• The graph above shows that there is a possitive correlation between amount of goal conceded and a teams position at the end of season is : 31.64%
• which shows that the more goals a team concedes the more a team losses matches

Research Question 3 Are there significance relationship between the amount of home games won and the the team position at the end of the season

top_20_team_with_most_homeWin_09_10.reset_index(inplace=True)

top_20_team_with_most_homeWin_09_10.drop("level_0", inplace=True, axis=1)

top_20_team_with_most_homeWin_09_10

	index	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
0	0	8634	FC Barcelona	38	18	13	1	5	0	1	31	1	6	99	98	24	74
1	1	8633	Real Madrid CF	38	18	13	0	3	1	3	31	4	3	96	102	35	67
2	0	8455	Chelsea	38	17	10	1	4	1	5	27	6	5	86	103	32	71
3	1	10260	Manchester United	38	16	11	1	3	2	5	27	7	4	85	86	28	58
4	1	8686	Roma	38	15	9	1	7	3	3	24	6	8	80	68	41	27
5	4	8661	RCD Mallorca	38	15	3	1	7	3	9	18	12	8	62	59	44	15
6	2	9825	Arsenal	38	15	8	2	4	2	7	23	9	6	75	83	41	42
7	0	8636	Inter	38	15	9	4	6	0	4	24	4	10	82	75	34	41
8	0	8592	Olympique de Marseille	38	14	9	3	6	2	4	23	6	9	78	69	36	33
9	3	8586	Tottenham Hotspur	38	14	7	2	5	3	7	21	10	7	70	67	41	26
10	3	8639	LOSC Lille	38	14	7	3	4	2	8	21	10	7	70	72	40	32
11	4	8540	Palermo	38	13	5	6	5	0	9	18	9	11	65	59	47	12
12	6	8650	Liverpool	38	13	5	3	6	3	8	18	11	9	63	61	35	26
13	3	9882	Sampdoria	38	13	6	6	4	0	9	19	9	10	67	49	41	8
14	6	10205	Villarreal CF	38	13	3	3	5	3	11	16	14	8	56	58	57	1
15	2	10267	Valencia CF	38	13	8	5	3	1	8	21	9	8	71	59	40	19
16	4	8456	Manchester City	38	12	6	4	9	3	4	18	7	13	67	73	45	28
17	2	8564	Milan	38	12	8	5	5	2	6	20	8	10	70	60	39	21
18	1	9748	Olympique Lyonnais	38	12	8	4	8	3	3	20	6	12	72	64	38	26
19	4	10249	Montpellier Hérault SC	38	12	8	4	5	3	6	20	9	9	69	50	40	10
20	0	9823	FC Bayern Munich	34	12	8	4	6	1	3	20	4	10	70	72	31	41

top_20_team_with_most_homeWin_09_10.plot(x="HW",y="index",kind="scatter", title="A graph of Games Won v Position 09/10")
plt.xlabel("games won on home ground")
plt.ylabel("team position")
plt.show()
print("The graph above shows that there is a seem to negative correlation between amount of game won at home and and the team position at the end of the season of  : {}%".format(round(top_20_team_with_most_homeWin_09_10["HW"].corr(top_20_team_with_most_homeWin_09_10["index"])*100, 2)))

The graph above shows that there is a seem to negative correlation between amount of game won at home and and the team position at the end of the season of  : -46.71%

top_20_team_with_most_homeWin_09_10.plot(x="team_name",y="HW",kind="bar")
plt.xlabel("goals conceeded")
plt.ylabel("games Loss")
plt.legend(["Teams with most home win"])
plt.show()

• Here it shows that Barcelona performed better at home than every other teams in europe in terms of home games won
• We can see also that atleast four teams amongst top 20 in europe with most home win is not among the top 4 teams in it's league

top_20_team_with_most_homeWin_09_10.plot(x="HW",y="W",kind="scatter")
    plt.xlabel("games won on home ground")
    plt.ylabel("team perfomance on games won")
plt.show()
print("The graph above shows that there is a positive correlation between game won  scored and games won which is : {}%".format(round(top_20_team_with_most_homeWin_09_10["HW"].corr(top_20_team_with_most_homeWin_09_10["W"])*100, 2)))

The graph above shows that there is a positive correlation between game won  scored and games won which is : 87.25%

top_20_team_with_most_homeWin_09_10.plot(x="HW",y="index",kind="scatter")
plt.legend([" corr btw Home win against team postion"])
plt.show()
print("The graph above shows that there is a negative correlation between game won at home and team position at the end of the season which is : {}%".format(round(top_20_team_with_most_homeWin_09_10["HW"].corr(top_20_team_with_most_homeWin_09_10["index"])*100, 2)))

The graph above shows that there is a negative correlation between game won at home and team position at the end of the season which is : -46.71%

• the information I can deduce from the here is that a team will be in the top 4 (four) at the end of the season in thier various league if they win 90% of their home games.

this are the other facts that we can deduce from the dataset.

top_20_team_with_most_awayWin_09_10

	index	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
0	0	8634	FC Barcelona	38	18	13	1	5	0	1	31	1	6	99	98	24	74
1	1	8633	Real Madrid CF	38	18	13	0	3	1	3	31	4	3	96	102	35	67
21	1	10260	Manchester United	38	16	11	1	3	2	5	27	7	4	85	86	28	58
20	0	8455	Chelsea	38	17	10	1	4	1	5	27	6	5	86	103	32	71
62	2	8697	SV Werder Bremen	34	8	9	6	4	3	4	17	7	10	61	71	40	31
42	2	8583	AJ Auxerre	38	11	9	5	6	3	4	20	7	11	71	42	29	13
25	5	10252	Aston Villa	38	8	9	8	5	3	5	17	8	13	64	52	39	13
40	0	8592	Olympique de Marseille	38	14	9	3	6	2	4	23	6	9	78	69	36	33
78	0	8636	Inter	38	15	9	4	6	0	4	24	4	10	82	75	34	41
79	1	8686	Roma	38	15	9	1	7	3	3	24	6	8	80	68	41	27
3	3	8302	Sevilla FC	38	10	9	6	0	3	10	19	13	6	63	65	49	16
61	1	10189	FC Schalke 04	34	11	8	2	6	4	3	19	7	8	65	53	31	22
80	2	8564	Milan	38	12	8	5	5	2	6	20	8	10	70	60	39	21
44	4	10249	Montpellier Hérault SC	38	12	8	4	5	3	6	20	9	9	69	50	40	10
41	1	9748	Olympique Lyonnais	38	12	8	4	8	3	3	20	6	12	72	64	38	26
2	2	10267	Valencia CF	38	13	8	5	3	1	8	21	9	8	71	59	40	19
22	2	9825	Arsenal	38	15	8	2	4	2	7	23	9	6	75	83	41	42
60	0	9823	FC Bayern Munich	34	12	8	4	6	1	3	20	4	10	70	72	31	41
67	7	8721	VfL Wolfsburg	34	7	7	2	6	8	4	14	12	8	50	64	58	6
65	5	10269	VfB Stuttgart	34	8	7	5	5	4	5	15	9	10	55	51	41	10
51	11	8481	AS Nancy-Lorraine	38	6	7	4	5	9	7	13	16	9	48	46	53	-7

top_20_team_with_most_awayWin_09_10.plot(x="team_name",y="AW",kind="bar")
plt.legend(["Teams with most Away win"])
plt.show()

top_20_team_with_most_awayWin_09_10.plot(x="AW",y="index",kind="scatter")
plt.legend(["corr btw Away win and team position "])
plt.show()
print("The graph above shows that there is a negative correlation between game won at home and team position at the end of the season which is : {}%".format(round(top_20_team_with_most_awayWin_09_10["F"].corr(top_20_team_with_most_awayWin_09_10["index"])*100, 2)))

The graph above shows that there is a negative correlation between game won at home and team position at the end of the season which is : -55.68%

top_20_team_with_most_Wins_09_10

	index	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
0	0	8634	FC Barcelona	38	18	13	1	5	0	1	31	1	6	99	98	24	74
1	1	8633	Real Madrid CF	38	18	13	0	3	1	3	31	4	3	96	102	35	67
21	1	10260	Manchester United	38	16	11	1	3	2	5	27	7	4	85	86	28	58
20	0	8455	Chelsea	38	17	10	1	4	1	5	27	6	5	86	103	32	71
79	1	8686	Roma	38	15	9	1	7	3	3	24	6	8	80	68	41	27
78	0	8636	Inter	38	15	9	4	6	0	4	24	4	10	82	75	34	41
22	2	9825	Arsenal	38	15	8	2	4	2	7	23	9	6	75	83	41	42
40	0	8592	Olympique de Marseille	38	14	9	3	6	2	4	23	6	9	78	69	36	33
23	3	8586	Tottenham Hotspur	38	14	7	2	5	3	7	21	10	7	70	67	41	26
43	3	8639	LOSC Lille	38	14	7	3	4	2	8	21	10	7	70	72	40	32
2	2	10267	Valencia CF	38	13	8	5	3	1	8	21	9	8	71	59	40	19
42	2	8583	AJ Auxerre	38	11	9	5	6	3	4	20	7	11	71	42	29	13
44	4	10249	Montpellier Hérault SC	38	12	8	4	5	3	6	20	9	9	69	50	40	10
80	2	8564	Milan	38	12	8	5	5	2	6	20	8	10	70	60	39	21
60	0	9823	FC Bayern Munich	34	12	8	4	6	1	3	20	4	10	70	72	31	41
41	1	9748	Olympique Lyonnais	38	12	8	4	8	3	3	20	6	12	72	64	38	26
81	3	9882	Sampdoria	38	13	6	6	4	0	9	19	9	10	67	49	41	8
45	5	9827	Girondins de Bordeaux	38	12	7	4	3	3	9	19	12	7	64	58	40	18
61	1	10189	FC Schalke 04	34	11	8	2	6	4	3	19	7	8	65	53	31	22
3	3	8302	Sevilla FC	38	10	9	6	0	3	10	19	13	6	63	65	49	16
24	4	8456	Manchester City	38	12	6	4	9	3	4	18	7	13	67	73	45	28

top_20_team_with_most_Wins_09_10.plot(x="team_name",y="W",kind="bar")
plt.legend(["Teams with most wins"])
plt.show()

top_20_team_with_most_homeloss_09_10

	index	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
39	19	8462	Portsmouth	38	5	2	3	4	11	13	7	24	7	28	34	66	-32
77	17	8177	Hertha BSC Berlin	34	1	4	6	3	10	10	5	20	9	24	34	56	-22
55	15	9874	FC Sochaux-Montbéliard	38	8	3	1	7	10	9	11	19	8	41	28	52	-24
58	18	4170	US Boulogne Cote D'Opale	38	4	3	5	5	10	11	7	21	10	31	31	62	-31
59	19	9855	Grenoble Foot 38	38	4	1	5	3	10	15	5	25	8	23	31	61	-30
15	15	8696	Racing Santander	38	4	5	5	7	10	7	9	17	12	39	42	59	-17
97	19	8537	Livorno	38	5	2	5	3	9	14	7	23	8	29	27	61	-34
19	19	9868	Xerez Club Deportivo	38	6	2	4	6	9	11	8	20	10	34	38	66	-28
76	16	9911	VfL Bochum	34	2	4	6	4	9	9	6	18	10	28	33	64	-31
51	11	8481	AS Nancy-Lorraine	38	6	7	4	5	9	7	13	16	9	48	46	53	-7
93	15	8533	Chievo Verona	38	6	6	5	3	8	10	12	18	8	44	37	42	-5
75	15	8165	1. FC Nürnberg	34	5	3	4	3	8	11	8	19	7	31	32	58	-26
96	18	8551	Siena	38	4	3	7	3	8	13	7	21	10	31	40	67	-27
34	14	8602	Wolverhampton Wanderers	38	5	4	6	5	8	10	9	18	11	38	32	56	-24
67	7	8721	VfL Wolfsburg	34	7	7	2	6	8	4	14	12	8	50	64	58	6
72	12	8722	1. FC Köln	34	3	6	6	5	8	6	9	14	11	38	33	42	-9
74	14	9904	Hannover 96	34	5	4	4	2	8	11	9	19	6	33	43	67	-24
73	13	8358	SC Freiburg	34	5	4	4	4	8	9	9	17	8	35	35	59	-24
89	11	8543	Lazio	38	5	6	6	7	8	6	11	14	13	46	39	43	-4
17	17	10281	Real Valladolid	38	4	3	8	7	7	9	7	16	15	36	37	62	-25
33	13	8559	Bolton Wanderers	38	6	4	6	3	7	12	10	19	9	39	42	67	-25

top_20_team_with_most_homeloss_09_10.plot(x="team_name",y="HL",kind="bar")
plt.legend(["Teams with most home loss"])
plt.show()

top_20_team_with_most_awayloss_09_10

	index	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
37	17	8191	Burnley	38	7	1	5	1	7	17	8	24	6	30	42	82	-40
57	17	8682	Le Mans FC	38	6	2	6	2	7	15	8	22	8	32	36	59	-23
59	19	9855	Grenoble Foot 38	38	4	1	5	3	10	15	5	25	8	23	31	61	-30
18	18	9867	CD Tenerife	38	8	1	6	3	5	15	9	20	9	36	40	74	-34
97	19	8537	Livorno	38	5	2	5	3	9	14	7	23	8	29	27	61	-34
95	17	8524	Atalanta	38	7	2	5	3	7	14	9	21	8	35	37	53	-16
35	15	8528	Wigan Athletic	38	6	3	7	2	6	14	9	20	9	36	37	79	-42
96	18	8551	Siena	38	4	3	7	3	8	13	7	21	10	31	40	67	-27
32	12	8472	Sunderland	38	9	2	7	4	3	13	11	16	11	44	48	56	-8
11	11	8371	CA Osasuna	38	7	4	8	2	4	13	11	17	10	43	37	46	-9
39	19	8462	Portsmouth	38	5	2	3	4	11	13	7	24	7	28	34	66	-32
38	18	8667	Hull City	38	6	0	6	6	7	13	6	20	12	30	34	75	-41
86	8	10233	Genoa	38	11	3	5	4	3	12	14	15	9	51	57	61	-4
33	13	8559	Bolton Wanderers	38	6	4	6	3	7	12	10	19	9	39	42	67	-25
36	16	8654	West Ham United	38	7	1	5	6	7	12	8	19	11	35	47	66	-19
92	14	8600	Udinese	38	10	1	5	6	4	12	11	16	11	44	54	59	-5
28	8	8658	Birmingham City	38	8	5	9	2	2	12	13	14	11	50	38	47	-9
10	10	8558	RCD Espanyol	38	9	2	6	5	4	12	11	16	11	44	29	46	-17
8	8	9906	Atlético Madrid	38	11	2	3	5	5	12	13	17	8	47	57	61	-4
56	16	9853	AS Saint-Étienne	38	5	5	7	3	7	11	10	18	10	40	27	45	-18
74	14	9904	Hannover 96	34	5	4	4	2	8	11	9	19	6	33	43	67	-24

top_20_team_with_most_awayloss_09_10.plot(x="team_name",y="AL",kind="bar")
plt.legend(["Teams with most Away loss"])
plt.show()

top_20_team_with_most_loss_09_10

	index	team_id	team_name	GP	HW	AW	HD	AD	HL	AL	W	L	D	P	F	A	+/-
59	19	9855	Grenoble Foot 38	38	4	1	5	3	10	15	5	25	8	23	31	61	-30
39	19	8462	Portsmouth	38	5	2	3	4	11	13	7	24	7	28	34	66	-32
37	17	8191	Burnley	38	7	1	5	1	7	17	8	24	6	30	42	82	-40
97	19	8537	Livorno	38	5	2	5	3	9	14	7	23	8	29	27	61	-34
57	17	8682	Le Mans FC	38	6	2	6	2	7	15	8	22	8	32	36	59	-23
58	18	4170	US Boulogne Cote D'Opale	38	4	3	5	5	10	11	7	21	10	31	31	62	-31
96	18	8551	Siena	38	4	3	7	3	8	13	7	21	10	31	40	67	-27
95	17	8524	Atalanta	38	7	2	5	3	7	14	9	21	8	35	37	53	-16
18	18	9867	CD Tenerife	38	8	1	6	3	5	15	9	20	9	36	40	74	-34
38	18	8667	Hull City	38	6	0	6	6	7	13	6	20	12	30	34	75	-41
77	17	8177	Hertha BSC Berlin	34	1	4	6	3	10	10	5	20	9	24	34	56	-22
35	15	8528	Wigan Athletic	38	6	3	7	2	6	14	9	20	9	36	37	79	-42
19	19	9868	Xerez Club Deportivo	38	6	2	4	6	9	11	8	20	10	34	38	66	-28
55	15	9874	FC Sochaux-Montbéliard	38	8	3	1	7	10	9	11	19	8	41	28	52	-24
74	14	9904	Hannover 96	34	5	4	4	2	8	11	9	19	6	33	43	67	-24
75	15	8165	1. FC Nürnberg	34	5	3	4	3	8	11	8	19	7	31	32	58	-26
33	13	8559	Bolton Wanderers	38	6	4	6	3	7	12	10	19	9	39	42	67	-25
36	16	8654	West Ham United	38	7	1	5	6	7	12	8	19	11	35	47	66	-19
76	16	9911	VfL Bochum	34	2	4	6	4	9	9	6	18	10	28	33	64	-31
34	14	8602	Wolverhampton Wanderers	38	5	4	6	5	8	10	9	18	11	38	32	56	-24
56	16	9853	AS Saint-Étienne	38	5	5	7	3	7	11	10	18	10	40	27	45	-18

top_20_team_with_most_loss_09_10.plot(x="team_name",y="L",kind="bar")
plt.legend(["Teams with most loss"])
plt.show()

Conclusions

Tip: After carrying out the analysis on this dataset I was able to give answers to the questions asked in the Questions section

After the analysis I carried on this dataset i was able to determine the following

1. Chelsea scored the most goals in all of europe
2. Camp nou of FC Barcelona is the most difficult place to get a win in all of europe
3. If a team scored between 59_100 goals there is possibility of getting into european cup competitions
4. any team that conceed 59-90 goals will probably be in the lower end of the table at the end of the season

the above listed observations are proves that the more a team the chances of winning more matches. we can also deduce that the best form defense any team can have is scoring more goals as we were able to see amongst the top 20 teams with most goals conceeds lesser goals. We are also able to see that teams that conceeded the most goals in their respective leagues get relegated most often times. ### LIMITATIONS: > **Tip**: Finally, the players dataset need more intensive cleaning because of which I can achive due to the deadline I will have to put hold further analysis at this point so I can avoid being revoked from carrying on with the work i intended doing. But once, I am done submitting i hope can further carry out more analysis and learning from this intresting dataset. ### REFERENCES:

1. Connor Anderson, Kevin Velasco, and Alex Shropshire.Hypothesis Testing European Soccer Data; Home Field Advantage, Ideal Formations, and Inter-League Attributes Explored in Python, May 11, 2019
2. Yiou Wang August 2020 Data Analyses of European Soccer
3. Soccer Data Analysis