LaLiga - Applied Statistics

Domain: Sports

Context: La Liga is the men's top professional football division of the Spanish football league system. The dataset contains information on all the teams that have participated in all the past tournaments. It has data about how many goals each team scored, conceded, how many times they came within the first 6 positions, how many seasons they have qualified, their best position in the past, etc. Data Description: Laliga.csv - The data set contains information on all the teams so far participated in all the past tournaments

Attribute Information: Pos - Position in among the list of all teams Team Seasons - how many seasons team has played so far Points - total number of points of the team GamesPlayed - total number of games played so far GamesWon - total number of games won so far GamesDrawn - total number of games drawn so far GamesLost - total number of games lost so far GoalsFor - total number of goals by the team GoalsAgainst - total number of goals against the team Champion - total number of times it team is a champion Runner-up - total number of times it team is a runner-up Third / Fourth/ Fifth/ Sixth - total number of times it team came in a third/fourth.fifth/sixth position Debut - debut year BestPosition - best position of the team

Objective: We want to use statistical techniques to come up with metrics with which can be used to gauge the winning team in the upcoming La Liga cup (Football tournament)

# importing libraries
import numpy as np
import pandas as pd

1. Read the data set and replace dashes with 0 to make sure you can perform arithmetic operations on the data.

# reading data set from provided CSV to a dataframe 'laliga' using pandas
laliga = pd.read_csv('Laliga.csv')

# printing head of the dataframe 'laliga'
laliga.head()

	Pos	Team	Seasons	Points	GamesPlayed	GamesWon	GamesDrawn	GamesLost	GoalsFor	GoalsAgainst	Champion	Runner-up	Third	Fourth	Fifth	Sixth	T	Debut	Since/LastApp	BestPosition
0	1	Real Madrid	86	4385	2762	1647	552	563	5947	3140	33	23	8	8	3	4	79	1929	1929	1
1	2	Barcelona	86	4262	2762	1581	573	608	5900	3114	25	25	12	12	4	6	83	1929	1929	1
2	3	Atletico Madrid	80	3442	2614	1241	598	775	4534	3309	10	8	16	9	7	6	56	1929	2002-03	1
3	4	Valencia	82	3386	2664	1187	616	861	4398	3469	6	6	10	11	10	7	50	1931-32	1987-88	1
4	5	Athletic Bilbao	86	3368	2762	1209	633	920	4631	3700	8	7	10	5	8	10	49	1929	1929	1

#checking shape of the 'laliga' dataframe
laliga.shape

(61, 20)

#checking data types of all columns
laliga.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 20 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Pos            61 non-null     int64 
 1   Team           61 non-null     object
 2   Seasons        61 non-null     int64 
 3   Points         61 non-null     object
 4   GamesPlayed    61 non-null     object
 5   GamesWon       61 non-null     object
 6   GamesDrawn     61 non-null     object
 7   GamesLost      61 non-null     object
 8   GoalsFor       61 non-null     object
 9   GoalsAgainst   61 non-null     object
 10  Champion       61 non-null     object
 11  Runner-up      61 non-null     object
 12  Third          61 non-null     object
 13  Fourth         61 non-null     object
 14  Fifth          61 non-null     object
 15  Sixth          61 non-null     object
 16  T              61 non-null     object
 17  Debut          61 non-null     object
 18  Since/LastApp  61 non-null     object
 19  BestPosition   61 non-null     int64 
dtypes: int64(3), object(17)
memory usage: 5.5+ KB

# using replace function to replace '-' with '0' which will allow us arithamatic operations
laliga.replace('-',0,inplace=True)
laliga

	Pos	Team	Seasons	Points	GamesPlayed	GamesWon	GamesDrawn	GamesLost	GoalsFor	GoalsAgainst	Champion	Runner-up	Third	Fourth	Fifth	Sixth	T	Debut	Since/LastApp	BestPosition
0	1	Real Madrid	86	4385	2762	1647	552	563	5947	3140	33	23	8	8	3	4	79	1929	1929	1
1	2	Barcelona	86	4262	2762	1581	573	608	5900	3114	25	25	12	12	4	6	83	1929	1929	1
2	3	Atletico Madrid	80	3442	2614	1241	598	775	4534	3309	10	8	16	9	7	6	56	1929	2002-03	1
3	4	Valencia	82	3386	2664	1187	616	861	4398	3469	6	6	10	11	10	7	50	1931-32	1987-88	1
4	5	Athletic Bilbao	86	3368	2762	1209	633	920	4631	3700	8	7	10	5	8	10	49	1929	1929	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
56	57	Xerez	1	34	38	8	10	20	38	66	0	0	0	0	0	0	0	2009-10	2009-10	20
57	58	Condal	1	22	30	7	8	15	37	57	0	0	0	0	0	0	0	1956-57	1956-57	16
58	59	Atletico Tetuan	1	19	30	7	5	18	51	85	0	0	0	0	0	0	0	1951-52	1951-52	16
59	60	Cultural Leonesa	1	14	30	5	4	21	34	65	0	0	0	0	0	0	0	1955-56	1955-56	15
60	61	Girona	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2017-18	2017-18	9

61 rows × 20 columns

2. Print all the teams which have started playing between 1930-1980. Use “Debut” column. ( Include year 1930 only)

# converting values of 'Debut' column into string datatype
laliga['Debut'] = laliga['Debut'].astype(str)

# getting team details to new dataframe 'Debut Year' based on Debut in between 1930 to 1980 (including 1930 but excluding 1980)
Debut_Year = laliga[laliga['Debut'].str[:4].between('1930','1980')]

# printing team name and debut year from 'Debut_Year' dataframe
Debut_Year[['Team','Debut']]

	Team	Debut
3	Valencia	1931-32
5	Sevilla	1934-35
8	Zaragoza	1939-40
9	Real Betis	1932-33
10	Deportivo La Coruna	1941-42
11	Celta Vigo	1939-40
12	Valladolid	1948-49
14	Sporting Gijon	1944-45
15	Osasuna	1935-36
16	Malaga	1949-50
17	Oviedo	1933-34
18	Mallorca	1960-61
19	Las Palmas	1951-52
21	Granada	1941-42
22	Rayo Vallecano	1977-78
23	Elche	1959-60
25	Hercules	1935-36
26	Tenerife	1961-62
27	Murcia	1940-41
28	Alaves	1930-31
29	Levante	1963-64
30	Salamanca	1974-75
31	Sabadell	1943-44
32	Cadiz	1977-78
34	Castellon	1941-42
37	Cordoba	1962-63
39	Recreativo	1978-79
40	Burgos CF	1971-72
41	Pontevedra	1963-64
46	Gimnastic	1947-48
49	Alcoyano	1945-46
50	Jaen	1953-54
52	AD Almeria	1979-80
54	Lleida	1950-51
57	Condal	1956-57
58	Atletico Tetuan	1951-52
59	Cultural Leonesa	1955-56

3. Print the list of teams which came Top 5 in terms of points.

# copying 'Team' and 'Points' column to new dataframe 'laliga_sort'
laliga_sort = laliga[['Team','Points']].copy()

# converting values of 'Points' column into int datatype
laliga_sort['Points'] = laliga_sort['Points'].astype(int)

# sorting the dataframe 'laliga_sort' based on 'Points' value
laliga_sort.sort_values(by='Points', ascending=False, inplace=True)

#printing head as top 5 teams in terms of points
laliga_sort.head(5)

	Team	Points
0	Real Madrid	4385
1	Barcelona	4262
2	Atletico Madrid	3442
3	Valencia	3386
4	Athletic Bilbao	3368

4. Write a function with the name “Goal_diff_count” which should return all the teams with their Goal Differences.

# converting values of 'GoalsFor' and 'GoalsAgainst' column into int datatype
laliga['GoalsFor'] = laliga['GoalsFor'].astype(int)
laliga['GoalsAgainst'] = laliga['GoalsAgainst'].astype(int)

# defining function 'Goal_diff_count()' as per the problem statement needs
def Goal_diff_count():   
     laliga['Goal_diff_count'] = laliga['GoalsFor']-laliga['GoalsAgainst']
     return laliga[['Team','Goal_diff_count']]

# calling 'Goal_diff_count()' function and storing it to 'Goal'
Goal = Goal_diff_count()

# sorting data and printing data from 'Goal'
Goal.sort_values(by = 'Goal_diff_count',ascending=False)

	Team	Goal_diff_count
0	Real Madrid	2807
1	Barcelona	2786
2	Atletico Madrid	1225
4	Athletic Bilbao	931
3	Valencia	929
...	...	...
27	Murcia	-385
19	Las Palmas	-399
14	Sporting Gijon	-399
12	Valladolid	-413
13	Racing Santander	-525

61 rows × 2 columns

# new column 'Goal_diff_count' added to dataframe so shape is changed from (61, 20)to (61, 21)
laliga.shape

(61, 21)

# conforming addition of new column to dataframe by printing head
laliga.head()

	Pos	Team	Seasons	Points	GamesPlayed	GamesWon	GamesDrawn	GamesLost	GoalsFor	GoalsAgainst	...	Runner-up	Third	Fourth	Fifth	Sixth	T	Debut	Since/LastApp	BestPosition	Goal_diff_count
0	1	Real Madrid	86	4385	2762	1647	552	563	5947	3140	...	23	8	8	3	4	79	1929	1929	1	2807
1	2	Barcelona	86	4262	2762	1581	573	608	5900	3114	...	25	12	12	4	6	83	1929	1929	1	2786
2	3	Atletico Madrid	80	3442	2614	1241	598	775	4534	3309	...	8	16	9	7	6	56	1929	2002-03	1	1225
3	4	Valencia	82	3386	2664	1187	616	861	4398	3469	...	6	10	11	10	7	50	1931-32	1987-88	1	929
4	5	Athletic Bilbao	86	3368	2762	1209	633	920	4631	3700	...	7	10	5	8	10	49	1929	1929	1	931

5 rows × 21 columns

5.Using the same function, find the team which has the maximum and minimum goal difference.

# printed first entry of sorted dataframe by calling 'Goal_diff_count()' function 
Goal_diff_count().head(1) # max goal difference

	Team	Goal_diff_count
0	Real Madrid	2807

# printed last entry of sorted dataframe by calling 'Goal_diff_count()' function 
Goal_diff_count().tail(1) # min goal difference

	Team	Goal_diff_count
60	Girona	0

6. Create a new column with the name “Winning Percent” and append it to the data set.

# converting values of 'GamesWon' and 'GamesPlayed' column into int datatype
laliga['GamesWon'] = laliga['GamesWon'].astype(int)
laliga['GamesPlayed'] = laliga['GamesPlayed'].astype(int)

# calculate winning percentage for each team and storing value to new column 'Win Per'
laliga['Win Per'] = (laliga['GamesWon']/laliga['GamesPlayed']) *100

# replacing Null values with 0%
laliga['Win Per'].fillna(0,inplace = True)

# printing team name and winning percentage for each team
laliga[['Team','Win Per']]

	Team	Win Per
0	Real Madrid	59.630702
1	Barcelona	57.241130
2	Atletico Madrid	47.475134
3	Valencia	44.557057
4	Athletic Bilbao	43.772629
...	...	...
56	Xerez	21.052632
57	Condal	23.333333
58	Atletico Tetuan	23.333333
59	Cultural Leonesa	16.666667
60	Girona	0.000000

61 rows × 2 columns

# new column 'Win  Per' added to dataframe so shape is changed from (61, 21) to (61, 22)
laliga.shape

(61, 22)

7. Print the top 5 teams which have the highest Winning percentage.

# printing top 5 teams with highest winning percentage
laliga[['Team','Win Per']].head(5)

	Team	Win Per
0	Real Madrid	59.630702
1	Barcelona	57.241130
2	Atletico Madrid	47.475134
3	Valencia	44.557057
4	Athletic Bilbao	43.772629

8. Group teams based on their “Best position” and print the sum of their points for all positions.

# converting values of 'Points' and 'BestPosition' column into int datatype
laliga['Points'] = laliga['Points'].astype(int)
laliga['BestPosition'] = laliga['BestPosition'].astype(int)

# grouping teams based on 'BeastPosition' column
group_Best = laliga[['Team','Points','BestPosition']].groupby('BestPosition')

# computing sum of grouped values on 'BestPosition' and print them
group_Best['Points'].sum()

BestPosition
1     27933
2      6904
3      5221
4      6563
5      1884
6      2113
7      1186
8      1134
9        96
10      450
11      445
12      511
14       71
15       14
16       81
17      266
19       81
20       34
Name: Points, dtype: int32

And, Project is over!!!

Completed by: Ganpat Patel Email: ganpat.patel.012@gmail.com