LFC Data Analysis: A Striking Trio¶

See Terry's blog LFC: A Striking Trio for a discussion of of the data generated by this analysis.

This notebook analyses Liverpool FC's goalscoring data from 1892-1893 to 2014-2015. In particular Liverpool's top scoring trio is identified and compared to Barcelona's best from 2014-2015. The analysis uses IPython Notebook, python, pandas and matplotlib to explore the data.

Notebook Change Log¶

In [1]:

%%html
<! left align the change log table in next cell >
<style>
table {float:left}
</style>

Date	Change Description
1st July 2015	Initial baseline

Set-up¶

Import the modules needed for the analysis.

In [2]:

import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sys 
from datetime import datetime
from __future__ import division

# enable inline plotting
%matplotlib inline

Print version numbers.

In [3]:

print 'python version: {}'.format(sys.version)
print 'pandas version: {}'.format(pd.__version__)
print 'matplotlib version: {}'.format(mpl.__version__)
print 'numpy version: {}'.format(np.__version__)

python version: 2.7.8 |Anaconda 2.2.0 (64-bit)| (default, Jul  2 2014, 15:12:11) [MSC v.1500 64 bit (AMD64)]
pandas version: 0.15.2
matplotlib version: 1.4.3
numpy version: 1.9.2

Load the LFC scorers data into a dataframe and munge¶

In [4]:

dflfc_scorers = pd.read_csv('data\lfchistory_goalscorers.csv', sep=';')
# sort by season, total goals, then league goals, etc
# same as on lfchistory.net season archive / goalscorers
dflfc_scorers = dflfc_scorers.sort(['season', 'total', 'league', 'facup', 'lccup', 'europe', 'other', 'player'], ascending=False)

In [5]:

# check sort order
dflfc_scorers[dflfc_scorers.season == '1983-1984'].head()

Out[5]:

	season	player	league	facup	lccup	europe	total
439	1983-1984	Ian Rush	32	2	8	5	47
440	1983-1984	Graeme Souness	7	0	5	0	12
442	1983-1984	Kenny Dalglish	7	0	2	3	12
441	1983-1984	Michael Robinson	6	1	3	2	12
443	1983-1984	Ronnie Whelan	4	0	3	2	9

Replace unwanted 'special' non-ascii characters

In [6]:

# for example, check the mapping for Jan Molby
dflfc_scorers[dflfc_scorers.player.str.startswith('Jan')].head(1)

Out[6]:

	season	player	league	facup	lccup	europe	other	total
305	1994-1995	Jan M�lby	2	0	0	0	0	2

In [7]:

# replace known non-ascii names using a mapping dictionary
name_mapper = {'Jan M\xf8lby': 'Jan Molby',
               'Emiliano Ins\xfaa': 'Emiliano Insua',
               'F\xe1bio Aur\xe9lio': 'Fabio Aurelio',
               '\xc1lvaro Arbeloa': 'Alvaro Arbeloa',
               'Djibril Ciss\xe9': 'Djibril Cisse',
               'Djimi Traor\xe9': 'Djimi Traore',
               '\xd8yvind Leonhardsen': 'Oyvind Leonhardsen',
               'Stig Inge Bj\xf8rnebye': 'Stig Inge Bjornebye',
               'Glenn Hys\xe9n': 'Glenn Hysen'
              }
dflfc_scorers['player'] = dflfc_scorers['player'].apply(lambda x: name_mapper[x] if x in name_mapper else x)

In [8]:

# for example, check the mapping for Jan Molby
dflfc_scorers[dflfc_scorers.player.str.startswith('Jan')].head()

Out[8]:

	season	player	league	facup	lccup	europe	total
305	1994-1995	Jan Molby	2	0	0	0	2
313	1993-1994	Jan Molby	2	0	1	0	3
330	1992-1993	Jan Molby	3	0	0	0	3
342	1991-1992	Jan Molby	3	1	0	1	5
358	1990-1991	Jan Molby	9	0	0	0	9

In [9]:

dflfc_scorers.head()

Out[9]:

	season	player	league	facup	lccup	europe	total
0	2014-2015	Steven Gerrard	9	2	0	2	13
1	2014-2015	Raheem Sterling	7	1	3	0	11
2	2014-2015	Philippe Coutinho	5	3	0	0	8
3	2014-2015	Jordan Henderson	6	0	0	1	7
4	2014-2015	Adam Lallana	5	1	0	0	6

In [10]:

dflfc_scorers.tail()

Out[10]:

	season	player	facup	other	total
1400	1892-1893	Jock Smith	0	5	5
1404	1892-1893	Hugh McQueen	1	3	4
1403	1892-1893	Jim McBride	0	4	4
1405	1892-1893	Joe McQue	0	2	2
1406	1892-1893	John McCartney	1	0	1

Analyse the data¶

Ask a question and find the answer.

Who are all time top goal scorers?

cross-check the answer with http://www.lfchistory.net/Stats/PlayerGoalscorers

In [11]:

dflfc_scorers[['player', 'total']].groupby('player').sum().sort('total', ascending=False).head(10)

Out[11]:

	total
player
Ian Rush	346
Roger Hunt	285
Gordon Hodgson	241
Billy Liddell	228
Steven Gerrard	186
Robbie Fowler	183
Kenny Dalglish	172
Michael Owen	158
Harry Chambers	151
Sam Raybould	130

Who scored the all time most goals scored in a season?

In [12]:

dflfc_scorers[['player', 'season', 'total']].groupby(['player', 'season']).sum().sort('total', ascending=False).head(10)

Out[12]:

		total
player	season
Ian Rush	1983-1984	47
Roger Hunt	1961-1962	42
Ian Rush	1986-1987	40
Roger Hunt	1964-1965	37
Gordon Hodgson	1930-1931	36
Robbie Fowler	1995-1996	36
John Evans	1954-1955	33
Fernando Torres	2007-2008	33
Ian Rush	1985-1986	33
Roger Hunt	1963-1964	33

Who are the top 10 all time most league goals scored in a season?

In [13]:

dflfc_scorers[['player', 'season', 'league']].groupby(['player', 'season']).sum().sort('league', ascending=False).head(10)

Out[13]:

		league
player	season
Roger Hunt	1961-1962	41
Gordon Hodgson	1930-1931	36
Ian Rush	1983-1984	32
Sam Raybould	1902-1903	31
Luis Suarez	2013-2014	31
Roger Hunt	1963-1964	31
Gordon Hodgson	1928-1929	30
Jack Parkinson	1909-1910	30
Billy Liddell	1954-1955	30
Ian Rush	1986-1987	30

What was most league goals in a season?

In [14]:

dflfc_scorers[['season', 'league']].groupby(['season']).sum().sort('league', ascending=False).head(1)

Out[14]:

	league
season
1895-1896	106

Create new dataframe of top level seasons¶

In [15]:

LANCS_YRS = ['1892-1893']
SECOND_DIV_YRS = ['1893-1894', '1895-1896', '1904-1905', '1961-1962', 
                  '1954-1955', '1955-1956', '1956-1957', '1957-1958', 
                  '1958-1959', '1959-1960', '1960-1961']
WAR_YRS = ['1945-1946'] # note that the other war years already excluded
NOT_TOP_LEVEL_YRS = LANCS_YRS + SECOND_DIV_YRS + WAR_YRS
dflfc_scorers_tl = dflfc_scorers[~dflfc_scorers.season.isin(NOT_TOP_LEVEL_YRS)].copy()

In [16]:

# show most league goals in a season in top level
# cross-check with http://en.wikipedia.org/wiki/List_of_Liverpool_F.C._records_and_statistics#Goalscorers
# expect 101 in 2013-14
dflfc_scorers_tl[['season', 'league']].groupby(['season']).sum().sort('league', ascending=False).head(1)

Out[16]:

	league
season
2013-2014	96

96 is correct as the dataframe does not include own goals - OG was 5 in 2013-14

In [17]:

# show highest goals at top level
dflfc_scorers_tl_sum = dflfc_scorers_tl.groupby('season').sum().sort('total', ascending=False)
dflfc_scorers_tl_sum.reset_index(inplace=True)
dflfc_scorers_tl_sum.head()

Out[17]:

	season	league	facup	lccup	europe	other	total
0	1985-1986	89	18	19	0	12	138
1	1981-1982	79	7	27	14	0	127
2	2000-2001	70	17	20	18	0	125
3	1982-1983	86	5	14	13	1	119
4	2007-2008	64	12	6	34	0	116

In [18]:

# show top individual scorer in a top level season
dflfc_scorers_tl.sort('total', ascending=False).head()

Out[18]:

	season	player	league	facup	lccup	europe	other	total
439	1983-1984	Ian Rush	32	2	8	5	0	47
401	1986-1987	Ian Rush	30	0	4	0	6	40
690	1964-1965	Roger Hunt	25	5	0	7	0	37
1002	1930-1931	Gordon Hodgson	36	0	0	0	0	36
286	1995-1996	Robbie Fowler	28	6	2	0	0	36

Take a quick look at the top scoring partnership in the league

In [19]:

# show best total for a striking partnerships in the league
dflfc_scorers_tl_top2_lg = dflfc_scorers_tl[['season', 'league']].groupby('season').head(2).groupby('season').sum()
# reset index and move season to column in dataframe
dflfc_scorers_tl_top2_lg.reset_index(inplace=True)
# show top
dflfc_scorers_tl_top2_lg.sort('league', ascending=False).head(10)

Out[19]:

	season	league
48	1963-1964	52
98	2013-2014	52
30	1930-1931	50
39	1946-1947	48
13	1909-1910	48
28	1928-1929	46
34	1934-1935	46
25	1925-1926	44
31	1931-1932	43
47	1962-1963	43

Note that 1963-64 and have 2013-14 have top scoring partnership.

In [20]:

TOP_PARTNERSHIPS = ['1963-1964', '2013-2014']
dflfc_scorers_tl[['season', 'player', 'league']][dflfc_scorers_tl.season.isin(TOP_PARTNERSHIPS)].groupby('season').head(2)

Out[20]:

	season	player	league
18	2013-2014	Luis Suarez	31
19	2013-2014	Daniel Sturridge	21
707	1963-1964	Roger Hunt	31
708	1963-1964	Ian St John	21

Remarkably Hunt and Suarez scored 31 and St John and Sturridge scored 21.

Let's now focus on the top scoring trio in the league¶

In [21]:

# create dataframe filtered for the league goals
dflfc_scorers_tl_lg = dflfc_scorers_tl[['season', 'player', 'league']]
dflfc_scorers_tl_lg.head()

Out[21]:

	season	player	league
0	2014-2015	Steven Gerrard	9
1	2014-2015	Raheem Sterling	7
2	2014-2015	Philippe Coutinho	5
3	2014-2015	Jordan Henderson	6
4	2014-2015	Adam Lallana	5

In [22]:

# show best total for 3 strikers working together
dflfc_scorers_tl_top3_lg = dflfc_scorers_tl_lg[['season', 'league']].groupby('season').head(3).groupby('season').sum()
# reset index and move season to column in dataframe
dflfc_scorers_tl_top3_lg.reset_index(inplace=True)
# show top
dflfc_scorers_tl_top3_lg.sort('league', ascending=False).head(10)

Out[22]:

	season	league
48	1963-1964	67
98	2013-2014	65
30	1930-1931	60
13	1909-1910	60
39	1946-1947	58
31	1931-1932	56
72	1987-1988	56
34	1934-1935	56
28	1928-1929	55
22	1922-1923	55

Now find the top3 scorers for these seasons, in order.

In [23]:

# capture top league seasons for top 3, in order
NUMBER_SEASONS = 10
top_seasons_lg = dflfc_scorers_tl_top3_lg.sort('league', ascending=False).head(NUMBER_SEASONS).season.values
top_seasons_lg

Out[23]:

array(['1963-1964', '2013-2014', '1930-1931', '1909-1910', '1946-1947',
       '1931-1932', '1987-1988', '1934-1935', '1928-1929', '1922-1923'], dtype=object)

In [24]:

# show top 3 scorers for top seasons
dflfc_scorers_tl_lg[dflfc_scorers_tl_lg.season.isin(top_seasons_lg)].groupby('season').head(3)

Out[24]:

	season	player	league
18	2013-2014	Luis Suarez	31
19	2013-2014	Daniel Sturridge	21
20	2013-2014	Steven Gerrard	13
391	1987-1988	John Aldridge	26
392	1987-1988	Peter Beardsley	15
393	1987-1988	John Barnes	15
707	1963-1964	Roger Hunt	31
708	1963-1964	Ian St John	21
709	1963-1964	Alf Arrowsmith	15
901	1946-1947	Jack Balmer	24
902	1946-1947	Albert Stubbins	24
903	1946-1947	Cyril Done	10
954	1934-1935	Gordon Hodgson	27
955	1934-1935	Vic Wright	19
956	1934-1935	Berry Nieuwenhuys	10
991	1931-1932	Gordon Hodgson	26
992	1931-1932	Gordon Gunson	17
993	1931-1932	Dave Wright	13
1002	1930-1931	Gordon Hodgson	36
1003	1930-1931	Jimmy Smith	14
1004	1930-1931	Archie McPherson	10
1028	1928-1929	Gordon Hodgson	30
1029	1928-1929	Dick Edmed	16
1031	1928-1929	Harry Race	9
1094	1922-1923	Harry Chambers	22
1095	1922-1923	Dick Forshaw	19
1096	1922-1923	Dick Johnson	14
1201	1909-1910	Jack Parkinson	30
1202	1909-1910	Jimmy Stewart	18
1203	1909-1910	Arthur Goddard	12

In [25]:

# check if any of 4ths are same as 3rds
import itertools
f = dflfc_scorers_tl_lg[dflfc_scorers_tl_lg.season.isin(top_seasons_lg)].groupby('season').head(4)
f = f.reset_index(drop=True)

# print 3rd and 4th and inspect visually
f.irow(list(itertools.chain.from_iterable((i-1, i) for i in range(3, len(f), 4))))

Out[25]:

	season	player	league
2	2013-2014	Steven Gerrard	13
3	2013-2014	Raheem Sterling	9
6	1987-1988	John Barnes	15
7	1987-1988	Steve McMahon	9
10	1963-1964	Alf Arrowsmith	15
11	1963-1964	Ian Callaghan	8
14	1946-1947	Cyril Done	10
15	1946-1947	Billy Liddell	7
18	1934-1935	Berry Nieuwenhuys	10
19	1934-1935	Alf Hanson	9
22	1931-1932	Dave Wright	13
23	1931-1932	Harold Barton	8
26	1930-1931	Archie McPherson	10
27	1930-1931	Dave Wright	6
30	1928-1929	Harry Race	9
31	1928-1929	Bob Clark	9
34	1922-1923	Dick Johnson	14
35	1922-1923	Donald Mackinlay	5
38	1909-1910	Arthur Goddard	12
39	1909-1910	Sam Bowyer	6

Note that in 1928-1929 both Harry Race and Bob Clark scored 9.

ok, back to the strking trio - need to get these in order

In [26]:

# create dataframe of top 3 league scorers
dflfc_trio = dflfc_scorers_tl_lg[dflfc_scorers_tl_lg.season.isin(top_seasons_lg)].groupby('season').head(3)
dflfc_trio.head(6)

Out[26]:

	season	player	league
18	2013-2014	Luis Suarez	31
19	2013-2014	Daniel Sturridge	21
20	2013-2014	Steven Gerrard	13
391	1987-1988	John Aldridge	26
392	1987-1988	Peter Beardsley	15
393	1987-1988	John Barnes	15

In [27]:

# create custom dict with key of seasons and value of order (0 is first)
custom_dict = {s:idx for idx, s in enumerate(top_seasons_lg)} 
custom_dict

Out[27]:

{'1909-1910': 3,
 '1922-1923': 9,
 '1928-1929': 8,
 '1930-1931': 2,
 '1931-1932': 5,
 '1934-1935': 7,
 '1946-1947': 4,
 '1963-1964': 0,
 '1987-1988': 6,
 '2013-2014': 1}

In [28]:

# now add a column with the rank for each season using the custom dict
dflfc_trio['top_rank'] = dflfc_trio['season'].map(custom_dict)
dflfc_trio.head()

Out[28]:

	season	player	league	top_rank
18	2013-2014	Luis Suarez	31	1
19	2013-2014	Daniel Sturridge	21	1
20	2013-2014	Steven Gerrard	13	1
391	1987-1988	John Aldridge	26	6
392	1987-1988	Peter Beardsley	15	6

In [29]:

# now show the striking trios in order, highest first
dflfc_trio.sort(['top_rank', 'league'], ascending=[True, False], inplace=True)
dflfc_trio.drop('top_rank', axis=1, inplace=True)
dflfc_trio.head(6)

Out[29]:

	season	player	league
707	1963-1964	Roger Hunt	31
708	1963-1964	Ian St John	21
709	1963-1964	Alf Arrowsmith	15
18	2013-2014	Luis Suarez	31
19	2013-2014	Daniel Sturridge	21
20	2013-2014	Steven Gerrard	13

In [30]:

# print the list, in order
this_season = None
for season, player, league in dflfc_trio.values:
    if this_season != season:
        print '\n'
        this_season = season
    print season, player, league


1963-1964 Roger Hunt 31
1963-1964 Ian St John 21
1963-1964 Alf Arrowsmith 15


2013-2014 Luis Suarez 31
2013-2014 Daniel Sturridge 21
2013-2014 Steven Gerrard 13


1930-1931 Gordon Hodgson 36
1930-1931 Jimmy Smith 14
1930-1931 Archie McPherson 10


1909-1910 Jack Parkinson 30
1909-1910 Jimmy Stewart 18
1909-1910 Arthur Goddard 12


1946-1947 Jack Balmer 24
1946-1947 Albert Stubbins 24
1946-1947 Cyril Done 10


1931-1932 Gordon Hodgson 26
1931-1932 Gordon Gunson 17
1931-1932 Dave Wright 13


1987-1988 John Aldridge 26
1987-1988 Peter Beardsley 15
1987-1988 John Barnes 15


1934-1935 Gordon Hodgson 27
1934-1935 Vic Wright 19
1934-1935 Berry Nieuwenhuys 10


1928-1929 Gordon Hodgson 30
1928-1929 Dick Edmed 16
1928-1929 Harry Race 9


1922-1923 Harry Chambers 22
1922-1923 Dick Forshaw 19
1922-1923 Dick Johnson 14

In [31]:

# pretty print with single row per season
# and create a new dataframe to hold this for good measure
df_top3_sum = pd.DataFrame(columns=['Season', 'Goals', 'Goalscorers'])
for idx, season in enumerate(dflfc_trio.season.unique(), 1):
    #print season
    scorers = []
    league_tot = 0
    for player, league in dflfc_trio[dflfc_trio.season == season][['player', 'league']].values:
        league_tot += int(league)
        scorer = '{} ({})'.format(player, league)
        #print scorer
        scorers.append(scorer)
    print season, league_tot, ', '.join(scorers)
    df_top3_sum.loc[idx] = (season, league_tot, ', '.join(scorers))

1963-1964 67 Roger Hunt (31), Ian St John (21), Alf Arrowsmith (15)
2013-2014 65 Luis Suarez (31), Daniel Sturridge (21), Steven Gerrard (13)
1930-1931 60 Gordon Hodgson (36), Jimmy Smith (14), Archie McPherson (10)
1909-1910 60 Jack Parkinson (30), Jimmy Stewart (18), Arthur Goddard (12)
1946-1947 58 Jack Balmer (24), Albert Stubbins (24), Cyril Done (10)
1931-1932 56 Gordon Hodgson (26), Gordon Gunson (17), Dave Wright (13)
1987-1988 56 John Aldridge (26), Peter Beardsley (15), John Barnes (15)
1934-1935 56 Gordon Hodgson (27), Vic Wright (19), Berry Nieuwenhuys (10)
1928-1929 55 Gordon Hodgson (30), Dick Edmed (16), Harry Race (9)
1922-1923 55 Harry Chambers (22), Dick Forshaw (19), Dick Johnson (14)

In [32]:

# set pandas option that allows all Goalscorers to be displayed
# this avoids the default curtailing of long rows with ...
pd.set_option('display.max_colwidth', -1)
df_top3_sum

Out[32]:

	Season	Goals	Goalscorers
1	1963-1964	67	Roger Hunt (31), Ian St John (21), Alf Arrowsmith (15)
2	2013-2014	65	Luis Suarez (31), Daniel Sturridge (21), Steven Gerrard (13)
3	1930-1931	60	Gordon Hodgson (36), Jimmy Smith (14), Archie McPherson (10)
4	1909-1910	60	Jack Parkinson (30), Jimmy Stewart (18), Arthur Goddard (12)
5	1946-1947	58	Jack Balmer (24), Albert Stubbins (24), Cyril Done (10)
6	1931-1932	56	Gordon Hodgson (26), Gordon Gunson (17), Dave Wright (13)
7	1987-1988	56	John Aldridge (26), Peter Beardsley (15), John Barnes (15)
8	1934-1935	56	Gordon Hodgson (27), Vic Wright (19), Berry Nieuwenhuys (10)
9	1928-1929	55	Gordon Hodgson (30), Dick Edmed (16), Harry Race (9)
10	1922-1923	55	Harry Chambers (22), Dick Forshaw (19), Dick Johnson (14)

In [33]:

# show top 3 trios
df_top3_sum.head(3)

Out[33]:

	Season	Goals	Goalscorers
1	1963-1964	67	Roger Hunt (31), Ian St John (21), Alf Arrowsmith (15)
2	2013-2014	65	Luis Suarez (31), Daniel Sturridge (21), Steven Gerrard (13)
3	1930-1931	60	Gordon Hodgson (36), Jimmy Smith (14), Archie McPherson (10)

Check to see if there is a correlation between top trios and league position¶

Load the league data¶

In [34]:

dflfc_league = pd.read_csv('data\lfchistory_league.csv')

In [35]:

dflfc_league.tail()

Out[35]:

	Season	League	Pos	PLD	HW	HD	HL	HF	HA	AW	AD	AL	AF	AA	PTS	GF	GA	GD
107	2010-2011	Premier League	6	38	12	4	3	37	14	5	3	11	22	30	58	59	44	15
108	2011-2012	Premier League	8	38	6	9	4	24	16	8	1	10	23	24	52	47	40	7
109	2012-2013	Premier League	7	38	9	6	4	33	16	7	7	5	38	27	61	71	43	28
110	2013-2014	Premier League	2	38	16	1	2	53	18	10	5	4	48	32	84	101	50	51
111	2014-2015	Premier League	6	38	10	5	4	30	20	8	3	8	22	28	62	52	48	4

Create new dataframe with league position and key goal data

In [36]:

dflfc_league_pos = dflfc_league[['Season', 'Pos', 'GF', 'GA', 'GD']].copy()
dflfc_league_pos.rename(columns={'Season': 'season', 'Pos': 'pos'}, inplace=True)
dflfc_league_pos.tail()

Out[36]:

	season	pos	GF	GA	GD
107	2010-2011	6	59	44	15
108	2011-2012	8	47	40	7
109	2012-2013	7	71	43	28
110	2013-2014	2	101	50	51
111	2014-2015	6	52	48	4

Now check league position of the top3s

In [37]:

dflfc_scorers_tl_top3_lg.head()

Out[37]:

	season	league
0	1894-1895	34
1	1896-1897	25
2	1897-1898	22
3	1898-1899	28
4	1899-1900	26

In [38]:

dflfc_scorers_tl_top3_lg_pos = dflfc_scorers_tl_top3_lg.merge(dflfc_league_pos)
dflfc_scorers_tl_top3_lg_pos.sort('league', ascending=False).head(10)

Out[38]:

	season	league	pos	GF	GA	GD
48	1963-1964	67	1	92	45	47
98	2013-2014	65	2	101	50	51
30	1930-1931	60	9	86	85	1
13	1909-1910	60	2	78	57	21
39	1946-1947	58	1	84	52	32
31	1931-1932	56	10	81	93	-12
72	1987-1988	56	1	87	24	63
34	1934-1935	56	7	85	88	-3
28	1928-1929	55	5	90	64	26
22	1922-1923	55	1	70	31	39

In [39]:

dfp = dflfc_scorers_tl_top3_lg_pos.sort('league', ascending=False).head(10)
t = dfp.pos[dfp.pos == 1].count()
print 'total league wins in top 10 of top3s is: {}'.format(t)

total league wins in top 10 of top3s is: 4

6 out of 10 of seasons with top3 scores did not result in a title.

In [ ]:

Back to the top trios, let's now plot the data.

Plot The Top Trios¶

In [40]:

print len(dflfc_scorers_tl_top3_lg)
dflfc_scorers_tl_top3_lg.head()

Out[40]:

	season	league
0	1894-1895	34
1	1896-1897	25
2	1897-1898	22
3	1898-1899	28
4	1899-1900	26

In [41]:

# create a list of missing years
START_YR = 1890
END_YR = 2015
all_years = ['{}-{}'.format(i, i+1) for i in range(START_YR, END_YR)]
years_in_df = dflfc_scorers_tl_top3_lg.season.unique()
missing_years = [s for s in all_years if s not in years_in_df]
print 'there are {} missing years, here are first 5: {}'.format(len(missing_years), missing_years[0:5])

there are 25 missing years, here are first 5: ['1890-1891', '1891-1892', '1892-1893', '1893-1894', '1895-1896']

In [42]:

# add missing years to dataframe, sort and reset the index
dflfc_scorers_tl_top3_lg_full = dflfc_scorers_tl_top3_lg.copy()
for s in missing_years:
    dflfc_scorers_tl_top3_lg_full.loc[len(dflfc_scorers_tl_top3_lg_full)]=(s, np.NaN)
dflfc_scorers_tl_top3_lg_full = dflfc_scorers_tl_top3_lg_full.sort('season')
dflfc_scorers_tl_top3_lg_full.reset_index(drop=True, inplace=True)
print len(dflfc_scorers_tl_top3_lg_full)
dflfc_scorers_tl_top3_lg_full.head()

Out[42]:

	season	league
0	1890-1891	NaN
1	1891-1892	NaN
2	1892-1893	NaN
3	1893-1894	NaN
4	1894-1895	34

In [43]:

top_seasons_lg

Out[43]:

array(['1963-1964', '2013-2014', '1930-1931', '1909-1910', '1946-1947',
       '1931-1932', '1987-1988', '1934-1935', '1928-1929', '1922-1923'], dtype=object)

In [44]:

# The aim is to highlight the top 10 trios on the plot, so these need their own column.

# create series for top 10 seasons containing the top3 scorers
top3_top10 = dflfc_scorers_tl_top3_lg_full.apply(lambda row: row.league 
                                                             if row.season in top_seasons_lg else np.NaN, axis=1)

# create series for the other seasons, the ones that don't containing the top 10 top3 scorers
top3_other = dflfc_scorers_tl_top3_lg_full.apply(lambda row: np.NaN
                                                             if row.season in top_seasons_lg else row.league, axis=1)

# add these series as columns to the dataframe
dflfc_scorers_tl_top3_lg_full['top3_top10'] = top3_top10
dflfc_scorers_tl_top3_lg_full['top3_other'] = top3_other
dflfc_scorers_tl_top3_lg_full.tail()

Out[44]:

	season	league	top3_top10	top3_other
120	2010-2011	32	NaN	32
121	2011-2012	22	NaN	22
122	2012-2013	42	NaN	42
123	2013-2014	65	65	NaN
124	2014-2015	21	NaN	21

In [73]:

# And now plot using different shapes for the top3_top10 and top3_other columns 
DF = dflfc_scorers_tl_top3_lg_full
FIG_SIZE = (9, 6)

fig = plt.figure()

tot_yrs = len(DF)
tot_goals = int(DF.top3_top10.max())
XTICKS = range(0, tot_yrs+10, 10)
YTICKS = range(0, tot_goals+30, 10)

ax = DF.plot(style='r.', figsize=FIG_SIZE, x='season', y='top3_other', 
             legend=False, rot='vertical', xticks=XTICKS, yticks=YTICKS)
DF.plot(ax=ax, style='ro', figsize=FIG_SIZE, x='season', y='top3_top10', 
        legend=False, rot='vertical', xticks=XTICKS, yticks=YTICKS)

ax.set_ylabel('total league goals by striking trio')
ax.set_xlabel('top level season')
ax.set_title('total league goals by LFC striking trio in top level season')
ax.text(1, 1, 'prepared by: @terry8dolan')

fig = plt.gcf() # save current figure
plt.show()
fig.savefig('SeasonvsTrioGoals.png', bbox_inches='tight')

<matplotlib.figure.Figure at 0xea8dfd0>

Compare The Striking Trios: LFC (1963-64) with Barcelona (2014-15)¶

Create a new dataframe for the Barca trio

In [46]:

# create dictionary with Barca stats for 2014-15
# ref: https://en.wikipedia.org/wiki/2014%E2%80%9315_FC_Barcelona_season
barca_201415 = {'season': ['2014-2015', '2014-2015', '2014-2015'],
                'team': ['FCB', 'FCB', 'FCB'], 
                'player': ['Messi', 'Neymar', 'Suarez'], 
                'appearance': [38, 33, 26], 
                'league': [43, 22, 16]}

In [47]:

# create a dataframe from the dict
dfb_trio = pd.DataFrame(data=barca_201415, columns=['season', 'team', 'player', 'appearance', 'league'])
dfb_trio = dfb_trio.set_index('season')
dfb_trio['GPA'] = (dfb_trio.league/dfb_trio.appearance).round(2)
dfb_trio['APG'] = (dfb_trio.appearance/dfb_trio.league).round(2)
dfb_trio.head()

Out[47]:

	team	player	appearance	league	GPA	APG
season
2014-2015	FCB	Messi	38	43	1.13	0.88
2014-2015	FCB	Neymar	33	22	0.67	1.50
2014-2015	FCB	Suarez	26	16	0.62	1.62

In [48]:

dfb_trio.league.sum()

Out[48]:

In [49]:

dfb_trio.plot(kind='bar', x='player', y=['appearance', 'league'])

Out[49]:

<matplotlib.axes._subplots.AxesSubplot at 0xd021358>

Create a new dataframe for the LFC trio

In [50]:

# create dictionary with LFC stats for 1963-64
lfc_196364 = {'season': ['1963-1964', '1963-1964', '1963-1964'],
                'team': ['LFC', 'LFC', 'LFC'], 
                'player': ['Hunt', 'St John', 'Arrowsmith'], 
                'appearance': [41, 40, 20], 
                'league': [31, 21, 15]}

In [51]:

# create a dataframe from the dict
dfl_trio = pd.DataFrame(data=lfc_196364, columns=['season', 'team', 'player', 'appearance', 'league'])
dfl_trio = dfl_trio.set_index('season')
dfl_trio['GPA'] = (dfl_trio.league/dfl_trio.appearance).round(2)
dfl_trio['APG'] = (dfl_trio.appearance/dfl_trio.league).round(2)
dfl_trio.head()

Out[51]:

	team	player	appearance	league	GPA	APG
season
1963-1964	LFC	Hunt	41	31	0.76	1.32
1963-1964	LFC	St John	40	21	0.52	1.90
1963-1964	LFC	Arrowsmith	20	15	0.75	1.33

In [52]:

dfl_trio.league.sum()

Out[52]:

In [53]:

dfl_trio.plot(kind='bar', x='player', y=['appearance', 'league'], )

Out[53]:

<matplotlib.axes._subplots.AxesSubplot at 0xdd3a358>

Create a new combined dataframe with LFC and Barca data

In [54]:

df_trio = pd.DataFrame()
df_trio = pd.concat([dfl_trio, dfb_trio])

In [55]:

df_trio

Out[55]:

	team	player	appearance	league	GPA	APG
season
1963-1964	LFC	Hunt	41	31	0.76	1.32
1963-1964	LFC	St John	40	21	0.52	1.90
1963-1964	LFC	Arrowsmith	20	15	0.75	1.33
2014-2015	FCB	Messi	38	43	1.13	0.88
2014-2015	FCB	Neymar	33	22	0.67	1.50
2014-2015	FCB	Suarez	26	16	0.62	1.62

In [56]:

df_trio.sort('APG')

Out[56]:

	team	player	appearance	league	GPA	APG
season
2014-2015	FCB	Messi	38	43	1.13	0.88
1963-1964	LFC	Hunt	41	31	0.76	1.32
1963-1964	LFC	Arrowsmith	20	15	0.75	1.33
2014-2015	FCB	Neymar	33	22	0.67	1.50
2014-2015	FCB	Suarez	26	16	0.62	1.62
1963-1964	LFC	St John	40	21	0.52	1.90

In [57]:

df_trio.plot(kind='bar', x='player', y=['appearance', 'league'])

Out[57]:

<matplotlib.axes._subplots.AxesSubplot at 0xdc7ac88>

Plot goals

In [116]:

FIG_SIZE = (9, 6)
fig = plt.figure()

# sort the dataframe by league goals
df_trio_lg_sorted = df_trio.sort('league', ascending=False)

# produce list of colour based on team
team_colours = ['r' if team is 'LFC' else 'b' for team in df_trio_lg_sorted.team.values]

# plot dataframe
ax = df_trio_lg_sorted.plot(kind='bar', x='player', y='league', 
                            legend=False, color=['b', 'r', 'b', 'r', 'b', 'r'],
                            title='Total League Goals for Top 3 Strikers: Barca 2014-15 and LFC 1963-64',
                            figsize=FIG_SIZE, ylim=(0, 50))

# set the axis labels
ax.set_xlabel('Player')
ax.set_ylabel('Total League Goals')

# create fake legend
l1 = plt.Line2D([], [], linewidth=10, color='b') 
l2 = plt.Line2D([], [], linewidth=10, color='r')
labels = ['FCB 2014-2015', 'LFC 1963-1964']
ax.legend([l1, l2], labels)
ax.text(-.4, 48, 'prepared by: @terry8dolan')

fig = plt.gcf() # save current figure
plt.show()
fig.savefig('PlayervsGoals.png', bbox_inches='tight')

<matplotlib.figure.Figure at 0xf007a58>

Plot Goals per Game

In [125]:

FIG_SIZE = (9, 6)
fig = plt.figure()

# sort the dataframe by GPA
df_trio_GPA_sorted = df_trio.sort('GPA', ascending=False)

# produce list of colour based on team
team_colours = ['r' if team is 'LFC' else 'b' for team in df_trio_GPA_sorted.team.values]

# plot the dataframe
ax = df_trio_GPA_sorted.plot(kind='bar', x='player', y='GPA', 
                             legend=False, color=team_colours,
                             title='League Goals per Game for Top 3 strikers: Barca 2014-15 and LFC 1963-64',
                             figsize=FIG_SIZE, ylim=(0, 1.4))

# set the axis labels
ax.set_xlabel('Player')
ax.set_ylabel('League Goals Per Game')

# create fake legend
l1 = plt.Line2D([], [], linewidth=10, color='b') 
l2 = plt.Line2D([], [], linewidth=10, color='r')
labels = ['FCB 2014-2015', 'LFC 1963-1964']
ax.legend([l1, l2], labels)
ax.text(-.4, 1.35, 'prepared by: @terry8dolan')

# save current figure and plot
fig = plt.gcf() 
plt.show()
fig.savefig('PlayervsGPG.png', bbox_inches='tight')

<matplotlib.figure.Figure at 0x10b614a8>

In [ ]:

Find key data for title winning years¶

In [60]:

WINNERS = ['1900-1901', '1905-1906', '1921-1922', '1922-1923', '1946-1947', '1963-1964', '1965-1966', '1972-1973', '1975-1976', 
           '1976-1977', '1978-1979', '1979-1980', '1981-1982', '1982-1983', '1983-1984', '1985-1986', '1987-1988', '1989-1990']
dfw = dflfc_scorers_tl_lg[dflfc_scorers_tl_lg.season.isin(WINNERS)].sort(['season', 'league'], ascending=False)

In [61]:

# check all 18 title winning seasons have matched
len(dfw.season.unique())

Out[61]:

In [62]:

# print average number of goals by striker in title winning season
dfw_1 = dfw[['season', 'league']].groupby('season').head(1).groupby('season').sum()
round(dfw_1.sort('league', ascending=False)['league'].mean())

Out[62]:

22.0

In [63]:

# print average number of goals by partner in title winning season
dfw_2 = dfw[['season', 'league']].groupby('season').head(2).groupby('season').nth(1)
round(dfw_2.sort('league', ascending=False)['league'].mean())

Out[63]:

15.0

In [64]:

# print average number of goals by partnership in title winning season
dfw_p = dfw[['season', 'league']].groupby('season').head(2).groupby('season').sum()
rp = round(dfw_p.sort('league', ascending=False)['league'].mean())
print "Liverpool's history says that to win the league we need a striker partnership that will score {} goals on average.".format(rp)

Liverpool's history says that to win the league we need a striker partnership that will score 37.0 goals on average.