I’ve always wanted to try the makeover monday since I learned about the project. But busy being busy, you know.
Last week, however, two things happened:
So it was decided - I’m creating a visualisation.
# Imports
import pandas as pd
import plotly.express as px
%matplotlib inline
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
import plotly.io as pio
pio.renderers.default = "notebook"
import warnings
warnings.filterwarnings(action='ignore')
pd.set_option('display.max_columns', None)
df = pd.read_excel('Arsenal Player Stats 2018-19.xlsx')print(f'columns: {df.columns}')columns: Index(['Rank', 'Player', 'Nationality', 'Metric', 'Stat'], dtype='object')df.head()| Rank | Player | Nationality | Metric | Stat | |
|---|---|---|---|---|---|
| 0 | 1 | Pierre-Emerick Aubameyang | Gabon | Appearances | 36 |
| 1 | 2 | Alex Iwobi | Nigeria | Appearances | 35 |
| 2 | 2 | Alexandre Lacazette | France | Appearances | 35 |
| 3 | 4 | Lucas Torreira | Uruguay | Appearances | 34 |
| 4 | 5 | Matteo Guendouzi | France | Appearances | 33 |
You can see that data is stored as rows per player/metric pair. Let’s pivot to get the player’s statistics in each row.
training = df.pivot(index='Player', columns='Metric', values='Stat')
training.head()| Metric | Appearances | Assists | Big Chances Missed | Clearances | Clearances Off Line | Dispossessed | Fouls | Goals | High Claim | Hit Woodwork | Minutes Played | Offsides | Own Goal | Passes | Punches | Red Cards | Saves | Shots | Tackles | Touches | Yellow Cards |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Player | |||||||||||||||||||||
| Aaron Ramsey | 28 | 6 | 2 | 3 | 0 | 27 | 21 | 4 | 0 | 0 | 1328 | 4 | 0 | 773 | 0 | 0 | 0 | 33 | 34 | 1029 | 0 |
| Ainsley Maitland-Niles | 16 | 1 | 0 | 11 | 1 | 17 | 8 | 1 | 0 | 0 | 986 | 1 | 0 | 451 | 0 | 1 | 0 | 5 | 33 | 782 | 1 |
| Alex Iwobi | 35 | 6 | 3 | 10 | 0 | 56 | 8 | 3 | 0 | 0 | 1972 | 2 | 0 | 951 | 0 | 0 | 0 | 35 | 28 | 1415 | 0 |
| Alexandre Lacazette | 35 | 8 | 13 | 29 | 0 | 61 | 51 | 13 | 0 | 1 | 2505 | 24 | 0 | 771 | 0 | 0 | 0 | 81 | 35 | 1313 | 2 |
| Bernd Leno | 32 | 0 | 0 | 32 | 0 | 1 | 0 | 0 | 10 | 0 | 2835 | 0 | 0 | 922 | 16 | 0 | 105 | 0 | 0 | 1276 | 0 |
I wanted to cluster players together to see if there are any patterns and groups. Then realised - it doesn’t make a good visualisation: hey, look, these players have more Saves - they are probably goalkeepers. So I decided to add player field position to the dataset.
With this data, let’s look at performance by position. Maybe a radar chart can work for such kind of comparison?
There is a limited amount of axes on a graph for users to still make sense of it. How to choose metrics to display? There are obvious choices of course: saves for goalkeepers, tackles for defenders, goals for forwards. But how about common attributes such as passes or touches?
To identify potentially interesting data points, I started with a correlation plot. I’m looking for interesting patterns, i.e. metrics that are not linearly correlated with the minutes played.
position_lookup = {k:'' for k in training.index.tolist()}
for gk in ['Bernd Leno', 'Petr Cech']:
position_lookup[gk] = 'GK'
for df in [
'Carl Jenkinson','Héctor Bellerín','Laurent Koscielny','Rob Holding','Sead Kolasinac'
,'Shkodran Mustafi','Sokratis','Stephan Lichtsteiner'
]:
position_lookup[df] = 'DF'
for mf in [
'Aaron Ramsey','Ainsley Maitland-Niles','Denis Suárez','Granit Xhaka','Henrikh Mkhitaryan'
,'Joe Willock','Konstantinos Mavropanos','Lucas Torreira', 'Matteo Guendouzi','Mesut Özil'
,'Mohamed Elneny','Nacho Monreal',
]:
position_lookup[mf] = 'MF'
for fw in [
'Alex Iwobi','Alexandre Lacazette','Bukayo Saka','Danny Welbeck','Eddie Nketiah','Pierre-Emerick Aubameyang'
]:
position_lookup[fw] = 'FW'
training['position'] = training.index.map(position_lookup)data_per_position = training.groupby('position').sum()
data_per_position| Metric | Appearances | Assists | Big Chances Missed | Clearances | Clearances Off Line | Dispossessed | Fouls | Goals | High Claim | Hit Woodwork | Minutes Played | Offsides | Own Goal | Passes | Punches | Red Cards | Saves | Shots | Tackles | Touches | Yellow Cards |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| position | |||||||||||||||||||||
| DF | 143 | 12 | 2 | 459 | 1 | 64 | 143 | 6 | 0 | 0 | 11479 | 17 | 1 | 6968 | 0 | 0 | 0 | 64 | 201 | 9185 | 34 |
| FW | 120 | 20 | 39 | 59 | 0 | 156 | 76 | 40 | 0 | 5 | 7425 | 51 | 0 | 2488 | 0 | 0 | 0 | 217 | 88 | 3984 | 2 |
| GK | 39 | 0 | 0 | 39 | 0 | 1 | 0 | 0 | 16 | 0 | 3420 | 0 | 0 | 1146 | 18 | 0 | 133 | 0 | 1 | 1591 | 0 |
| MF | 229 | 20 | 9 | 205 | 2 | 188 | 193 | 23 | 0 | 3 | 15241 | 21 | 0 | 10203 | 0 | 2 | 0 | 186 | 319 | 13366 | 36 |
# Common metrics
training['passes_per_min_played'] = training['Passes'] / training['Minutes Played']
training['passes_per_appearance'] = training['Passes'] / training['Appearances']
training['min_played_per_apperance'] = training['Minutes Played'] / training['Appearances']
training['touches_per_appearance'] = training['Touches'] / training['Appearances']
training['touches_per_min_played'] = training['Touches'] / training['Minutes Played']
# GK metrics
training['high_claim_per_min_played'] = training['High Claim'] / training['Minutes Played']
training['punches_per_min_played'] = training['Punches'] / training['Minutes Played']
training['saves_per_min_played'] = training['Saves'] / training['Minutes Played']
training['high_claim_per_appearance'] = training['High Claim'] / training['Appearances']
training['punches_per_appearance'] = training['Punches'] / training['Appearances']
training['saves_per_appearance'] = training['Saves'] / training['Appearances']
# DF metrics
training['clearances_per_min_played'] = training['Clearances'] / training['Minutes Played']
training['clearances_per_appearance'] = training['Clearances'] / training['Appearances']
training['clearances_per_touch'] = training['Clearances'] / training['Touches']
training['fouls_per_appearance'] = training['Fouls'] / training['Appearances']
training['fouls_per_clearance'] = training['Fouls'] / training['Clearances']
training['fouls_per_touch'] = training['Fouls'] / training['Touches']
training['yc_per_min_played'] = training['Yellow Cards'] / training['Minutes Played']
training['yc_per_tackle'] = training['Yellow Cards'] / training['Tackles']
training['yc_per_foul'] = training['Yellow Cards'] / training['Fouls']
training['tackles_per_appearance'] = training['Tackles'] / training['Appearances']
training['tackles_per_min_played'] = training['Tackles'] / training['Minutes Played']
# Midfield metrics
training['assists_per_appearance'] = training['Assists'] / training['Appearances']
training['assists_per_min_played'] = training['Assists'] / training['Minutes Played']
training['assists_per_pass'] = training['Assists'] / training['Passes']
training['assists_per_touch'] = training['Assists'] / training['Touches']
training['dispossessed_per_appearance'] = training['Dispossessed'] / training['Appearances']
training['dispossessed_per_min_played'] = training['Dispossessed'] / training['Minutes Played']
training['dispossessed_per_pass'] = training['Dispossessed'] / training['Passes']
training['dispossessed_per_touch'] = training['Dispossessed'] / training['Touches']
training['fouls_per_appearance'] = training['Fouls'] / training['Appearances']
training['fouls_per_clearance'] = training['Fouls'] / training['Clearances']
training['fouls_per_min_played'] = training['Fouls'] / training['Minutes Played']
training['fouls_per_touch'] = training['Fouls'] / training['Touches']
training['goals_per_appearance'] = training['Goals'] / training['Appearances']
training['goals_per_min_played'] = training['Goals'] / training['Minutes Played']
training['goals_per_shot'] = training['Goals'] / training['Shots']
training['goals_per_touch'] = training['Goals'] / training['Touches']
training['passes_per_appearance'] = training['Passes'] / training['Appearances']
training['passes_per_min_played'] = training['Passes'] / training['Minutes Played']
training['passes_per_touches'] = training['Passes'] / training['Touches']
training['shots_per_appearance'] = training['Shots'] / training['Appearances']
training['shots_per_min_played'] = training['Shots'] / training['Minutes Played']
training['shots_per_touch'] = training['Shots'] / training['Touches']
training['tackles_per_appearance'] = training['Tackles'] / training['Appearances']
training['tackles_per_min_played'] = training['Tackles'] / training['Minutes Played']
training['tackles_per_touch'] = training['Tackles'] / training['Touches']
training['tackles_per_clearance'] = training['Tackles'] / training['Clearances']
training['touches_per_apperance'] = training['Touches'] / training['Appearances']
training['touches_per_min_played'] = training['Touches'] / training['Minutes Played']
# FW metrics
training['assists_per_min_played'] = training['Assists'] / training['Minutes Played']
training['missed_chances_per_shot'] = training['Big Chances Missed'] / training['Shots']
training['missed_chances_per_min_played'] = training['Big Chances Missed'] / training['Minutes Played']
training['goals_per_min_played'] = training['Goals'] / training['Minutes Played']
training['offsides_per_min_played'] = training['Offsides'] / training['Minutes Played']
training['shots_per_min_played'] = training['Shots']/ training['Minutes Played']
training['assists_per_appearance'] = training['Assists'] / training['Appearances']
training['missed_chances_per_appearance'] = training['Big Chances Missed'] / training['Appearances']
training['goals_per_appearance'] = training['Goals'] / training['Appearances']
training['offsides_per_appearance'] = training['Offsides'] / training['Appearances']
training['shots_per_appearance'] = training['Shots']/ training['Appearances']ax = scatter_matrix(
training.query("position == 'DF'")[
[
'Clearances',
'Clearances Off Line',
'Fouls',
'Tackles',
'Red Cards',
'Yellow Cards',
'Touches',
'Minutes Played']
],
figsize=(20,12))
There are some:
What would be the best way to put it on the graph?
If we want to create player profiles, we cannot just say Cech made X saves while Leno made Y. We need to normalise it. The most precise would be to do it by minutes played. So tackles per minute played or passes per minute played.
Having these numbers, we can already iterate to find the best representation.
The good part here - the real values are displayed for the KPIs. We can see Mustafi is completing around 0.06 clearances per minute played, more than anyone else. The problem - it’s hard to have all the numbers at the same scale, so some metrics skew the scale towards one direction and so it becomes hard to see values for smaller values.
Mustafi - first in memes but also completing around 0.06 clearances and 0.025 tackles per minute, more than any other defender in a team.
We can try to play with axis scales to get a more equal distribution of numbers. As you can see now graphs are more equally distributed, but it’s a mess in readability.
We can divide values by the maximum for the metric at this position. So all players will get a percentage from the maximum for the position in a team. As for the example above: Mustafi will get 1 for the clearances, Sokratis around 0.8 from that and so on.
Personally, I like the last approach most, even though with it we are losing the interpretability.
gk = training.query("position == 'GK'")[
['high_claim_per_appearance', 'punches_per_appearance', 'saves_per_appearance'
,'passes_per_appearance','clearances_per_appearance', 'Minutes Played']
].rename(columns={'high_claim_per_appearance':'High Claims', 'punches_per_appearance': 'Punches',
'saves_per_appearance':'Saves', 'passes_per_appearance': 'Passes',
'clearances_per_appearance': 'Clearances'})
max_map = gk.max().to_dict()
gk = pd.DataFrame(gk.unstack()).reset_index()
gk.columns = ['kpi', 'name', 'value']
gk['denom'] = gk.kpi.map(max_map)
gk['value'] = gk.value / gk.denom
gk.T| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| kpi | High Claims | High Claims | Punches | Punches | Saves | Saves | Passes | Passes | Clearances | Clearances | Minutes Played | Minutes Played |
| name | Bernd Leno | Petr Cech | Bernd Leno | Petr Cech | Bernd Leno | Petr Cech | Bernd Leno | Petr Cech | Bernd Leno | Petr Cech | Bernd Leno | Petr Cech |
| value | 0.364583 | 1.0 | 1.0 | 0.571429 | 0.820312 | 1.0 | 0.900391 | 1.0 | 1.0 | 1.0 | 1.0 | 0.206349 |
| denom | 0.857143 | 0.857143 | 0.5 | 0.5 | 4.0 | 4.0 | 32.0 | 32.0 | 1.0 | 1.0 | 2835.0 | 2835.0 |
fig = px.line_polar(gk, r='value', theta='kpi', line_dash='name', color='name', line_close=True
, template="plotly_white", width=800)
fig.update_traces(mode='lines', line_width=5)
fig.update_layout(legend=dict(x=0.7, y=1.1))
fig.show()
Cech was getting around 20% of playing time compared to Leno.
Distinctive styles: Cech is doing much more high claims while Leno uses more punches.
Cech has a better “saves per minute” ratio, but also - might be the result of him playing much less.
df = training.query("position == 'DF' and Appearances > 18")[
['clearances_per_min_played', 'fouls_per_min_played', 'yc_per_min_played'
,'tackles_per_min_played','passes_per_min_played', 'Minutes Played', 'assists_per_min_played']
].rename(columns={'clearances_per_min_played': 'Clearances', 'fouls_per_min_played': 'Fouls'
, 'yc_per_min_played': 'Yellow Cards','tackles_per_min_played': 'Tackles'
,'passes_per_min_played': 'Passes', 'assists_per_min_played':'Assists'})
max_map = df.max().to_dict()
df = pd.DataFrame(df.unstack()).reset_index()
df.columns = ['kpi', 'name', 'value']
df['denom'] = df.kpi.map(max_map)
df['value'] = df.value / df.denom
df.T| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| kpi | Clearances | Clearances | Clearances | Clearances | Fouls | Fouls | Fouls | Fouls | Yellow Cards | Yellow Cards | Yellow Cards | Yellow Cards | Tackles | Tackles | Tackles | Tackles | Passes | Passes | Passes | Passes | Minutes Played | Minutes Played | Minutes Played | Minutes Played | Assists | Assists | Assists | Assists |
| name | Héctor Bellerín | Sead Kolasinac | Shkodran Mustafi | Sokratis | Héctor Bellerín | Sead Kolasinac | Shkodran Mustafi | Sokratis | Héctor Bellerín | Sead Kolasinac | Shkodran Mustafi | Sokratis | Héctor Bellerín | Sead Kolasinac | Shkodran Mustafi | Sokratis | Héctor Bellerín | Sead Kolasinac | Shkodran Mustafi | Sokratis | Héctor Bellerín | Sead Kolasinac | Shkodran Mustafi | Sokratis | Héctor Bellerín | Sead Kolasinac | Shkodran Mustafi | Sokratis |
| value | 0.349637 | 0.214844 | 1.0 | 0.797709 | 0.620299 | 0.66036 | 0.954555 | 1.0 | 0.358611 | 0.484788 | 0.630688 | 1.0 | 0.412695 | 0.758747 | 1.0 | 0.805571 | 0.758673 | 0.761175 | 1.0 | 0.934498 | 0.586233 | 0.722753 | 1.0 | 0.840918 | 1.0 | 0.811111 | 0.0 | 0.278854 |
| denom | 0.061568 | 0.061568 | 0.061568 | 0.061568 | 0.016826 | 0.016826 | 0.016826 | 0.016826 | 0.005457 | 0.005457 | 0.005457 | 0.005457 | 0.023709 | 0.023709 | 0.023709 | 0.023709 | 0.674952 | 0.674952 | 0.674952 | 0.674952 | 2615.0 | 2615.0 | 2615.0 | 2615.0 | 0.003262 | 0.003262 | 0.003262 | 0.003262 |
fig = px.line_polar(df, r='value', theta='kpi'
,color='name', line_dash='name', line_close=True, template="plotly_white")
fig.update_traces(mode='lines', line_width=4)
fig.update_layout(legend=dict(x=0, y=1.1))
fig.show()
Mustafi leads on most defensive metrics. He also played most minutes from all defenders (3rd overall, after Leno and Aubameyang).
Sokratis has a similar profile, with the exception that he leads by far in yellow cards.
Bellerin and Kolasinac have more offensive profiles, both taking third place in overall team assists rank (5 each, Bellerin played less).
mf = training.query("position == 'MF' and Appearances > 20")[
['assists_per_min_played', 'dispossessed_per_min_played'
,'goals_per_min_played','passes_per_min_played','shots_per_min_played', 'tackles_per_min_played'
,'touches_per_min_played', 'Minutes Played']
].rename(columns={'assists_per_min_played':'Assists', 'dispossessed_per_min_played':'Dispossessed'
,'goals_per_min_played':'Goals','passes_per_min_played':'Passes','shots_per_min_played':'Shots'
, 'tackles_per_min_played':'Tackles','touches_per_min_played':'Touches'})
max_map = mf.max().to_dict()
mf = pd.DataFrame(mf.unstack()).reset_index()
mf.columns = ['kpi', 'name', 'value']
mf['denom'] = mf.kpi.map(max_map)
mf['value'] = mf.value / mf.denom
mf.T| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| kpi | Assists | Assists | Assists | Assists | Assists | Assists | Assists | Dispossessed | Dispossessed | Dispossessed | Dispossessed | Dispossessed | Dispossessed | Dispossessed | Goals | Goals | Goals | Goals | Goals | Goals | Goals | Passes | Passes | Passes | Passes | Passes | Passes | Passes | Shots | Shots | Shots | Shots | Shots | Shots | Shots | Tackles | Tackles | Tackles | Tackles | Tackles | Tackles | Tackles | Touches | Touches | Touches | Touches | Touches | Touches | Touches | Minutes Played | Minutes Played | Minutes Played | Minutes Played | Minutes Played | Minutes Played | Minutes Played |
| name | Aaron Ramsey | Granit Xhaka | Henrikh Mkhitaryan | Lucas Torreira | Matteo Guendouzi | Mesut Özil | Nacho Monreal | Aaron Ramsey | Granit Xhaka | Henrikh Mkhitaryan | Lucas Torreira | Matteo Guendouzi | Mesut Özil | Nacho Monreal | Aaron Ramsey | Granit Xhaka | Henrikh Mkhitaryan | Lucas Torreira | Matteo Guendouzi | Mesut Özil | Nacho Monreal | Aaron Ramsey | Granit Xhaka | Henrikh Mkhitaryan | Lucas Torreira | Matteo Guendouzi | Mesut Özil | Nacho Monreal | Aaron Ramsey | Granit Xhaka | Henrikh Mkhitaryan | Lucas Torreira | Matteo Guendouzi | Mesut Özil | Nacho Monreal | Aaron Ramsey | Granit Xhaka | Henrikh Mkhitaryan | Lucas Torreira | Matteo Guendouzi | Mesut Özil | Nacho Monreal | Aaron Ramsey | Granit Xhaka | Henrikh Mkhitaryan | Lucas Torreira | Matteo Guendouzi | Mesut Özil | Nacho Monreal | Aaron Ramsey | Granit Xhaka | Henrikh Mkhitaryan | Lucas Torreira | Matteo Guendouzi | Mesut Özil | Nacho Monreal |
| value | 1.0 | 0.176996 | 0.538524 | 0.186073 | 0.0 | 0.25426 | 0.356797 | 1.0 | 0.334326 | 0.74795 | 0.578892 | 0.71183 | 0.960538 | 0.079288 | 0.825301 | 0.438225 | 1.0 | 0.230349 | 0.0 | 0.786904 | 0.147233 | 0.648453 | 1.0 | 0.539397 | 0.744561 | 0.831103 | 0.715386 | 0.699188 | 0.833723 | 0.389036 | 1.0 | 0.352575 | 0.250615 | 0.211982 | 0.126199 | 0.937048 | 0.760976 | 0.623358 | 1.0 | 0.751821 | 0.252269 | 0.68834 | 0.696084 | 1.0 | 0.654089 | 0.776378 | 0.824114 | 0.714137 | 0.762703 | 0.530988 | 1.0 | 0.657337 | 0.95122 | 0.856457 | 0.696122 | 0.744102 |
| denom | 0.004518 | 0.004518 | 0.004518 | 0.004518 | 0.004518 | 0.004518 | 0.004518 | 0.020331 | 0.020331 | 0.020331 | 0.020331 | 0.020331 | 0.020331 | 0.020331 | 0.00365 | 0.00365 | 0.00365 | 0.00365 | 0.00365 | 0.00365 | 0.00365 | 0.897641 | 0.897641 | 0.897641 | 0.897641 | 0.897641 | 0.897641 | 0.897641 | 0.029805 | 0.029805 | 0.029805 | 0.029805 | 0.029805 | 0.029805 | 0.029805 | 0.027322 | 0.027322 | 0.027322 | 0.027322 | 0.027322 | 0.027322 | 0.027322 | 1.113155 | 1.113155 | 1.113155 | 1.113155 | 1.113155 | 1.113155 | 1.113155 | 2501.0 | 2501.0 | 2501.0 | 2501.0 | 2501.0 | 2501.0 | 2501.0 |
fig = px.line_polar(mf, r='value', theta='kpi', color='name', line_dash='name',
line_close=True, template="plotly_white",
width=800) # , color_discrete_sequence= px.colors.sequential.Plasma[-3::-1])
fig.update_traces(mode='lines', line_width=5)
fig.update_layout(legend=dict(x=1, y=1.1))
fig.show()
There are two clear patterns on the chart: defensive and offensive. Xhaka and Torreira are representatives of the former, while Mkhitaryan and Özil of the latter.
Ramsey is an interesting exception of a “complete” midfielder here, leading by assists per minute played, but also participating a lot in tackles.
fw = training.query("position == 'FW' and Appearances > 10")[
['assists_per_min_played', 'missed_chances_per_min_played', 'goals_per_min_played'
,'offsides_per_min_played', 'shots_per_min_played', 'dispossessed_per_min_played'
,'fouls_per_min_played']
].rename(columns={'assists_per_min_played':'Assists', 'missed_chances_per_min_played':'Missed Chances'
,'goals_per_min_played':'Goals', 'offsides_per_min_played': 'Offsides'
,'shots_per_min_played':'Shots', 'dispossessed_per_min_played':'Dispossessed'
,'fouls_per_min_played':'Fouls'})
max_map = fw.max().to_dict()
fw = pd.DataFrame(fw.unstack()).reset_index()
fw.columns = ['kpi', 'name', 'value']
fw['denom'] = fw.kpi.map(max_map)
fw['value'] = fw.value / fw.denom
fw.T| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| kpi | Assists | Assists | Assists | Missed Chances | Missed Chances | Missed Chances | Goals | Goals | Goals | Offsides | Offsides | Offsides | Shots | Shots | Shots | Dispossessed | Dispossessed | Dispossessed | Fouls | Fouls | Fouls |
| name | Alex Iwobi | Alexandre Lacazette | Pierre-Emerick Aubameyang | Alex Iwobi | Alexandre Lacazette | Pierre-Emerick Aubameyang | Alex Iwobi | Alexandre Lacazette | Pierre-Emerick Aubameyang | Alex Iwobi | Alexandre Lacazette | Pierre-Emerick Aubameyang | Alex Iwobi | Alexandre Lacazette | Pierre-Emerick Aubameyang | Alex Iwobi | Alexandre Lacazette | Pierre-Emerick Aubameyang | Alex Iwobi | Alexandre Lacazette | Pierre-Emerick Aubameyang |
| value | 0.952713 | 1.0 | 0.573279 | 0.180638 | 0.616211 | 1.0 | 0.188848 | 0.644221 | 1.0 | 0.105857 | 1.0 | 0.879028 | 0.51565 | 0.939445 | 1.0 | 1.0 | 0.857514 | 0.425511 | 0.19926 | 1.0 | 0.233808 |
| denom | 0.003194 | 0.003194 | 0.003194 | 0.008422 | 0.008422 | 0.008422 | 0.008056 | 0.008056 | 0.008056 | 0.009581 | 0.009581 | 0.009581 | 0.03442 | 0.03442 | 0.03442 | 0.028398 | 0.028398 | 0.028398 | 0.020359 | 0.020359 | 0.020359 |
fig = px.line_polar(fw, r='value', theta='kpi', color='name', line_dash='name',
line_close=True, template="plotly_white",
width=800, text='value')
fig.update_traces(mode='lines', line_width=5)
fig.update_layout(legend=dict(x=.8, y=1.1))
fig.show()
Lacazette and Aubameyang are 2nd and 4th in most minutes played during the season.
Lacazette fouls five times more than Aubameyang or Iwobi.
Aubameyang leads on goal effectiveness, while Iwobi and Lacazette are great in assists.
I used canva to assemble the graphs above into the final image, added some red-white colours. I didn’t come up with any breakthroughs, of course. But I had a couple of interesting observations and discoveries during the process. And had some fun too!
The greatest benefit was to document the process itself. As Jason Fried urges people to write more to really think the idea through, just sitting down and writing the process of analysis was rewarding.