Top Bowlers and Top Fielders Analysis¶

import pandas as pd
import numpy as np
import seaborn as sn
import plotly.express as px
import plotly.graph_objects as go
import plotly.offline as py
from plotly.subplots import make_subplots
import matplotlib.pyplot as plt
%matplotlib inline

deliveries = pd.read_csv('deliveries.csv')
pd.set_option('display.max_columns',None)
deliveries.head()

df_csk = deliveries[(deliveries['bowling_team']=='Chennai Super Kings')]
df_mi = deliveries[(deliveries['bowling_team']=='Mumbai Indians')]
df_rcb = deliveries[(deliveries['bowling_team']=='Royal Challengers Bangalore')]
df_kkr = deliveries[(deliveries['bowling_team']=='Kolkata Knight Riders')]
df_rr = deliveries[(deliveries['bowling_team']=='Rajasthan Royals')]
df_kxip = deliveries[(deliveries['bowling_team']=='Kings XI Punjab')]
df_dd = deliveries[(deliveries['bowling_team']=='Delhi Daredevils')]
df_srh = deliveries[(deliveries['bowling_team']=='Sunrisers Hyderabad')]

teams = ['csk','mi','rcb','kkr','rr','kxip','dd','srh']

df_csk = df_csk[(df_csk['batsman']==df_csk['player_dismissed'])]
df_mi = df_mi[(df_mi['batsman']==df_mi['player_dismissed'])]
df_rcb = df_rcb[(df_rcb['batsman']==df_rcb['player_dismissed'])]
df_kkr = df_kkr[(df_kkr['batsman']==df_kkr['player_dismissed'])]
df_rr = df_rr[(df_rr['batsman']==df_rr['player_dismissed'])]
df_kxip = df_kxip[(df_kxip['batsman']==df_kxip['player_dismissed'])]
df_dd = df_dd[(df_dd['batsman']==df_dd['player_dismissed'])]
df_srh = df_srh[(df_srh['batsman']==df_srh['player_dismissed'])]

df_srh.shape

(627, 21)

def getmap(df):
    bowler = list(df['bowler'])
    bowler_map = dict(zip(bowler,map(bowler.count,bowler)))
    bowler_map = dict(sorted(bowler_map.items(),key = lambda x:x[1],reverse=True))
    bowler_map = dict(list(bowler_map.items())[:5])
    keys = []
    values = []
    for key,value in bowler_map.items():
        keys.append(key)
        values.append(value)
    df = pd.DataFrame()
    df['Player'] = keys
    df['TotalWickets'] = values
    return df

top_bowl_csk = getmap(df_csk)
top_bowl_mi  = getmap(df_mi)
top_bowl_rcb = getmap(df_rcb)
top_bowl_kkr = getmap(df_kkr)
top_bowl_rr = getmap(df_rr)
top_bowl_kxip = getmap(df_kxip)
top_bowl_dd = getmap(df_dd)
top_bowl_srh = getmap(df_srh)

top_bowl_csk

teams = ['csk','mi','rcb','kkr','rr','kxip','dd','srh']

fig = make_subplots(rows=4,cols=2,subplot_titles=('CSK','MI','RCB','KKR',
                                                 'RR','KXIP','DD','SRH'),start_cell = 'top-left',
                                  specs=[[{'type':'polar'},{'type':'polar'}],
                                         [{'type':'polar'},{'type':'polar'}],
                                         [{'type':'polar'},{'type':'polar'}],
                                         [{'type':'polar'},{'type':'polar'}]],shared_yaxes=True)

# fig = make_subplots(rows=4,cols=2,specs=[[{'type':'polar'},{'type':'polar'}],
#                                          [{'type':'polar'},{'type':'polar'}],
#                                          [{'type':'polar'},{'type':'polar'}],
#                                          [{'type':'polar'},{'type':'polar'}]])

fig.add_trace(go.Barpolar(theta=list(top_bowl_csk['Player'].unique()),r = list(top_bowl_csk['TotalWickets'].unique())),1,1)

fig.add_trace(go.Barpolar(theta=list(top_bowl_mi['Player'].unique()),r = list(top_bowl_mi['TotalWickets'].unique())),1,2)

fig.add_trace(go.Barpolar(theta=list(top_bowl_rcb['Player'].unique()),r = list(top_bowl_rcb['TotalWickets'].unique())),2,1)

fig.add_trace(go.Barpolar(theta=list(top_bowl_kkr['Player'].unique()),r = list(top_bowl_kkr['TotalWickets'].unique())),2,2)

fig.add_trace(go.Barpolar(theta=list(top_bowl_rr['Player'].unique()),r = list(top_bowl_rr['TotalWickets'].unique())),3,1)

fig.add_trace(go.Barpolar(theta=list(top_bowl_kxip['Player'].unique()),r = list(top_bowl_kxip['TotalWickets'].unique())),3,2)

fig.add_trace(go.Barpolar(theta=list(top_bowl_dd['Player'].unique()),r = list(top_bowl_dd['TotalWickets'].unique())),4,1)

fig.add_trace(go.Barpolar(theta=list(top_bowl_srh['Player'].unique()),r = list(top_bowl_srh['TotalWickets'].unique())),4,2)


# fig.update_traces(hoverinfo="label+percent+name")

# fig.update_layout(
#     title_text="Global Emissions 1990-2011",
#     # Add annotations in the center of the donut pies.
#     annotations=[dict(text='GHG', x=0.18, y=0.5, font_size=20, showarrow=False),
#                  dict(text='CO2', x=0.82, y=0.5, font_size=20, showarrow=False),
#                 dict(text='CO2', x=0.82, y=0.5, font_size=20, showarrow=False),
#                 dict(text='CO2', x=0.82, y=0.5, font_size=20, showarrow=False),
#                 dict(text='CO2', x=0.82, y=0.5, font_size=20, showarrow=False),
#                 dict(text='CO2', x=0.82, y=0.5, font_size=20, showarrow=False),
#                 dict(text='CO2', x=0.82, y=0.5, font_size=20, showarrow=False),
#                 dict(text='CO2', x=0.82, y=0.5, font_size=20, showarrow=False)])

fig.update_layout(height =1600,width = 1000,title_text = "Top Performing Bowlers From Each team")
# annot =list(fig.layout.annotations)
# fig.layout.annotations = annot
fig.show()

df_csk.head()

teams = ['csk','mi','rcb','kkr','rr','kxip','dd','srh']
csk_bowler = list(top_bowl_csk['Player'].unique())
mi_bowler = list(top_bowl_mi['Player'].unique())
rcb_bowler = list(top_bowl_rcb['Player'].unique())
kkr_bowler = list(top_bowl_kkr['Player'].unique())
rr_bowler = list(top_bowl_rr['Player'].unique())
kxip_bowler = list(top_bowl_kxip['Player'].unique())
dd_bowler = list(top_bowl_dd['Player'].unique())
srh_bowler = list(top_bowl_srh['Player'].unique())

temp = df_csk[(df_csk['bowler']=='DJ Bravo')]
temp.shape

(114, 21)

deliveries['dismissal_kind'].unique()

array([nan, 'caught', 'bowled', 'run out', 'lbw', 'caught and bowled',
       'stumped', 'retired hurt', 'hit wicket', 'obstructing the field'],
      dtype=object)

dismissal_kind = list(temp['dismissal_kind'])
dismissal_mapper = dict(zip(dismissal_kind,map(dismissal_kind.count,dismissal_kind)))
dismissal_mapper

{'caught': 85, 'caught and bowled': 4, 'bowled': 11, 'lbw': 4, 'run out': 10}

def getmap(df,bowler):
    temp = df[(df['bowler']==bowler)]
    dismissal_kind = list(temp['dismissal_kind'])
    dismissal_mapper = dict(zip(dismissal_kind,map(dismissal_kind.count,dismissal_kind)))
    to_use = ['caught','caught and bowled','bowled','lbw','stumped','hit wicket']
    mapper = {}
    for key,val in dismissal_mapper.items():
        if key in to_use:
            mapper[key] = val
            
    return mapper


a = getmap(df_csk,'DJ Bravo')
a

{'caught': 85, 'caught and bowled': 4, 'bowled': 11, 'lbw': 4}

csk_bowler

['DJ Bravo', 'R Ashwin', 'RA Jadeja', 'JA Morkel', 'MM Sharma']

teams = ['csk','mi','rcb','kkr','rr','kxip','dd','srh']

teams = [csk_bowler,mi_bowler,rcb_bowler,kkr_bowler,rr_bowler,kxip_bowler,dd_bowler,srh_bowler]

def getdetails(df,player_list):
    final_mapper = {}
    for player in player_list:
        temp = df[(df['bowler']==player)]
        dismissal_kind = list(temp['dismissal_kind'])
        dismissal_mapper = dict(zip(dismissal_kind,map(dismissal_kind.count,dismissal_kind)))
        to_use = ['caught','caught and bowled','bowled','lbw','stumped','hit wicket']
        mapper = {}
        for key,val in dismissal_mapper.items():
            if key in to_use:
                mapper[key] = val
                
                
        final_mapper[player] = mapper
        temp = pd.DataFrame(final_mapper)
        temp = temp.T
        temp = temp.fillna(0)
        temp = temp.astype('int32')
        temp['Player'] = temp.index
        temp = temp.reset_index()
        temp = temp.drop(['index'],axis = 1)
        
    return temp

teams = ['csk','mi','rcb','kkr','rr','kxip','dd','srh']

bowl_csk = getdetails(df_csk,csk_bowler)
bowl_mi = getdetails(df_mi,mi_bowler)
bowl_rcb = getdetails(df_rcb,rcb_bowler)
bowl_kkr = getdetails(df_kkr,kkr_bowler)
bowl_rr = getdetails(df_rr,rr_bowler)
bowl_kxip = getdetails(df_kxip,kxip_bowler)
bowl_dd = getdetails(df_dd,dd_bowler)
bowl_srh = getdetails(df_srh,srh_bowler)

bowl_csk

bowl_csk.columns

Index(['caught', 'caught and bowled', 'bowled', 'lbw', 'stumped', 'hit wicket',
       'Player'],
      dtype='object')

def getnameandruns(df_i):
    final = []
    for i in range(df_i.shape[0]):
        first = df_i.iloc[[i]]
        temp = []
        for i in first.columns:
            a = []
            a.append(list(first[i]))
            a = sum(a,[])
            temp.append(a)

        final.append(temp)


    final = sum(final,[])
    final = sum(final,[])
    name = [i for i in final if type(i)!=int]
    runs = [i for i in final if type(i)==int]
    final_runs = []
    start = 0
    end = 5
    while end<=len(runs):
        temp = runs[start:end]
        final_runs.append(temp)
        start = end
        end+=5
        
    return name,final_runs

csk_name,csk_bowl = getnameandruns(bowl_csk)
mi_name,mi_bowl = getnameandruns(bowl_mi)
rcb_name,rcb_bowl = getnameandruns(bowl_rcb)
kkr_name,kkr_bowl = getnameandruns(bowl_kkr)
rr_name,rr_bowl = getnameandruns(bowl_rr)
kxip_name,kxip_bowl = getnameandruns(bowl_kxip)
dd_name,dd_bowl = getnameandruns(bowl_dd)
srh_name,srh_bowl = getnameandruns(bowl_srh)

def drawplots(player_wickets,player_names,player_name,top_bowl,text):
    
    wickets =  ['caught', 'caught and bowled', 'bowled', 'lbw', 'stumped', 'hit wicket']
    fig = make_subplots(rows=2,cols = 3,subplot_titles=tuple(player_names),
                       specs=[[{'type':'domain'},{'type':'domain'},{'type':'domain'}],
                              [{'type':'domain'},{'type':'domain'},{'type':'domain'}]],shared_yaxes=True)
    
    fig.add_trace(go.Pie(labels=wickets,values=player_wickets[0],name = player_names[0]),1,1)
    fig.add_trace(go.Pie(labels=wickets,values=player_wickets[1],name = player_names[1]),1,2)
    fig.add_trace(go.Pie(labels=wickets,values=player_wickets[2],name = player_names[2]),1,3)
    fig.add_trace(go.Pie(labels=wickets,values=player_wickets[3],name = player_names[3]),2,1)
    fig.add_trace(go.Pie(labels=wickets,values=player_wickets[4],name = player_names[4]),2,2)
    fig.add_trace(go.Pie(labels = player_name,values=list(top_bowl['TotalWickets'].unique())[:5]),2,3)
    fig.update_layout(height = 750,width = 900,title_text = text)
    fig.show()

final = []
teams = [csk_bowler,mi_bowler,rcb_bowler,kkr_bowler,rr_bowler,kxip_bowler,dd_bowler,srh_bowler]
for i in range(len(teams)):
    final.append(teams[i])
    
final

[['DJ Bravo', 'R Ashwin', 'RA Jadeja', 'JA Morkel', 'MM Sharma'],
 ['SL Malinga',
  'Harbhajan Singh',
  'JJ Bumrah',
  'MJ McClenaghan',
  'KA Pollard'],
 ['YS Chahal', 'R Vinay Kumar', 'Z Khan', 'S Aravind', 'A Kumble'],
 ['SP Narine', 'PP Chawla', 'AD Russell', 'UT Yadav', 'Shakib Al Hasan'],
 ['SK Trivedi', 'SR Watson', 'SK Warne', 'JP Faulkner', 'MM Patel'],
 ['PP Chawla', 'Sandeep Sharma', 'AR Patel', 'IK Pathan', 'P Awana'],
 ['A Mishra', 'M Morkel', 'UT Yadav', 'S Nadeem', 'A Nehra'],
 ['B Kumar', 'Rashid Khan', 'S Kaul', 'KV Sharma', 'DW Steyn']]

teams = ['csk','mi','rcb','kkr','rr','kxip','dd','srh']
top_bowl = [top_bowl_csk,top_bowl_mi,top_bowl_rcb,top_bowl_kkr,top_bowl_rr,top_bowl_kxip,top_bowl_dd,top_bowl_srh]
player_name = [csk_name,mi_name,rcb_name,kkr_name,rr_name,kxip_name,dd_name,srh_name]
player_runs = [csk_bowl,mi_bowl,rcb_bowl,kkr_bowl,rr_bowl,kxip_bowl,dd_bowl,srh_bowl]

for i,j,k,l,m in zip(range(len(teams)),range(len(player_name)),range(len(player_runs)),range(len(top_bowl)),range(len(final))):
    
    drawplots(player_runs[k],player_name[j],final[m],top_bowl[l],text = "Top 5 Bowlers From"+" "+str(teams[i]).upper())
    print("-"*120)

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Observation

From Chennai Super Kings,Dwayne Bravo,R Ashwin,R Jadeja have very good Figures

From Mumbai Indians,L Malinga,Harbajan Singh,JJ Bumrah have very good Figures

From Royal Challengers Banglore,Y Chahal,R Vinay Kumar,Z Khan have very good Figures

From Kolkata Knight Riders,S Narine,P Chawla,A Russel have very good Figures

From Rajasthan Royals,SK Trivedi,S Watson,S Warne have very good Figures

From Kings XI Punjab,PP Chawla,Sandeep Sharma,Axar Patel have very good Figures

From Delhi DareDevils,A Mishra,U Yadav,M Morkel have very good Figures

From Surises Hyderabad,Bhuvneshwar Kumar,Rahid Khan,S Kaul have very good Figures

Fielder Analysis¶

df_csk = deliveries[(deliveries['bowling_team']=='Chennai Super Kings')&(deliveries['dismissal_kind']=='caught')]
df_mi = deliveries[(deliveries['bowling_team']=='Mumbai Indians')&(deliveries['dismissal_kind']=='caught')]
df_rcb = deliveries[(deliveries['bowling_team']=='Royal Challengers Bangalore')&(deliveries['dismissal_kind']=='caught')]
df_kkr = deliveries[(deliveries['bowling_team']=='Kolkata Knight Riders')&(deliveries['dismissal_kind']=='caught')]
df_rr = deliveries[(deliveries['bowling_team']=='Rajasthan Royals')&(deliveries['dismissal_kind']=='caught')]
df_kxip = deliveries[(deliveries['bowling_team']=='Kings XI Punjab')&(deliveries['dismissal_kind']=='caught')]
df_dd = deliveries[(deliveries['bowling_team']=='Delhi Daredevils')&(deliveries['dismissal_kind']=='caught')]
df_srh = deliveries[(deliveries['bowling_team']=='Sunrisers Hyderabad')&(deliveries['dismissal_kind']=='caught')]

def getmap(df):
    bowler = list(df['fielder'])
    bowler_map = dict(zip(bowler,map(bowler.count,bowler)))
    bowler_map = dict(sorted(bowler_map.items(),key = lambda x:x[1],reverse=True))
    bowler_map = dict(list(bowler_map.items())[:5])
    keys = []
    values = []
    for key,value in bowler_map.items():
        keys.append(key)
        values.append(value)
    df = pd.DataFrame()
    df['Player'] = keys
    df['TotalCatches'] = values
    return df

top_catch_csk = getmap(df_csk)
top_catch_mi  = getmap(df_mi)
top_catch_rcb = getmap(df_rcb)
top_catch_kkr = getmap(df_kkr)
top_catch_rr = getmap(df_rr)
top_catch_kxip = getmap(df_kxip)
top_catch_dd = getmap(df_dd)
top_catch_srh = getmap(df_srh)

def getnameandruns(df_i):
    
    player = list(df_i['Player'])
    catches = list(df_i['TotalCatches'])
        
    return player,catches

csk_name,csk_bowl = getnameandruns(top_catch_csk)
mi_name,mi_bowl = getnameandruns(top_catch_mi)
rcb_name,rcb_bowl = getnameandruns(top_catch_rcb)
kkr_name,kkr_bowl = getnameandruns(top_catch_kkr)
rr_name,rr_bowl = getnameandruns(top_catch_rr)
kxip_name,kxip_bowl = getnameandruns(top_catch_kxip)
dd_name,dd_bowl = getnameandruns(top_catch_dd)
srh_name,srh_bowl = getnameandruns(top_catch_srh)

fig = make_subplots(rows=4,cols = 2,subplot_titles=('CSK','MI','RCB','KKR',
                                                 'RR','KXIP','DD','SRH'),
                      specs=[[{'type':'domain'},{'type':'domain'}],
                            [{'type':'domain'},{'type':'domain'}],
                            [{'type':'domain'},{'type':'domain'}],
                            [{'type':'domain'},{'type':'domain'}]],shared_yaxes=True)

fig.add_trace(go.Pie(labels=csk_name,values=csk_bowl),1,1)
fig.add_trace(go.Pie(labels=mi_name,values=mi_bowl),1,2)
fig.add_trace(go.Pie(labels=rcb_name,values=rcb_bowl),2,1)
fig.add_trace(go.Pie(labels=kkr_name,values=kkr_bowl),2,2)
fig.add_trace(go.Pie(labels=rr_name,values=rr_bowl),3,1)
fig.add_trace(go.Pie(labels=kxip_name,values=kxip_bowl),3,2)
fig.add_trace(go.Pie(labels=dd_name,values=dd_bowl),4,1)
fig.add_trace(go.Pie(labels=srh_name,values=srh_bowl),4,2)

fig.update_traces(hole=.4, hoverinfo="label+percent+value")
fig.update_layout(height = 1400,width = 900,title_text = "Top Feilders of Each Team")

fig.show()

	match_id	inning	batting_team	bowling_team	over	ball	batsman	non_striker	bowler	wide_runs	batsman_runs	extra_runs	total_runs	player_dismissed	dismissal_kind	fielder
0	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	1	DA Warner	S Dhawan	TS Mills	0	0	0	0	NaN	NaN	NaN
1	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	2	DA Warner	S Dhawan	TS Mills	0	0	0	0	NaN	NaN	NaN
2	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	3	DA Warner	S Dhawan	TS Mills	0	4	0	4	NaN	NaN	NaN
3	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	4	DA Warner	S Dhawan	TS Mills	0	0	0	0	NaN	NaN	NaN
4	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	5	DA Warner	S Dhawan	TS Mills	2	0	2	2	NaN	NaN	NaN

	match_id	inning	batting_team	bowling_team	over	ball	batsman	non_striker	bowler	player_dismissed	dismissal_kind	fielder
14246	61	2	Kings XI Punjab	Chennai Super Kings	6	6	K Goel	JR Hopes	MS Gony	K Goel	caught	PA Patel
14275	61	2	Kings XI Punjab	Chennai Super Kings	11	2	JR Hopes	KC Sangakkara	P Amarnath	JR Hopes	caught	Joginder Sharma
14300	61	2	Kings XI Punjab	Chennai Super Kings	15	2	Yuvraj Singh	KC Sangakkara	Joginder Sharma	Yuvraj Singh	caught	P Amarnath
14320	61	2	Kings XI Punjab	Chennai Super Kings	18	4	KC Sangakkara	SM Katich	M Muralitharan	KC Sangakkara	caught	Joginder Sharma
15636	67	2	Mumbai Indians	Chennai Super Kings	4	4	ST Jayasuriya	RV Uthappa	MS Gony	ST Jayasuriya	caught	Joginder Sharma

	caught	caught and bowled	bowled	lbw	stumped	hit wicket	Player
0	85	4	11	4	0	0	DJ Bravo
1	56	3	16	8	7	0	R Ashwin
2	47	3	20	6	5	0	RA Jadeja
3	53	2	15	5	0	1	JA Morkel
4	42	0	12	4	0	0	MM Sharma