IPL_Analytics/app.py at main · Codon-s/IPL_Analytics · GitHub

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
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
''' IPL Analytics Dashboard '''
import json
import streamlit as st
import pandas as pd
from series_analysis import no_of_wins, pair_analysis, venue_run, tosschoice_bb
from team_performance import (
    sunburst, match_count, overseas_players, performance, toss_performance, toss_choice
    )
from match_analysis import (
    dismissals, boundaries, batsman_perf, bowler_perf, fielder_perf
)

st.set_page_config(
    page_title = 'IPL Dashboard',
    layout = 'wide',
    initial_sidebar_state = 'collapsed'
)


st.title("IPL Analytics")

tab1, tab2, tab3 = st.tabs(['Series Analysis', 'Team Performance Analysis', 'Match Analysis'])

with tab1: #Series Analysis
    match_list = pd.read_csv('data/match_list-9jun25.csv')
    match_info = pd.read_csv('data/match_info-10jun25.csv')

    col = st.columns([0.7, 0.3], gap='medium')

    with col[0]:
        st.plotly_chart(venue_run, use_container_width=True)
        # with col2[1]:
        #     st.plotly_chart(tosschoice_venue, use_container_width=True)

        st.dataframe(pair_analysis)

    with col[1]:
        with st.container().markdown("**Right Container 1**"):
            st.plotly_chart(no_of_wins, use_container_width=True)

        with st.container().markdown("**Right Container 2**"):
            st.plotly_chart(tosschoice_bb, use_container_width=True)

with tab2: #Team Performance
    match_list = pd.read_csv('data/match_list-9jun25.csv')
    match_info = pd.read_csv('data/match_info-10jun25.csv')

    team_names = match_info.Team1.str.replace(' ', '_').unique().tolist()

    team_name = st.selectbox(
        'Select a Team: ',
        team_names
    )

    col = st.columns(3, gap='small')

    with col[0]:
        with st.container():
            st.plotly_chart(performance(team_name), use_container_width=True)
            st.plotly_chart(toss_performance(team_name), use_container_width=True)

    with col[1]:
        with st.container():
            st.plotly_chart(sunburst(team_name), use_container_width=True)
            st.write('Overseas Player:')
            st.dataframe(overseas_players(team_name))

    with col[2]:
        with st.container():
            st.plotly_chart(match_count(team_name), use_container_width=True)
            st.plotly_chart(toss_choice(team_name), use_container_width=True)

with tab3: #Match Analysis
    match_list = pd.read_csv('data/match_list-9jun25.csv')

    selection = st.selectbox(
        'Select a Match',
        (match_list.MatchDate + ', ' + match_list.MatchName).to_list()
    )

    match_id = match_list.MatchID.loc[match_list.MatchName == selection.split(', ')[1]]

    @st.cache_data
    def load_match_data(match):
        ''' Read data for Match Data Files '''
        with open(f'data/scorecard/{match}.json', 'r', encoding="utf-8") as f:
            data = json.load(f)

        return data

    # Data Ingestion
    inn1 = load_match_data(match_id.values[0])[0]
    inn2 = load_match_data(match_id.values[0])[1]
    inn1_name = inn1['inning'].replace(' Inning 1', '')
    inn2_name = inn2['inning'].replace(' Inning 1', '')

    # Data Preprocessing
    ## Match First Half
    batting = pd.DataFrame.from_records(inn1['batting'])
    batting.batsman = pd.json_normalize(batting.batsman)['name']
    batting.bowler = pd.json_normalize(batting.bowler)['name']
    batting.catcher = pd.json_normalize(batting.catcher)['name']

    bowling = pd.DataFrame.from_records(inn1['bowling'])
    bowling.bowler = pd.json_normalize(bowling.bowler)['name']
    bowling['extras'] = bowling['nb'] + bowling['wd']

    # KPIs
    total_runs = batting['r'].sum()
    total_balls = batting['b'].sum()
    team_strike = round((total_runs / total_balls) * 100, 2)
    total_dismissals = batting['dismissal'].notna().sum()

    col = st.columns(2, gap='small')
    # Dashoard
    with col[0]:
        with st.container(border=True):
            st.header(inn1_name, anchor=False)
            inner_col = st.columns(4, gap='small')
            with inner_col[0]:
                st.metric('Total Runs', total_runs, border=True)
            with inner_col[1]:
                st.metric('Total Balls Faced', total_balls, border=True)
            with inner_col[2]:
                st.metric('Team Strike Rate', team_strike, border=True)
            with inner_col[3]:
                st.metric('Total Dismissals', total_dismissals, border=True)

            inner_col2 = st.columns(2, gap='small')
            with inner_col2[0]:
                # Visualization: Dismissals
                dismissial_counts = batting['dismissal'].value_counts()
                st.plotly_chart(dismissals(dismissial_counts))
            with inner_col2[1]:
                # Visualization: Boundaries
                boundaries_count = batting.groupby('batsman')[['4s', '6s']].sum().reset_index()
                st.plotly_chart(boundaries(boundaries_count))

            # Visualization: Batsman Performance
            st.plotly_chart(batsman_perf(batting))

            # Visualization: Bowler Performance
            st.plotly_chart(bowler_perf(bowling))

            # Visualization: Fielder Performance
            st.plotly_chart(fielder_perf(batting))

    ## Match Second Half
    batting1 = pd.DataFrame.from_records(inn2['batting'])
    batting1.batsman = pd.json_normalize(batting1.batsman)['name']
    batting1.bowler = pd.json_normalize(batting1.bowler)['name']
    batting1.catcher = pd.json_normalize(batting1.catcher)['name']

    bowling1 = pd.DataFrame.from_records(inn2['bowling'])
    bowling1.bowler = pd.json_normalize(bowling1.bowler)['name']
    bowling1['extras'] = bowling1['nb'] + bowling1['wd']

    total_runs = batting1['r'].sum()
    total_balls = batting1['b'].sum()
    team_strike = round((total_runs / total_balls) * 100, 2)
    total_dismissals = batting1['dismissal'].notna().sum()

    # Dashoard
    with col[1]:
        with st.container(border=True):
            st.header(inn2_name, anchor=False)
            inner_col = st.columns(4, gap='small')
            with inner_col[0]:
                st.metric('Total Runs', total_runs, border=True)
            with inner_col[1]:
                st.metric('Total Balls Faced', total_balls, border=True)
            with inner_col[2]:
                st.metric('Team Strike Rate', team_strike, border=True)
            with inner_col[3]:
                st.metric('Total Dismissals', total_dismissals, border=True)

            inner_col2 = st.columns(2, gap='small')
            with inner_col2[0]:
                # Visualization: Dismissals
                dismissial_counts = batting1['dismissal'].value_counts()
                st.plotly_chart(dismissals(dismissial_counts))
            with inner_col2[1]:
                # Visualization: Boundaries
                boundaries_count = batting1.groupby('batsman')[['4s', '6s']].sum().reset_index()
                st.plotly_chart(boundaries(boundaries_count))

            # Visualization: Batsman Performance
            st.plotly_chart(batsman_perf(batting1))

            # Visualization: Bowler Performance
            st.plotly_chart(bowler_perf(bowling1))

            # Visualization: Fielder Performance
            st.plotly_chart(fielder_perf(batting1))