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ipl-app.py
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ipl-app.py
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import warnings
warnings.simplefilter(action="ignore", category=UserWarning)
from dash_core_components.Dropdown import Dropdown
from dash_core_components.Graph import Graph
from dash_core_components.Markdown import Markdown
from dash_html_components.Div import Div
from dash_html_components.H2 import H2
from dash_html_components.I import I
from dash_html_components.Label import Label
from dash_html_components.P import P
from pandas._config.config import options
from pandas.core.indexes import multi
from pandas.io.formats import style
import dash
import dash_core_components as dcc
import dash_bootstrap_components as dbc
import dash_html_components as html
from dash.dependencies import Input, Output
import dash_table
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import numpy as np
import os
import base64
# function for loading data
file_path = os.path.join(os.getcwd(), "data")
def load_data(filename, file_path=file_path):
csv_path = os.path.join(file_path, filename)
return pd.read_csv(csv_path)
# read the data
points_table = load_data("points_table.csv")
points_table["Net R/R"] = points_table["Net R/R"].round(3)
wins_losses = load_data("wins_losses.csv")
wins_losses.sort_values(by=["Titles", "Win %"], ascending=[False, False], inplace=True)
wins_losses.drop("Span", axis=1, inplace=True)
# read batting data
batting = load_data("batting.csv")
batting.iloc[batting[batting["PLAYER"] == "Rohit Sharma"].index, 16] = "Mumbai Indians"
batting_players_list = list(batting["PLAYER"].unique())
# batting aggregated data
batting_agg = load_data("batting_all_time.csv")
# read bowling data
bowling = load_data("bowling.csv")
bowling = bowling.rename(columns={"Maid": "Maiden"})
bowling_players_list = list(bowling["PLAYER"].unique())
# create a new column
bowling["Runs/Inns"] = (bowling["Runs"] / bowling["Inns"]).round(2)
# read bowling aggregated data
bowling_agg = load_data("bowling_all_time.csv")
# copy the data that is not avialable in aggregated csv
bowling_subset = bowling[["PLAYER", "Dots", "Maiden"]].copy()
# calculate aggregates and join
bs_groupby = bowling_subset.groupby("PLAYER").sum().reset_index()
bowling_agg = pd.merge(
left=bowling_agg, right=bs_groupby, left_on="PLAYER", right_on="PLAYER"
)
# delete un-necessary column
bowling_agg.drop("Player Link", axis=1, inplace=True)
# create a new column
bowling_agg["Runs/Inns"] = (bowling_agg["Runs"] / bowling_agg["Inns"]).round(2)
batting_metrics_list = [
"Runs",
"HS",
"Avg",
"BF",
"SR",
"100",
"50",
"4s",
"6s",
"Mat",
"Inns",
"NO",
]
bowling_metrics_list = [
"Wkts",
"Econ",
"Avg",
"SR",
"Runs/Inns",
"Dots",
"4w",
"5w",
"Maiden",
"Ov",
]
team_list = [
"All Teams",
"Sunrisers Hyderabad",
"Kings Xi Punjab",
"Mumbai Indians",
"Delhi Capitals",
"Kolkata Knight Riders",
"Royal Challengers Bangalore",
"Chennai Super Kings",
"Rajasthan Royals",
]
heading_markdown = """
# IPL Stats (2008-2019)
### by Bhola Prasad
#### Website - [Life With Data](https://www.lifewithdata.com/)
"""
# year list
year_list = [year for year in range(2019, 2007, -1)]
# players runs distribution plot
runs_dist_plot = px.histogram(batting, x="Runs")
runs_dist_plot.update_layout(title="Distribution of Player Runs(2008-2019)")
# players runs kde plot
import plotly.figure_factory as ff
unique_teams = batting["Team"].unique()
hist_data = [
batting[batting["Team"] == team]["Runs"] for team in unique_teams if team != "Nan"
]
group_labels = ["SRH", "KXIP", "MI", "DC", "KKR", "RCB", "CSK", "RR"]
colors = ["Orange", "Silver", "Blue", "Black", "Gold", "Red", "Yellow", "Green"]
runs_kde_plot = ff.create_distplot(
hist_data, group_labels, show_hist=False, show_rug=False, colors=colors
)
runs_kde_plot.update_layout(
title="Kde Plot of Runs", xaxis=dict(title="Runs"), yaxis=dict(title="Density")
)
# function for adding local images
def encode_image(image_file):
encoded = base64.b64encode(open(image_file, "rb").read())
return "data:image/png;base64,{}".format(encoded.decode())
# Batting Feature Importances figures
batting_bar = os.getcwd() + "/images/batting_bar.png"
batting_beeswarm = os.getcwd() + "/images/batting_beeswarm.png"
batting_bf = os.getcwd() + "/images/batting_bf.png"
batting_sr = os.getcwd() + "/images/batting_sr.png"
# Bowling Feature Importances figures
bowling_bar = os.getcwd() + "/images/bowling_bar.png"
bowling_beeswarm = os.getcwd() + "/images/bowling_beeswarm.png"
bowling_sr = os.getcwd() + "/images/bowling_sr.png"
bowling_dots = os.getcwd() + "/images/bowling_dots.png"
###### Bowling plots
# wickets histogram
wickets_histogram = px.histogram(bowling, x="Wkts")
wickets_histogram.update_layout(
title="Number of Wickets In A Season(2008-2019)",
yaxis=dict(title="Player Count"),
xaxis=dict(title="Number Of Wickets"),
)
# wickets taken by teams distribution
team_wickets_dist = px.box(bowling[bowling["Team"] != "Nan"], x="Wkts", y="Team")
team_wickets_dist.update_layout(
title="Wickets Taken Per Season (2008-2019)",
yaxis=dict(title="Players Team"),
xaxis=dict(title="Total Wickets"),
)
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.SOLAR])
server = app.server
app.layout = html.Div(
[
html.H1("IPL Stats (2008-2019)"),
html.H4("by Bhola Prasad"),
dcc.Markdown("##### Website - [Life With Data](https://www.lifewithdata.com/)"),
##### Points Table
html.Div([], style={"height": "100px"}),
html.H3("Points Table"),
html.Div(
[
html.Div(
[
html.Label("Select a Season"),
dcc.Dropdown(
id="points-year-selector",
options=[
{"label": str(year), "value": year}
for year in year_list
],
value=2019,
),
],
style={"width": "20%", "display": "inline-block"},
),
]
),
html.Div(
[
html.Div(
[
# points table data
dash_table.DataTable(
id="points-table",
columns=[
{"name": i, "id": i} for i in points_table.columns
],
data=points_table.to_dict("records"),
sort_action="native",
style_cell={"textAlign": "left"},
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "rgb(248, 248, 248)",
},
{
"if": {"column_id": "Team"},
"backgroundColor": "#3D9970",
"color": "white",
},
{
"if": {
"column_id": "Points",
"row_index": [0, 1, 2, 3],
},
"backgroundColor": "#3D9970",
"color": "white",
},
],
),
],
style={"margin": "15px", "width": "70%"},
)
]
),
####### Team Wins and Losses Table
html.Div([], style={"height": "45px"}),
html.H3("Team Records"),
html.Div(
[
dash_table.DataTable(
id="wins-losses-table",
columns=[{"name": i, "id": i} for i in wins_losses.columns],
data=wins_losses.to_dict("records"),
sort_action="native",
style_cell={"textAlign": "left"},
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "rgb(248, 248, 248)",
},
{
"if": {"column_id": "Team"},
"backgroundColor": "#3D9970",
"color": "white",
},
{
"if": {"column_id": "Win %", "row_index": [0, 1]},
"backgroundColor": "#3D9970",
"color": "white",
},
{
"if": {"column_id": "Titles", "row_index": [0, 1]},
"backgroundColor": "#3D9970",
"color": "white",
},
],
),
],
style={"margin": "15px", "width": "70%"},
),
###### Batting stats
html.Div([], style={"height": "45px"}),
html.H2("Batting Records"),
html.Div(
[
html.Div([], style={"height": "25px"}),
html.H4("Player Runs Per Season"),
html.B("Select Players"),
html.P("Use dropdown to select multiple players or remove them."),
dcc.Dropdown(
id="select-player-ts",
options=[
{"label": player, "value": player}
for player in batting_players_list
],
value=[
"Virat Kohli",
"Rohit Sharma",
"David Warner",
"KL Rahul",
],
multi=True,
),
# Players Runs Time-Series Chart
dcc.Graph(id="players-runs-time-series"),
],
style={"width": "70%"},
),
###### Runs Distributions
html.Div([], style={"height": "45px"}),
html.H3("Runs Distributions"),
# Histogram of Runs Distributions
html.Div(
[
html.Div(
[
html.P(
"We can see that the distribution is right skewed. Many "
"players makes low or medium runs, while few players makes "
"lots of runs. The median of this distribution is 126 which "
"means that 50% of the players makes less than 126 runs and "
"50% more than this. 406 is the 90th percentile, meaning 90% "
"of the players makes less than 406 runs. So, any players who "
"is making more than 400 runs in a season is really doing well. "
"They are in the top 10%."
)
],
style={
"width": "30%",
"display": "inline-block",
"margin-top": "60px",
},
),
html.Div(
[dcc.Graph(id="runs-dist-plot", figure=runs_dist_plot)],
style={"width": "60%", "float": "right", "display": "inline-block"},
),
],
style={"margin": "40px", "height": 500},
),
# Kernal density estimation of Runs distributions
html.Div(
[
html.Div(
[
html.P(
"After segmenting the runs distribution by team, "
"it is clear that percentage of players making runs "
"within the 300 range is lower for csk compared to other "
"teams and the higher within the range 300 to 600, which is "
"what you want.Less percentage at the lower end and higher at "
"the middle and upper end. On average, csk players make 235 "
"runs,compared to 187 runs by Mumbai Indians which is the second "
"highest."
)
],
style={
"width": "30%",
"display": "inline-block",
"margin-top": "100px",
},
),
html.Div(
[
dcc.Graph(
id="runs-kde-plot",
figure=runs_kde_plot,
)
],
style={"width": "60%", "float": "right", "display": "inline-block"},
),
],
style={"margin": "40px", "height": 500},
),
# Batting Leaderboard - All Time
html.H3("Batting Leaderboard"),
html.Div([], style={"height": "25px"}),
html.H4("All Time Records"),
html.Div([], style={"height": "20px"}),
html.Div(
[
dcc.Tabs(
[
dcc.Tab(
label="Chart",
children=[
html.P(""),
html.Div(
[
html.Div(
[
html.Label("Select a Metric"),
dcc.Dropdown(
id="all-time-metric-selector",
options=[
{
"label": metric,
"value": metric,
}
for metric in batting_metrics_list
],
value="Runs",
),
],
style={
"width": "35%",
"display": "inline-block",
},
),
html.Div(
[
html.Label("Select Team"),
dcc.Dropdown(
id="all-time-team-selector",
options=[
{
"label": team,
"value": team,
}
for team in team_list
],
value="All Teams",
),
],
style={
"width": "35%",
"float": "right",
"display": "inline-block",
},
),
]
),
html.Div([dcc.Graph(id="all-time-graph")]),
],
),
dcc.Tab(
label="Table",
children=[
dash_table.DataTable(
id="all-time-records",
columns=[
{"name": i, "id": i}
for i in batting_agg.columns
],
data=batting_agg.to_dict("records"),
sort_action="native",
style_cell={"textAlign": "left"},
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "rgb(248, 248, 248)",
},
],
style_table={"overflowX": "auto"},
style_cell_conditional=[
{
"if": {"column_id": "PLAYER"},
"textAlign": "center",
},
],
page_current=0,
page_size=15,
page_action="native",
)
],
),
]
)
],
style={"width": "75%"},
),
# Season Records
html.Div([], style={"height": "45px"}),
html.H4("Season Records"),
html.Div([], style={"height": "20px"}),
html.Div(
[
dcc.Tabs(
[
dcc.Tab(
label="Chart",
children=[
html.P(""),
html.Div(
[
html.Div(
[
html.Label("Select a Metric"),
dcc.Dropdown(
id="season-metric-selector",
options=[
{
"label": metric,
"value": metric,
}
for metric in batting_metrics_list
],
value="Runs",
),
],
style={
"width": "25%",
"float": "left",
"padding-right": "25px",
"display": "inline-block",
},
),
html.Div(
[
html.Label("Season"),
dcc.Dropdown(
id="season-year-selector",
options=[
{
"label": str(year),
"value": year,
}
for year in range(
2019, 2007, -1
)
],
value=2019,
),
],
style={
"width": "25%",
"float": "middle",
"display": "inline-block",
},
),
html.Div(
[
html.Label("Team"),
dcc.Dropdown(
id="season-team-selector",
options=[
{"label": team, "value": team}
for team in team_list
],
value="All Teams",
),
],
style={
"width": "25%",
"float": "right",
"display": "inline-block",
},
),
],
),
html.Div([dcc.Graph(id="season-graph")]),
],
),
dcc.Tab(
label="Table",
children=[
dash_table.DataTable(
id="season-records",
columns=[
{"name": i, "id": i}
for i in batting.columns
if i != "Player Link"
],
data=batting.to_dict("records"),
sort_action="native",
style_cell={"textAlign": "left"},
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "rgb(248, 248, 248)",
},
],
style_table={"overflowX": "auto"},
style_cell_conditional=[
{
"if": {"column_id": "POS"},
"textAlign": "center",
},
],
page_current=0,
page_size=15,
page_action="native",
)
],
),
]
)
],
style={"width": "75%"},
),
# Features importances
html.Div([], style={"height": "80px"}),
html.H4("Important features for predicting players runs."),
html.Div([], style={"height": "25px"}),
html.Div(
[
html.Div(
[
dcc.Tabs(
id="tabs-with-classes",
value="tab-1",
parent_className="custom-tabs",
className="custom-tabs-container",
children=[
dcc.Tab(
label="Feature Importances",
children=[html.Img(src=encode_image(batting_bar))],
),
dcc.Tab(
label="FI Beeswarm",
children=[
html.Img(src=encode_image(batting_beeswarm))
],
),
dcc.Tab(
label="Ball Faced",
children=[html.Img(src=encode_image(batting_bf))],
),
dcc.Tab(
label="Strike Rate",
children=[html.Img(src=encode_image(batting_sr))],
),
],
)
],
style={"width": "88%"},
),
html.Div([], style={"height": "25px"}),
html.Div(
[
html.H5("Feature Importances: "),
html.P(
"BF - This is the most important feature in predicting how much runs a player will make and"
"as the number of ball faced increases the number of runs also increases, so spending more"
"time on the field is even much more important than having a very higher strike rate. "
),
html.P(
"SR - Second most important feature is strike rate and having a higher strike rate is good."
),
html.P(
"4s and 6s - Hitting 4s is slightly more important than hitting 6s in making runs in the long "
"run. The reason cloud be hitting 4s is much easier than hitting 6s so most of the time players "
"tends to have more 4s than 6s and when added together, in the long run 4s generate more runs."
),
html.P(
"Avg - Batting avg is also important but not as much as the above mentioned metrics."
),
],
style={"width": "85%"},
),
],
style={"width": "75%"},
),
# Bowling Records
html.Div([], style={"height": "45px"}),
html.H2("Bowling Records"),
html.Div([], style={"height": "25px"}),
html.H4("Player Wickets Per Season"),
html.Div(
[
html.B("Select Players"),
html.P(""),
dcc.Dropdown(
id="select-player-wkts-ts",
options=[
{"label": player, "value": player}
for player in bowling_players_list
],
value=[
"Jasprit Bumrah",
"Rashid Khan",
"Kagiso Rabada",
"Sunil Narine",
"Deepak Chahar",
],
multi=True,
),
dcc.Graph(id="players-wickets-time-series"),
],
style={"width": "70%"},
),
html.Div([], style={"height": "45px"}),
html.H4("Wickets Distributions"),
# Histogram of Wickets Distributions
html.Div(
[
html.Div(
[
html.P(
"Just like runs, wickets distribution is also right skewed, means "
"many players takes low or medium number of wickets, while few "
"players takes lots of wickets.The median of this distribution is 5 "
"which means that 50% of the players taks less than 5 wickets and "
"50% more than 5 wickets. 15 is the 90th percentile, meaning 90% "
"of the players takes less than 15 wickets. So, any players who "
"is taking more than or equal to 15 wickets in a season is doing "
"exceptionally well. "
)
],
style={
"width": "30%",
"display": "inline-block",
"margin-top": "60px",
},
),
html.Div(
[dcc.Graph(id="wickets-hist-plot", figure=wickets_histogram)],
style={"width": "60%", "float": "right", "display": "inline-block"},
),
],
style={"margin": "40px", "height": 500},
),
# Team wickets distributions
html.Div(
[
html.Div(
[
html.P(
"On average, Chennai Super Kings bowlers also performs better "
"than any other teams. Rajasthan Royals has some good balance in "
"their team which is why their median wickets is second highest "
"after CSK but overall gets outperformed by other teams. And there "
"is lot of variability in Sunrisers hyderabad team, they have really "
"some high wickets takers but the team is not much balanced which is "
"why they have a very low median value. Out of all Royal Challengers "
"Banglore and Delhi Capitals is performing very poorly. "
)
],
style={
"width": "30%",
"display": "inline-block",
"margin-top": "100px",
},
),
html.Div(
[
dcc.Graph(
id="team-wickets-dist",
figure=team_wickets_dist,
)
],
style={"width": "60%", "float": "right", "display": "inline-block"},
),
],
style={"margin": "40px", "height": 500},
),
# Bowling Leaderboard
html.H3("Bowling Leaderboard"),
html.Div([], style={"height": "25px"}),
html.H4("All Time Records"),
html.Div([], style={"height": "20px"}),
html.Div(
[
dcc.Tabs(
[
dcc.Tab(
label="Chart",
children=[
html.P(""),
html.Div(
[
html.Div(
[
html.Label("Select a Metric"),
dcc.Dropdown(
id="all-time-metric-selector-bowling",
options=[
{
"label": metric,
"value": metric,
}
for metric in bowling_metrics_list
],
value="Wkts",
),
],
style={
"width": "35%",
"display": "inline-block",
},
),
html.Div(
[
html.Label("Select Team"),
dcc.Dropdown(
id="all-time-team-selector-bowling",
options=[
{
"label": team,
"value": team,
}
for team in team_list
],
value="All Teams",
),
],
style={
"width": "35%",
"float": "right",
"display": "inline-block",
},
),
]
),
html.Div([dcc.Graph(id="all-time-graph-bowling")]),
],
),
dcc.Tab(
label="Table",
children=[
dash_table.DataTable(
id="all-time-records-bowling",
columns=[
{"name": i, "id": i}
for i in bowling_agg.columns
],
data=bowling_agg.to_dict("records"),
sort_action="native",
style_cell={
"textAlign": "left",
},
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "rgb(248, 248, 248)",
},
],
style_table={"overflowX": "auto"},
style_cell_conditional=[
{
"if": {"column_id": "POS"},
"textAlign": "center",
},
],
page_current=0,
page_size=15,
page_action="native",
)
],
),
]
)
],
style={"width": "75%"},
),
# Season Records - Bowling
html.Div([], style={"height": "45px"}),
html.H4("Season Records"),
html.Div([], style={"height": "20px"}),
html.Div(
[
dcc.Tabs(
[
dcc.Tab(
label="Chart",
children=[
html.P(""),
html.Div(
[
html.Div(
[
html.Label("Select a Metric"),
dcc.Dropdown(
id="season-metric-selector-bowling",
options=[
{
"label": metric,
"value": metric,
}
for metric in bowling_metrics_list
],
value="Wkts",
),
],
style={
"width": "25%",
"float": "left",
"padding-right": "25px",
"display": "inline-block",
},
),
html.Div(
[
html.Label("Season"),
dcc.Dropdown(
id="season-year-selector-bowling",
options=[
{
"label": str(year),
"value": year,
}
for year in range(
2019, 2007, -1
)
],
value=2019,
),
],
style={
"width": "25%",
"float": "middle",
"display": "inline-block",
},
),
html.Div(
[
html.Label("Team"),
dcc.Dropdown(
id="season-team-selector-bowling",
options=[
{"label": team, "value": team}
for team in team_list
],
value="All Teams",
),
],
style={
"width": "25%",
"float": "right",
"display": "inline-block",
},
),
],
),
html.Div([dcc.Graph(id="season-graph-bowling")]),
],
),
dcc.Tab(
label="Table",
children=[
dash_table.DataTable(
id="season-records-bowling",
columns=[
{"name": i, "id": i}
for i in bowling.columns
if i != "Player Link"
],
data=bowling.to_dict("records"),
sort_action="native",
style_cell={"textAlign": "left"},
style_data_conditional=[
{
"if": {"row_index": "odd"},
"backgroundColor": "rgb(248, 248, 248)",
},
],
style_table={"overflowX": "auto"},
style_cell_conditional=[
{
"if": {"column_id": "POS"},
"textAlign": "center",
},
],
page_current=0,
page_size=15,
page_action="native",
)
],
),
]
)
],
style={"width": "75%"},
),
# bowler Performance
html.Div([], style={"height": "80px"}),
html.H4("Important features for predicting bowlers wickets."),
html.Div([], style={"height": "25px"}),
html.Div(
[
html.Div(
[
dcc.Tabs(
id="tabs-with-classes-bowling",
parent_className="custom-tabs-bowling",
className="custom-tabs-container-bowling",
children=[
dcc.Tab(
label="Feature Importance",
children=[html.Img(src=encode_image(bowling_bar))],
),
dcc.Tab(
label="FI Beeswarm",
children=[
html.Img(src=encode_image(bowling_beeswarm))
],
),
dcc.Tab(
label="Strike Rate",