Guide

Introduction

Ball-by-Ball data in cricket, records the information for every ball bowled in the game, like the batsman on strike, the bowler who is bowling, runs scored on that ball, if a wicket is taken on that ball etc. By using this data, we can compute metrics like the Strike Rate of a Batsman, Runs per over, Economy of a bowler and all the other standard metrics used in cricket.
To know more about Ball-by-Ball data, the sources for this type of data and the standard metrics which can be calculated using this data, visit this article.
In this article, we will be learning how to handle Ball-by-Ball data with Python using the basic libraries Python provides.
The dataset used for this article can be directly downloaded from here.

Tutorial

Importing the Libraries and Packages

Let’s start this tutorial by importing all the libraries and packages required.

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler

The numpy library is used for dealing with arrays and making numerical operations.
The pandas library is used for handling and analyzing data.
The matplotlib library is a data visualization library used to draw necessary graphs and plots.
The MinMaxScaler package is a package from SciKit-Learn library used for normalizing the data.

Loading the data

The datasets provided are in a CSV (Comma Separated Values) format. We load the datasets using the read_csv function available in pandas. This function reads the CSV and loads it into a dataframe.

In [2]:

dfDeliveries = pd.read_csv('D:\Analytics\Cricket\Data\IPL_Ball_by_Ball_2022.csv') 
dfMatches = pd.read_csv('D:\Analytics\Cricket\Data\IPL_Matches_2022.csv')

After loading the dataframes into variables, we can explore the dataframes by using the head() function which returns the first 5 rows of the dataframe.

In [3]:

pd.set_option('max_columns',100)

In [4]:

dfDeliveries.head()

Out[4]:

	ID	innings	ballnumber	batter	bowler	non-striker	extra_type	batsman_run	extras_run	total_run	player_out	kind	fielders_involved	BattingTeam
0	1312200	1	1	YBK Jaiswal	Mohammed Shami	JC Buttler	NaN	0	0	0	NaN	NaN	NaN	Rajasthan Royals
1	1312200	1	2	YBK Jaiswal	Mohammed Shami	JC Buttler	legbyes	0	1	1	NaN	NaN	NaN	Rajasthan Royals
2	1312200	1	3	JC Buttler	Mohammed Shami	YBK Jaiswal	NaN	1	0	1	NaN	NaN	NaN	Rajasthan Royals
3	1312200	1	4	YBK Jaiswal	Mohammed Shami	JC Buttler	NaN	0	0	0	NaN	NaN	NaN	Rajasthan Royals
4	1312200	1	5	YBK Jaiswal	Mohammed Shami	JC Buttler	NaN	0	0	0	NaN	NaN	NaN	Rajasthan Royals

In [5]:

dfMatches.head()

Out[5]:

	ID	City	Date	Season	MatchNumber	Team1	Team2	Venue	TossWinner	TossDecision	SuperOver	WinningTeam	WonBy	Margin	method	Player_of_Match	Team1Players	Team2Players	Umpire1	Umpire2
0	1312200	Ahmedabad	2022-05-29	2022	Final	Rajasthan Royals	Gujarat Titans	Narendra Modi Stadium, Ahmedabad	Rajasthan Royals	bat	N	Gujarat Titans	Wickets	7	NaN	HH Pandya	[‘YBK Jaiswal’, ‘JC Buttler’, ‘SV Samson’, ‘D …	[‘WP Saha’, ‘Shubman Gill’, ‘MS Wade’, ‘HH Pan…	CB Gaffaney	Nitin Menon
1	1312199	Ahmedabad	2022-05-27	2022	Qualifier 2	Royal Challengers Bangalore	Rajasthan Royals	Narendra Modi Stadium, Ahmedabad	Rajasthan Royals	field	N	Rajasthan Royals	Wickets	7	NaN	JC Buttler	[‘V Kohli’, ‘F du Plessis’, ‘RM Patidar’, ‘GJ …	[‘YBK Jaiswal’, ‘JC Buttler’, ‘SV Samson’, ‘D …	CB Gaffaney	Nitin Menon
2	1312198	Kolkata	2022-05-25	2022	Eliminator	Royal Challengers Bangalore	Lucknow Super Giants	Eden Gardens, Kolkata	Lucknow Super Giants	field	N	Royal Challengers Bangalore	Runs	14	NaN	RM Patidar	[‘V Kohli’, ‘F du Plessis’, ‘RM Patidar’, ‘GJ …	[‘Q de Kock’, ‘KL Rahul’, ‘M Vohra’, ‘DJ Hooda…	J Madanagopal	MA Gough
3	1312197	Kolkata	2022-05-24	2022	Qualifier 1	Rajasthan Royals	Gujarat Titans	Eden Gardens, Kolkata	Gujarat Titans	field	N	Gujarat Titans	Wickets	7	NaN	DA Miller	[‘YBK Jaiswal’, ‘JC Buttler’, ‘SV Samson’, ‘D …	[‘WP Saha’, ‘Shubman Gill’, ‘MS Wade’, ‘HH Pan…	BNJ Oxenford	VK Sharma
4	1304116	Mumbai	2022-05-22	2022	70	Sunrisers Hyderabad	Punjab Kings	Wankhede Stadium, Mumbai	Sunrisers Hyderabad	bat	N	Punjab Kings	Wickets	5	NaN	Harpreet Brar	[‘PK Garg’, ‘Abhishek Sharma’, ‘RA Tripathi’, …	[‘JM Bairstow’, ‘S Dhawan’, ‘M Shahrukh Khan’,…	AK Chaudhary	NA Patwardhan

Data PreProcessing

As we can see from the above Deliveries DataFrame, we do not have the Name of the Bowling team present in it.
To get the Name of the Bowling team for each delivery, we’ll be using the Matches DataFrame by merging/joining it with the Deliveries DataFrame and using the where function provided by numpy to write a conditional clause.

In [6]:

dfDeliveries = dfDeliveries.merge(dfMatches, on='ID')

In [7]:

dfDeliveries['BowlingTeam'] = np.where(dfDeliveries['BattingTeam']==dfDeliveries['Team1'],
                                       dfDeliveries['Team2'],
                                       dfDeliveries['Team1'])

In [8]:

dfDeliveries.head()

Out[8]:

	ID	innings	ballnumber	batter	bowler	non-striker	extra_type	batsman_run	extras_run	total_run	player_out	kind	fielders_involved	BattingTeam	City	Date	Season	MatchNumber	Team1	Team2	Venue	TossWinner	TossDecision	SuperOver	WinningTeam	WonBy	Margin	method	Player_of_Match	Team1Players	Team2Players	Umpire1	Umpire2	BowlingTeam
0	1312200	1	1	YBK Jaiswal	Mohammed Shami	JC Buttler	NaN	0	0	0	NaN	NaN	NaN	Rajasthan Royals	Ahmedabad	2022-05-29	2022	Final	Rajasthan Royals	Gujarat Titans	Narendra Modi Stadium, Ahmedabad	Rajasthan Royals	bat	N	Gujarat Titans	Wickets	7	NaN	HH Pandya	[‘YBK Jaiswal’, ‘JC Buttler’, ‘SV Samson’, ‘D …	[‘WP Saha’, ‘Shubman Gill’, ‘MS Wade’, ‘HH Pan…	CB Gaffaney	Nitin Menon	Gujarat Titans
1	1312200	1	2	YBK Jaiswal	Mohammed Shami	JC Buttler	legbyes	0	1	1	NaN	NaN	NaN	Rajasthan Royals	Ahmedabad	2022-05-29	2022	Final	Rajasthan Royals	Gujarat Titans	Narendra Modi Stadium, Ahmedabad	Rajasthan Royals	bat	N	Gujarat Titans	Wickets	7	NaN	HH Pandya	[‘YBK Jaiswal’, ‘JC Buttler’, ‘SV Samson’, ‘D …	[‘WP Saha’, ‘Shubman Gill’, ‘MS Wade’, ‘HH Pan…	CB Gaffaney	Nitin Menon	Gujarat Titans
2	1312200	1	3	JC Buttler	Mohammed Shami	YBK Jaiswal	NaN	1	0	1	NaN	NaN	NaN	Rajasthan Royals	Ahmedabad	2022-05-29	2022	Final	Rajasthan Royals	Gujarat Titans	Narendra Modi Stadium, Ahmedabad	Rajasthan Royals	bat	N	Gujarat Titans	Wickets	7	NaN	HH Pandya	[‘YBK Jaiswal’, ‘JC Buttler’, ‘SV Samson’, ‘D …	[‘WP Saha’, ‘Shubman Gill’, ‘MS Wade’, ‘HH Pan…	CB Gaffaney	Nitin Menon	Gujarat Titans
3	1312200	1	4	YBK Jaiswal	Mohammed Shami	JC Buttler	NaN	0	0	0	NaN	NaN	NaN	Rajasthan Royals	Ahmedabad	2022-05-29	2022	Final	Rajasthan Royals	Gujarat Titans	Narendra Modi Stadium, Ahmedabad	Rajasthan Royals	bat	N	Gujarat Titans	Wickets	7	NaN	HH Pandya	[‘YBK Jaiswal’, ‘JC Buttler’, ‘SV Samson’, ‘D …	[‘WP Saha’, ‘Shubman Gill’, ‘MS Wade’, ‘HH Pan…	CB Gaffaney	Nitin Menon	Gujarat Titans
4	1312200	1	5	YBK Jaiswal	Mohammed Shami	JC Buttler	NaN	0	0	0	NaN	NaN	NaN	Rajasthan Royals	Ahmedabad	2022-05-29	2022	Final	Rajasthan Royals	Gujarat Titans	Narendra Modi Stadium, Ahmedabad	Rajasthan Royals	bat	N	Gujarat Titans	Wickets	7	NaN	HH Pandya	[‘YBK Jaiswal’, ‘JC Buttler’, ‘SV Samson’, ‘D …	[‘WP Saha’, ‘Shubman Gill’, ‘MS Wade’, ‘HH Pan…	CB Gaffaney	Nitin Menon	Gujarat Titans

PlayerWise Aggregation – Batting Metrics

In this section, we will be calculating the standard batting metrics for each and every batsmen in IPL 2022. The standard metrics we will be calculating are,

Number of Innings Played as a Batsman (Innings)
Number of Runs Scored (Runs)
Number of Outs/Dismissals (Dismissals)
Number of Balls Faced (Balls Faced)
Number of 4s scored (4s)
Number of 6s scored (6s)
Highest Score (High Score)
Number of Half Centuries scored (50)
Number of Centuries scored (100)
Batting Strike Rate (Strike Rate)
Batting Average (Batting Average)

For calculating these metrics, we will be using basic conditional dataframe slicing and the groupby, agg, merge, reset_index functions provided by pandas along with some basic math functions like sum, max, count and nunique.

Innings, Runs and Dismissals

To calculate the number of innings played, runs scored and dismissals for each batsmen, we will use groupby function to group over the deliveries of each batsmen and use the agg function to get the number of innings by getting the unique Match IDs with nunique, runs scored by summing all the batsmen runs and dismissals by summing all the wicket deliveries.

We can convert this aggregated dataframe into a proper dataframe by resetting the index using the reset_index function.

In [9]:

dfBatsmen = dfDeliveries.groupby('batter')\
            .agg({'ID':'nunique','batsman_run':'sum','isWicketDelivery':'sum'})\
            .reset_index()

In [10]:

dfBatsmen.head()

Out[10]:

	batter	ID	batsman_run	isWicketDelivery
0	A Badoni	11	161	9
1	A Manohar	7	108	6
2	A Nortje	1	1	0
3	A Tomar	1	4	1
4	AD Russell	12	335	9

Now, we rename the columns to their correct descriptions and display it with the head function to see our resultant dataframe.

In [11]:

dfBatsmen.columns = ['Batter','Innings','Runs','Dismissals']

In [12]:

dfBatsmen.head()

Out[12]:

	Batter	Innings	Runs	Dismissals
0	A Badoni	11	161	9
1	A Manohar	7	108	6
2	A Nortje	1	1	0
3	A Tomar	1	4	1
4	AD Russell	12	335	9

We use the same aggregation methods with a combination of conditional slicing by excluding all the wide balls for calculating the number of balls faced, by including all the runs which are 4s and boundaries for calculating the number of 4s scored and similarly for 6s.

Balls Faced

In [13]:

dfBallsFaced = dfDeliveries[(dfDeliveries['extra_type']!='wides')].groupby('batter')['ID'].count().reset_index()
dfBallsFaced.columns = ['Batter','Balls Faced']

In [14]:

dfBallsFaced.head()

Out[14]:

	Batter	Balls Faced
0	A Badoni	130
1	A Manohar	75
2	A Nortje	6
3	A Tomar	8
4	AD Russell	192

Boundaries – 4s and 6s

In [15]:

dfBatter4s = dfDeliveries[(dfDeliveries['batsman_run']==4)&(dfDeliveries['non_boundary']==0)]\
            .groupby('batter')['ID']\
            .count()\
            .reset_index()\
            .sort_values('ID')
dfBatter4s.columns = ['Batter','4s']

In [16]:

dfBatter4s.head()

Out[16]:

	Batter	4s
50	KK Nair	1
25	DJ Mitchell	1
60	M Ashwin	1
28	DR Sams	1
62	M Vohra	1

In [17]:

dfBatter6s = dfDeliveries[(dfDeliveries['batsman_run']==6)&(dfDeliveries['non_boundary']==0)]\
            .groupby('batter')['ID']\
            .count()\
            .reset_index()\
            .sort_values('ID')
dfBatter6s.columns = ['Batter','6s']

In [18]:

dfBatter6s.head()

Out[18]:

	Batter	6s
55	Mohsin Khan	1
90	SE Rutherford	1
17	DJ Bravo	1
82	RV Patel	1
94	SP Jackson	1

To calculate the High Scores, Number of Half Centuries and Centuries, we need to group over the batsmen and the Match ID and perform a sum function over all the batsmen runs to get the runs scored for each batsmen in each match.

In [19]:

dfMatchWise = dfDeliveries.groupby(['batter','ID'])['batsman_run'].sum().reset_index()

In [20]:

dfMatchWise.head()

Out[20]:

	batter	ID	batsman_run
0	A Badoni	1304050	54
1	A Badoni	1304053	19
2	A Badoni	1304058	19
3	A Badoni	1304061	10
4	A Badoni	1304066	5

We now use this match-wise aggregated dataframe and again group over the batsmen and perform the max function to get the highest score scored by every batsmen.

High Score

In [21]:

dfHighScore = dfMatchWise.groupby(['batter'])['batsman_run'].max().reset_index()
dfHighScore.columns = ['Batter','High Score']

In [22]:

dfHighScore.head()

Out[22]:

	Batter	High Score
0	A Badoni	54
1	A Manohar	43
2	A Nortje	1
3	A Tomar	4
4	AD Russell	70

Similarly, by using the match-wise aggregated dataframe along with conditional slicing of the dataframe by getting total runs scored in each match greater than or equal to 50 and less than 100 and counting it we calculate the number of half centuries scored and, getting total runs scored in each match greater than or equal to 100 and counting it we calculate the number of centuries scored.

50s and 100s

In [23]:

df50s = dfMatchWise[(dfMatchWise['batsman_run']>=50)&(dfMatchWise['batsman_run']<100)]\
        .groupby('batter')['batsman_run']\
        .count()\
        .reset_index()\
        .sort_values('batsman_run',ascending=False)
df50s.columns = ['Batter','50']

In [24]:

df50s.head()

Out[24]:

	Batter	50
10	DA Warner	5
46	Shubman Gill	4
18	JC Buttler	4
16	HH Pandya	4
23	LS Livingstone	4

In [25]:

df100s = dfMatchWise[(dfMatchWise['batsman_run']>=100)]\
        .groupby('batter')['batsman_run']\
        .count()\
        .reset_index()\
        .sort_values('batsman_run',ascending=False)
df100s.columns = ['Batter','100']

In [26]:

df100s.head()

Out[26]:

	Batter	100
0	JC Buttler	4
1	KL Rahul	2
2	Q de Kock	1
3	RM Patidar	1

Finally, we join all the dataframes we have created so far for each statistic using the merge function on the Batter column.
(Note: We need to join these dataframes in outer join by specifying the ‘how’ argument in the merge function as ‘outer’.)

Merging all the DataFrames

In [27]:

dfBatsmen = dfBatsmen.merge(dfBallsFaced, on='Batter',how='outer').merge(dfBatter4s, on='Batter',how='outer')\
                     .merge(dfBatter6s, on='Batter',how='outer').merge(dfHighScore, on='Batter',how='outer')\
                     .merge(df50s, on='Batter',how='outer').merge(df100s, on='Batter',how='outer')

In [28]:

dfBatsmen.head()

Out[28]:

	Batter	Innings	Runs	Dismissals	Balls Faced	4s	6s	High Score	50	100
0	A Badoni	11	161	9	130	11.0	7.0	54	1.0	NaN
1	A Manohar	7	108	6	75	14.0	3.0	43	NaN	NaN
2	A Nortje	1	1	0	6	NaN	NaN	1	NaN	NaN
3	A Tomar	1	4	1	8	1.0	NaN	4	NaN	NaN
4	AD Russell	12	335	9	192	18.0	32.0	70	1.0	NaN

Using this newly joined dataframe we can derive metrics like Batting Strike Rate, by dividing the runs scored with the balls faced as a multiple of 100 and Batting Average, by dividing the runs scored with the number of dismissals.

In [29]:

dfBatsmen['Strike Rate'] = (dfBatsmen['Runs']/dfBatsmen['Balls Faced'])*100
dfBatsmen['Batting Average'] = dfBatsmen['Runs']/dfBatsmen['Dismissals']

Let’s fill the NaN values with 0 using the fillna function and sort the rows in the dataframe by using sort_values function with runs scored as the key to be sorted on and display the head of this dataframe.

In [30]:

dfBatsmen.fillna(0, inplace=True)

In [31]:

dfBatsmen.sort_values(by='Runs', ascending=False).head()

Out[31]:

	Batter	Innings	Runs	Dismissals	Balls Faced	4s	6s	High Score	50	100	Strike Rate	Batting Average
49	JC Buttler	17	863	15	579	83.0	45.0	116	4.0	4.0	149.050086	57.533333
65	KL Rahul	14	616	11	455	45.0	30.0	103	4.0	2.0	135.384615	56.000000
112	Q de Kock	15	508	15	341	47.0	23.0	140	3.0	1.0	148.973607	33.866667
42	HH Pandya	15	487	10	371	49.0	12.0	87	4.0	0.0	131.266846	48.700000
154	Shubman Gill	16	483	15	365	51.0	11.0	96	4.0	0.0	132.328767	32.200000

These aggregated standard metrics can be directly found on IPL’s website.
Why go through all this trouble to calculate something that’s readily available on the internet?
This is because the aggregation methods used to calculate all the standard metrics are used as the foundation to make much more in-depth analysis.

TeamWise Aggregation – Bowling Metrics in Death Overs

A T20 game can be divided into three phases. They are,

Power Play (1-6 overs)
Middle Overs (7-15 overs)
Death Overs (16-20 overs)
Let’s create a small function that returns the phase of the game based on the over.
If we look at the unique values in the Overs column of the dataframe, the overs start from 0th over and end at the end of the 19th over.
This is why we will shift all the starting and ending overs of each phase by -1.

In [32]:

def overClassifier(val):
    if val>=0 and val<6:
        return 'Power Play'
    elif val>=6 and val<15:
        return 'Middle Overs'
    else:
        return 'Death Overs'

Let’s map this function to the overs column and get the phase of the play of the game at every delivery.

In [33]:

dfDeliveries['PhaseOfPlay'] = dfDeliveries['overs'].map(overClassifier)

In [34]:

dfDeliveries[['overs','PhaseOfPlay']].sort_values('overs').drop_duplicates().reset_index(drop=True)

Out[34]:

	overs	PhaseOfPlay
0	0	Power Play
1	1	Power Play
2	2	Power Play
3	3	Power Play
4	4	Power Play
5	5	Power Play
6	6	Middle Overs
7	7	Middle Overs
8	8	Middle Overs
9	9	Middle Overs
10	10	Middle Overs
11	11	Middle Overs
12	12	Middle Overs
13	13	Middle Overs
14	14	Middle Overs
15	15	Death Overs
16	16	Death Overs
17	17	Death Overs
18	18	Death Overs
19	19	Death Overs

Using the conditional slicing, let’s take all the deliveries which occurred in the Death Overs of the game.
Using the same aggregation methods we performed above, we will be calculating the Runs Conceded, Wickets Taken, Balls Bowled by grouping over each Bowling Team. We merge all the dataframes of bowling metrics we have created and display the head of this dataframe.

In [35]:

dfDeathOvers = dfDeliveries[dfDeliveries['PhaseOfPlay']=='Death Overs']

Innings, Runs and Dismissals

In [36]:

dfDeathBowling = dfDeathOvers.groupby('BowlingTeam').agg({'total_run':'sum','isWicketDelivery':'sum'}).reset_index()

In [37]:

dfDeathBowling.columns = ['Team','Runs Conceded','Wickets']

Balls Bowled

In [38]:

dfDOBallsBowled = dfDeliveries[~((dfDeliveries['extra_type']=='wides'))&(dfDeliveries['PhaseOfPlay']=='Death Overs')]\
                  .groupby('BowlingTeam')['ID']\
                  .count().reset_index()
dfDOBallsBowled.columns = ['Team','Balls Bowled']

In [39]:

dfDeathBowling = dfDeathBowling.merge(dfDOBallsBowled, on='Team')

Overs, Bowling Average, Bowling Strike Rate and Economy Rate

In [40]:

dfDeathBowling['Overs'] = dfDeathBowling['Balls Bowled']/6
dfDeathBowling['Bowling Average'] = dfDeathBowling['Runs Conceded']/dfDeathBowling['Wickets']
dfDeathBowling['Bowling Strike Rate'] = dfDeathBowling['Balls Bowled']/dfDeathBowling['Wickets']
dfDeathBowling['Economy Rate'] = dfDeathBowling['Runs Conceded']/dfDeathBowling['Overs']

Let’s look at the teams with the best Economy Rate in the Death Overs.
To do that, we will sort the dataframe using the sort_values function in pandas and sort it on Economy Rate in ascending order since a lower Economy Rate is better.

In [41]:

dfDeathBowling = dfDeathBowling.sort_values('Economy Rate',ascending=False).reset_index(drop=True)

In [42]:

dfDeathBowling

Out[42]:

	Team	Runs Conceded	Wickets	Balls Bowled	Overs	Bowling Average	Bowling Strike Rate	Economy Rate
0	Lucknow Super Giants	776	35	418	69.666667	22.171429	11.942857	11.138756
1	Sunrisers Hyderabad	669	33	369	61.500000	20.272727	11.181818	10.878049
2	Mumbai Indians	639	33	354	59.000000	19.363636	10.727273	10.830508
3	Royal Challengers Bangalore	744	31	422	70.333333	24.000000	13.612903	10.578199
4	Rajasthan Royals	813	39	469	78.166667	20.846154	12.025641	10.400853
5	Chennai Super Kings	599	27	346	57.666667	22.185185	12.814815	10.387283
6	Delhi Capitals	711	35	414	69.000000	20.314286	11.828571	10.304348
7	Kolkata Knight Riders	640	30	376	62.666667	21.333333	12.533333	10.212766
8	Punjab Kings	561	29	342	57.000000	19.344828	11.793103	9.842105
9	Gujarat Titans	645	38	415	69.166667	16.973684	10.921053	9.325301

Finally, let’s make a simple visualization for the teams with the best Economy Rate in the form of a horizontal bar graph.

In [43]:

fig,ax = plt.subplots(figsize=(12,10))

# Setting background colors
fig.set_facecolor('black')
ax.patch.set_facecolor('black')

# Setting the grid for axis
ax.set_axisbelow(True)
ax.grid(color='grey',which='major',linestyle='--',alpha=0.75)

# Embellishing the spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_color('white')
ax.spines['bottom'].set_color('white')
ax.spines['left'].set_linewidth(3)
ax.spines['bottom'].set_linewidth(3)

# Normalizing the Economy Rate values
scaler = MinMaxScaler(feature_range=(0.2,1))
# Using the normalized Economy Rate values as alpha values
alphaList = scaler.fit_transform(dfDeathBowling['Economy Rate'].values.reshape(-1,1)).flatten().tolist()
alphaList.reverse()

# Plotting the horizontal bar graph
for index,row in dfDeathBowling.iterrows():
    ax.barh(dfDeathBowling['Team'][index],dfDeathBowling['Economy Rate'][index],
            color='#0217fa',alpha=alphaList[index])
    
    ax.barh(dfDeathBowling['Team'][index],dfDeathBowling['Economy Rate'][index],
            color='None',edgecolor='white',lw=2)

# Setting the fontsize, color and font-style of the tick labels    
plt.yticks(fontsize=16,color='white',fontname='Arial Rounded MT Bold')
plt.xticks(fontsize=13,color='white',fontname='Arial Rounded MT Bold')

# Setting the labels and the title
plt.xlabel('Economy Rate',fontsize=18,color='white',fontname='Arial Rounded MT Bold')

plt.title(x=0.35,y=1.05,label='Economy Rate in Death Overs\nIndian Premier League - 2022',
          fontsize=28,color='white',fontname='Arial Rounded MT Bold')

plt.show()