Visualizing and Predicting Analysis of Cricket Match - Part 1

Some Interesting Analysis

I. One of the analyzes looking at IPL matches played between 2008 and 2017 shows that Eden Garden and M Chinnaswamy Stadium are the best venues to track results. Tossing and fielding would be a better option. Let's do the same analysis for the IPL match. 

Dataset Description: This contains a total of 17 columns. Let's see them.

Attribute Information :

• team1
• team2
• id
• City
• date
• player_of_match
• toss_winner
• toss_decision
• winner
• result
• result_margin
• eliminator
• method
• venue
• umpire1
• umpire2 

In the code section, we will directly see the main part of the code. To know detailed descriptions, you can directly download the Jupyter Notebook.

Let's load the libraries:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

Look at the dataset:

df = pd. read_csv(r'C:USERhp Matches 2008-2020.csv')

Let's remove the columns that are not of use from the dataset:

df.drop(labels = ['id', 'date', 'player_of_match', 'result', 'neutral_venue', 'result_margin','umpire1', 'umpire2', 'eliminator', 'method'], axis = 1,inplace = True)

Now, analyzing the optimized or correct option the toss win:

win_target = loss_target = win_chassing = loss_chassing = 0 
for i in range(len(df)) : 
    if df.toss_result.iloc[i] == 'bat' :
        if df.winner_toss_team.iloc[i] == df.winner.iloc[i] : 
            win_target += 1
        else :
            loss_target += 1
    else : 
        if df.winner_toss_team.iloc[i] == df.winner.iloc[i] : 
            win_chassing += 1
        else :
            loss_chassing += 1
print('{} times captain have chosen batting over fielding and win the match.'.format(win_target))
print('{} times captain have chosen batting option over fielding but loose the match.'.format(loss_target))
print('{} times captain have chosen fielding option over batting and win the match.'.format(win_chassing))
print('{} times captain have chosen fielding option but loose the match.'.format(loss_chassing))

Let's create a particular column and describe how the team wins the match(by chasing the score or by giving the target):

for i in range(len(df)) : 
    if df.toss_result.iloc[i] == 'bat' :    
        if df.toss_result.iloc[i] == df.winner.iloc[i] : 
            # captain chooses the batting option and win the match, then it will count as a target.
            df['target'].iloc[i] = 1       
        else :
            # captain chooses batting option and loses the match, then it will count as chasing.
            df['chase'].iloc[i] = 1   
    else :          
        if df.toss_result.iloc[i] == df.winner.iloc[i] : 
            # captain chooses fielding option and wins the match, then it will count in chasing.
            df['chase'].iloc[i] = 1   
        else :
            # captain chooses fielding option and loses the match, which will count in a target.
            df['target'].iloc[i] = 1

Let's take out some more useful information from the data:

targetlist = []
chaselist = []
for i in top10_stadium : 
    print('Analysis on "{} Stadium"'.format(i))
    aa = np.sum(df[df.venue1 == i].target)
    bb = np.sum(df[df.venue1 == i].chase)
    print(aa, 'times team gave good target and win the match.')
    print(bb, 'times team easily chase the score and win the match.') 
    targetlist.append(aa)
    chaselist.append(bb)
    print()
 

Let's visualize the above data for better understanding:

top10_stadium = ['Eden Gardens, Kolkata',
                              'Feroz Shah Kotla, Delhi',
                              'Wankhede Stadium, Mumbai',
                              'M Chinnaswamy Stadium, Bangalore',
                              'Rajiv Gandhi International Stadium, Uppal, Hyderabad',
                              'Sawai Mansingh Stadium, Jaipur', 
                              'MA Chidambaram Stadium, Chepauk, Chennai',
                              'Punjab Cricket Association Stadium, Mohali, Chandigarh',
                              'Sheikh Zayed Stadium, Abu Dhabi',
                              'Maharashtra Cricket Association Stadium, Pune']
data = {'target': [30, 34, 36, 26, 27, 35, 15, 15, 19, 10],
        'chase': [47, 39, 37, 36, 37, 22, 32, 20, 14, 16]}
df1 = pd.DataFrame(data,columns=['target', 'chase'], index = top10_stadium)
df1.plot.barh(figsize = (10,10))
plt.style.use('seaborn-bright')
plt.title('Top-10 Stadiums')
plt.ylabel('Stadiums')
plt.xlabel('No. of Matches Win')
plt.xticks(np.arange(0, 54, 3))
plt.show()

 The graph above shows how often teams score good goals or easily chase goals at a given stadium. Look at the horizontal bar in the stadium "Eden Garden, Kolkata." This bar shows over 45 times, the game was easily scored and won, and about 30 times, the team aimed and won the game. From this, we can easily conclude that this stadium is good for chasing points. Therefore, if a match is played at this location and the team wins a draw, then the defense is the better option. Similarly, we can quickly analyze the entire plot.

Let's convert the above data in terms of percentage for better understanding:

target = []
chase = []
for i in top10_stadium : 
    print(i)
    aa = np.sum(df[df.venue1 == i].target)
    bb = np.sum(df[df.venue1 == i].chase)
    total = aa + bb
    t = ((aa / tot) * 100)
    c = ((bb / tot) * 100)
    target.append(round(t, 2))
    chase.append(round(c, 2))
    print('{:.2f}% probability that if you choose to bat, then you will win the match.'.format((aa / tot) * 100))
    print('{:.2f}% probability that if you choose to field, then you will win the match.'.format((bb / tot) * 100))
    print()

 Let's visualize the above data for better understanding:

top10_stadium = ['Eden Gardens, Kolkata',
                              'Feroz Shah Kotla, Delhi',
                              'Wankhede Stadium, Mumbai',
                              'M Chinnaswamy Stadium, Bangalore',
                              'Rajiv Gandhi International Stadium, Uppal, Hyderabad',
                              'Sawai Mansingh Stadium, Jaipur', 
                              'MA Chidambaram Stadium, Chepauk, Chennai',
                              'Punjab Cricket Association Stadium, Mohali, Chandigarh',
                              'Sheikh Zayed Stadium, Abu Dhabi',
                              'Maharashtra Cricket Association Stadium, Pune']
data = {'Bat_first': target,
        'Field_first': chase}
df2 = pd.DataFrame(data,columns=['Bat_first', 'Field_first'], index = top10_stadium)

df2.plot.barh(figsize = (10,10))
plt.style.use('seaborn-bright')
plt.title('Top-10 Stadiums')
plt.ylabel('Stadiums')
plt.xlabel('Probability to win')
plt.xticks(np.arange(0, 75, 3))
plt.show()

From this, we can easily conclude that the stadium is more suitable for scoring goals against the opposing team. Therefore, batting is the better option if a match is played at this venue and either team wins a draw. Similarly, we can easily analyze the entire plot. For more information, you can directly download the jupyter notebook.

II. Further analysis considered batting averages and batting averages for all IPL players and concluded that all players under the age of 35 had batting averages. He averaged 24.51 while his strike rate was 126.84, an average strike rate for players over the age of 35. The batting average is significantly below the 112.1 batting average from 21.34. This shows that teams should prioritize younger players if they need to improve their performance.

Frequently Asked Questions

How do Data Scientists use statistics?

Statistics plays a powerful role in Data Science. It is one of the most important disciplines to provide tools and methods to find structure and to give deeper insight into data. It serves a great impact on data acquisition, exploration, analysis, validation, etc.

What is bias in data science?

Historical data bias occurs when socio-cultural prejudices and beliefs are mirrored in systematic processes.

What is heuristic thinking?

Heuristics are mental shortcuts that can facilitate problem-solving and probability judgments. These strategies are generalizations, or rules-of-thumb, reduce cognitive load and can be effective for making immediate judgments, however, they often result in irrational or inaccurate conclusions.

Conclusion

This article visualizes and predicts the analysis of cricket matches. We will continue this article in part 2. 

To learn more, see Visualizing and Predicting Analysis of Cricket part 2, Cloud Computing, Microsoft Azure, C++ with Data Structure, DBMS, Operating System by Coding Ninjas, and keep practicing on our platform Coding Ninjas Studio.

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