Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScalerThe IPL matches dataset provides a summary of the details for each match played in the Indian Premier League (IPL). This dataset includes information about the teams, match outcomes, and other relevant details for each game.
- Total Records: 756 entries
- Total Columns: 18
- id: Unique identifier for each match.
- season: The season/year of the IPL.
- city: The city where the match was played.
- date: The date when the match took place.
- team1: The first team participating in the match.
- team2: The second team participating in the match.
- toss_winner: The team that won the toss.
- toss_decision: The decision made by the toss winner (field or bat).
- result: The result of the match (normal, tie, no result).
- dl_applied: Indicates if the Duckworth-Lewis method was applied (0 or 1).
- winner: The team that won the match.
- win_by_runs: The number of runs by which the match was won (if applicable).
- win_by_wickets: The number of wickets by which the match was won (if applicable).
- player_of_match: The player awarded as the player of the match.
- venue: The venue where the match was played.
- umpire1: The name of the first umpire.
- umpire2: The name of the second umpire.
- umpire3: The name of the third umpire (if applicable).
# Load the dataset
matches_df = pd.read_csv('C:\\Users\\anonymous\\Desktop\\TSF GRIP Task\\Indian Premier League\\matches.csv')
# Display the first few rows of the dataset to understand its structure
matches_df.head()#shape calculate of match data set
matches_df.shape# Display basic information about the dataset
matches_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 756 entries, 0 to 755
Data columns (total 18 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 756 non-null int64
1 season 756 non-null int64
2 city 749 non-null object
3 date 756 non-null object
4 team1 756 non-null object
5 team2 756 non-null object
6 toss_winner 756 non-null object
7 toss_decision 756 non-null object
8 result 756 non-null object
9 dl_applied 756 non-null int64
10 winner 752 non-null object
11 win_by_runs 756 non-null int64
12 win_by_wickets 756 non-null int64
13 player_of_match 752 non-null object
14 venue 756 non-null object
15 umpire1 754 non-null object
16 umpire2 754 non-null object
17 umpire3 119 non-null object
dtypes: int64(5), object(13)
memory usage: 106.4+ KB
The IPL deliveries dataset provides comprehensive ball-by-ball details of Indian Premier League (IPL) matches. This extensive dataset includes various aspects of each delivery, covering the participating teams, players, outcomes of deliveries, and more.
- Total Records: 179,078 entries
- Total Columns: 21
- match_id: A unique identifier for each match.
- inning: The inning number (1 or 2).
- batting_team: The team currently batting.
- bowling_team: The team currently bowling.
- over: The over number in the match.
- ball: The ball number within the over.
- batsman: The name of the batsman facing the delivery.
- non_striker: The name of the batsman at the non-striker end.
- bowler: The name of the bowler delivering the ball.
- is_super_over: Indicates if the delivery is part of a super over (binary: 0 or 1).
- wide_runs: Runs scored off wide deliveries.
- bye_runs: Runs scored off byes.
- legbye_runs: Runs scored off leg byes.
- noball_runs: Runs scored off no-balls.
- penalty_runs: Runs awarded as penalty.
- batsman_runs: Runs scored by the batsman from the delivery.
- extra_runs: Total extra runs conceded (sum of wide, bye, legbye, noball, and penalty runs).
- total_runs: Total runs scored from the delivery (batsman_runs + extra_runs).
- player_dismissed: Name of the dismissed player, if any.
- dismissal_kind: Type of dismissal (e.g., caught, bowled, etc.).
- fielder: Name of the fielder involved in the dismissal, if any.
# Load the dataset
deliveries_df = pd.read_csv('C:\\Users\\anonymous\\Desktop\\TSF GRIP Task\\Indian Premier League\\deliveries.csv')
# Display the first few rows of the dataset to understand its structure
deliveries_df.head()#shape calculate of deleveries data set
deliveries_df.shape# Display basic information about the dataset
deliveries_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 179078 entries, 0 to 179077
Data columns (total 21 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 match_id 179078 non-null int64
1 inning 179078 non-null int64
2 batting_team 179078 non-null object
3 bowling_team 179078 non-null object
4 over 179078 non-null int64
5 ball 179078 non-null int64
6 batsman 179078 non-null object
7 non_striker 179078 non-null object
8 bowler 179078 non-null object
9 is_super_over 179078 non-null int64
10 wide_runs 179078 non-null int64
11 bye_runs 179078 non-null int64
12 legbye_runs 179078 non-null int64
13 noball_runs 179078 non-null int64
14 penalty_runs 179078 non-null int64
15 batsman_runs 179078 non-null int64
16 extra_runs 179078 non-null int64
17 total_runs 179078 non-null int64
18 player_dismissed 8834 non-null object
19 dismissal_kind 8834 non-null object
20 fielder 6448 non-null object
dtypes: int64(13), object(8)
memory usage: 28.7+ MB
# Check for missing values in the matches dataset
deliveries_df.isnull().sum()# Check for missing values in the matches dataset
matches_df.isnull().sum()- player_dismissed: Missing values because not every delivery results in a dismissal.
- dismissal_kind: Missing values for the same reason as player_dismissed.
- fielder: Missing values when no fielder is involved in the dismissal.
- city: 7 missing entries.
- winner: 4 missing entries.
- player_of_match: 4 missing entries.
- umpire1: 2 missing entries.
- umpire2: 2 missing entries.
- umpire3: 637 missing entries (often umpire3 is not present).
Drop the unnecessary Column
# Drop the umpire3 column
matches_df.drop(columns=['umpire3'], inplace=True)
# Check for remaining missing values in the dataset
matches_df.isnull().sum()# Drop the dismissal-related columns with inplace=True
deliveries_df.drop(columns=['player_dismissed', 'dismissal_kind', 'fielder'], inplace=True)
# Check the structure of the modified dataset to confirm the columns have been dropped
deliveries_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 179078 entries, 0 to 179077
Data columns (total 18 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 match_id 179078 non-null int64
1 inning 179078 non-null int64
2 batting_team 179078 non-null object
3 bowling_team 179078 non-null object
4 over 179078 non-null int64
5 ball 179078 non-null int64
6 batsman 179078 non-null object
7 non_striker 179078 non-null object
8 bowler 179078 non-null object
9 is_super_over 179078 non-null int64
10 wide_runs 179078 non-null int64
11 bye_runs 179078 non-null int64
12 legbye_runs 179078 non-null int64
13 noball_runs 179078 non-null int64
14 penalty_runs 179078 non-null int64
15 batsman_runs 179078 non-null int64
16 extra_runs 179078 non-null int64
17 total_runs 179078 non-null int64
dtypes: int64(13), object(5)
memory usage: 24.6+ MB
# Clean the matches dataset
# Fill missing city values based on the venue
venue_city_mapping = {
"Dubai International Cricket Stadium": "Dubai",
# Add more mappings as necessary
}
matches_df['city'] = matches_df.apply(
lambda row: venue_city_mapping.get(row['venue'], row['city']) if pd.isnull(row['city']) else row['city'],
axis=1
)
# Replace missing values in the winner column with 'Match Cancelled' where matches were canceled due to rain
matches_df['winner'] = matches_df.apply(
lambda row: 'Match Cancelled' if pd.isnull(row['winner']) else row['winner'],
axis=1
)
# Fill missing values in the player_of_match column with 'No Award'
matches_df['player_of_match'].fillna('No Award', inplace=True)
# Replace missing values in the umpire1 column with 'Sundaram Ravi'
matches_df['umpire1'].fillna('Sundaram Ravi', inplace=True)
# Replace missing values in the umpire2 column with 'Virender Sharma'
matches_df['umpire2'].fillna('Virender Sharma', inplace=True)
matches_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 756 entries, 0 to 755
Data columns (total 17 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 756 non-null int64
1 season 756 non-null int64
2 city 756 non-null object
3 date 756 non-null object
4 team1 756 non-null object
5 team2 756 non-null object
6 toss_winner 756 non-null object
7 toss_decision 756 non-null object
8 result 756 non-null object
9 dl_applied 756 non-null int64
10 winner 756 non-null object
11 win_by_runs 756 non-null int64
12 win_by_wickets 756 non-null int64
13 player_of_match 756 non-null object
14 venue 756 non-null object
15 umpire1 756 non-null object
16 umpire2 756 non-null object
dtypes: int64(5), object(12)
memory usage: 100.5+ KB
- Columns: match_id, inning, batting_team, bowling_team, over, ball, batsman, non_striker, bowler, is_super_over, wide_runs, bye_runs, legbye_runs, noball_runs, penalty_runs, batsman_runs, extra_runs, total_runs
- Removed Columns: player_dismissed, dismissal_kind, fielder
- Status: No missing values
- Columns: id, season, city, date, team1, team2, toss_winner, toss_decision, result, dl_applied, winner, win_by_runs, win_by_wickets, player_of_match, venue, umpire1, umpire2
- Handled Missing Values:
- city: filled based on venue
- winner: filled with "Match Cancelled" where applicable
- player_of_match: filled with "No Award"
- umpire1: filled with "Sundaram Ravi"
- umpire2: filled with "Virender Sharma"
- Status: No missing values
Exploratory Data Analysis (EDA) is an analysis approach that identifies general patterns in the data. These patterns include outliers and features of the data that might be unexpected.
- Model building
- Analysis and reporting
- Validate assumptions
- Handling missing values
- Feature engineering
- Detecting outliers
matches_df.describe()deliveries_df.describe()Step 1: Identify the Most Successful Team
# Calculate total number of wins for each team
team_wins = matches_df['winner'].value_counts()
print("Total Wins by Team:\n", team_wins)
# Identify the team with the most wins
most_successful_team = team_wins.idxmax()
print("Most Successful Team:", most_successful_team)
# Plot total wins by team
plt.figure(figsize=(10, 6))
team_wins.plot(kind='bar')
plt.title('Total Wins by Team')
plt.xlabel('Team')
plt.ylabel('Number of Wins')
plt.show()Total Wins by Team:
winner
Mumbai Indians 109
Chennai Super Kings 100
Kolkata Knight Riders 92
Royal Challengers Bangalore 84
Kings XI Punjab 82
Rajasthan Royals 75
Delhi Daredevils 67
Sunrisers Hyderabad 58
Deccan Chargers 29
Gujarat Lions 13
Pune Warriors 12
Rising Pune Supergiant 10
Delhi Capitals 10
Kochi Tuskers Kerala 6
Rising Pune Supergiants 5
Match Cancelled 4
Name: count, dtype: int64
Most Successful Team: Mumbai Indians
Step 2: Identify the Most Successful Batsmen
# Calculate total runs scored by each batsman
batsman_runs = deliveries_df.groupby('batsman')['batsman_runs'].sum()
print("Total Runs by Batsmen:\n", batsman_runs.sort_values(ascending=False).head(10))
# Identify the batsman with the highest total runs
top_batsman = batsman_runs.idxmax()
print("Most Successful Batsman:", top_batsman)
# Plot top 10 batsmen by runs
plt.figure(figsize=(10, 6))
batsman_runs.sort_values(ascending=False).head(10).plot(kind='bar')
plt.title('Top 10 Batsmen by Total Runs')
plt.xlabel('Batsman')
plt.ylabel('Total Runs')
plt.show()Total Runs by Batsmen:
batsman
V Kohli 5434
SK Raina 5415
RG Sharma 4914
DA Warner 4741
S Dhawan 4632
CH Gayle 4560
MS Dhoni 4477
RV Uthappa 4446
AB de Villiers 4428
G Gambhir 4223
Name: batsman_runs, dtype: int64
Most Successful Batsman: V Kohli
Step 3: Factors Contributing to Win or Loss
Toss Decisions
# Analyze the impact of toss decisions on match outcomes
toss_impact = pd.crosstab(matches_df['toss_decision'], matches_df['winner'] == matches_df['toss_winner'])
print("Impact of Toss Decision:\n", toss_impact)
# Plot the impact of toss decisions
toss_impact.plot(kind='bar', stacked=True, figsize=(10, 6))
plt.title('Impact of Toss Decisions on Match Outcomes')
plt.xlabel('Toss Decision')
plt.ylabel('Number of Matches')
plt.show()Impact of Toss Decision:
col_0 False True
toss_decision
bat 159 134
field 204 259
Winning Margin
# Analyze the impact of winning margin (runs)
plt.figure(figsize=(10, 6))
matches_df['win_by_runs'].hist(bins=30, edgecolor='black')
plt.title('Winning Margin by Runs Distribution')
plt.xlabel('Winning Margin (Runs)')
plt.ylabel('Frequency')
plt.show()
# Analyze the impact of winning margin (wickets)
plt.figure(figsize=(10, 6))
matches_df['win_by_wickets'].hist(bins=9, edgecolor='black')
plt.title('Winning Margin by Wickets Distribution')
plt.xlabel('Winning Margin (Wickets)')
plt.ylabel('Frequency')
plt.show()
Venue Impact
# Analyze the impact of different venues
venue_wins = matches_df.groupby('venue')['winner'].value_counts().unstack().fillna(0)
print("Wins by Venue:\n", venue_wins)
# Plot the impact of venues
venue_wins.sum(axis=1).sort_values(ascending=False).head(10).plot(kind='bar', figsize=(10, 6))
plt.title('Top Venues by Number of Matches')
plt.xlabel('Venue')
plt.ylabel('Number of Matches')
plt.show()Wins by Venue:
winner Chennai Super Kings \
venue
ACA-VDCA Stadium 1.0
Barabati Stadium 0.0
Brabourne Stadium 1.0
Buffalo Park 1.0
De Beers Diamond Oval 1.0
Dr DY Patil Sports Academy 3.0
Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Sta... 1.0
Dubai International Cricket Stadium 2.0
Eden Gardens 5.0
Feroz Shah Kotla 5.0
Feroz Shah Kotla Ground 1.0
Green Park 0.0
Himachal Pradesh Cricket Association Stadium 1.0
Holkar Cricket Stadium 0.0
IS Bindra Stadium 0.0
JSCA International Stadium Complex 3.0
Kingsmead 1.0
M Chinnaswamy Stadium 5.0
M. A. Chidambaram Stadium 6.0
M. Chinnaswamy Stadium 0.0
MA Chidambaram Stadium, Chepauk 34.0
Maharashtra Cricket Association Stadium 5.0
Nehru Stadium 0.0
New Wanderers Stadium 1.0
Mumbai Indians
Reason: Mumbai Indians have consistently performed well in the IPL, winning the most titles. Their strong team performance and large fan base make them an excellent choice for endorsement.
Virat Kohli
Reason: Virat Kohli is one of the top-performing batsmen with a significant number of runs in the IPL. He is highly popular and has a strong presence both on and off the field, making him an ideal choice for endorsement.
- Team to Endorse: Mumbai Indians
- Player to Endorse: Virat Kohli
# Team Recommendation
team_wins = matches_df['winner'].value_counts()
most_successful_team = team_wins.idxmax()
# Player Recommendation
batsman_runs = deliveries_df.groupby('batsman')['batsman_runs'].sum()
top_batsman = batsman_runs.idxmax()
print("Most Successful Team:", most_successful_team)
print("Most Successful Batsman:", top_batsman)Most Successful Team: Mumbai Indians
Most Successful Batsman: V Kohli