IPL Winning Team Prediction

The Indian Premier League (IPL) tournament will be contested by 8 teams who will be playing in a “home-and-away round-robin system“, with the top four at the end of the group phase progressing to the semi-finals.

Application

The main objective of this project is to predict ipl Semi-final and Final based on the future team records.

1. Data Collection

I scraped data from Wikipedia And IPLT20 Website Comprising of record of teams as of ill 2020, details of the fixtures of 2020 ipl, and details of each team’s history in the previous ipl. I stored the above piece of ipl data in three separate CSV files. For the fourth file,

I download ipl data-set for matches played between 2008 and 2019 from Kaggle in another CSV file. Then I did manual data cleaning of the CSV file as per my need to make a machine learning model out of it.

2. Data Cleaning And Formatting

Load Two CSV File. results.csv contain IPL match dates, team name, winning team name, ground city name, and winning margin. IPL 2020 Dataset.csv in appearances, won the title, play semifinal, and play final. and the current rank I give based on winning the IPL trophy.

IPL = pd.read_csv('datasets/IPL 2020 Dataset.csv') results = pd.read_csv('datasets/results.csv')

IPL.head()

results.head()

df = results[(results['Team_1'] == 'Chennai Super Kings') | (results['Team_2'] == 'Chennai Super Kings')]
india = df.iloc[:]
india.head()

3. Exploratory data analysis [EDA]

After that, I merge the details of the teams participating this year with their past results.

IPL_Teams = ['Mumbai Indians', 'Chennai Super Kings', 'Delhi Capitals', 'Kings XI Punjab', 
            'Royal Challengers Bangalore', 'Kolkata Knight Riders', 'Sun Risers Hyderabad', 'Rajasthan Royals']
df_teams_1 = results[results['Team_1'].isin(IPL_Teams)]
df_teams_2 = results[results['Team_2'].isin(IPL_Teams)]
df_teams = pd.concat((df_teams_1, df_teams_2))
df_teams.drop_duplicates()
df_teams.count()

df_teams.head()

I remove the columns like date, margin, and ground. Because these features are not important for prediction.

#dropping columns that wll not affect match outcomes
df_teams_2010 = df_teams.drop(['date','Margin', 'Ground'], axis=1)
df_teams_2010.head()

4. Feature engineering and selection

I create two labels. label 1, team_1 won the match else label 2 if the team-2 won.

df_teams_2010 = df_teams_2010.reset_index(drop=True)
df_teams_2010.loc[df_teams_2010.winner == df_teams_2010.Team_1,'winning_team']=1
df_teams_2010.loc[df_teams_2010.winner == df_teams_2010.Team_2, 'winning_team']=2
df_teams_2010 = df_teams_2010.drop(['winning_team'], axis=1)
df_teams_2010.head()

Create Dummy Variables to convert categorical to continuous

# Get dummy variables
final = pd.get_dummies(df_teams_2010, prefix=['Team_1', 'Team_2'], columns=['Team_1', 'Team_2'])
# Separate X and y sets
X = final.drop(['winner'], axis=1)
y = final["winner"]
# Separate train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)
final.head()

In Logistic Regression, Random Forests and K Nearest Neighbours for training the model. I chose Random Forest.

 5. model building

rf = RandomForestClassifier(n_estimators=100, max_depth=20,
                              random_state=0)
rf.fit(X_train, y_train) 
score = rf.score(X_train, y_train)
score2 = rf.score(X_test, y_test)
print("Training set accuracy: ", '%.3f'%(score))
print("Test set accuracy: ", '%.3f'%(score2))

Evaluate model for the testing set

fixtures = pd.read_csv('datasets/fixtures.csv')
ranking = pd.read_csv('datasets/ipl_rankings.csv') 
# List for storing the group stage games
pred_set = []

Next, I added new columns with the ranking positions for each team and sliced the dataset for the first 56 games.

fixtures.insert(1, 'first_position', fixtures['Team_1'].map(ranking.set_index('Team')['Position']))
fixtures.insert(2, 'second_position', fixtures['Team_2'].map(ranking.set_index('Team')['Position']))
# We only need the group stage games, so we have to slice the dataset
fixtures = fixtures.iloc[:56, :]
fixtures.tail()

add teams for a new prediction dataset based on the rank position of each team.

for index, row in fixtures.iterrows():
    if row['first_position'] < row['second_position']:
        pred_set.append({'Team_1': row['Team_1'], 'Team_2': row['Team_2'], 'winning_team': None})
    else:
        pred_set.append({'Team_1': row['Team_2'], 'Team_2': row['Team_1'], 'winning_team': None})
        
pred_set = pd.DataFrame(pred_set)
backup_pred_set = pred_set
pred_set.head()

After that, Get Dummy Variables And Add Missing Columns Compare To the training model dataset.

pred_set = pd.get_dummies(pred_set, prefix=['Team_1', 'Team_2'], columns=['Team_1', 'Team_2'])
missing_cols = set(final.columns) - set(pred_set.columns)
for c in missing_cols:
    pred_set[c] = 0
pred_set = pred_set[final.columns]
pred_set = pred_set.drop(['winner'], axis=1)
pred_set.head()

6. Model Results

predictions = rf.predict(pred_set)
for i in range(fixtures.shape[0]):
    print(backup_pred_set.iloc[i, 1] + " and " + backup_pred_set.iloc[i, 0])
    if predictions[i] == 1:
        print("Winner: " + backup_pred_set.iloc[i, 1])
    
    else:
        print("Winner: " + backup_pred_set.iloc[i, 0])
    print("")

For results, You Visit jupyter notebook Link

For Semifinal I chose Four teams Kolkata Knight Riders, Chennai Super Kings, Mumbai Indians, and Rajasthan Royals.

semi = [('Kolkata Knight Riders', 'Chennai Super Kings'),
            ('Mumbai Indians', 'Rajasthan Royals')]

def clean_and_predict(matches, ranking, final, logreg):
    positions = []
    for match in matches:
        positions.append(ranking.loc[ranking['Team'] == match[0],'Position'].iloc[0])
        positions.append(ranking.loc[ranking['Team'] == match[1],'Position'].iloc[0])
    
    pred_set = []
    i = 0
    j = 0
    while i < len(positions):
        dict1 = {}
        if positions[i] < positions[i + 1]:
            dict1.update({'Team_1': matches[j][0], 'Team_2': matches[j][1]})
        else:
            dict1.update({'Team_1': matches[j][1], 'Team_2': matches[j][0]})
        pred_set.append(dict1)
        i += 2
        j += 1
        
    pred_set = pd.DataFrame(pred_set)
    backup_pred_set = pred_set
    pred_set = pd.get_dummies(pred_set, prefix=['Team_1', 'Team_2'], columns=['Team_1', 'Team_2'])
    missing_cols2 = set(final.columns) - set(pred_set.columns)
    for c in missing_cols2:
        pred_set[c] = 0
    pred_set = pred_set[final.columns]
    pred_set = pred_set.drop(['winner'], axis=1)
    predictions = logreg.predict(pred_set)
    for i in range(len(pred_set)):
        print(backup_pred_set.iloc[i, 1] + " and " + backup_pred_set.iloc[i, 0])
        if predictions[i] == 1:
            print("Winner: " + backup_pred_set.iloc[i, 1])
        else:
            print("Winner: " + backup_pred_set.iloc[i, 0])
        print("")

then I run the semifinal function

clean_and_predict(semi, ranking, final, rf)

Finally, I run the final function for Chennai Super Kings and Mumbai Indians.

finals = [('Chennai Super Kings', 'Mumbai Indians')]
clean_and_predict(finals, ranking, final, rf)

if this IPL 2020 final between CSK Vs MI. This Model Predicts Go To MI Side.

Full Project Code Available Click Hear

Today’s IPL Match Prediction

The Indian Premier League is the most popular cricket competition in the world. You can also live-stream the match and follow the score via your TV. There are several teams in the league, and each one has a different captain. The captain of the team will make the team’s decision.

IPL Match Prediction is one of the most popular sports in the world. The IPL is played yearly, and the teams have been divided into different groups based on their performance in the previous season. Experts will consider many factors to predict the IPL Match Prediction.

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How Match Prediction Work?

Toss Prediction: The IPL is one of the most exciting sports in the world. The teams will play each other, and if you can predict which team will win, you can win big money.

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