IPL Prediction Using Machine Learning

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.


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 Data Head
Result data head
df = results[(results['Team_1'] == 'Chennai Super Kings') | (results['Team_2'] == 'Chennai Super Kings')]
india = df.iloc[:]
ipl Team result win ground

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))
ipl team 2020

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)
ipl 2020 team prediction

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)
ipl 2020 team prediction

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)
ipl dummy variable

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,
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))
IPL Prediction Model Accurccy

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, :]
IPL Latest Model Data

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})
        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
IPL Ranking Postion

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)
IPL compare model data

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])
        print("Winner: " + backup_pred_set.iloc[i, 0])

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]})
            dict1.update({'Team_1': matches[j][1], 'Team_2': matches[j][0]})
        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])
            print("Winner: " + backup_pred_set.iloc[i, 0])

then I run the semifinal function

clean_and_predict(semi, ranking, final, rf)
ipl semifinal result

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)
IPL Final Match Prediction

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.

Today’s IPL Match Prediction is the best way to ensure you don’t miss any games. As a sports enthusiast, IPL predictions make you feel like you are watching an exciting game.

IPL Match Prediction is a great way to stay in touch with the latest cricket and keep track of the teams’ performances. A cricket betting system is essential to get the best and most accurate predictions. A cricket betting website will have the best and most accurate cricket predictions.

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.

Many sports betting sites offer particular markets for the IPL, the world’s most popular cricket tournament. You can also bet on outright winners, team props, and more. They provide accurate predictions, so you don’t have to worry about losing.

Choosing a team based on the players’ form is essential to making an IPL prediction. The captain is the one who needs to be the captain of the group.

If you want to bet on today’s IPL match, it will help you to make informed decisions. Because your tips are based on the form of the player and the scoreboards, you don’t have to worry about losing or winning a match.

IPL Match Predictions will include a team’s form. The team should have a good record in the last two seasons. If you have a bad history, it can be dangerous.

Analyzing the records of a team can help you predict whether they will win a game. For example, Mumbai Indians are the underdogs in today’s IPL season, while the Delhi Capitals are the favourites.

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