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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Build a Winning Dream 11 Team Using Python and AI

Harshit Ahluwalia
Harshit Ahluwalia Last Updated : 25 Nov, 2024
5 min read

The exponential rise of IPL in India has correlatively resulted in the growth of fantasy cricket in the country. In recent years, we have seen more and more people playing on platforms like Dream 11, My Circle, etc. every day. Dream11 is a fantasy sports app where you can create your fantasy team for sports like cricket, football, kabaddi, etc. based on real-life matches. Once you build your team, you can join competitions and win daily cash prizes. In these games, for every single match, you have to create an 11-player sports team. The aim is to play with this team and earn maximum points to win maximum rewards. In this article, I will show you how to create a Dream 11 fantasy cricket team with which you can win up to Rs. 1 Crore in India, using Python and AI. So let’s hit it off! In this article you will get to know about the how to make dream team with ai dream 11 team generator how you can make ai fantasy team generator

In this article, you will explore the best AI fantasy team generator to enhance your gaming experience. Discover how to utilize an AI team generator free to create competitive lineups effortlessly. We’ll also delve into a fantasy team generator free option and provide insights on the Dream11 team generator for today’s match, ensuring you make informed choices for your fantasy league!

Build a Winning Dream 11 Team Using Python and AI

Rules and Constraints for Selecting the Dream 11 Team

The team you will create on Dream 11 will have 11 players out of 22 players. Out of these, a maximum of 7 players can be from the same team in real life. Your Dream11 can have different combinations of players, but has to be within the 100-credit cap and must fulfill the following team selection criteria:

Player selection rules and criteria

Building Your Dream 11 Team

Once you are done with your team, now it’s time to select your captain and vice-captain. In this article, as an example, I am going to create a team for a match between Chennai Super Kings and Punjab Kings.

We begin by running the following code:

!pip install openai==1.12.0

!pip install gradio==4.19.0

from bs4 import BeautifulSoup
import pandas as pd
from openai import OpenAI
import gradio as gr
from google.colab import userdata

client = OpenAI(api_key=userdata.get('OPENAI_API_KEY'))

def pull_match_data(m_link):
    r = requests.get(m_link)
    soup = BeautifulSoup(r.text, 'html.parser')

    batting_columns = []
    for i in soup.find_all(class_='ds-w-full ds-table ds-table-md ds-table-auto ci-scorecard-table')[:1]:
        for j in i.find_all('th'):
            batting_columns.append(j.text)
    
    batting = []
    for i in soup.find_all(class_='ds-w-full ds-table ds-table-md ds-table-auto ci-scorecard-table')[:1]:
        rows = i.find_all('td', class_='ds-min-w-max')
        for i in range(0, len(rows), 8):
            batter = []
            for j in range(i, i+8):
                try:
                    batter.append(rows[j].text)
                except IndexError:
                    break
            batting.append(batter)
    innings1_batting = pd.DataFrame(batting, columns=batting_columns)

    batting_columns = []
    for i in soup.find_all(class_='ds-w-full ds-table ds-table-md ds-table-auto ci-scorecard-table')[1:2]:
        for j in i.find_all('th'):
            batting_columns.append(j.text)
    
    batting = []
    for i in soup.find_all(class_='ds-w-full ds-table ds-table-md ds-table-auto ci-scorecard-table')[1:2]:
        rows = i.find_all('td', class_='ds-min-w-max')
        for i in range(0, len(rows), 8):
            batter = []
            for j in range(i, i+8):
                try:
                    batter.append(rows[j].text)
                except IndexError:
                    break
            batting.append(batter)
    innings2_batting = pd.DataFrame(batting, columns=batting_columns)

    bowling_columns = []
    for i in soup.find_all(class_='ds-w-full ds-table ds-table-md ds-table-auto')[:1]:
        for j in i.find_all('th'):
            bowling_columns.append(j.text)
    
    bowling = []
    for i in soup.find_all(class_='ds-w-full ds-table ds-table-md ds-table-auto')[:1]:
        rows = i.find_all('td')
        bowler = []
        for i in range(0, len(rows)):
            if len(bowler) < 11:
                if len(rows[i].text) < 20:
                    try:
                        bowler.append(rows[i].text)
                    except IndexError:
                        break
            else:
                bowling.append(bowler)
                bowler = []
                if len(rows[i].text) < 20:
                    bowler.append(rows[i].text)
    innings1_bowling = pd.DataFrame(bowling, columns=bowling_columns)

    bowling_columns = []
    for i in soup.find_all(class_='ds-w-full ds-table ds-table-md ds-table-auto')[1:2]:
        for j in i.find_all('th'):
            bowling_columns.append(j.text)
    
    bowling = []
    for i in soup.find_all(class_='ds-w-full ds-table ds-table-md ds-table-auto')[1:2]:
        rows = i.find_all('td')
        bowler = []
        for i in range(0, len(rows)):
            if len(bowler) < 11:
                if len(rows[i].text) < 20:
                    try:
                        bowler.append(rows[i].text)
                    except IndexError:
                        break
            else:
                bowling.append(bowler)
                bowler = []
                if len(rows[i].text) < 20:
                    bowler.append(rows[i].text)
    innings2_bowling = pd.DataFrame(bowling, columns=bowling_columns)

    return innings1_batting, innings1_bowling, innings2_batting, innings2_bowling

def get_completion(m1_link, m1_batting_team, m1_bowling_team, m2_link, m2_batting_team, m2_bowling_team, m3_team1, m3_team2, model="gpt-4-turbo-2024-04-09"):
    m1_innings1_batting, m1_innings1_bowling, m1_innings2_batting, m1_innings2_bowling = pull_match_data(m1_link)
    m2_innings1_batting, m2_innings1_bowling, m2_innings2_batting, m2_innings2_bowling = pull_match_data(m2_link)

    system_prompt = """
    You are a huge cricket fan, and good with analysing match data.
    """

    prompt = """
    I will provide you batting & bowling data for two cricket matches.
    Match 1 data provided between three backticks.
    Match 2 data provided between separator ####.
    Now, one team from each of these two matches are playing tonight.
    Your task is to analyse the two matches data.
    And create a Fantasy team for tonight's match between {m3_team1} & {m3_team2}.
    This fantasy team should have 11 players.

    ```

    Match 1: {m1_batting_team} vs {m1_bowling_team}
    Innings 1 >> {m1_batting_team} Batting
    {m1_innings1_batting}
    Innings 1 >> {m1_bowling_team} Bowling
    {m1_innings1_bowling}
    Innings 2 >> {m1_bowling_team} Batting
    {m1_innings2_batting}
    Innings 2 >> {m1_batting_team} Bowling
    {m1_innings2_bowling}
    ```

    ####

    Match 1: {m2_batting_team} vs {m2_bowling_team}
    Innings 1 >> {m2_batting_team} Batting
    {m2_innings1_batting}
    Innings 1 >> {m2_bowling_team} Bowling
    {m1_innings1_bowling}
    Innings 2 >> {m2_bowling_team} Batting
    {m2_innings2_batting}
    Innings 2 >> {m2_batting_team} Bowling
    {m2_innings2_bowling}
    ####
    """

    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": prompt}
    ]

    response = client.chat.completions.create(
        model="gpt-4-turbo-2024-04-09",
        messages=messages,
        temperature=0
    )

    return response.choices[0].message.content

iface = gr.Interface(
    fn=get_completion,
    inputs=[
        gr.Textbox(label="Team 1 Last Match Scorecard", lines=1

After running the code, you will get this interface:

Fantasy XI using AI

Here’s how to create your Dream11 fantasy cricket team using the above interface.

  1. Fill in ‘Team 1 Last Match Scorecard’

    Input the value of your first team’s scorecard from the last match. For reference, you can copy-paste this website link which contains the match between Chennai Super Kings and Sunrisers Hyderabad.

  2. Select ‘Team Batting First’

    Input the team that chose to bat first in that match.

  3. Select ‘Team Bowling’

    Input the team that chose to bowl first in that match.

  4. Add ‘Team 2 Last Match Scorecard’

    Input the value of your second team’s scorecard from the previous match. For reference, you can copy-paste this website link which contains the match between Kolkata Knight Riders and Punjab Kings.

  5. Select ‘Team Batting First’

    Input the team that chose to bat first in that match.

  6. Select ‘Team Bowling’

    Input the team that chose to bowl first in that match.

  7. Select ‘Teams Playing Today’

    Choose the two teams who will be playing today’s fantasy match.

  8. Enter ‘GPT Model API’

    Enter your GPT Model API or choose ‘gpt-3.5-turbo-1106’, if unsure.

  9. Click Submit

    Dream 11 fantasy cricket team

And with that final click, here is your dream team!

Your fantasy cricket dream 11 team

Conclusion

In this article, we explored the exciting possibility of using Python and AI to create a winning Dream11 team. By leveraging historical data and machine learning techniques, you can potentially gain an edge in selecting the best players and maximizing your points. Remember, while AI can be a powerful tool, factors like player form and pitch conditions also play a crucial role. So, for the ultimate Dream11 success, combine AI insights with your own cricketing knowledge. Now go ahead and take on the world of fantasy sports. Hope you like the article and know how you can make team with ai dream 11 team generator.

Frequently Asked Questions

Q1.Can AI make a Dream11 team?

Yes, AI can analyze player statistics, recent performances, and match conditions to suggest optimized Dream11 teams, but outcomes still depend on real-life match results.

Q2.How can I get a 100% accurate team in Dream11?

No team is 100% accurate as cricket is unpredictable. However, thorough research on player form, pitch conditions, and expert analysis can improve your chances.

Q3.How to make a Dream11 team combination?

Balance your team with in-form players, including:
1-2 wicketkeepers
3-5 batsmen
1-3 all-rounders
3-5 bowlers.
Allocate credits wisely and select a strong captain and vice-captain.

Harshit Ahluwalia
Harshit Ahluwalia

Growth Hacker | Generative AI | LLMs | RAGs | FineTuning | 62K+ Followers https://www.linkedin.com/in/harshit-ahluwalia/ https://www.linkedin.com/in/harshit-ahluwalia/ https://www.linkedin.com/in/harshit-ahluwalia/

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Responses From Readers

Clear

Vraj Anandpara
Vraj Anandpara

Hey Harshit, Loved the cleverness you did while creating this project hats off to you, but their an issue in code despite all the fix still its pickuping the data from espncricinfo in output it's not giving real time data It would be great if you revise the code

123

Akash Pacharane
Akash Pacharane

can get your help in coding same on dream11

123

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