Who Is The Host Of 2023 Cricket World Cup – The International Cricket Council (ICC) ODI World Cup is one of the most anticipated events in the cricket calendar. Held every four years, the tournament brings together the world’s best cricketing nations to compete for the prestigious title of World Champions.
The ICC World Cup 2023 will start in India from October 5, 2023 and continue till November 19, 2023 but the excitement around the event is already building. India will be hosting the tournament exclusively on their home soil for the first time. In the past, in 1987, 1996 and 2011, India hosted the event in collaboration with its neighbors Pakistan, Sri Lanka and Bangladesh. On Tuesday, June 27, the ICC released the ODI World Cup 2023 schedule. The first match between England and New Zealand will be played at the Narendra Modi Stadium in Ahmedabad on October 5.
Who Is The Host Of 2023 Cricket World Cup
The ICC Men’s 50 Over World Cup will follow the same format as the 2019 World Cup. There will be total 10 teams. 48 matches will be held. The top eight teams India, Pakistan, Australia, England, South Africa, New Zealand, Afghanistan and Bangladesh have qualified for the tournament. The remaining 2 teams will be selected from the World Cup Super League matches. The tournament follows a round-robin format where each team plays once against the other 9 teams. After the round-robin stage, the top 4 teams qualify for the semi-finals and the top two teams advance to the finals.
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The 10 teams playing in the 2023 ICC Cricket World Cup are India, England, Australia, South Africa, New Zealand, Pakistan, Bangladesh, Sri Lanka, West Indies and Afghanistan.
The matches will be held at various locations across the country including Ahmedabad, Chennai, Bangalore, Hyderabad, Kolkata, Delhi, Indore, Dharamshala, Guwahati, Rajkot, Raipur and Mumbai. Below is the list of ICC Cricket World Cup 2023 stadiums:
Time wise, the day/night matches will start at 2 PM IST. Matches of the day start at 10:30 AM IST. In total we have 6 day matches and 42 D/N matches. India, England and South Africa are the only three teams not playing ODIs. Bangladesh will play a total of three one-day matches.
Team India will begin their World Cup campaign against the victorious Australia in Chennai on October 8. Other big matches are Pakistan vs Ahmedabad (October 15), England vs Lucknow (October 29) and South Africa (November 5) in Kolkata. Below is the official schedule of Team India for ICC ODI World Cup 2023.
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Apart from the above mentioned matches, Team India will play 2 practice matches before the tournament. While India will play England in Guwahati on September 30, they will play Qualifier 1 in Thiruvananthapuram on October 3. The 2023 ICC Men’s Cricket World Cup marks the 13th installment of the prestigious quadrennial One Day International (ODI) cricket tournament, which includes the national men’s game. The teams are fighting for supremacy under the auspices of the International Cricket Council (ICC). This edition is set to be played from October 5 to November 19, 2023 with India as the host nation. Notably, the original schedule planned for February to March 2023 had to be postponed due to the global COVID-19 pandemic.
A total of ten teams, including 2019 defending champion England, are preparing to participate in the tournament. What sets this edition apart is that it will be the sole host of the Men’s Cricket World Cup for the first time. In the past, India has collaborated with other countries of the Indian subcontinent to co-host the event in 1987, 1996 and 2011.
The grand finale of this cricket extravaganza will unfold on November 19, 2023 at the Narendra Modi Stadium in Ahmedabad. Additionally, the semifinals will be held at the iconic Wankhede Stadium in Mumbai and the historic Eden Gardens in Kolkata.
The main objective of predicting ICC World Cup 2023 winners through machine learning is to use data-driven techniques to make accurate predictions so that a cricket team can emerge as the champion of the tournament, helping fans, analysts and teams gain insight into the performance. And the results.
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Data related to ICC World Cup Winners includes historical records of previous World Cup tournaments, including details such as winning teams, years, venues and matches, and possible statistical information about players. This data serves as an invaluable resource for analysis, prediction and understanding of tournament history and trends.
I collected data from HowStat.com which includes ODI match results of 2015 World Cup. While I agree that the accuracy of the model is not exceptionally high, I believe it provides a good idea of the trends. I chose not to include matches before the 2015 World Cup because I think recent results are more influential, with older data weighing less. For rest of the data files, I have collected them from Cricbuzz website.
Next, I loaded the dataset with the latest ODI match results and the latest rankings of teams from the 2015 ODI World Cup.
I analyzed the World Cup dataset by creating visualizations based on various factors, including the number of titles won by each team, their latest ICC ODI rankings and their winning percentage in previous World Cups. For visualization purposes, I used matplotlib and seaborn.
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I analyzed the team statistics by selecting top 5 favorites for the World Cup, then I started analyzing and visualizing their performance against other teams in ODIs and World Cups.
I have removed columns like match date, margin of victory and ground where the match was played. These characteristics do not appear to be important for our future.
Feature engineering is the process of creating new, meaningful features or modifying existing features in a dataset to improve the performance of a machine learning model. This is an important step in the data preprocessing pipeline. The goal of feature engineering is to provide the model with more relevant and informative input data, which can lead to better model performance and prediction.
Feature engineering is very important in machine learning because it directly affects the model’s ability to learn and make accurate predictions. Well-designed features can significantly improve model performance, often more than the choice of algorithm itself. In short, effective feature engineering is key to success in many machine learning tasks.
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Continued work and created a model. If team-1 wins the match, I assign label 1 to it; Otherwise, if team-2 wins, I assign label 2 to it.
This variable has only two answer choices: team 1 and team 2. This creates a new dataframe consisting of zeros and ones. In this case the dataframe will have one depending on the team in the particular game
Model selection in machine learning is like choosing the right tool for the job. Imagine you have a bunch of data and you want to make predictions or find patterns in it. Model selection is the process of deciding which machine learning algorithm or method to use to perform that task effectively.
It’s like choosing the right type of car for a trip. If you are going off-road, you can opt for a rugged SUV. If you are traveling in the city, a compact car is more practical. Similarly, in machine learning, you choose a model (algorithm) that fits the characteristics of your data and the specific problem you are trying to solve.
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Model selection involves trying different algorithms, comparing how well they perform on your data, and choosing the one that gives the best results. While choosing the right car can make your commute easier, choosing the right model can make your machine learning work more accurate and efficient.
Random Forest is a powerful machine learning algorithm that is similar to group decision makers. It builds multiple decision trees and combines their predictions to make more accurate and powerful predictions. Each tree “votes” on the results, and the most popular choice becomes the final prediction. Random forests are good for handling complex data, handling missing values, and avoiding overfitting. It is commonly used for tasks such as classification and regression and is known for its high accuracy and versatility.
Let’s continue. I have added the ICC ranking of the teams, preferring the top ranked team to win this year.
Next, I added new columns with the ranking positions for each team and sliced the dataset to the first 45 games since there are 45 league level games in total.
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Next, I added scripts to get duplicate variables and missing columns compared to the model training dataset.
For results, please feel free to read the corresponding notebook. The four teams that entered the semi-finals were Australia, India, England and Pakistan.
And then I created a function to repeat the above function. This is the ultimate task to predict the winner of ICC Cricket World Cup 2023.
So the two finalists are Australia and India, which is quite obvious as they are considered favorites to win this year. Well, they are
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