Parameters of Cricket Analysis

Parameters of Cricket Analysis/Dataset/README.md

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+# Exploratory Data Analysis (Sports)
+
+This project involves the analysis of cricket match data to uncover insights and patterns. The datasets used in this analysis include detailed information about deliveries and match outcomes.
+
+## Datasets
+
+1. **deliveries.csv**: This dataset contains ball-by-ball information for each match, including details such as:
+   - `match_id`: Identifier for the match.
+   - `inning`: Inning number.
+   - `batting_team`: Team that is batting.
+   - `bowling_team`: Team that is bowling.
+   - `over` and `ball`: Over and ball number.
+   - `batsman`, `non_striker`, `bowler`: Players involved.
+   - Various run categories and dismissal information.
+
+2. **matches.csv**: This dataset provides match-level information, including:
+   - `id`: Match identifier.
+   - `season`: Year of the match.
+   - `city` and `date`: Location and date of the match.
+   - `team1` and `team2`: Teams playing the match.
+   - `toss_winner` and `toss_decision`: Toss winner and their decision.
+   - `result`, `dl_applied`: Match result and whether Duckworth-Lewis method was applied.
+   - `winner`, `win_by_runs`, `win_by_wickets`: Winning team and margin of victory.
+   - `player_of_match`, `venue`: Player of the match and match venue.
+   - `umpire1`, `umpire2`, `umpire3`: Umpires officiating the match.
+
+## Objectives
+
+- Analyze player and team performances.
+- Identify key factors contributing to match outcomes.
+- Visualize trends and patterns in cricket matches.
+
+## Usage
+
+1. **Data Preprocessing**: Clean and prepare the datasets for analysis.
+2. **Exploratory Data Analysis (EDA)**: Perform statistical analysis and visualization to explore the data.
+3. **Insights and Conclusions**: Derive meaningful insights from the data and present conclusions.
+
+## Conclusion
+
+This project aims to provide a comprehensive analysis of cricket match data, helping to understand the dynamics of the game and the factors influencing outcomes.

Parameters of Cricket Analysis/Models/README.md

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+# Exploratory Data Analysis (Sports) 
+
+![](https://img.shields.io/badge/Programming_Language-Python-blue.svg)
+![](https://img.shields.io/badge/Main_Tool_Used-Jupyter_Notebook-orange.svg)
+![](https://img.shields.io/badge/Status-Complete-green.svg)
+
+## Problem Statement:
+- Perform ‘Exploratory Data Analysis’ on dataset ‘Indian Premier League’<br>
+- As a sports analysts, find out the most successful teams, players and factors<br>
+- contributing win or loss of a team.<br>
+- Suggest teams or players a company should endorse for its products.<br>
+- You can choose any of the tool of your choice
+(Python/R/Tableau/PowerBI/Excel/SAP/SAS)<br>
+
+## Feature Description
+It is a comprehensive guide for conducting EDA on sports-related datasets. It aims to equip users with the skills necessary to uncover insights and patterns from raw data, leveraging various statistical and graphical techniques. This project covers a range of essential EDA steps, including data cleaning, data visualization, and summary statistics. Users will learn how to handle missing values, identify outliers, and understand the distribution and relationships within the data. The project provides practical examples and code snippets to facilitate hands-on learning, making it an ideal resource for anyone looking to enhance their data analysis capabilities in the sports domain. By the end of the project, users will be able to perform robust EDA, gaining valuable insights that can inform decision-making and strategy in sports analytics.
+
+## Use Cases:
+1. Performance Analysis: Evaluate individual and team performance metrics to identify strengths and weaknesses.
+2. Player Comparison: Compare players based on various statistics to aid in scouting and recruitment.
+3. Injury Analysis: Analyze patterns in injuries to improve player health and safety protocols.
+4. Match Outcome Prediction: Identify factors that influence match outcomes for better strategic planning.
+5. Fan Engagement: Understand fan behavior and preferences to enhance engagement and marketing strategies.
+6. Training Optimization: Optimize training programs based on performance and fitness data.
+7. Game Strategy: Develop data-driven strategies for game planning and in-game decision-making.
+8. Trend Identification: Detect emerging trends in sports performance and fan engagement over time.