IPL: Analysing data of IPL with Python

In the world of sports, no event captures the imagination and enthusiasm of cricket fans quite like the Indian Premier League. With the IPL being one of the world's most popular sports franchises, renowned for its excitement, rivalry, and cultural importance across cricketing nations and around the world, it became evident that making its data available to the general public could democratize knowledge about the game, create a greater understanding among fans, and unlock untold stories that had previously been hidden in the minutiae of raw data.

Python is the foundation, with O(R) handling directory navigation, JSON handling data files, and PANDAS for data transformation into structured, easy-to-analyze formats. Apache Spark is considered, which is known for its ability to handle large amounts of data, but we felt our arsenal was too large for the job at hand.

Level 1: Match Summary Data, which provides a bird's eye view of each match. Here, we have distilled the data into a single line of metadata: Date, Teams, Venue, Toss Decision, and Match Outcome. This data is the entry point for anyone looking to measure trends, analyze the effect of toss decisions, and understand performance variations across venues.

Level 2:  Zoom in a bit closer at the player level. We aggregate data for each match, including runs, wickets, etc. This layer makes it easy to analyze player contributions, identify the stars in each match, and compare performances throughout the season. At

Level 3: This dataset is for the detail-obsessed. It provides insights into the game's mechanics, player tactics, and the small details that can decide the outcome of a match. To implement these levels, we needed to find a balance between depth and accessibility. The IPL Match Summary dataset was created by transforming raw JSON-formatted IPL match data, ball by ball, into a structured, analysis-ready dataset using Python's powerful data processing capabilities.

Data source and format

Crisheet.org is the primary data source for it. It provides detailed IPL match data in JSON format, which is a text-based format that is easy for humans and machines to interpret and generate. JSON data is structured, so it needs to be processed and transformed to be useful for data analysis.

Python script for data transformation

The script uses Python, which is well-known for its ease of use and powerful data handling and analysis libraries. Here is a step-by-step description of how the script works:

Open and read JSON files: The script uses Python's built-in JSON library to read every IPL match's JSON file. It iterates over all the match files within the specified directory, using the os library to navigate the filesystem.

Data extraction and aggregation: The main goal of the script is to extract important match information, including date, venue info, team info, toss decision info, match outcome info, and player info. For, Aggregating metrics, the script computes aggregate metrics like total run and wicket info for each team based on the delivery details from each inning.

Pandas DataFrame Creation: DataFrame creation with Pandas Data extracted and aggregated for each match is structured into a DataFrame using Pandas. Pandas is an excellent tool for manipulating and analyzing data in Python. It helps to organize and organize the data into tabular format, making it ideal for analysis.

Compiling Season Summary The script compiles the summaries of every match in the season into one DataFrame. The aggregated data gives a detailed overview of the season.

Libraries

json: Deletes JSON files into a Python data structure. pandas: Creates, manipulates, and structures the data into a DataFrame for easy analysis os: Navigates directories and file paths to dynamically access the JSON files for every match Choosing Python vs. Spark .

pandas: used to create, manipulate, and structure the data into DataFrame for easy analysis

os: used to navigate directories and file paths to dynamic access the JSON file for each match

Decision against Apache Spark

Apache Spark is an excellent tool for large data processing, but it was not necessary for the size of this dataset. I preferred simplicity and the relatively small size of the data did not justify Spark's distributed computing capabilities. I likened it to "cutting garlic cloves with a sword", emphasizing the overkill of Spark in this case.

Output & Accessibility

The output of the script will be a CSV file called match_summary.cv. This CSV file will store the structured match summaries data. Saving the CSV file ensures that the dataset is easy to access and compatible with a variety of data analysis tools & environments, which furthers the goal of making IPL data accessible to everyone.

Data Organization & Preparation To get started, we needed to align the JSON match data files

Organizing and Preparing the Data

To get started, we needed to organize our JSON match files and match summary records in a structured way. The solution was to: ·

Sort all JSON files (remove the '{}' extension from the match_id file) from the directory we want to use. This will ensure a sequential alignment of our match data files to our match summary records, making it easier for us to compare detailed match data to summary records.

The integration of Python for analyzing cricket in IPL indicates the beginning of a new era of excellence in the field of sports.