Analyze IPL with Python

The modern game of cricket generates a lot of statistical and user-generated data. This information is used by coaches and performance analysts to design strategies for future games and seek out new talent. In this article, I will analyze an IPL match with Python.

Let’s Analyze IPL: CSK Vs. MI

Here I will analyze the first match on IPL which was between CSK and MI. We know the result that CSK won the match but our task will we to make some sense with the data. So let’s get started with this task to analyze IPL with Python.

Also, Read – StandardScaler in Machine Learning.

I will start by importing the necessary libraries and the datasets we need for this task:

First, let’s take a look at the runs scored by both teams using bar charts and count plots:

analyze IPL
bar plot mi vs csk

The first two graphs show runs per over and the next two show the types of points scored by each team. The scoring graph (point types) and the distribution graph indicate that both rounds had similar scoring types with some small variations.

However, the bar graph (Runs per over) represents a significant contrast between the two runs. MI had an initial blast of power-play followed by declining innings, while CSK had an unsettling power play and recovered from the end of the power play (after 6 overs) and maintained a pace regular until the end of the match.

Let’s visualize the performance of Batsman vs Bowler using heat maps:

MI VS. CSK

Finally, let’s take a look at the word cloud representation of user comments displayed on the http://www.espncricinfo.com dashboard during the game. It sums up the game well and echoes the performance and feelings of the audience quite a bit:

IPL wordcloud

I hope you liked this article on analyzing IPL Match with Python. Feel free to play more with the data. You can ask your valuable questions in the comments section below.

Also, Read – Why Python is Best for Machine Learning?

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