The Indian Premier League or IPL is a T20 cricket tournament organized annually by the Board of Control for Cricket In India (BCCI). Eight city-based franchises compete with each other over 6 weeks to find the winner.
In this article, I'm going to analyze data from the IPL's past seasons to see which teams have won the most games, how teams behave when winning a toss, who has the greatest legacy, and so on.
I have done this analysis from a historical point of view, giving an overview of what has happened in the IPL over the years. I have used tools such as Pandas, Matplotlib and Seaborn along with Python to give a visual as well as numeric representation of the data in front of us.
Pandas stands for Python Data Analysis library. It is typically used for working with tabular data (similar to the data stored in a spreadsheet). Pandas provides helper functions to read data from various file formats like CSV, Excel spreadsheets, HTML tables, JSON, SQL and perform operations on them.
Matplotlib and Seaborn are two Python libraries that are used to produce plots. Matplotlib is generally used for plotting lines, pie charts, and bar graphs.
Seaborn provides some more advanced visualization features with less syntax and more customizations. I switch back-and-forth between them during the analysis.
Table of Contents
- Getting the Dataset
- Data Preparation and Cleaning
- Exploratory Analysis and Visualization
- Asking and Answering Questions
- Inferences From the Analysis
1. Getting the Dataset
I downloaded the dataset from Kaggle. You will see there are two CSV (Comma Separated Value) files, matches.csv and deliveries.csv. I chose to do my analysis on matches.csv.
To find more interesting datasets, you can look at this page.
2. Data Preparation and Cleaning
A dataset contains many columns and rows. It is always possible that certain rows have missing values or
NaN for one or more columns.
It is also possible that there might be certain columns or rows that you want to discard from your analysis. You can also combine two or more datasets for an in-depth analysis.
Cleaning the data involves making corrections to that data, leaving out unnecessary columns or rows, merging datasets, and so on.
Before taking these steps, I needed to install and import the tools (libraries) to be used during the analysis. I imported the libraries with different aliases such as
sns. I then set some basic styles for the plots.
Notice the special command
%matplotlib inline. It makes sure that plots are shown and embedded within the Jupyter notebook itself. Without this command, sometimes plots may show up in pop-up windows.
read_csv() method from the Pandas library, I loaded the matches.csv file.
Data from the file is read and stored in a
DataFrame object - one of the core data structures in Pandas for storing and working with tabular data. I used the
_df suffix in the variable names for data frames.
I used the name
matches_raw_df for the data frame. This indicates that this is unprocessed data that I will clean, filter, and modify to prepare a data frame that's ready for analysis.
shape property of a
Dataframe object, I found that the dataset contains 756 rows and 18 columns. To find the names of those columns I used the
columns property. It returned a list of the columns in a data frame.
To get a summary of what the data frame contains, I used
info(). This gives information about columns, number of non-null values in each column, their data type, and memory usage.
Almost all columns except
umpire3 have no or very few null values. The presence of null values could result from a lack of information or an incorrect data entry.
An interesting thing to observe is that, although there are no null values for the
result column, there are some for
player_of_match columns. Let's find out why.
I first accessed the
result column using dot notation (
matches_raw_df.result). Then I used
vaule_counts() method on the
value_counts() returns a series which contains counts of unique values. Here, it tells us about the different values present in
result and the total number for each of them.
So, out of 756 matches (rows), 4 matches ended as no result.
Cricket is an outdoor sport and unlike, say, football, play isn't possible when it's raining. It is very common to have matches abandoned due to incessant raining. Therefore, we have no winners or player of the match for these 4 matches.
For this analysis, the
umpire3 column isn't needed. So I removed the column using the
drop() method by passing the column name and axis value. If you want to remove multiple columns, the column names are to be given in a list.
I assigned this cleaned data frame to
matches_df. I used this data frame for further analysis.
3. Exploratory Analysis and Visualization
Exploratory analysis involves performing operations on the dataset to understand the data and find patterns. It helps us make sense of the data we have.
Visualization is the graphic representation of data. It involves producing charts that communicate those patterns among the represented data to viewers.
Now, let's take a look at the data I analyzed and what I learned in the process.
Number of matches and teams
I tried to find the number of matches played in each season in the IPL from its inception to 2019.
Since I needed matches played each season, it made sense to group our data according to different seasons. Pandas has a
groupby() method to achieve this, wherein I passed
season as an argument.
id is unique for each match (row), counting the number of ids for each season leads to what we want. I used the
count() method on the
id column to find the number of matches held each season. This series is assigned to the variable
I then used the
barplot() method from the Seaborn library to plot the series. The index of the series, that is the seasons, were given as the x-value while the values of those indices were given as y-values.
I used various
matpllotlib.pyplot methods such as
title() to set the size of the plot, title of the plot, and so on.
figure takes a parameter,
figsize, which I set to
(12,6). Notice that the size was given as a tuple. To
xticks(), I gave the
rotation parameter a value of
75 to make it easier to read.
Each season, almost 60 matches were played. However, we see a spike in the number of matches from 2011 to 2013. This is because two new franchises, the Pune Warriors and Kochi Tuskers Kerala, were introduced, increasing the number of teams to 10.
However, Kochi was removed in the very next season, while the Pune Warriors were removed in 2013, bringing the number down to 8 from 2014 onwards.
Before the start of the 2016 season, two teams, the Chennai Super Kings and Rajasthan Royals were banned for two seasons. To make up for their absence, two new teams (the Rising Pune Supergiants and Gujarat Lions) entered the competition.
When the Chennai Super Kings and Rajasthan Royals returned, these two teams were removed from the competition.
Analyzing the Toss results
One of the most significant events in any cricket match is the toss, which happens at the very start of a match. The toss winner can choose whether they want to bat first or second (fielding first).
Let's see what the trend has been amongst the teams across different seasons.
Again I grouped the rows by season and then counted the different values of the
toss_decision column by using
Since a percentage gives a clearer picture, I divided the above result with
matches_per_season and multiplied it by 100. This series was assigned to
toss_decision_percentage is a series with multi-index. If we print the index of the series using the
index property, we see it is of the form
(2008, 'bat'), (2008, 'field') and so on.
The series used both
toss_decision as an index. But I only wanted the seasons to be an index. I used
unstack() to achieve this.
By using the
unstack() method on the series, it converted the values of
toss_decision (that is,
field) into separate columns.
Next I used the
plot() method from Matplotlib to represent these values as bar charts.
plot() has a parameter
kind which decides what type of plot to draw. The value was set to
For 2008-2013, teams seemed to favour both batting first and second. For this period, teams chose to bat first more in 2009, 2010 and 2013. On the other hand, they chose fielding first more in 2008 and 2011. Things were even-steven in 2012.
This could be because IPL and T20 cricket in general was in its budding stages. So, teams were probably learning and trying to figure out which option would be more beneficial.
However, since 2014, teams have overwhelmingly chosen to bat second. Especially since 2016, teams have chosen to field first more than 80% of the time.
Batting first requires that the team gauge the conditions and the pitch and then set a target accordingly. Chasing is less complicated, as there is a fixed target to achieve.
Conditions have also become more batsman-friendly and the skills of the batsmen have increased tremendously (read more here).
Number of Wins
We saw how teams in the recent past have chosen to bat second more than 4 out of 5 times. Did this decision transform the results? Let's see.
wins_batting_first, the values of
win_by_wickets has to be 0. Also, the
result column should have a value of
normal since tied matches also have win margins as 0. This condition was stored as
wins_fielding_first, the the value of
win_by_runs has to be 0 and the
result column should have a value of
normal. This condition was stored as
In both the series, I used
count() method on
winner column to find the won matches in the filtered conditions. I divided the results with
matches_per_season calculated earlier to give a better understanding.
To plot these two series together, I combined them using Pandas'
concat() method. I passed the two series names as a list and set the value of
1. This gives us a new data frame which was stored as
Next I plotted
combined_wins_df as a bar chart using
We saw earlier that for 2008-2013, teams faced a conundrum whether to bat first or field first. This is partially visible in the results as well.
The wins from batting first are very close to that from fielding first. However, there is just one season where teams batting first won more, with things being equal in 2013.
Again, since 2014, things have been in favour of teams chasing except 2015. Leaving out 2015, things have been overwhelmingly in favour of teams fielding first.
So, teams choosing to field more have been justified in their decisions.
Teams with "History"
In leagues across different sports, there is always talk about teams with "history" – teams that have played the most in the league and continue to do so. Let's find those teams in the IPL.
Now, between two teams A and B, it can be "A vs B" or "B vs A", depending on how the data entry has been done. So I decided to count the total number of different values for both the
team2 columns using
value_counts(). Then I added them together.
I sorted the results in descending order using the
sort_values() method from Pandas. The
ascending parameter was set to
Here, I used
sns.barplot() to plot the graph.
The Mumbai Indians have played the most matches. They are followed by the Royal Challengers Bangalore, Kolkata Knight Riders, Kings XI Punjab and Chennai Super Kings.
The Chennai Super Kings and Rajasthan Royals could have been higher had they not been banned.
You will see there are two teams from Delhi, the Delhi Daredevils and Delhi Capitals. This resulted from a change in ownership and then team name in 2018.
It's a similar story for the Deccan Chargers and Sunrisers Hyderabad, as the Deccan Chargers were removed from the IPL in 2013 and the Sunrisers came in their place.
Also, there are two teams with almost same name: the Rising Pune Supergiants and Rising Pune Supergiant. They are same team, and there was no change in ownership – it has more to do with superstitions.
In the 2016 season, the Rising Pune Supergiants finished 7th. The owners changed the captain for 2017 and also dropped the 's' from Supergiants. Well, it paid off as they finished as runner-up that season!
Teams with "Legacy"
Now, teams may have a lot of history but it's their "legacy" – how often they win – that makes them popular and attracts new and neutral fans.
To find such teams, I simply used
value_counts() on the
winner column. This gives us the number of matches that each team has won.
So Mumbai has the most wins. But a better metric to judge would be the win percentage. To find the win percentage, I divided
total_matches_played to find the
win_percentage for each team.
The Rising Pune Supergiant and Delhi Capitals have the highest win percentage. This is largely because they have played fewer matches compared to most teams. Especially Rising Pune Supergiant, which technically became a new team after dropping the 's'.
The Chennai Super Kings, despite playing two fewer seasons than the Mumbai Indians, had only 9 fewer victories. They, along with the Mumbai Indians, are the only two teams in the top 5 that were also part of the IPL in 2008.
Chennai and Mumbai are the teams with the most legacy.
4. Asking and Answering Questions from the Data
We've already gained some insights about the IPL by exploring various columns of our dataset.
Let's ask some specific questions, and try to answer them using data frame operations and interesting visualizations.
Q. Who has won the IPL tournament?
- Group the rows according to seasons using
- Find the last match of each season, that is, the final using
tail(). It returns the last n rows from a Dataframe object or series based on position.
- Sort the values per season using
- Count the different winners and the times they won using
Then I plotted the series
Mumbai and Chennai, our legacy teams, have won the IPL at least 3 times. The Sunrisers Hyderabad are the only team that joined the league later and won the trophy.
Q. Which are the most and least consistent teams across all seasons?
- Created a data frame between different values of
- Plotted the data frame as a heatmap.
pd.crosstab() gives a simple cross-tabulation of the
season columns. For each different value of
pd.crosstab() finds its frequency for each different value in
Then I plotted
sns.heatmap(). I passed the data frame
True to have the values shown as well. Here, the darker color indicates more matches won.
The Chennai Super Kings have been the most consistent team, winning at least 8 matches in each of the seasons they have played. This is backed up by the fact that they are the only team to reach the playoffs stage every season.
At the other end of the spectrum are 3 teams, the Delhi Daredevils, Kings XI Punjab and Rajasthan Royals. All three of them have had two seasons where they performed really well. However, they have been pretty average during the other seasons.
Q. What has been the biggest margin of victory in terms of runs in the IPL?
- Filter the data frame using the required condition.
- Sort the values in descending order using
- Find the biggest 10 victories in the list using the
head()method. It works opposite to
tail(), returning the first n rows.
I plotted the filtered data frame
sns.scatterplot(). For the
x parameter I used
season, and I used
win_by_runs as the
y parameter. I made the size of the points bigger for the top 10 victories using the
To put emphasis on the top 10 victories, I used a different color as well as annotated those data points using
plt.annotate(). The first parameter is the text of the annotation. The position of the point to be annotated is given as a tuple.
The biggest margin of victory by runs is 146 runs. In 2017, the Mumbai Indians defeated the Delhi Daredevils by this margin. The Royal Challengers Bangalore have 3 victories amongst the top 5.
Q. Mumbai and Chennai are the two most successful teams so far. Which team leads in the head-to-head record?
- Filter the data frame using the required condition to find the matches played between the two teams.
- Use the
winnercolumn to find how many times each of the teams have won.
I plotted the series
mivcsk as a bar chart for a better visualization.
MI have dominated CSK and are leading the head-to-head record 17-11. We can see their dominance especially in the 2019 season, where the MI defeated the CSK 4 out of 4 times they met, including the playoff and the final.
5. Inferences from the Analysis
We have drawn some interesting inferences and now know more about the IPL than when we started. Here's a summary of what we learned through our analysis:
- Almost 60 matches are played in every IPL season amongst 8 teams.
- There has been an attempt to expand the IPL to 10 teams but the 8 teams idea was brought back and has been continued since.
- For the first six seasons (2008-2013), teams were figuring out whether batting first or chasing would be better after winning the toss. This could be down to the fact that the IPL and T20 cricket were both in their early stages so teams were trying different strategies.
- But, since 2014, teams have preferred chasing, especially in the past 4 seasons (2016-2019) where teams have chosen to field more than 4 times out of 5. This is likely because having a set total to chase makes things simpler. This could also result from teams preferring to chase in ODIs as well.
- Though teams have overwhelmingly chosen to field first, the win percentage after choosing to bat or field is not that one-sided. However, their difference is on the rise.
- Mumbai Indians have played the most matches in the IPL. Due to the brief expansion, change of owners, and removal and banning of teams, there have been 15 teams who have played in the IPL.
- Chennai and Mumbai are the two teams with the highest win percentage. The fact that they are the only two teams that were part of the first season as well, in the top 5, shows their dominance.
- Mumbai Indians have the won the IPL 4 times, the most. They are followed by Chennai at 3 and Kolkata Knight Riders at 2. Sunrisers Hyderabad, Deccan Chargers and Rajasthan Royals complete the IPL Champions list, all winning once each.
- 146 runs is the largest margin of victory by runs. Mumbai Indians defeated Delhi Daredevils by this margin in 2017. The largest margin for victory by wickets is 10, which has been achieved many times.
- The two heavyweights, Mumbai and Chennai, have a head-to-head record in favour of Mumbai at 17-11. Mumbai have had the upper hand in the 2019 season every time they met, including the final.
In this article, we did a bunch of analysis and saw some interesting visualizations. However, this was just scratching the surface.
You can perform more interesting analysis on matches.csv as a standalone data set. But combining deliveries.csv with this dataset could lead to more in-depth analysis.
I did this data analysis and visualization as a project for the 6-week course Data Analysis with Python: Zero to Pandas. This course was conducted by Jovian.ml in partnership with freeCodeCamp.org. Check out the project here.
Also, the IPL is on right now. Go watch it and enjoy!