Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/pb319/ipl_sports_magazine
Challenge #10: Analyse historical IPL data and provide insights on IPL 2024 for a Sports Magazine (Codebasics)
https://github.com/pb319/ipl_sports_magazine
Last synced: 11 days ago
JSON representation
Challenge #10: Analyse historical IPL data and provide insights on IPL 2024 for a Sports Magazine (Codebasics)
- Host: GitHub
- URL: https://github.com/pb319/ipl_sports_magazine
- Owner: pb319
- Created: 2024-03-28T03:59:32.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-04-13T05:39:47.000Z (10 months ago)
- Last Synced: 2024-04-13T20:08:24.436Z (10 months ago)
- Size: 75.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# IPL_-Sports_Magazine
- Challenge #10: Analyse historical IPL data and provide insights on IPL 2024 for a Sports Magazine (Codebasics).
- Project Presentation PDF: [Linkedin Post](https://www.linkedin.com/posts/pranaybiswas_ipl-sports-magazine-activity-7190769304886280192-8a2H?utm_source=share&utm_medium=member_desktop)
***
***
## Table of Contents:
0. [Project Link](https://codebasics.io/challenge/codebasics-resume-project-challenge).
1. [Problem Statement](https://github.com/pb319/IPL_Sports_Magazine#problem-statement)
2. [Data Modeling](https://github.com/pb319/IPL_Sports_Magazine?tab=readme-ov-file#data-loading)
3. [Primary Data Analysis](https://github.com/pb319/IPL_Sports_Magazine/blob/main/README.md#primary-data-analysis)
4. [Orange Cap and Purple Cap Prediction](https://github.com/pb319/IPL_Sports_Magazine/blob/main/README.md#the-orange-and-purple-cap-prediction)
5. [My Team 11](https://github.com/pb319/IPL_Sports_Magazine/blob/main/README.md#my-team-11)
## Problem Statement
**"Sports Basics"** is a sports blog company that entered space recently. They
wanted to get more traffic to their website by releasing a special edition magazine
on IPL 2024. This magazine aims to provide interesting insights and facts for
fans, analysts and teams based on the last 3 years' data.
The chief editor Tony Sharma oversees this publication, and he believes in data
analytics. He reached out to Peter Pandey, a journalist in his team who is a data-savvy cricket enthusiast.
## Data Loading
1. MySQL
- First I created a database called "ipl" using the following command
```CREATE DATABASE IF NOT EXISTS "ipl";```
- I used Mysql Workbench Table Data Import Wizard to import
- [fact_bowling_summary.csv](https://github.com/pb319/IPL_Sports_Magazine/files/14800053/fact_bowling_summary.csv)
- [dim_match_summary.csv](https://github.com/pb319/IPL_Sports_Magazine/files/14800054/dim_match_summary.csv)
- [dim_players.csv](https://github.com/pb319/IPL_Sports_Magazine/files/14800055/dim_players.csv)
- [fact_bating_summary.csv](https://github.com/pb319/IPL_Sports_Magazine/files/14800056/fact_bating_summary.csv)
Similarly, I added all other dimensions and fact tables into the Mysql Workbench:
The table names have been changed slightly using Mysql Workbench using the following bunch of code:
```
USE ipl;
ALTER TABLE dim_match_summary
RENAME TO dim_match;
ALTER TABLE fact_bating_summary
RENAME TO fact_bating;
ALTER TABLE fact_bowling_summary
RENAME TO fact_bowling;
```
Connecting Mysql Database with **Power BI**:
## Data Exploration
1. Mysql
```
-- Data Exploration
-- dim_match
SELECT * FROM dim_match LIMIT 5;
DESC dim_match;
SELECT COUNT(match_id) as No_of_Matches,
COUNT(DISTINCT(team1)) as No_of_Teams,
COUNT(DISTINCT(RIGHT(matchDate,4))) as `Year`
FROM dim_match;
-- Therefore 10 teams participated in 206 matches
-- Let's have a look into the margin column
select DISTINCT(
CASE
WHEN margin LIKE "%run%" THEN RIGHT(margin,4)
WHEN margin LIKE "%wicket%" THEN (RIGHT(margin,7)) END) AS Margin
FROM dim_match;
-- that is We have 4 different in the form of singular and plural
-- dim_players
SELECT * FROM dim_players LIMIT 5;
SELECT COUNT(DISTINCT(name)) as Player_Name,
COUNT(DISTINCT(team)) as No_of_Teams
FROM dim_players;
-- Data validation: Total Teams = 10, Total Number of Players 292
-- fact_bating
SELECT * FROM fact_bating LIMIT 5;
SELECT COUNT(DISTINCT(match_id)) as No_of_Matches,
COUNT(DISTINCT(batsmanName)) as Batsman_Count
FROM fact_bating;
-- Data Consistency: Total Matches played = 206, Total Number of Batsman is 262
-- fact_bowling
SELECT * FROM fact_bowling LIMIT 5; -- 5 Rows of `fact_bowling` table
SELECT COUNT(DISTINCT(match_id)) as No_of_Matches,
COUNT(DISTINCT(bowlerName)) as Bowler_Count
FROM fact_bowling;
-- AGAIN Corraboration of Total Matches played = 206
-- Total Number of Bowlers 202
```
For more detailed documentation [Click Here](https://github.com/pb319/IPL_Sports_Magazine/blob/main/Data_Exploration.sql)
## Data Cleaning
1. Mysql
- There were some issues with "May 28-29, 2023" entry in the matchYear field while changing the datatype of matchDate column. Hence the following SQL command has been employed to manupulate the data a bit:
```
set sql_safe_updates =0;
UPDATE dim_match
SET matchDate = 'May 29, 2023'
WHERE matchDate = 'May 28-29, 2023';
set sql_safe_updates =1;
```
1. Power BI
- "matchDate" datatype has been changed from text into Date:
```
= Table.TransformColumnTypes(ipl_dim_match,{{"matchDate", type date}})
```
- "matchYear" column added:
```
= Table.AddColumn(#"Changed Type", "matchYear", each Date.Year([matchDate]), Int64.Type)
```
## Primary Data Analysis:
1. Mysql (Validated by Power BI Dashboar in the background)
- Top 10 batsmen based on past 3 years total runs scored
```
-- Primary Analysis
-- ** Caution Edit-> Preferences-> SQL Editor -> read-timeout interval to be at least 60 seconds **
USE ipl; -- (ASSIGNING DEFAULT TABLE) MUST RUN CODE ELSE REST WILL NOT WORK
SELECT DENSE_RANK() OVER(ORDER BY SUM(runs) DESC) as Player_Rank,
batsmanName as Player_Name, SUM(runs)as Total_Run
FROM fact_bating
GROUP BY batsmanName
ORDER BY Total_Run DESC
LIMIT 10;
```
- Top 10 batsmen based on past 3 years batting average. (min 60 balls faced in each season)
```
-- [Bating Average is the total number of runs they have scored divided by the number of times they have been out]
WITH CTE1 AS(SELECT batsmanName, RIGHT(matchDate,4) AS Season
FROM fact_bating f
INNER JOIN dim_match d
ON f.match_id = d.match_id
GROUP BY 1, 2
HAVING SUM(balls)>60)
SELECT DENSE_RANK() OVER(ORDER BY ROUND(SUM(runs)/SUM(CASE WHEN `out/not_out` = "out" THEN 1 ELSE 0 END),2) DESC) as Player_Rank,
batsmanName as Player_Name,
ROUND(SUM(runs)/COUNT(CASE WHEN `out/not_out` = "out" THEN 1 ELSE NULL END),2) AS Bating_Average
FROM fact_bating F
INNER JOIN dim_match D -- `dim_match` and `fact_bating` has been Inner Joined using `match_id`
ON F.match_id = D.match_id
WHERE batsmanName IN(
SELECT batsmanName
FROM CTE1
GROUP BY 1
HAVING count(Season) = 3)
GROUP BY batsmanName
HAVING COUNT(DISTINCT(RIGHT(matchDate,4)))=3
ORDER BY Bating_Average DESC -- ordered in decsending order
LIMIT 10;
```
- Top 10 batsmen based on past 3 years strike rate (min 60 balls faced in each season)
```
SELECT DENSE_RANK() OVER(ORDER BY AVG(SR) DESC)as Player_Rank, batsmanName as Player_Name,
ROUND(avg(SR),2) as Avg_SR
FROM fact_bating F
INNER JOIN dim_match D
ON F.match_id = D.match_id
WHERE batsmanName IN(
SELECT batsmanName
FROM fact_bating f
INNER JOIN dim_match d
ON f.match_id = d.match_id
GROUP BY batsmanName , RIGHT(matchDate,4)
HAVING SUM(balls)>60)
GROUP BY batsmanName
ORDER BY Avg_SR DESC
LIMIT 10;
```
- Top 5 batsmen based on past 3 years boundary % (min 60 balls faced in each season)
```
-- Boundary % is the percentage of total run that comes from 4s and 6s (min 60 balls faced in each season)
WITH CTE1 AS(SELECT batsmanName, RIGHT(matchDate,4) AS Season
FROM fact_bating f
INNER JOIN dim_match d
ON f.match_id = d.match_id
GROUP BY 1, 2
HAVING SUM(balls)>60)
SELECT batsmanName AS Player_Name,
ROUND((100*(4*SUM(`4s`)+6*SUM(`6s`))/SUM(runs)),2) as 'Boundary%'
FROM fact_bating AS f
WHERE batsmanName IN(
SELECT batsmanName
FROM CTE1
GROUP BY 1
HAVING count(Season) = 3 )
GROUP BY batsmanName
ORDER BY `Boundary%` DESC
LIMIT 5;
```
- Top 10 bowlers based on past 3 years total wickets taken
```
SELECT DENSE_RANK() OVER(ORDER BY SUM(wickets) DESC) AS Player_Rank,
bowlerName AS Name,
SUM(wickets) AS Total_Wickets
FROM fact_bowling F
INNER JOIN dim_match D
ON F.match_id = D.match_id
GROUP BY `Name`
ORDER BY Total_Wickets DESC
LIMIT 10;
```
- Top 10 bowlers based on past 3 years bowling average. (min 60 balls bowled in each season)
```
WITH CTE2 AS(SELECT bowlerName, RIGHT(matchDate,4) AS Season -- CTE Filtered by Atleast 60 balls delivered
FROM fact_bowling f
INNER JOIN dim_match d
ON f.match_id = d.match_id
GROUP BY 1, 2
HAVING SUM(overs)*6 > 60)
SELECT DENSE_RANK() OVER(ORDER BY (SUM(runs)/SUM(wickets)) ASC) as Player_Rank, -- Ranking
bowlerName as Player_Name,
ROUND((SUM(runs)/SUM(wickets)),2) as Bowling_Avg
FROM fact_bowling F
INNER JOIN dim_match D
ON F.match_id = D.match_id
WHERE bowlerName IN(
SELECT bowlerName
FROM CTE2
GROUP BY 1
HAVING count(Season) = 3 )
GROUP BY bowlerName
HAVING Bowling_Avg IS NOT NULL
ORDER BY Bowling_Avg ASC
LIMIT 10;
```
- Top 10 bowlers based on past 3 years economy rate. (min 60 balls bowled in each season)
```
WITH CTE2 AS(SELECT bowlerName, RIGHT(matchDate,4) AS Season -- CTE Filtered by Atleast 60 balls delivered
FROM fact_bowling f
INNER JOIN dim_match d
ON f.match_id = d.match_id
GROUP BY 1, 2
HAVING SUM(overs)*6 > 60)
SELECT RANK() OVER(ORDER BY (ROUND(avg(economy),2)) ASC ) as Player_Rank,
bowlerName, ROUND(avg(economy),3) as Avg_Economy -- Rounded upto 2 decimal place
FROM fact_bowling f
INNER JOIN dim_match d
ON f.match_id = d.match_id -- `fact_bowling` and `dim_match` has been Inner Joined on `match_id`
WHERE bowlerName IN (
SELECT bowlerName
FROM CTE2
GROUP BY 1
HAVING count(Season) = 3 ) -- All of the Three Seasons Bowled
GROUP BY bowlerName
ORDER BY Avg_Economy ASC
LIMIT 10;
```
- Top 10 bowlers based on past 3 years dot ball % (min 60 balls bowled in each season)
```
-- dot ball % i.e. percentage of dot balls to total ball
WITH CTE2 AS(SELECT bowlerName, RIGHT(matchDate,4) AS Season
FROM fact_bowling f
INNER JOIN dim_match d
ON f.match_id = d.match_id
GROUP BY 1, 2
HAVING SUM(overs)*6 > 60)
SELECT bowlerName AS Player_Name,
ROUND(100*((SUM(`0s`))/(SUM(overs)*6)),2) as 'Dotball%'
FROM fact_bowling AS f
WHERE bowlerName IN (
SELECT bowlerName
FROM CTE2
GROUP BY 1
HAVING count(Season) = 3 )
GROUP BY bowlerName
ORDER BY `Dotball%` DESC
LIMIT 5;
```
- Top 4 teams based on past 3 years winning %
```
-- (the fraction of games or matches a team or individual has won)
-- I employed Common Table Expression to execute the query
WITH CTE3 AS(SELECT team1 AS team,
COUNT(match_id) as First_bat
FROM dim_match
GROUP BY 1
),
CTE4 AS(SELECT team2 AS team,
COUNT(match_id) as First_ball
FROM dim_match
GROUP BY 1
),
task AS(SELECT winner AS team,
COUNT(*) AS `#Win`
FROM dim_match
GROUP BY 1
)
SELECT team AS Team,
(`First_ball`+`First_bat`) AS Total_play,
ROUND((100*`#Win`/(`First_ball`+`First_bat`)),2) AS Win_Prc
FROM CTE3
JOIN CTE4 USING (team)
JOIN task USING (team)
ORDER BY 3
LIMIT 4;
```
- Top 2 teams with the highest number of wins achieved by chasing targets over the past 3 years
```
WITH temp AS(SELECT winner,
ROUND(COUNT(CAST(SUBSTRING(margin,1,LOCATE(" ",margin)-1)AS SIGNED)),2) AS Avg_Lead -- Substring `Lead_Number` absolute value of field like `26 wickets` '26' is Lead_Number and 'wickets' is filed
FROM dim_match
WHERE SUBSTRING(margin,LOCATE(" ",margin)+1,length(margin)) = "wickets" -- Those who chases run and wins must be won by wickets
GROUP BY winner , SUBSTRING(margin,LOCATE(" ",margin)+1,length(margin)) -- substring() gives ou "wickets"/'runs'
)
SELECT DENSE_RANK() OVER(ORDER BY Avg_Lead DESC) AS Team_Rank,
winner, Avg_Lead -- Average lead
FROM temp
ORDER BY Avg_Lead DESC
LIMIT 2;
```
In our first social update, we noticed anomalies in some SQL query outputs that require rectification. You may refer to [Old Primary SQL Analysis Docstring](https://github.com/pb319/IPL_Sports_Magazine/blob/main/Primary_Analysis.sql) or [Linked Post](https://www.linkedin.com/posts/pranaybiswas_mysql-query-drills-activity-7183933808914706432-f9d4?utm_source=share&utm_medium=member_desktop).
The Rectified SQL Queries are as follows:
You may refer to the docstring - ([Updated SQL Query](https://github.com/pb319/IPL_Sports_Magazine/blob/main/Primary_Analysis%20(Rectified).sql))
***
1. Power BI (Cleaning and Basic Analysis)
- Power Query (M Language)
- DAX Measures
> Both can be found in ([Final_Dashboard.pbix](https://github.com/pb319/IPL_Sports_Magazine/raw/main/Final_Dashboard.pbix))
## The Orange and Purple Cap Prediction
Here in this section, we will deal with various machine learning models and Statistical analysis to optimize those models mostly deployed through R and Python as a part of our "Predictive Analytics".
## Orange Cap:
#### __Objective__:
- To predict the player having maximum total run in the IPL season 2024.
#### __Methodologies__:
- Assumptions like _**Independence of errors, Normality of errors**_, etc. have been checked using various plots and measures.
- Primary model has been made through _**Stepwise Regression method**_.
- _**Multicollinearity**_ of independent variable has been identified by _**Variance Inflation Factor**_
- _**Multicollinearity**_ of the independent variable has been addressed by _**Principal Component Analysis**_
- _**Test of Significance**_ has been considered while model building
- Reliability has been reconfirmed by _**R-Squared and Adjusted R-Squared**_
## Purple Cap:
#### __Objective__:
- To predict the player with the most wickets in the IPL season 2024.
#### __Methodologies__:
- The approach mentioned earlier for "Orange Cap Prediction" has been imitated.
#### Resources
- Explanation: _[Linked Post](https://bit.ly/3io5qqX)_
- Resources: Datasets are available [Here](https://github.com/pb319/IPL_Sports_Magazine/tree/main/Predictive%20Analytics)
***
- The Ultimate Result:
## My Team 11
1. Power BI
- Data Analysis
- I Classified the pool of players into various homogeneous groups (like Opener, Middle Order, Lower Order, etc.)
- To classify some criteria are needed. I got it from Analysis using Power BI.
- Illustration: Say we want to make criteria for openers then:
1. Filter the pool using position 1,2. We can't allow middle-order players to play the role of opener.
2. Then Line Charts would depict 90 percentile points which may help us to put a cap for further filtering like Strike Rate, Average Ball Faced, etc.
3. Ordering players based on Standard Deviation of runs/Strike Rate/anything else may help choose the right ones.
- Actionable Insights
- ** [Explanation Video](https://lnkd.in/gj4eiFfv)
- The Ultimate Result:
Thank You So Much!! See You Soon, Until then Goodbye 👋👋
***