COIT20253 - Big Data Business Intelligence: Sports Analytics Report

This report explores the application of business intelligence using big data in the realm of sports analytics, specifically focusing on IPL (Indian Premier League) data from 2007 to 2017. The study aims to analyze individual batsman scores, bowler performances, and overall match dynamics, including head-to-head comparisons between teams. Data collection methods, storage solutions (cloud and central warehouses), and data processing techniques using R-Shiny are detailed. The report also covers the analysis of IPL batsman, including strike rates, runs per location, and performance evaluations, and discusses business continuity aspects, emphasizing the growing role of data analytics in sports management. Recommendations highlight the potential for strategic planning and player development through data-driven insights, concluding that sports analytics is a booming field with significant implications for both athletic performance and business opportunities.

Business Intelligence using Big Data
“Sports Analytics “

Table of Contents
Objectives……………………………………………………………………………………..1
Executive Summary…………………………………………………………………………..1
Introduction……………………………………………………………………………………2
Data Collection………………………………………………………………………………..2
IPL Batsman……………………………………………………………………………….3
IPL Bowlers………………………………………………………………………………..3
IPL Matches………………………………………………………………………………..4
Head to Head………………………………………………………………………………4
Data Storage…………………………………………………………………………………...4
Data Application and Processing…………………………………………………………….5
IPL Batsman Analysis………………………………………………………………………6
Strike Rate.…………………………………………………………………………………..6
Runs as per Location.………………………………………………………………………7
Analysis of Batsman Performance……………………………………………………….7
Business Continuity..…………………………………………………………………………..8
Recommendation. ……….……………………………………………………………………10
Conclusion………………………………………………………………………………………12
References……………………………………………………………………………………...13

Objectives
The main objective of this study is access and analyzed the difference in precipitation and
the necessity of sports analysis. In this study, we analyze and predicting the importance of
big data in sports industry.
For analysis purpose we will using IPL matches data from 2007 to 2017.
 We analyzing the Individual batsman scores
 Individual bowlers in IPL
 Analysis any IPL match from 2007 to 2017.
 All matches of an IPL team from starting to ending.
 Highest Scorer in IPL
 Highest wicket taker in IPL
Executive Summary
As per the study, Australia is the leader in the field of sports, thats why every people is
involved in sports activities.
This reports provides a more formal understanding of the high performance and sports
workforce with the purpose of informing and policy development of an government bodies
and education providers to aware about sports activities that can enhance the future
leaders in sports.
The data presented in this report is based on IPL matches and it provides the performance
about the players so that we can improve the weakness point.
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Introduction
We will analysis this records using R-Shiny library. It will predict the data according to
the batsman and bowlers, simultaneously visualize the data in dashboards so that we can
easily analyze the data.
Here we will analyze data as per individual batsman’s and bowlers performance. We will
add all features to the column using rbind function. Count the data as any IPL player could
have played more than one team.
We have all data records for particular team wise with batsman and bowlers performance
in each team. Further will form this data to data frames to analyze the list of records.
Data Collection
For the data analysis we will take some most informative features variables from the
dataset, which is given below :
IPL Batsman : This will having all the records of batsman who is playing in the IPL match.
Also will adding the performance if one player is playing in more then one team in different
IPL session year wise.
 Runs and Bowling’s
 Four & Sixes
 Out of the Batsman
 Runs with strike rate
 Average in an inning
 Average strike rate
 Runs against team wise
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 Runs at venue wise
 Predicting runs of Batsman for future innings
IPL Bowlers : This will used to analyze individual bowlers in IPL matches. The same as
most bowlers who played more than one game in IPL.
 Economy rate
 Means of the runs by a bowlers
 Moving Average
 Average Wickets
 Wickets plot
 Wickets against another teams
 Wicket counts at the venues
 Bowlers wickets prediction
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IPL Matches : It will be using for analysis for individual IPL matches at venues.
 Partnerships
 Bowlers vs Batsman
 Wicket type
 Wicket runs
 Wicket in a Match
 Graph for match
Head to Head : This will analysis the data for 2 IPL team. Ex. All matches between
Rajasthan Royal vs Kings Eleven Punjab.
 Team Batsman batting partnership in all matches
 Batsman vs Bowlers in all matches
 Bowlers wickets in all matches
 Runs for bowlers in all matches
 Matches loss or win
Data Storage
Data is collected in different phases and we stored data into cloud storage or in central
warehouse, where we can fetch the data as per requirements.
In data model we are collecting data from difference sources as mentioned and storing in
database management system.
We created three layer as per the data – Structured data, Unstructured data and
Semistructured data. Based on the analysis we can visualize the data in the form of graph,
charts, plot and dashboards.
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Data Application & Processing
Now in this process we will create an template for end to end analysis of IPL T20 matches.
So before going to analyze the data it requires some dependencies library to run the
analysis model.
 Yorkr package – library(yorkr)
 dplyr package – library(dplyr
In the first step we will we will take care about the dataset. The dataset is requires before
for any analysis for IPL batsman’s, bowler’s and matches.
There is 5 dataset directories :
 database
 IPL matches list
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 Matches list between two teams
 Matches played against with another team
 Batting and Bowling details in each match
IPL Batsman analysis : As per the data analysis of IPL batsman according to runs vs
bowls, we can see that the graph is continuously increasing with bowls.
Strike Rate : From the graph we can easily see that the batsman strike rate is
continuously increasing and creasing. It is not stable.
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Runs as per location : As per the location wise we can see the batsman is scoring
different runs. In some IPL matches the score is high and in some its less.
Analysis of Batsman Performance : Different match having better performance and in
some matches having low. From the dataset we can say that the batsman Ashwin having
better performance in first over but letter on its decreasing.
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So from the analysis of the dataset we get a point is that in big data we can also use
sports analysis to predict the future prospect based on the data and results.
Business continuity
Applied data analysis is the term that is used to refer to the use of quantitative methods to
and technology to gather, store and analyze the multi structures data in order to make
business decisions.
Rapid increasing in data analysis is important for sports management. All the sports
events having data analysis, they are analyzing each and every activities by the players.
Data analytics is not limited in one sport, but it is applied for all kind of sports like Crickets,
Football, Baseball, Athletics, Cycling and many more to improve sports analysis.
Why use Big Data in sports, because it not easy to collect all types of data from sports
activities. Data is generating in sports in unstructured format which is not easy to collect to
by traditional software or systems, thats why big data comes into the pictures to collect
these types of data.
The below applications of Big Data in Sports –
 Games data analysis
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 Player actions
 Sports broadcasting
 Fitness management
The streaming data comes into big data module kafka and collected all streaming data to
spark streaming system to store into database.
In the first step data are collected in kafka queue which is data warehouse for
organizational analysis purpose.
Kafka process the data to spark engine for filtering the data based on key and values
paired.
Http processed data on user portal portal in to form of dashboard to visualize the user
streaming data to insights form.
This is cricket match streaming process how big data application used in sports as well as
for business purpose. As per the analysis based on previous records, businessman can
interact with the batsman’s or players to take auction and sell themselves.
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Recommendation
Day by day increasing role of analytics in sports is good for business scope. Peoples can
be get training and make some strategy planning on the sports. Data analytics is the game
changer for sports to analysis the players data and records.
Many sports coaches believe that performance analytics is the most important point to
analyze the weakness of the players. Based on the analysis we find out the most weak
point in performance so that we can improve It.
Analytics for Winning : It is an strategic planning to win the game from opposition. But
before we analyzed the historical data for each activities. Based on the analysis we target
to player or opponent on key point.
High powered big data analytics algorithms and CCTV camera’s replacing human and
statistician helping them to find out the performance of the player and outcomes the game.
Study area
Sports is having huge amount of data which can not analyzed by basic software or
systems. It requires big data processing techniques and algorithms which can give the
result in seconds. Big data algorithm capturing each activities of the players and game and
generating amount of data.
Today’s world is focused on Artificial Intelligence which provides real time analytics and
data visualization quickly. The data scripting data processed in bulk amount and
algorithms find out the outcomes from the data.
Artificial Intelligence : It is the main source of data analytics. AI used deep machine
learning algorithms to find out patterns, sentiment analysis, taxonomy, language
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