Statistics & Facts - International Football (1872-2018)

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Course : 44-564_01 Design of Data-Intensive Systems

Northwest Missouri State University

Team - 1A

• Pair 01 - Abhijeet Agrawal & Sandeep Mulakala

  1. Abhijeet Agrawal:

     • Email id: [email protected]
     • Course: Applied Computer Science
     • Semester: 02

  2. Sandeep Mulakala

     • Email id: [email protected]
     • Course: Applied Computer Science
     • Semester: 04

• Pair 02 - Aditya Srimat Tirumala Pallerlamudi & Prathibha Kamani

  1. Aditya Srimat Tirumala Pallerlamudi

     • Email id: [email protected]
     • Course: Applied Computer Science
     • Semester: 04

  2. Prathibha Kamani

     • Email id: [email protected]
     • Course: Applied Computer Science
     • Semester: 04

Links

• Repository Link
• IssueTracker

Introduction

We are going to develop a map-reduce program to analyze the facts for international football. We have a dataset which includes results of international football matches starting from the very first official match in 1872 up to 2018. The matches range from World Cup to Baltic Cup to regular friendly matches.

Data Source

• Kaggle - https://www.kaggle.com/martj42/international-football-results-from-1872-to-2017/data
• No of Records : 38,759
• Size : 2.5 MB
• File format : CSV
• Data Format : Structured

Big Data Qualifications / Challenges

1. Volume: This dataset contains the list of International football results from 1872 to 2018. There are 38759 in total.
2. Variety: The data is structured and this data is in the forms of characters and integer values.
3. Velocity: Velocity for these statistics will vary for every tournament played between various teams and results for the matches will be different for every match in each month in a year.
4. Veracity: It gives us the statistical result for every game played at different cities and countries and it is trustworthy even though published in different websites but most of the times official results of the matches were published by the committee or the sponsors.
5. Value: It is very helpful to the people who follow the football matches and mainly it is used for the analysis purpose of the teams and performance of a particular team over a certain period.

Big Data Questions

1. For each team, how many wins as a home team? - Abhijeet Agrawal
2. For each tournament, how many matches were played? - Sandeep Mulakala
3. For each year, how many matches were drawn? - Prathibha Kamani
4. For each home team, how many matches were played? - Aditya Srimat Tirumala Pallerlamudi

Big Data Solutions

1. For each team, how many wins as a home team?

   Story :

   Our goal is to derive statistics related to number of wins, loss and draws while playing as a home team. To accomplish this, first we designed a mapper which list the home team and there respective scores for the match. We sort the list generated from the mapper output. After the sorting we aggregated the for each team and determined the number of total winns,loss and draws as a Home team.

   Data Flow

   • Mapper input : 1872-11-30,Scotland,England,0,0,Friendly,Glasgow,Scotland
   • Mapper Output / Reducer Input : Scotland,0,0
   • Reducer Output : Scotland 11
   • Kind of chart : Pie chart (Home vs Away wins)

   Graph

   

2. For each tournament, how many matches were played?

   Story :

   Our goal is to derive statistics related to number of matches played in a prticular tournment. To accomplish this, first we designed a mapper which list all the tournments. We sort the list generated from the mapper output. After the sorting we counted the number of times a tournment is repeated in the sorted output and calculated the number of gmes played in each tournment.

   Data Flow

   • Mapper input : 1872-11-30,Scotland,England,0,0,Friendly,Glasgow,Scotland
   • Mapper Output / Reducer Input : Friendly,1872-11-30,Scotland
   • Reducer Output : Friendly 10
   • Kind of chart : Bar chart (Tournament wise)

   Graph

   

3. For each year, how many matches were drawn?

   Story :

   Our goal is to derive statistics related to number of draw matches in an year. To calculate this, first we have designed a mapper which lists the home score and away score for the match which are of same score. We sort the list generated from the mapper output. After the sorting we have aggregated the drawn matches and reduced to total drawn matches in each and every year.

   Data Flow

   • Mapper input : 1872-11-30,Scotland,England,0,0,Friendly,Glasgow,Scotland
   • Mapper Output / Reducer Input : 1872-11-30, Scotland
   • Reducer Output : 1872, 1
   • Kind of chart : Scatter chart (Year wise)

   Graph

   

4. For each home team, how many matches were played?

   Story :

   Our goal is to calculate stats related to number of matches played as a home team. To calculate this, first we have designed a mapper which list all the home teams for all the years from 1872 to 2018. Then, we have sorted the list generated from the mapper output. After that, the sorted output is given as input for reducer and determined the number of total matches played as a Home team.

   Data Flow

   • Mapper input : 1872-11-30,Scotland,England,0,0,Friendly,Glasgow,Scotland
   • Mapper Output / Reducer Input : Scotland
   • Reducer Output : Scotland,369
   • Kind of chart : Pareto chart (Home Team wise)

   Graph