Analyzing Trends in Cricket Games

Proposal

Author

Affiliations

PlotWizards

High-level Goal

In this project we will create a dashboard of a live, ongoing cricket match that updates its plots at regular, automatic intervals.

Goal Details

Motivation:

For this project we wanted to showcase our data visualization skill-set with the challenge of a live dataset, and what better live dataset to use than sports, so we went with our favorite sport - cricket. For the purposes of demonstration we won’t use an actual real-time data as we found that there would be unwanted associated costs, so we will instead use a large historical dataset where our back-end server will automatically update the data table for the front-end at regular intervals, essentially mocking a live match.

Description of datasets:

We’ll use two datasets:

ODI_Match_Data.csv: Provides facts about the location and season of the cricket matches along with team information and the play results from each team member. We’ll need this one to investigate partnerships between batsmen. It’s dimensions are 155432 rows of data by 23 variable columns

Variables that we’ll use:

Variable Name	Data type	Description
match_id	double	A unique identifier for each ODI cricket match.
season	character	The season in which the match took place
start_date	character	The date on which the match started.
innings	double	The innings number (1st innings or 2nd innings).
ball	double	A numeric representation of the ball number bowled in the innings.
batting_team	character	The name of the batting team for the current innings.
bowling_team	character	The name of the bowling team for the current innings.
striker	character	The batsman who is currently facing the ball.
non_striker	character	The batsman at the non-striker’s end.
bowler	character	The bowler who is delivering the ball.
runs_off_bat	double	The number of runs scored off the bat (excluding extras).
extras	double	The total number of extra runs (wides, no-balls, byes, leg-byes, penalty) in the current ball.
wides	double	The number of wide deliveries bowled in the current ball.
noballs	double	The number of no-ball deliveries bowled in the current ball.
byes	double	The number of byes scored in the current ball.
legbyes	double	The number of leg-byes scored in the current ball.

ODI_Match_info.csv: Overlaps in data with the above but provides information on the umpire, performance, and the city the match took place. We’ll need this one to analyze the batting and bowling performance of each player. It’s dimensions are 2380 rows of data by 18 variable columns.

Variables that we’ll use:

Variable Name	Data type	Description
id	double	A unique identifier for each cricket match.
season	character	The season in which the match took place
date	date	The date on which the match was played.
team1	character	The name of the first cricket team participating in the match.
team2	character	The name of the second cricket team participating in the match.
result	character	The result of the match (e.g., “normal,” “tie,” “no result”).
winner	character	The winning team of the match.
win_by_runs	double	The margin of victory in runs (0 for wickets, if not applicable).
win_by_wickets	double	The margin of victory in wickets (0 for runs, if not applicable).

Research questions :

Below we have the questions we would like to answer with our dashboard along with what we will need to answer them including variables, plot representations, and insights we can extract from the visuals.

How did the (a) scoring rate, (b) player batting performances, and (c) bowlers’ economy rates, evolve throughout the match?

We chose this question because it encompasses critical aspects of a cricket match, including scoring dynamics, standout individual performances, bowling efficiency, partnerships’ influence on runs, and overall player contributions, providing a holistic view of the match progression.

a. Scorecard summary:

Variables: Overs (X-axis), Runs Scored per Over (Y-axis) - from ODI_Match_info.csv

Plot Representation: Line chart or bar chart

Insights: Visualize the scoring rate throughout the innings, show periods of acceleration or deceleration in runs scored.

b. Batting Performance:

Variables: Batsman Name (X-axis), Runs Scored (Y-axis) - from ODI_Match_info.csv

Plot Representation: Bar chart or stacked bar chart

Insights: Compares runs scored by each batsman, highlighting top performers.

For data wrangling: season, batsman, runs scored..

c. Bowler’s economy rates:

Variables: Bowler Name (X-axis), Economy Rate (Y-axis)

Plot Representation: Box plot or violin plot

Insights: Shows the distribution of economy rates among bowlers, indicating variations in performance.
How did (a) partnerships influence total runs, and (b) what insights do team performances, player contributions and live updates provide about the match dynamics?

We selected this question to highlight the significance of partnerships in contributing to total runs, while also delving into team performances and individual player contributions, offering insights into strategic game play and the match’s overall dynamics.

a. Partnerships Overview:

Variables: Batsman Pair (X-axis), Partnership Runs (Y-axis)

Plot Representation: Network graph or line chart

Insights: Highlights significant partnerships and their impact on total runs.

b.
- Player Comparison (merged from other questions):
  
  Variables: Player Name (X-axis), Batting/Bowling Statistics (Y-axis)
  
  Plot Representation: Radar chart or spider chart
  
  Insights: Provides a comprehensive comparison of players’ performances across multiple metrics.
- Team Comparison (merged from other questions):
  
  Variables: Team Name (X-axis), Runs Scored/Wickets Taken (Y-axis)
  
  Plot Representation: Pie chart or half-eye plot
  
  Insights: Visualizes the proportion of runs scored or wickets taken by each team, offering a quick comparison view.
- Dynamic Updates (merged from other questions):
  
  Variables: Live Data (e.g., Score, Commentary, Player Stats)
  
  Plot Representation: Live dashboard with real-time updates
  
  Insights: Access to latest match information for enhanced engagement.

The data visualization methods are subject to change while we analyze the data.

Plan of Attack

Mocking Live Data :

To avoid unnecessary costs associated with real-time data, we will split the data into two parts: past data and live data.

The past data will include information from years 2002 to 2022, while the live data will consist of data from the year 2023. Each entry from 2023 will be read from the actual CSV file and entered into a database table with an interval of 10 to 20 seconds between two consecutive entries. These entries will be considered as live data and will be sent to the API caller.

Diagram of plan:

Figure 1: Diagram of the methodology we’ll implement in this project.

Back-end APIs:

1) Batting Performance API:

Endpoint: /batting-performance

Parameters:

match_id: Filter data for a specific match.
player_name: Filter data for a specific player.

Response:

Returns data for batting performance including runs scored by each batsman.

API:

GET /batting-performance?match_id={id}&player_name={pname}

2) Bowling Performance API:

Endpoint: /bowling-performance

Parameters:

match_id: Filter data for a specific match.
player_name: Filter data for a specific player.

Response:

Returns data for bowling performance including wickets taken by each bowler and their economy rate.

API:

GET /bowling-performance?match_id={id}&player_name={pname}

3) Partnerships API:

Endpoint: /partnerships Parameters:

match_id: Filter data for a specific match.

Response:

Returns data for partnerships between batsmen.

API:

GET /partnerships?match_id={id}

4) Player Performance Tracking API:

Endpoint: /player-performance

Parameters:

match_id: The ongoing match ID.
player_name: The name of the player.

Response:

Returns real-time updates for the player’s performance including runs scored, wickets taken, strike rate, economy rate, etc.

API:

GET /player-performance?match_id={ongoing-id}&player_name={pname}

These APIs are subject to change, additions, or removals as we analyze the data.

Task Name	Status	Assigned to	Due	Priority	Task Summary
Backend: JDBC Connectivity		Srinivasan, Mohit	April 5th	High	Connect the database, MySQL, to JAVA using JDBC Connectivity
Back-end: API Creativity		Srinivasan, Mohit	April 12th	High	Create all the necessary APIs mentioned in the proposal. Addition/changes could be made.
Converting Dataset into database entries		Tejas, Mohit	April 7th	High	Merge both the .csv files and put that data in MySQL
Mocking live data		Mohit, Tejas	April 12th	Low	Try to mock the data from 2023 as live data
Data Wrangling		Anjani, Nandini, Alex	April 5th	High	Merging datasets and cleaning them
Designing the initial draft of the plots (Question 1)		Alex, Navya	April 10th	High	Designing basic plots for Question 1(complex plots will be done later)
Designing the initial draft of the plots (Question 2)		Anjani, Nandini	April 10th	High	Designing basic plots for Question 2(complex plots will be done later)
Deciding on more complex plots		All	April 15th-22th	High	Complex plots will be planned and crafted for both the questions
Integrating frontend with the backend through API calls		Srinivasan, Mohit	April 15th -17th	Low	Calling APIs hosted on the backend in the frontend.
Quarto Dashboards for the initial draft plots		Tejas, Anjani, Nandini, Navya, Alex	April 17th	High	Create dashboards for all the plots (simple and complex) using quarto dashboards.

Repository Organization

Our github repository will remain mostly unchaged from the defualt. We will insert our data files into the data/ folder along with a corresponding readme.md file. The frontend of the project will be saved under index.qmd. The final repository will deployed on github pages.

References

Utkarsh Tomar. (2023). ODI Men’s Cricket Match Data (2002-2023) [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/3780212

--- title: "Analyzing Trends in Cricket Games" subtitle: "Proposal" author: - name: "PlotWizards" affiliations: - name: "School of Information, University of Arizona" - description: "Analyzing cricket match data to understand team performances and match outcomes." format: html: code-tools: true code-overflow: wrap code-line-numbers: true embed-resources: true code-fold: true editor: visual code-annotations: hover execute: warning: false message: false error: false --- ## High-level Goal In this project we will create a dashboard of a live, ongoing cricket match that updates its plots at regular, automatic intervals. ## Goal Details ### Motivation: For this project we wanted to showcase our data visualization skill-set with the challenge of a live dataset, and what better live dataset to use than sports, so we went with our favorite sport - cricket. For the purposes of demonstration we won't use an actual real-time data as we found that there would be unwanted associated costs, so we will instead use a large historical dataset where our back-end server will automatically update the data table for the front-end at regular intervals, essentially mocking a live match. ### Description of datasets: We'll use two datasets: 1. **ODI_Match_Data.csv**: Provides facts about the location and season of the cricket matches along with team information and the play results from each team member. We'll need this one to investigate partnerships between batsmen. It's dimensions are 155432 rows of data by 23 variable columns Variables that we'll use: | Variable Name | Data type | Description | |:------------------|:------------------|:---------------------------------| | match_id | double | A unique identifier for each ODI cricket match. | | season | character | The season in which the match took place | | start_date | character | The date on which the match started. | | innings | double | The innings number (1st innings or 2nd innings). | | ball | double | A numeric representation of the ball number bowled in the innings. | | batting_team | character | The name of the batting team for the current innings. | | bowling_team | character | The name of the bowling team for the current innings. | | striker | character | The batsman who is currently facing the ball. | | non_striker | character | The batsman at the non-striker's end. | | bowler | character | The bowler who is delivering the ball. | | runs_off_bat | double | The number of runs scored off the bat (excluding extras). | | extras | double | The total number of extra runs (wides, no-balls, byes, leg-byes, penalty) in the current ball. | | wides | double | The number of wide deliveries bowled in the current ball. | | noballs | double | The number of no-ball deliveries bowled in the current ball. | | byes | double | The number of byes scored in the current ball. | | legbyes | double | The number of leg-byes scored in the current ball. | 2. **ODI_Match_info.csv**: Overlaps in data with the above but provides information on the umpire, performance, and the city the match took place. We'll need this one to analyze the batting and bowling performance of each player. It's dimensions are 2380 rows of data by 18 variable columns. Variables that we'll use: | Variable Name | Data type | Description | |:------------------|:------------------|:---------------------------------| | id | double | A unique identifier for each cricket match. | | season | character | The season in which the match took place | | date | date | The date on which the match was played. | | team1 | character | The name of the first cricket team participating in the match. | | team2 | character | The name of the second cricket team participating in the match. | | result | character | The result of the match (e.g., "normal," "tie," "no result"). | | winner | character | The winning team of the match. | | win_by_runs | double | The margin of victory in runs (0 for wickets, if not applicable). | | win_by_wickets | double | The margin of victory in wickets (0 for runs, if not applicable). | ### Research questions : Below we have the questions we would like to answer with our dashboard along with what we will need to answer them including variables, plot representations, and insights we can extract from the visuals. 1. **How did the (a) scoring rate, (b) player batting performances, and (c) bowlers' economy rates, evolve throughout the match?** We chose this question because it encompasses critical aspects of a cricket match, including scoring dynamics, standout individual performances, bowling efficiency, partnerships' influence on runs, and overall player contributions, providing a holistic view of the match progression. a\. **Scorecard summary:** Variables: Overs (X-axis), Runs Scored per Over (Y-axis) - from ODI_Match_info.csv Plot Representation: Line chart or bar chart Insights: Visualize the scoring rate throughout the innings, show periods of acceleration or deceleration in runs scored. b\. **Batting Performance:** Variables: Batsman Name (X-axis), Runs Scored (Y-axis) - from ODI_Match_info.csv Plot Representation: Bar chart or stacked bar chart Insights: Compares runs scored by each batsman, highlighting top performers. For data wrangling: season, batsman, runs scored.. c\. **Bowler's economy rates:** Variables: Bowler Name (X-axis), Economy Rate (Y-axis) Plot Representation: Box plot or violin plot Insights: Shows the distribution of economy rates among bowlers, indicating variations in performance. 2. **How did (a) partnerships influence total runs, and (b) what insights do team performances, player contributions and live updates provide about the match dynamics?** We selected this question to highlight the significance of partnerships in contributing to total runs, while also delving into team performances and individual player contributions, offering insights into strategic game play and the match's overall dynamics. a\. **Partnerships Overview:** Variables: Batsman Pair (X-axis), Partnership Runs (Y-axis) Plot Representation: Network graph or line chart Insights: Highlights significant partnerships and their impact on total runs. b\. - **Player Comparison (merged from other questions):** Variables: Player Name (X-axis), Batting/Bowling Statistics (Y-axis) Plot Representation: Radar chart or spider chart Insights: Provides a comprehensive comparison of players' performances across multiple metrics. - **Team Comparison (merged from other questions):** Variables: Team Name (X-axis), Runs Scored/Wickets Taken (Y-axis) Plot Representation: Pie chart or half-eye plot Insights: Visualizes the proportion of runs scored or wickets taken by each team, offering a quick comparison view. - **Dynamic Updates (merged from other questions):** Variables: Live Data (e.g., Score, Commentary, Player Stats) Plot Representation: Live dashboard with real-time updates Insights: Access to latest match information for enhanced engagement. *The data visualization methods are subject to change while we analyze the data.* ## Plan of Attack ### Mocking Live Data : To avoid unnecessary costs associated with real-time data, we will split the data into two parts: past data and live data. The past data will include information from years 2002 to 2022, while the live data will consist of data from the year 2023. Each entry from 2023 will be read from the actual CSV file and entered into a database table with an interval of 10 to 20 seconds between two consecutive entries. These entries will be considered as live data and will be sent to the API caller. ### Diagram of plan: ![](images/Capture.PNG) Figure 1: Diagram of the methodology we'll implement in this project. ### Back-end APIs: **1) Batting Performance API:** Endpoint: /batting-performance Parameters: - match_id: Filter data for a specific match. - player_name: Filter data for a specific player. Response: - Returns data for batting performance including runs scored by each batsman. API: - GET /batting-performance?match_id={id}&player_name={pname} **2) Bowling Performance API:** Endpoint: /bowling-performance Parameters: - match_id: Filter data for a specific match. - player_name: Filter data for a specific player. Response: - Returns data for bowling performance including wickets taken by each bowler and their economy rate. API: - GET /bowling-performance?match_id={id}&player_name={pname} **3) Partnerships API:** Endpoint: /partnerships Parameters: - match_id: Filter data for a specific match. Response: - Returns data for partnerships between batsmen. API: - GET /partnerships?match_id={id} **4) Player Performance Tracking API:** Endpoint: /player-performance Parameters: - match_id: The ongoing match ID. - player_name: The name of the player. Response: - Returns real-time updates for the player's performance including runs scored, wickets taken, strike rate, economy rate, etc. API: - GET /player-performance?match_id={ongoing-id}&player_name={pname} *These APIs are subject to change, additions, or removals as we analyze the data.* | | | | | | | |------------|------------|------------|------------|------------|------------| | **Task Name** | **Status** | **Assigned to** | **Due** | **Priority** | **Task Summary** | | Backend: JDBC Connectivity | | Srinivasan, Mohit | April 5th | High | Connect the database, MySQL, to JAVA using JDBC Connectivity | | Back-end: API Creativity | | Srinivasan, Mohit | April 12th | High | Create all the necessary APIs mentioned in the proposal. Addition/changes could be made. | | Converting Dataset into database entries | | Tejas, Mohit | April 7th | High | Merge both the .csv files and put that data in MySQL | | Mocking live data | | Mohit, Tejas | April 12th | Low | Try to mock the data from 2023 as live data | | Data Wrangling | | Anjani, Nandini, Alex | April 5th | High | Merging datasets and cleaning them | | Designing the initial draft of the plots (Question 1) | | Alex, Navya | April 10th | High | Designing basic plots for Question 1(complex plots will be done later) | | Designing the initial draft of the plots (Question 2) | | Anjani, Nandini | April 10th | High | Designing basic plots for Question 2(complex plots will be done later) | | Deciding on more complex plots | | All | April 15th-22th | High | Complex plots will be planned and crafted for both the questions | | Integrating frontend with the backend through API calls | | Srinivasan, Mohit | April 15th -17th | Low | Calling APIs hosted on the backend in the frontend. | | Quarto Dashboards for the initial draft plots | | Tejas, Anjani, Nandini, Navya, Alex | April 17th | High | Create dashboards for all the plots (simple and complex) using quarto dashboards. | ## Repository Organization Our github repository will remain mostly unchaged from the defualt. We will insert our data files into the data/ folder along with a corresponding readme.md file. The frontend of the project will be saved under index.qmd. The final repository will deployed on github pages. ## References 1. Utkarsh Tomar. (2023). ODI Men's Cricket Match Data (2002-2023) \[Data set\]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/3780212