Cricket Metrics

INFO 526 - Project Final

Author

Affiliation

Plot Wizards

Abstract

This project aims to create a dashboard for a live cricket match using historical data and API mock-ups. The motivation stems from showcasing data visualization skills within the context of a dynamic, real-time dataset, albeit simulated through historical data updates. Our data provides comprehensive information about cricket matches, including team performance, player statistics, and match outcomes. The project’s research questions focus on analyzing scoring rates, player batting and bowling performances, partnerships’ influence on total runs, and team dynamics throughout the match.

The backend APIs play a crucial role in providing real-time or pseudo-real-time data updates for batting performance, bowling performance, partnerships, and player performance tracking. The team is organized into tasks such as JDBC connectivity, API creation, data wrangling, mock data creation, and frontend-backend integration. The GitHub repository structure includes a data folder for datasets, a readme.md file, and index.qmd for the frontend, with deployment planned on GitHub Pages.

Overall, this project showcases a comprehensive approach to data visualization, backend API integration, and dashboard creation for a live cricket match experience, blending historical data insights with dynamic dashboard updates.

Introduction

“Analyzing Trends in Cricket Games” aims to develop a dynamic dashboard that visualizes cricket match data to simulate a live game experience. This initiative will utilize a large historical dataset to create an environment that mirrors real-time data feeds without the associated costs. By mocking live data updates, our dashboard will provide insights into various match aspects such as scoring rates, player performances, and team dynamics. The project highlights our proficiency in data visualization and addresses the challenge of working with live datasets by implementing innovative solutions like automated data refreshes and API-driven analytics. This approach not only enhances our understanding of cricket game trends but also showcases the potential of real-time sports analytics in a cost-effective manner.

Question1

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

Approach

To analyze how scoring rates, player batting performances, and bowlers’ economy rates evolve throughout a cricket match, we’ll start by importing and cleaning our datasets, ensuring data integrity and consistency. We’ll then process the data to calculate runs per over, total runs by each batsman, and economy rates for bowlers, adjusting for factors like incomplete overs. Visualizations will be crafted to display these metrics effectively: scoring rates will be shown via line or bar charts, batting performances through bar charts, and bowlers’ economy rates with box or violin plots. These visual tools will allow us to explore key performance trends and insights, providing a holistic view of the match’s strategic dynamics.

1a. Scorecard Summary

Insights : The “Scoring Rate Evolution” graph depicts the number of runs provided to the opponent per over throughout two different innings of a cricket match. The red line depicts the scoring rate in the first innings, while the blue line indicates the scoring rate in the second innings. The graphic indicates that both innings have variable scoring rates, with many peaks indicating overs with very high scoring.

1b. Batting Performance

Insights : The bar chart depicts ranks for batsmen based on the total runs they scored during the selected cricket season (2023/24). The batsmen, including M. Labuschagne, D. Warner, and others, are displayed along the x-axis with their corresponding runs on the y-axis, highlighting M. Labuschagne as the highest scorer with around 380 runs, while the others have scored between 100 to 350 runs.

Question2

How did (a) partnerships influence total runs, and (b) what insights do team performances, player contributions and live updates provide about the match dynamics?

Approach

To explore the impact of batting partnerships on total runs and extract insights from team and player performances in cricket matches, we’ll analyze and aggregate data to highlight contributions made by player pairs and overall team efforts. Using player and match identifiers, we’ll sum runs for each partnership to assess their influence on scoring. Additionally, we’ll compile team performance metrics to discern trends and outliers over various matches. To simulate a live match experience, our approach will also include real-time data updates, providing a dynamic view of ongoing match dynamics. This method will offer a detailed perspective on both the strategic role of partnerships and the evolving nature of game play.

2a. Partnerships Overview

Insights : This bar chart represents the top 10 batting partnerships in terms of runs scored during the 2023/24 cricket season. Each pair of batsmen is represented by a different color and labeled under the chart. The pair of K. Klaasen and D.A. Miller leads with the highest number of runs, reaching around 160, while other partnerships, like Q. de Kock & T. Bavuma and M. Labuschagne & D.A. Warner, also have significant contributions, all scoring over 100 runs. The chart effectively shows the variability and success of different batting combinations in contributing significant runs to their teams totals.

2b. Team Comparision

Insights : The donut chart represents the percentage distribution of total cricket runs scored by different teams in the 2023/24 season. The blue segment, representing 54% of the total runs, indicates a team that significantly outperformed others in terms of runs scored. The red segment, accounting for 30.6%, and the green segment, accounting for 15.4%, represent the second and third most scoring teams, respectively. This visualization highlights the dominance of the leading team in run-scoring compared to the others.

Overall discussion/conclusion

In this project we created a dynamic dashboard that visualizes a live cricket match experience by simulating historical data. The dashboard uses mocked live data updates to present vital insights into several areas of cricket matches, including as scoring rates, player performances, and team dynamics. While historical data is useful to sports fans and analytics to gauge the performance of a team, the real-time sports dashboard can confirm or disprove our prior judgements.

--- title: "Cricket Metrics" subtitle: "INFO 526 - Project Final" author: "*Plot Wizards*" affiliations: "School of Information, University of Arizona" format: html: code-fold: true code-tools: true code-overflow: wrap embed-resources: true editor: visual execute: warning: false echo: false --- ## Abstract This project aims to create a dashboard for a live cricket match using historical data and API mock-ups. The motivation stems from showcasing data visualization skills within the context of a dynamic, real-time dataset, albeit simulated through historical data updates. Our data provides comprehensive information about cricket matches, including team performance, player statistics, and match outcomes. The project's research questions focus on analyzing scoring rates, player batting and bowling performances, partnerships' influence on total runs, and team dynamics throughout the match. The backend APIs play a crucial role in providing real-time or pseudo-real-time data updates for batting performance, bowling performance, partnerships, and player performance tracking. The team is organized into tasks such as JDBC connectivity, API creation, data wrangling, mock data creation, and frontend-backend integration. The GitHub repository structure includes a data folder for datasets, a readme.md file, and index.qmd for the frontend, with deployment planned on GitHub Pages. Overall, this project showcases a comprehensive approach to data visualization, backend API integration, and dashboard creation for a live cricket match experience, blending historical data insights with dynamic dashboard updates. ## Introduction *"**Analyzing Trends in Cricket Games**"* aims to develop a dynamic dashboard that visualizes cricket match data to simulate a live game experience. This initiative will utilize a large historical dataset to create an environment that mirrors real-time data feeds without the associated costs. By mocking live data updates, our dashboard will provide insights into various match aspects such as scoring rates, player performances, and team dynamics. The project highlights our proficiency in data visualization and addresses the challenge of working with live datasets by implementing innovative solutions like automated data refreshes and API-driven analytics. This approach not only enhances our understanding of cricket game trends but also showcases the potential of real-time sports analytics in a cost-effective manner. ## Question1 How did the (a) scoring rate, (b) player batting performances, and (c) bowlers' economy rates, evolve throughout the match? ## Approach To analyze how scoring rates, player batting performances, and bowlers' economy rates evolve throughout a cricket match, we'll start by importing and cleaning our datasets, ensuring data integrity and consistency. We'll then process the data to calculate runs per over, total runs by each batsman, and economy rates for bowlers, adjusting for factors like incomplete overs. Visualizations will be crafted to display these metrics effectively: scoring rates will be shown via line or bar charts, batting performances through bar charts, and bowlers' economy rates with box or violin plots. These visual tools will allow us to explore key performance trends and insights, providing a holistic view of the match's strategic dynamics. ## 1a. Scorecard Summary ![](images/clipboard-595703620.png) **Insights :** The "Scoring Rate Evolution" graph depicts the number of runs provided to the opponent per over throughout two different innings of a cricket match. The red line depicts the scoring rate in the first innings, while the blue line indicates the scoring rate in the second innings. The graphic indicates that both innings have variable scoring rates, with many peaks indicating overs with very high scoring. ## **1b. Batting Performance** ![](images/clipboard-150279252.png)**Insights :** The bar chart depicts ranks for batsmen based on the total runs they scored during the selected cricket season (2023/24). The batsmen, including M. Labuschagne, D. Warner, and others, are displayed along the x-axis with their corresponding runs on the y-axis, highlighting M. Labuschagne as the highest scorer with around 380 runs, while the others have scored between 100 to 350 runs. ## Question2 How did (a) partnerships influence total runs, and (b) what insights do team performances, player contributions and live updates provide about the match dynamics? ## Approach To explore the impact of batting partnerships on total runs and extract insights from team and player performances in cricket matches, we'll analyze and aggregate data to highlight contributions made by player pairs and overall team efforts. Using player and match identifiers, we'll sum runs for each partnership to assess their influence on scoring. Additionally, we'll compile team performance metrics to discern trends and outliers over various matches. To simulate a live match experience, our approach will also include real-time data updates, providing a dynamic view of ongoing match dynamics. This method will offer a detailed perspective on both the strategic role of partnerships and the evolving nature of game play. ## 2a. Partnerships Overview ![](images/clipboard-3865215917.png)**Insights :** This bar chart represents the top 10 batting partnerships in terms of runs scored during the 2023/24 cricket season. Each pair of batsmen is represented by a different color and labeled under the chart. The pair of K. Klaasen and D.A. Miller leads with the highest number of runs, reaching around 160, while other partnerships, like Q. de Kock & T. Bavuma and M. Labuschagne & D.A. Warner, also have significant contributions, all scoring over 100 runs. The chart effectively shows the variability and success of different batting combinations in contributing significant runs to their teams totals. ## 2b. Team Comparision ![](images/clipboard-3079618900.png)**Insights :** The donut chart represents the percentage distribution of total cricket runs scored by different teams in the 2023/24 season. The blue segment, representing 54% of the total runs, indicates a team that significantly outperformed others in terms of runs scored. The red segment, accounting for 30.6%, and the green segment, accounting for 15.4%, represent the second and third most scoring teams, respectively. This visualization highlights the dominance of the leading team in run-scoring compared to the others. ## Overall discussion/conclusion In this project we created a dynamic dashboard that visualizes a live cricket match experience by simulating historical data. The dashboard uses mocked live data updates to present vital insights into several areas of cricket matches, including as scoring rates, player performances, and team dynamics. While historical data is useful to sports fans and analytics to gauge the performance of a team, the real-time sports dashboard can confirm or disprove our prior judgements.