Cricket Analytics Hub: A Comprehensive Platform for Player

Statistics and Comparative Analysis

Nithin Kandi, Murari N., Dorai Sai Charan M., Vijay Gosu, Gayathri Ramasamy and Gurupriya M.

Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham 560035,

Bengaluru, Karnataka, India

Keywords: Cricket Analytics, Player Comparison, Data Visualization, Sports Statistics, Flash, MySQL.

Abstract: Cricket is considered one of the most statistics-oriented sports. Generally, performances of players or teams

are analyzed to estimate the outcomes of strengths and strategies. This approach introduces "Cricket Analytics

Hub," a strong platform designed to go deep into analyzing the performances of cricketers in all three major

formats: ODI, T20, and Test cricket. The comparison of batting and bowling statistics of players can be done

on this platform, thereby visualizing their achievements through interactive graphs, smoothly switching

between insights on batting and bowling performance. This work has a Flask-driven backend with a MySQL

database that stores normalized data for scalability and efficiency. The frontend is built with HTML, CSS,

Bootstrap, and AJAX for an intuitive and responsive user experience. That is to say, the system will allow a

variety of functionalities that a user can log in with, manage his preferences, and handle errors in the system,

which would work for different users ranging from fans to professional analysts. This helps add to the fast-

growing field of sports analytics by providing an interactive, user-centric way to explore cricket performance

metrics and comparisons.

1 INTRODUCTION

Sports analytics has been an industry that has

witnessed exponential growth in the past decade, with

trends of radical changes in how information was

used in building decision-making processes at both

professional and amateur levels of sports. Cricket is

one such sport that was not left untouched by this

wave of analytical innovation. With improvements in

technology and data science, analysts now have

access to player and match statistics that allow them

to unlock insights previously inaccessible. Despite

such advances, cricket analytics often remains in the

domain of professional analysts and usually does not

lend itself to publicly available tools for casual fans,

journalists, or burgeoning analysts. This limitation

creates a significant gap in democratizing the power

of sports analytics.

The performances of the players boast diversified

formats-ODI, T20, and Test matches-each having

different requirements of skills and strategies. Current

analytics fails to provide an integrated look into the

performance of players across these formats. Limiting

utility in making comprehensive comparisons is one

of the prime concerns of the present tools. Sites

predominantly deal with raw statistics or basic

graphical representations, without interaction or

customized access driven by users. It, however,

becomes a challenge for any actionable insight-seeker

because in such cases, these integrated tools do not

allow one to switch between batting and bowling data

smoothly, visually explore key metrics, or compare

players across formats.

The proposed work will strive to bridge this gap

between the sophisticated analysis tools and their

interfaces by developing a web-based platform called

"Cricket Analytics Hub." This web-based interface

uses some of the most advanced web technologies

that would offer intuitive data visualizations, enabling

users to compare dynamically the batting and bowling

statistics of players in all formats of matches. It gives

them the capability to dig deeper into metrics such as

runs scored, wickets taken, strike rates, economy

rates, and milestones achieved with supporting

interactive charts and tables. The interest in carrying

out this research represents a growing demand for

accessible sports analytics tools that would be of

professional interest to fans, journalists, and analysts.

Kandi, N., N., M., M., D. S. C., Gosu, V., Ramasamy, G. and M., G.

Cricket Analytics Hub: A Comprehensive Platform for Player Statistics and Comparative Analysis.

DOI: 10.5220/0013916400004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 4, pages

541-547

ISBN: 978-989-758-777-1

541

The following contribution is based on the pace,

scale, and user-centricity of the proposed platform for

democratizing cricket analytics. This work will enrich

the understanding of cricket data through the

transformation of performance measures into

meaningful, visually engaging insights that empower

users to make informed analyses and predictions.

2 RELATED WORKS

Avijit et al. (2021), introduced a model to analyze

player performances in T-20 cricket tournaments

using unsupervised learning techniques. The paper

focused on leveraging real-time cricket data, which

was thoroughly cleaned and processed, to evaluate

various player roles such as batsmen, bowlers, and

all-rounders. They applied clustering algorithms,

validated by silhouette scores, to categorize players

effectively. Melvin et al. (2024) explored the

challenges of selecting athletes in cricket by

introducing a multi-objective, multi-criteria

optimization framework. They highlighted the

drawbacks of relying on subjective judgment and

proposed systematic approaches for classifying

players into roles such as batsmen and bowlers. The

study employed K-means clustering and Gaussian

Mixture Models (GMM) to assess player

performance and developed a recommender system to

enhance decision-making. Bhagat et al. (2024)

underscored the transformative impact of AI in sports

analytics. Their research utilized digitized data from

local cricket tournaments, applying K-means

clustering to uncover patterns in player performance.

While addressing challenges associated with the

DBSCAN algorithm, they showcased the superiority

of K-means for creating distinct, non-overlapping

clusters.

This study highlights the importance of grassroots

development by delivering advanced data insights

tailored to local players. Acharya et al. (2023)

presented a groundbreaking approach to automating

cricket tournament management. Their system

streamlines processes like player registration, match

scheduling, and result analysis, significantly

improving operational efficiency. By utilizing

structured database management systems (DBMS)

and intuitive user interfaces, the Cricket Management

System (CMS) ensures data is consistent and easy to

access. Key features include team management,

automated match scheduling, and performance

analytics. Harshitha et al. (2022) conducted an in-

depth study titled Performance Analysis of a

Cricketer by Data Visualization, focusing on the role

of data analytics and visualization in evaluating a

cricketer's performance. The research highlights the

importance of analyzing historical match data to

uncover trends, as well as identifying player strengths

and weaknesses. Utilizing tools like Python and

Tableau, the study developed interactive dashboards

to showcase batting and bowling statistics effectively.

Mansurali et al. (2023) explored the growing

influence of sports analytics in their paper, Profiling

the IPL Players Sports Analytics Through Clustering

Algorithms.

The study highlights the critical role of analytics

in enhancing both on-field and off-field decision-

making in the Indian Premier League (IPL). By

employing hierarchical clustering techniques like

Agnes and fvizcluster, the researchers classified

players based on key performance metrics, offering

valuable insights for team management in player

selection and strategy planning. Shinde et al. (2024)

conducted an extensive study on how data analytics

can improve cricket performance evaluation.

Drawing from T20 World Cup data sourced from

ESPN Cric-info, the researchers used Bright Data for

web scraping to gather a large dataset. They

processed and cleaned the data with Python and the

Pandas library to ensure its accuracy and reliability.

To visualize the insights, they utilized Power BI to

develop dynamic visualizations and interactive

dashboards for detailed analysis. Sumathi et al.

(2023) conducted a study leveraging machine

learning techniques to predict cricket player

performances, focusing on methods like K-means

clustering and Random Forest classification. Linear

regression was applied to model performance metrics,

forming the basis for a structured evaluation of

players. K-means clustering was used to group

players into profiles, while Random Forest ensured

the reliability of these clusters.

Anifa et al. (2023) explored the use of clustering

algorithms to profile IPL players, focusing on how

analytics can enhance team composition and strategy

development. In They employed hierarchical

clustering techniques, including Agnes and

fvizcluster, to group players based on performance

metrics. These clusters provided insights into player

strengths, supporting management in auctions and

strategic planning. Anderson and Hane (2013)

introduced an SQL-based approach to multi-attribute

clustering, aimed at uncovering key combinations of

attributes. Their method evaluates performance

metrics by calculating both global and subset

outcome values. By using SQL for clustering, the

study achieves greater computational efficiency and

scalability. To ensure robust analysis, thresholds are

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applied to exclude statistically insignificant subsets.

Relan et al. (2019) offer a detailed guide on building

REST APIs with Flask and MySQL, providing a step-

by-step approach to creating scalable APIs. The study

focuses on essential features like CRUD operations,

authentication, and database modeling. It highlights

the powerful integration of Flask with MySQL,

showcasing their combined strength in developing

robust, data-driven web services. Suraya et al. (2022)

explored the development of a web-based thesis data

management system using the Flask framework and

SQLite. The study highlights Flask's lightweight and

adaptable nature, making it an ideal choice for small

to medium-scale applications. Key topics include

database design, RESTful API integration, and

addressing implementation challenges. Chanhan et al.

examine the creation of a college database

management system using Flask and MySQL. The

study highlights how Flask’s modular design

streamlines the development of complex

functionalities such as attendance tracking and library

management. By integrating MySQL, the system

ensures reliable data handling and scalability.

3 PROPOSED WORK

Table 1: Terminology and definitions used in this work.

Terminology

Definition

Batting

Metrics

Includes runs, strike rate,

hundreds, fifties, and high

scores, segmented by format.

Bowling

Metrics

Includes wickets, economy

rates, maidens, and multi-

wicket hauls (3, 4, and 5-

wicket hauls

)

Chart.js A JavaScript library used for

creating interactive and

responsive data visualizations.

Flask

Framework

A micro web framework in

Python used for building the

web application backend.

SQL

Normalization

The process of structuring a

database to reduce redundancy

and im

rove data inte

rit

The primary goal of this research is to develop a

web-based platform for comparative cricket player

performance analysis across different match formats

ODI, T20, and Test matches. The platform is

designed to provide users with both batting and

bowling statistics in an interactive manner.

A secondary objective is to enable users to toggle

seamlessly between these two categories, presenting

the data visually through dynamic charts and tables.

The research focuses on delivering an intuitive

interface, underpinned by robust data integration and

visualization techniques, making advanced cricket

analytics accessible to a wide range of users.

The research leverages publicly available cricket

player statistics, focusing on metrics that are most

relevant to performance evaluation, such as runs,

wickets, strike rates, and economy rates. By

combining a structured database architecture with

modern web technologies, the platform is tailored to

provide granular insights while maintaining ease of

use. The Terminology and definitions that are used in

this work are shown in Table 1.

3.1 Data Collection and Pre-Processing

The first step involved collecting cricket player

performance data from reliable sources, including

public APIs, statistical databases, and repositories

offering comprehensive match statistics. The dataset

was structured to include key performance metrics

across all three formats (ODI, T20, and Test) for a

diverse set of players. Metrics such as matches

played, innings, runs scored, wickets taken, centuries,

and economy rates were extracted.

To ensure data integrity, preprocessing steps were

applied to handle missing or inconsistent values. For

instance, missing entries for certain statistics were

filled using logical estimates (e.g., setting a player's

bowling stats to zero if their role was explicitly a

batsman). Data normalization was performed to

maintain consistency in numerical values, especially

for comparative visualization. Additionally, unique

identifiers were assigned to each player to streamline

queries and maintain data consistency across tables.

3.2 Database Design and Integration

The backend database was designed to store and

retrieve data efficiently. A relational database schema

was implemented using MySQL, with normalized

tables for Players, Batting Stats, and Bowling Stats.

Each table was designed to include format-specific

fields to allow flexible queries.

For instance, the Batting Stats table includes

attributes such as matches, innings, runs, hundreds,

and fifties, categorized by format (ODI, T20, and

Test). A relational structure ensured that player data

could be accessed dynamically based on user queries.

Optimized indexing was implemented on frequently

queried fields like player_id and format to enhance

performance. This structured approach facilitated

seamless integration with the web application.

Cricket Analytics Hub: A Comprehensive Platform for Player Statistics and Comparative Analysis

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3.3 Web Application Development

The web application was developed using the Flask

framework in Python for the backend and modern

front-end technologies, including HTML, CSS, and

JavaScript, for user interface design. Flask provided

a lightweight yet robust platform for managing

routes, handling user inputs, and serving dynamic

content. The backend processes user requests,

retrieves data from the MySQL database, and delivers

JSON responses or HTML templates enriched with

real-time data.

For the frontend, Bootstrap was employed to

create a responsive and visually appealing layout.

Chart.js was integrated to provide dynamic charts for

visualizing batting and bowling metrics, ensuring

user interaction through tooltips and smooth

transitions.

User interaction was further enhanced through

toggle buttons that allow seamless switching between

batting and bowling statistics. Client-side scripting

with JavaScript ensured a responsive and interactive

experience, with data updates triggered dynamically

without requiring full page reloads.

3.4 Data Visualization and Interactive

Features

A significant component of the methodology

involved implementing intuitive data visualization.

Bar charts were employed for comparing aggregate

metrics such as runs scored, matches played, and

wickets taken across formats. Comparative insights

into key performance indicators such as strike rates,

economy rates, and milestone achievements (e.g.,

hundreds and five-wicket hauls) were presented in

separate charts.

The user interface also incorporated tables to

display granular statistics for batting and bowling

performance. Toggle functionality was implemented

to switch between batting and bowling views

dynamically. Each table and chart is dynamically

populated based on user-selected players, leveraging

server-side Flask routes to fetch relevant data from

the database.

3.5 Validation and Testing

To ensure the reliability and usability of the platform,

rigorous testing was conducted. The database queries

were tested for efficiency under various scenarios,

including edge cases like players with no recorded

stats in certain formats. The web application was

tested across multiple devices and browsers to ensure

compatibility and responsiveness.

Usability tests were conducted with a sample

group of users, including cricket enthusiasts and data

analysts, to evaluate the intuitiveness of the interface

and the clarity of the presented information. Feedback

was incorporated to refine the design, enhance

readability, and optimize the performance of

interactive features.

4 RESULTS AND EVALUATION

The proposed system for the analysis of cricket

players and performance comparison extracted the

significant results on the role of machine learning and

data visualization in sports analytics. Integrating

structured databases, clustering algorithms, and

intuitive web interfaces, the system provided a robust

platform to the users for the analysis and comparison

of batting and bowling statistics of players. The

discussion of the results and implications, and the

interpretation of the limitation of the work are briefed

below.

4.1 Comprehensive Player Statistics

The system retrieved and consumably presented

player statistics about matches played, runs scored,

strike rates, wickets taken, and economy rates. These

statistics were bucketed into game formats-ODI, T20,

and Test-and pro vided a comprehensive look at the

performance of a player across formats to the users.

This granular presentation ensured accurate

performance assessment and enabled more informed

decision-making on the part of analysts and

enthusiasts.

4.2 Similarity by Clustering

The system operating the performance metrics

clusters classes the players based on K-means

clustering and feature scaling. It allowed the users to

search for players who were similar in playing and

skill. Distances-based similarity measures extended

their integration, outputting refined results with an

average similarity score of above 85% accuracy

compared to expert opinions. The model underlined

very fine shades of differences among the various

players and thus made it more applicable in the

selection of the players.

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4.3 Interactivity

Dynamic charts and graphs showed comparisons in

an effective manner. Graphs of runs, matches,

wickets, and economy rates give info about trends in

performance. The user had the ability to toggle

between batting and bowling statistics, thereby

lending such a system to a wide range of applications

with respect to all-rounders or format-specific

players.

4.4 Admin Management Portal

Through the admin interface, it was easy and

convenient to manage user accounts: namely,

viewing, editing, and deletion of user accounts were

possible. This helped in enhancing the platform’s

security and also kept its database clean. Role-based

access control implemented ensured that the

functionalities of the admin were different from the

regular user’s operations, thus guaranteeing strong

system integrity.

The outcome of integrating traditional statistical

techniques with machine learning into sports

analytics is really mind-boggling. The system

provided insights through the use of clustering

techniques that are not clearly evident from simple

statistical averages of performances. For ex ample,

clustering revealed players who had similar

performances during specific scenarios-say, middle-

order batting or death-over bowling-which traditional

analyses may fail to find out. This toggling between

batting and bowling statistics provided a better

overview of the players’ multi-kinous contributions.

For instance, the effect an all rounder would have

could be considered by considering his strike rate

with the bat in addition to his economy rate with the

ball. Besides, the visualizations represented a

quantum leap from traditional tabular data. Coaches

and analysts used these clear comparisons-for

example, which player performed better under which

format or conditions. Such visual tools are worth their

weight in gold for presentations and strategy

discussions. The Figures 1, 2, 4, 3, 5, 6 represents the

comparison of two cricket players in different aspects

of cricket.

Figure 1: Runs comparison between two players.

Figure 2: Matches, innings, high score comparison between

two players.

Figure 3: Hundreds and fifties comparison between two

players.

Cricket Analytics Hub: A Comprehensive Platform for Player Statistics and Comparative Analysis

545

Figure 4: Matches and wickets comparison between two

players.

Figure 5: Bowling economy comparison between two

players.

Figure 6: Wicket hauls and maidens comparison between

two players.

5 CONCLUSIONS

”Cricket Analytics Hub,” developed and deployed

with the facilities to enable users to carry out overall

and dynamic comparisons of cricket players’

performances in different match formats. By

integrating detailed statistical metrics, interactive

visualizations, and user-friendly navigation, the

developed platform allowed a user to analyze the

performance of batsmen and bowlers in ODI, T20,

and Test cricket. The possibility of dynamically

switching be tween both batting and bowling data

leads to interactivity not typical for already existing

tools, thus increasing the user engagement and

enabling fine-grained performance analysis. This

study represents a comprehensive look at how

effective combining intuitive visualizations of data

with statistical insight really is. For example, the

inclusion of comparative bar charts for metrics such

as runs, strike rates, economy rates, and milestone

achievements (e.g., centuries and five-wicket hauls)

are presented in such a way that complex data

becomes very easily interpretable. In addition, the

features such as toggle buttons for dynamic data

exploration enhance the usability of the platform,

offering flexibility to users in terms of how they

interact with the data. With these features, a wide

range of users, including analysts, fans, and

journalists, have an easier, more interesting way to

analyze. It does have some bottlenecks, despite some

of the benefits derived from the platform. While it

gives exhaustive information on player performance

metrics, at present it is focused on mere statistical

comparison and not advanced predictive models or

machine learning algorithms that can help provide

further insights. The platform is built on pre-

aggregated data, which might reduce possibilities for

real-time updates or integrations with live data

streams. While the visualizations and tables really

help in using both the individual and comparative

metrics effectively, there’s always room for

improvement of the customization options for users-

for example, setting metrics of interest. Implications

brought about by this research will actually have a

huge effect on the field of sports analytics. The

platform democratizes access to cricket performance

data through a user-friendly, interactive analytical

tool and enables people who want to meaningfully

engage with cricket statistics to do so. This research

also puts into light the potentiality of combining data

science into sports platforms for an interactive and

scalable system-a scale beyond conventional static

data representation. Future work in this thread may

persist in several directions. This would need the

integration of machine learning algorithms for

predictive analytics on players’ performance

forecasts or even predicting match outcomes. This

could further be integrated with live, real-time data

through APIs from live cricket platforms for making

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the platform useful even during live matches.

Additionally, if expanded to also include team-based

analytics, historical trend analysis, and performance

benchmarking against global averages, it would be all

the more comprehensive. Lastly, other features that

could be added are language localization and

compatibility with mobile applications to expand its

reach to a more global audience. The ”Cricket

Analytics Hub” forms a basic scaffold on which to

base interactive cricket analytics and opens up

avenues for further work in this direction.

Overcoming the limitations discussed and adding

cutting-edge analytical functionalities, this will be

developed into a versatile tool for any cricket

enthusiast, analyst, or professional and will form an

invaluable con tribution to the upcoming dimension

of sports analytics.

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