Smart Cricket Companion (CricNest): Implementing Artificial Intelligence Chatbots and Dynamic Rankings in a Mobile app

Smart Cricket Companion (CricNest): Implementing Artificial Intelligence Chatbots and Dynamic Rankings in a Mobile app

Shravya P

Department of Computer Applications

St Joseph Engineering College Mangalore, India

Mr. Hareesh B Associate Professor Department of Computer Applications

St Joseph Engineering College Vamanjoor, Mangalore, Karnataka

Rakshitha P Department of Computer Applications

St Joseph Engineering College Mangalore, India

ABSTRACT: Crickets massive fanbase and the growing demand for real-time digital interaction have led to the creation of CricNest, an intelligent mobile application that redefines how users engage with cricket data. This paper presents the key features and capabilities of CricNest, which combines a natural language processing (NLP) chatbot with a ranking engine to deliver an interactive experience. Users can ask simple questions like Who is the top ODI batsman? and get accurate, up-to- date responses without navigating complex menus.

Built with Flutter and Firebase, the app offers a responsive interface and real-time updates. During testing, the chatbot achieved over 90% accuracy, and the rankings matched the ICCs official July 2025 data.

CricNest is designed to make following cricket more fun and personal. It gives fans easy access to player stats and match insights, all in one place. Whether you're into fantasy leagues, coaching, cricket journalism, or just love watching the game, CricNest has something for you.What makes it even better is that its built to grow. Features like voice control, smart match predictions, and live data updates could easily be added in the future.By bringing AI and real time cricket updates together, CricNest isnt just another cricket app its a smarter way to enjoy the game. It helps fans stay connected while also offering useful tools for professionals, setting a fresh standard in how we experience cricket today.

1. INTRODUCTION

   In places like India, Australia, England, Pakistan, South Africa, and Sri Lanka, cricket isnt just a sport its something people grow up with. It brings excitement, pride, and a sense of connection. Over the years, how fans follow the game has changed a lot. These days, everyone turns to apps like Cricbuzz, ESPNcricinfo, or the ICC website to stay on top of scores, stats, and match updates.

   But heres the thing even though those apps are full of information, they can feel a bit overwhelming. There's a lot of tapping around just to find a players recent performance,

   rankings, or even simple team comparisons. The experience often ends up feeling slow, and honestly, kind of dull.

   Thats where cricnest steps in. Its designed to make following cricket feel more natural and personal. Powered by AI, it includes a chatbot that actually understands your questions. So instead of digging through menus, you can just type something like How did Kohli play last week? and get a direct answer. Its quick, simple, and makes cricket feel more connected, just like chatting with a friend who knows everything about the game. Instead of browsing through filters, users can ask direct questions like Whos the top T20 batsman right now? and receive real time, accurate responses instantly.

   What sets CricNest apart is its dynamic ranking system that constantly analyzes recent player performances, strike rates, batting averages, and other key metrics to offer updated player and team rankings similar to ICCs system but more immediate and flexible. Built using Flutter, the app combines a clean, user friendly interface with real time data and AI driven responses, creating an engaging and intelligent platform.

   In an era where cricket is becoming more data focused, CricNest moves beyond traditional score apps. It transforms how fans interact with the game from static browsing to smart, conversational engagement. This paper explores the design, architecture, and real world impact of CricNest, demonstrating its potential to reshape digital cricket experiences.

2. LITERATURE REVIEW

Many existing platforms have aimed to enhance how fans access cricket-related data. Popular websites like Cricbuzz and ESPNcricinfo provide live scores, statistics, and player

rankings. However, they still lack AI-driven conversational capabilities that could simplify user interaction. Research by Kumar et al. (2022) focused on using machine learning to predict match outcomes, and Sharma et al. (2021) explored AI-based dashboards for sports analytics. While these efforts contribute to automated cricket analysis, they dont offer live, interactive AI support for users. Mukherjee (2012) introduced a graph theory-based ranking model to evaluate cricket captains and teams, but the emphasis was on historical match data rather than live performance or player-specific queries. Similarly, Pappalardo et al. (2019) presented PlayeRank, a dynamic ranking system developed for soccer using multidimensional player data. However, this concept hasnt been tailored or implemented in the cricket domain. Further studies by Chhabra et al. (2020) and Abbas & Haider (2019) addressed team recommendation systems and performance prediction models but lacked a user-facing, conversational approach. Khot et al. (2021) proposed methods to identify rising cricket talent through statistical methods but did not incorporate live updates or mobile application support

Gap Identified: These papers collectively highlight the growing importance of data in cricket analytics but demonstrate a key research gap: lack of easy-to-use, live, Artificial Intelligencepowered interfaces for interacting with cricket data. Most models work in the background for analysts or administrators but do not engage directly with users. There is also limited use of natural language processing in this domain, especially for mobile platforms. CricNest addresses these gaps by combining live performance-based ranking, conversational Artificial Intelligence, and an intuitive mobile interaction.

• Data Handling: Player and match data structured in Firestore collections

  Implementation

  When users input queries like Top T20 all-rounders, the conversational assistant parses it using keyword mapping and fetches appropriate data from Firestore. The dynamic rankings are updated based on player performance metrics and match recency.

  1. RESULTS AND ASSESSMENT

     Conversational assistant Accuracy

     Tested with over 100 cricket-related queries, the conversational assistant provided correct responses in 90% of the cases. Common queries included team squads, top-ranked players, player profiles, and live match details.

     Ranking assessment As of July 2025:

• T20I Batsmen: Suryakumar Yadav, Mohammad Rizwan, and Travis Head aligned with ICC ranks.

• Test Bowlers: Jasprit Bumrah and Pat Cummins were correctly ranked using performance stats.

  The app is very easy to use and brings all cricket-related information together in one convenient place. Whether you're looking for player rankings, team standings, or answers to specific cricket questions, everything is just a tap away. It offers quick, accurate, and reliable responses, making it a great tool for both casual fans and cricket enthusiasts. The smooth interface and fast performance make the overall experience simple and enjoyable.

  1. METHODOLOGY

     Data Sources

     The app uses live data from ICCs official rankings, Cricbuzz, and ESPNcricinfo. Statistics are categorized by match format (Test, ODI, T20) and player role (batsman, bowler).

     System Architecture

     CricNest consists of hree core modules:

• Artificial Intelligence conversational assistant Engine :Interprets user queries using rule-based and synonym detection NLP.

• Dynamic Ranking Module : Ranks players using weighted metrics (average, SR, economy, etc.).

• Flutter-Based Mobile UI :Displays live match stats, player profiles, and Artificial Intelligence results.

  Technologies Used

• Frontend: Flutter (Dart)

• Backend: Firebase (Firestore, Realtime DB)

• Artificial Intelligence/NLP: Dart string processing and intent handling

  1. CONCLUSION:

     CricNest marks a major advancement in the field of cricket analytics by blending AI-driven conversations with real-time performance tracking and ranking features. It transforms the way fans connect with cricket data, offering a more personalized, faster, and smarter experience. Rather than simply displaying static information, CricNest delivers tailored insights based on current performance such as identifying the top-performing T20 bowler of the week or analyzing trends in a players recent form.

     By integrating natural language processing (NLP), live data handling, and a clean, mobile-friendly interface, the app ensures that users even those without a technical background can easily navigate and understand complex cricket statistics. Its modular system design also lays the groundwork for future capabilities, including AI-generated player suggestions, voice-based queries, predictive analytics, and fantasy sports integration.

     With its user-first approach, cutting-edge technologies, and cricket-specific expertise, CricNest provides a blueprint for the next generation of interactive sports applications delivering not just data, but meaningful, real-time experiences.

  2. FUTURE WORK:

     To scale and enhance the app further, the following features are proposed:

• combination with ICC API for live stat syncing

• Multilingual voice command support (Hindi, Kannada)

• Fantasy team builder with Artificial Intelligence- based suggestions

• Analytics dashboard for match predictions

• Admin dashboard to monitor data accuracy

These improvements will make CricNest more robust, inclusive, and impactful for a broader audience.

1. REFERENCES

1. Cricbuzz. (2025). Player Stats and Rankings.

2. ICC Rankings. (2025).

3. Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing. Pearson.

4. Kumar, A., & Rani, M. (2022). Cricket Match Outcome Prediction Using Machine Learning, IEEE Conference.

5. Sharma, R., et al. (2021). Artificial Intelligence Dashboards for Sports Analytics, Int. Journal of Artificial Intelligence & Applications.

6. Mukherjee, S. (2012). Identifying the Greatest Team and Captain,.

7. Shun Motegi, Naoki Masuda (2012) :A Network-Based Dynamical Ranking System for Competitive Sports

8. Luca Pappalardo et al. (2019) :PlayeRank: Data-Driven Player assessment in Soccer, ACM Transactions.

9. Jayaraj J. et al. (2021) :Re-Ranking ODI Batsman Using JJ Metric, Springer AISC Series.