CRICTRS: Embeddings based Statistical and Semi Supervised Cricket Team Recommendation System

TL;DR

This paper introduces CRICTRS, a semi-supervised embedding-based system for cricket team recommendation that models player strengths and weaknesses, considering opposition and team composition factors to optimize team selection.

Contribution

It presents a novel semi-supervised approach using player embeddings and a rating system that accounts for opposition strength and team composition dynamics.

Findings

Effective player embeddings capturing performance metrics

Improved team composition recommendations based on embeddings

Incorporates opposition strength into team selection process

Abstract

Team Recommendation has always been a challenging aspect in team sports. Such systems aim to recommend a player combination best suited against the opposition players, resulting in an optimal outcome. In this paper, we propose a semi-supervised statistical approach to build a team recommendation system for cricket by modelling players into embeddings. To build these embeddings, we design a qualitative and quantitative rating system which considers the strength of opposition also for evaluating player performance. The embeddings obtained, describes the strengths and weaknesses of the players based on past performances of the player. We also embark on a critical aspect of team composition, which includes the number of batsmen and bowlers in the team. The team composition changes over time, depending on different factors which are tough to predict, so we take this input from the user and use the player embeddings to decide the best possible team combination with the given team composition.