Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach

• URL: http://arxiv.org/abs/2209.06346v2
• Date: Sat, 22 Jul 2023 15:44:41 GMT
• Title: Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach
• Authors: Ashish V Shenoy, Arjun Singhvi, Shruthi Racha, Srinivas Tunuguntla
• Abstract summary: We try four different machine learning approaches for predicting the results of T20 Cricket Matches. Specifically we take in account: previous performance statistics of the players involved in the competing teams, ratings of players obtained from reputed cricket statistics websites, clustering the players' with similar performance statistics and propose a novel method using an ELO based approach to rate players. We compare the performances of each of these feature engineering approaches by using different ML algorithms, including logistic regression, support vector machines, bayes network, decision tree, random forest.
• Score: 1.417373050337415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20, is a short form of cricket. In a Twenty20 game the two teams of 11 players have a single innings each, which is restricted to a maximum of 20 overs. This version of cricket is especially unpredictable and is one of the reasons it has gained popularity over recent times. However, in this paper we try four different machine learning approaches for predicting the results of T20 Cricket Matches. Specifically we take in to account: previous performance statistics of the players involved in the competing teams, ratings of players obtained from reputed cricket statistics websites, clustering the players' with similar performance statistics and propose a novel method using an ELO based approach to rate players. We compare the performances of each of these feature engineering approaches by using different ML algorithms, including logistic regression, support vector machines, bayes network, decision tree, random forest.

Related papers

• FanCric : Multi-Agentic Framework for Crafting Fantasy 11 Cricket Teams [0.0]
  This study concentrates on Dream11, India's leading fantasy cricket league for IPL, where participants craft virtual teams based on real player performances to compete internationally. This research introduces the FanCric framework, an advanced multi-agent system leveraging Large Language Models (LLMs) and a robust orchestration framework to enhance fantasy team selection in cricket. FanCric employs both structured and unstructured data to surpass traditional methods by incorporating sophisticated AI technologies.
  arXiv  Detail & Related papers  (2024-10-02T08:01:28Z)
• Impact of a Batter in ODI Cricket Implementing Regression Models from Match Commentary [0.0]
  This paper seeks to understand the conundrum behind this impactful performance by determining how much control a player has over the circumstances. We collected data for the entire One Day International career of 3 prominent cricket players: Rohit G Sharma, David A Warner, and Kane S Williamson. We used Multiple Linear Regression (MLR), Polynomial Regression, Support Vector Regression (SVR), Decision Tree Regression, and Random Forest Regression on each player's data individually to train them and predict the Impact the player will have on the game.
  arXiv  Detail & Related papers  (2023-02-22T06:42:20Z)
• GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports [84.55775845090542]
  We propose a semi-supervised win prediction model for esports based on graph convolutional networks. GCN-WP integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL.
  arXiv  Detail & Related papers  (2022-07-26T21:38:07Z)
• Retrospective on the 2021 BASALT Competition on Learning from Human Feedback [92.37243979045817]
  The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks. Rather than mandating the use of LfHF techniques, we described four tasks in natural language to be accomplished in the video game Minecraft. Teams developed a diverse range of LfHF algorithms across a variety of possible human feedback types.
  arXiv  Detail & Related papers  (2022-04-14T17:24:54Z)
• Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
  We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
  arXiv  Detail & Related papers  (2022-03-10T02:37:39Z)
• Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity [49.68758494467258]
  We study how to construct diverse populations of agents by carefully structuring how individuals within a population interact. Our approach is based on interaction graphs, which control the flow of information between agents during training. We provide evidence for the importance of diversity in multi-agent training and analyse the effect of applying different interaction graphs on the training trajectories, diversity and performance of populations in a range of games.
  arXiv  Detail & Related papers  (2021-10-08T11:29:52Z)
• Prediction of IPL Match Outcome Using Machine Learning Techniques [0.0]
  The Indian Premier League (IPL) is a national cricket match where players are drawn from regional teams of India, National Team and also international team. Many factors like live streaming, radio, TV broadcast made this league as popular among cricket fans. The prediction of the outcome of the IPL matches is very important for online traders and sponsors.
  arXiv  Detail & Related papers  (2021-09-30T09:45:34Z)
• A study on Machine Learning Approaches for Player Performance and Match Results Prediction [2.82163744818616]
  Predicting the outcome of a cricket match has become a fundamental problem as we are advancing in the field of machine learning. Multiple researchers have tried to predict the outcome of a cricket match or a tournament, or to predict the performance of players during a match, or to predict the players who should be selected as per their current performance, form, morale, etc. We discuss some of these techniques along with a brief comparison among these techniques.
  arXiv  Detail & Related papers  (2021-08-23T12:49:57Z)
• Bandit Modeling of Map Selection in Counter-Strike: Global Offensive [55.41644538483948]
  In Counter-Strike: Global Offensive (CSGO) matches, two teams first pick and ban maps, or virtual worlds, to play. We introduce a contextual bandit framework to tackle the problem of map selection in CSGO and to investigate teams' pick and ban decision-making. We find that teams have suboptimal map choice policies with respect to both picking and banning. We also define an approach for rewarding bans, which has not been explored in the bandit setting, and find that incorporating ban rewards improves model performance.
  arXiv  Detail & Related papers  (2021-06-14T23:47:36Z)
• Analysing Long Short Term Memory Models for Cricket Match Outcome Prediction [0.0]
  Recently, various machine learning techniques have been used to analyse the cricket match data and predict the match outcome as win or lose. Here we propose a novel Recurrent Neural Network model which can predict the win probability of a match at regular intervals given the ball-by-ball statistics.
  arXiv  Detail & Related papers  (2020-11-04T04:49:11Z)
• Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport [51.20042288437171]
  We propose a Two-Stage Spatial-Temporal Network (TSSTN) that can provide accurate real-time win predictions. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
  arXiv  Detail & Related papers  (2020-08-14T12:00:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.