Regression to Forecast: An In-Play Outcome Prediction for One-Day Cricket Matches

Subramanian, R. Raja; Karthick, P. Vijaya; Dhanasekaran, S.; Sudharsan, R. Raja; Hariharasitaraman, S.; Rajasekaran, S.; Murugan, B. S.

doi:10.1007/978-981-19-2358-6_5

Cited by 1 publication

(1 citation statement)

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“…It fills a gap in the research on fantasy cricket team selection. Anuraj et al [41] and Subramanian et al [42] focus on cricket match prediction. Anuraj et al used sports data mining to forecast cricket matches, particularly the T20 International World Cup matches.…”

Section: Context-aware Strategiesmentioning

confidence: 99%

RunsGuard Framework: Context Aware Cricket Game Strategy for Field Placement and Score Containment

Hussain,

Arshad,

Hassan

2024

Applied Sciences

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Sports analytics utilizes data analysis techniques and computational methods to gain insights, make informed decisions, and facilitate improvements in the performance of individuals and teams. Cricket is one of the most popular games and continues to evolve worldwide. The availability of ball-by-ball data demands in-depth investigation of player strategies, team dynamics, and the impact of contextual variables. Existing studies explored various aspects of cricket analytics, including detecting key events, predicting outcomes, and ranking teams. However, the literature lacks a comprehensive integrated framework that processes unstructured sports commentary, extracts actionable insights, conducts a thorough player analysis, and develops strategic plans while considering contextual factors. This work aims to propose a bowling and fielding strategy to contain a batsman. For this purpose, we developed a comprehensive context-aware framework that collects data, extracts insights from commentary, identifies player strengths and weaknesses, and proposes cricket bowling and fielding strategies according to the given context. To evaluate this work, we implemented a case study that simulated different scenarios, and our framework suggested bowling and fielding strategies. In these simulations, the proposed strategies consistently demonstrated a substantial reduction in the number of runs that were scored. On average, the framework reduces the batsman’s score rate by 33%. These findings underscore the practical effectiveness of research in optimizing field placement and effectively reducing scoring opportunities. Finally, by bridging the gap between data analytics and cricket game strategy, this methodology provides a competitive advantage to coaches, captains, and players. In the future, we aim to involve temporal patterns to understand the evolving behavior of players.

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Section: Context-aware Strategiesmentioning

confidence: 99%