Field hockey from the performance analyst’s perspective: A systematic review

Lord, Felicity; Pyne, David B.; Welvaert, Marijke; Mara, Jocelyn K

doi:10.1177/17479541211008903

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…Analysis of fine-grained sports data plays a pivotal role in data-driven decision-making in all aspects of sports management (Fried & Mumcu, 2016). Many machine learning models have been proposed for game modeling and match outcome prediction for soccer (Bai, Gedik, & Egilmez, 2022;Davis, Bransen, Decroos, Robberechts, & Haaren, 2019;, basketball (Deshpande & Jensen, 2016), and hockey (Liu & Schulte, 2018;Lord, Pyne, Welvaert, & Mara, 2022). However, data-driven decision-making has not received much attention in cricket, which has the second-highest viewership (Sankaranarayanan, Sattar, & Lakshmanan, 2014) after soccer and is a multi-billion dollar industry.…”

Section: Introductionmentioning

confidence: 99%

CAMP: A context-aware cricket players performance metric

Ayub

Ullah

Ali

et al. 2023

Journal of the Operational Research Society

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Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, camp, to quantify individual players' contributions toward a cricket match outcome. camp employs data mining methods and enables effective datadriven decision-making for selection and drafting, coaching and training, team lineups, and strategy development. camp incorporates the exact context of performance, such as opponents' strengths and specific circumstances of games, such as pressure situations. We empirically evaluate camp on data of limited-over cricket matches between 2001 and 2019. In every match, a committee of experts declares one player as the best player, called Man of the Match (MoM). The top two rated players by camp match with MoM in 83% of the 961 games. Thus, the camp rating of the best player closely matches that of the domain experts. By this measure, camp significantly outperforms the current best-known players' contribution measure based on the Duckworth-Lewis-Stern (dls) method.

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