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CAMP: A Context-Aware Cricket Players Performance Metric

arXiv 2307.13700 ↗First posted 14 Jul 2023cs.AI cs.CY cs.LGcited by 0

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Abstract (arXiv)

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 data-driven decision-making for selection and drafting, coaching and training, team line-ups, 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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References · 4

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2018Actions Speak Louder Than Goals: Valuing Player Actions in Soccercited by 90
  3. 2018Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluationcited by 23
  4. 2013Predicting college basketball match outcomes using machine learning techniques: some results and lessons learnedcited by 1

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