CAMP: A context-aware cricket players performance metric
提出一种情境感知的板球运动员表现度量CAMP，通过数据挖掘方法量化球员对比赛结果的贡献，在961场比赛中其最佳球员评分与专家评选的“比赛最佳球员”在83%的场次中一致，优于现有方法。
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.