How do evaluation systems compress multidimensional performance information into summary ratings? Using expert assessments of 9,669 professional soccer players on 28 attributes, we characterize the dimensional structure of evaluation outputs. The first principal component explains 40.6% of attribute variance, indicating a strong general factor, but formal noise discrimination procedures retain four components and bootstrap resampling confirms that this structure is highly stable. Internal consistency is high without evidence of redundancy. In out of sample prediction of expert overall ratings, a comprehensive model using the full attribute set substantially outperforms a single-factor summary (cross-validated R squared = 0.814). Overall, performance evaluations exhibit moderate information compression; they combine shared variance with stable residual dimensions that are economically meaningful for evaluation outcomes, with direct implications for the design of measurement systems.

翻译：评估系统如何将多维绩效信息压缩为综合评分？基于对9,669名职业足球运动员在28项属性上的专家评估，本文刻画了评估输出的维度结构。第一主成分解释了40.6%的属性方差，表明存在显著的一般性因子，但通过正式噪声判别程序保留了四个成分，且自助重抽样验证该结构具有高度稳定性。内部一致性较高且未发现冗余证据。在专家总体评分的样本外预测中，使用完整属性集的综合模型显著优于单因子概括模型（交叉验证R平方=0.814）。总体而言，绩效评估呈现中等程度的信息压缩：它们融合了共享方差与稳定的残差维度，这些维度对评估结果具有实际意义，并对测量系统的设计具有直接启示。