## 4 Discussion  
The main objective of this study was to introduce the CR-ICC, a unified metric capable of quantifying performance in critical moments, facilitating the evaluation of individual contributions in decisive scenarios [15]. Our results show that the CR-ICC is a useful metric for evaluating performance in critical moments, with significant predictive capacity (AUC--87.1%), though lower than Ptos (AUC--93.9%) and Eff A (AUC--93.0%). This may be because these metrics focus on terminal events directly related to the score, which have a strong correlation with victory [8]. However, the CR-ICC also incorporates continuity actions and contextual aspects, making it less predictive in absolute terms but valuable for identifying performance patterns under pressure in decisive situations. This can be explained by the fact that Ptos and Eff A focus on terminal events directly related to the score. According to [8], terminal metrics have a stronger correlation with victory due to their immediate and visible impact. In contrast, the CR-ICC also integrates continuity actions and contextual aspects, making it less predictive in absolute terms but more useful for identifying performance patterns under pressure. Compared to Eff R (AUC--56.0%) and balance (AUC--82.3%), the CR-ICC stands out by capturing the contextual impact of players. Eff R, although key for organizing attacks and providing more options for the setter with quality receptions [46], depends heavily on subsequent actions. [PERSON] et al. [47] point out that receptions are merely the foundation for offensive plays, whose effectiveness depends on the tempo and type of attack. [PERSON] et al. [48] add that, even with good receptions, decision-making and execution under pressure are crucial to scoring points. On the other hand, balance, although measuring net contributions, does not include the competitive context, where the CR-ICC excels in evaluating performance in critical moments [49]. At an individual level, the results show a more limited predictive capacity for all metrics, with AUC values close to randomness. The CR-ICC, with an AUC of 60.7%, outperforms Eff A (58.8%) and balance (60.5%), demonstrating its utility in capturing key contributions. However, Ptos, highly predictive at a team level (AUC--93.9%), significantly declines at an individual level (AUC--56.7%), reflecting the importance of collaborative context in volleyball, conditioned by specific roles that influence team dynamics [20,50]. According to [PERSON] and [PERSON] [51], success depends on standout talents who can keep the team afloat under pressure [52]. The CR-ICC identifies these exceptional players in key contexts, capturing their greater involvement during critical moments [49]. This approach complements the belief in \"superstars\" capable of influencing outcomes [53] and helps to identify talents with impact in decisive moments [54]. When analyzing player roles, we observe that the CR-ICC favor's key offensive roles. According to Table 3, opposites (Opposite) stand out in critical contexts with positive coefficients compared to liberos (OR = 1.274, \(p\) = 0.241), while middle blockers and wing spikers have a relatively lower impact. This finding aligns with [PERSON] et al. [54], who highlight the offensive importance of opposites, especially in decisive moments, and with [PERSON] et al. [20], who emphasized their ability to execute effective attacks even against double blocks or in high-pressure contexts. In high-pressure contexts, such as decisive sets, the CR-ICC excels. Our data show that performance under pressure is higher in final sets (OR = 1.143 for third, fourth, and fifth sets), validating its ability to identify outstanding players in these moments. This finding aligns with the literature that highlights the importance of situational and psychological factors in performance under pressure [49]. Regarding alternative approaches such as MCDA or machine learning, these present significant technical advantages but face practical barriers.