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A too-much-talent effect in football: top talent benefited performance up to a point after which the marginal benefit of talent decreased and eventually turned negative (Observed Data)
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Five studies examined the relationship between talent and team performance. Two survey studies found that people believe there is a linear and nearly monotonic relationship between talent and performance: participants expected that more talent increases performance and that this relationship would never turn negative. However, building off research...
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... with the lay intuition documented in Study 1, the linear relationship between talent and football team performance was positive and significant (Table 2, Model 1). However, Study 2 also revealed a significant quadratic effect of top talent: top talent benefited performance only up to a point, after which the marginal benefit of talent decreased and turned negative (Table 2, Model 2) (Figure 2). The linear and curvilinear effects were significant when control variables were omitted (B=5.95, ...
Citations
... Authors in [13] suggest TC depends on even more intangible traits like "trust" which is not computable in the technical sense, but even so, remains very difficult to quantify and measure. Related to TC is an overabundance of talent discussed in [14], showing that players' stats are not additive. The authors discuss that increasing players' stats contributes to the team's overall performance up to a certain point, beyond which, intrateam coordination (or TC) becomes critical to judge whether an increase in players' stats contributes to an increase in team performance. ...
Fantasy Sports has a current market size of $27B and is expected to grow more than $84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA's API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://dalkilic.luddy.indiana.edu/fantasyleague/, and the source code can be found at https://github.com/gany-15/nbafan.
... Hence, in the present studies, we deliberately decided to manipulate prenegotiation mimicry and examine its effects on first-offer anchoring. This procedure constitutes a rather conservative test because it builds on the idea that the effect of mimicry in one task transfers to the mimickee's behavior in a different, subsequent task. 2 The terminology of the "too-much-mimicry" effect follows previous research on, for instance, the toomuch-talent effect (Swaab et al., 2014) or the too-much-precision effect (Loschelder et al., 2016), both of which predict and observe a conceptually similar outcome pattern. Both publications were conceptually motivated by Grant and Schwarz's (2011) ideas about inverted-U-shaped effects, claiming that first an effect that is positive compared with a baseline control condition emerges. ...
... Both publications were conceptually motivated by Grant and Schwarz's (2011) ideas about inverted-U-shaped effects, claiming that first an effect that is positive compared with a baseline control condition emerges. For example, more talent leads to better team performance in sports (Swaab et al., 2014), and precise offers exert a stronger anchoring effect in negotiations (Loschelder et al., 2016). However, too much talent in sports teams and too much precision in negotiation offers backfire in the sense that the initially advantageous effect disappears and leads to no difference compared with the control condition. ...
We examined whether mimicking an interaction partner is universally advantageous or, provided the mimicry is particularly strong, whether it has detrimental impacts on interpersonal and negotiation outcomes. Participants interacted with a confederate who engaged in no, subtle, or strong mimicry and then negotiated. In laboratory Experiment 1 (N = 71) and Experiment 2 (N = 149), subtly (vs. not) mimicked participants liked the confederate more, while strongly (vs. subtly) mimicked participants liked and trusted less. In Experiment 2, strongly (vs. subtly) mimicked participants were less susceptible to the first-offer anchor. The online Experiment 3 (N = 180) corroborated the too-much-mimicry effect: When participants became aware of mimicry, it exerted detrimental effects on liking and trust irrespective of the experimental condition. Experiment 1 and Experiment 3 found no too-much-mimicry effect on anchoring susceptibility. These findings show that (a) sufficiently subtle mimicry positively influences interpersonal outcomes and (b) too much mimicry backfires.
... Inputs are antecedent variables that predict team member interactions. Some of the prominent inputs that have been examined within the group dynamics in sport literature include team composition (e.g., Swaab et al., 2014), leadership (e.g., Callow et al., 2009), and organizational influences (e.g., Arnold et al., 2017). In some cases, inputs can help facilitate team functioning (e.g., effective leadership behaviors; Callow et al., 2009), whereas in other cases, they can constrain it (e.g., organizational stress; Arnold et al., 2017). ...
In the current study, the structural and external validity of data derived from two shorter versions of the Multidimensional Assessment of Teamwork in Sport (MATS) were examined using multilevel analyses. Evidence of model–data fit was shown for both a 5-factor model comprising 19 items (with subscales assessing teamwork preparation, execution, evaluation, adjustments, and management of team maintenance) and a single-factor model comprising five items (providing a global estimate of teamwork). In general, data from both versions were positively and significantly correlated with (and distinct from) athletes’ perceptions of team cohesion, collective efficacy, performance satisfaction, enjoyment in their sport, and commitment to their team and their coaches’ transformational leadership. The measures appear well suited to detect between-teams differences, as evidenced by intraclass correlation coefficients and acceptable reliability estimates of team-level scores. In summary, the 19-item Multidimensional Assessment of Teamwork in Sport-Short and five-item Multidimensional Assessment of Teamwork in Sport-Global provide conceptually and psychometrically sound questionnaires to briefly measure teamwork in sport.
... -Collaboration: More collaborative teams generally perform better [20], implying a direct relationship between collaboration and performance. However, talent and collaboration may sometimes be inversely proportional [30]. -Leadership: Teams exhibiting superior leadership and trust usually perform better than those facing leadership crises [13]. ...
Rooted in collaborative efforts, cybersecurity spans the scope of cyber competitions and warfare. Despite extensive research into team strategy in sports and project management, empirical study in cyber-security is minimal. This gap motivates this paper, which presents the Collaborative Cyber Team Formation (CCTF) Simulation Framework. Using Agent-Based Modeling, we delve into the dynamics of team creation and output. We focus on exposing the impact of structural dynamics on performance while controlling other variables carefully. Our findings highlight the importance of strategic team formations, an aspect often overlooked in corporate cybersecurity and cyber competition teams.
... -Collaboration: More collaborative teams generally perform better [20], implying a direct relationship between collaboration and performance. However, talent and collaboration may sometimes be inversely proportional [30]. -Leadership: Teams exhibiting superior leadership and trust usually perform better than those facing leadership crises [13]. ...
Rooted in collaborative efforts, cybersecurity spans the scope of cyber competitions and warfare. Despite extensive research into team strategy in sports and project management, empirical study in cyber-security is minimal. This gap motivates this paper, which presents the Collaborative Cyber Team Formation (CCTF) Simulation Framework. Using Agent-Based Modeling, we delve into the dynamics of team creation and output. We focus on exposing the impact of structural dynamics on performance while controlling other variables carefully. Our findings highlight the importance of strategic team formations, an aspect often overlooked in corporate cybersecurity and cyber competition teams.
... According to Smart and colleagues (Smart, 2012;Smart & Smart, 1997), A-players are believed to do everything better and should comprise at least 75% of employees, with almost no C-players. An example is the 1992 and 1996 Olympic men's American teams as an elite sports team where topgrading may work best, and includes other professional sports leagues such as the National Basketball Association or the National Hockey League where only athletes who display the highest levels of performance are recruited such that the whole team is truly talented (Downs & Swailes, 2013;Swaab et al., 2014). This is echoed by Swailes et al. (2014) who explain that topgrading only works in small, niche organizations or where required skills are in short supply, such as in start-up businesses. ...
... Finally, we further examined the effect of digitalization and the inverted U-shape of export product quality via the quadratic regression. Based on the method [49], we first found the maximum point Digitalization_max on the inverted U-shaped curve and then calculated the upper and lower values of digitalization indicators: ...
Digitalization has imposed new production and trade requirements on enterprises. Quality upgrading of export products, a requirement of international consumption upgrading, is also a product of enterprise industrial upgrading. We examine the impact of the digitalization of Chinese listed firms on the quality of their export products from 2011 to 2015 using the panel fixed effects model and chain mediating effects model. The results demonstrate that digitalization has an inverted U-shaped effect on the quality of export products. Further, digitalization can promote the quality upgrading of enterprises’ export products. However, the supporting capacity of digital infrastructure and the decreasing consumption upgrading of foreign customers limit this process. Empirical results show that digitalization can alleviate the financing constraints to improve the quality of export products. Because digitalization can improve human capital quality, this effect can improve the export product quality. A chain mediation effect also occurs. Digitalization alleviates financing constraints to improve human capital and enterprises’ export product quality. By clarifying digitalization⇒, alleviating financing constraints⇒, improving human capital quality⇒ improve export product quality relationships, executives can improve the competitiveness of enterprises in the international market.
... A special role in teams could be played by star inventors, the extremely prolific ones. Even though they could be associated to negative effects in organizations due to coordination costs and conflicts with collaborators [40], [41], star scientists provide directly with extremely superior innovation output, and indirectly with support to an organization's activities [22] and to the attraction of resources and skilled personnel [42]. In particular, their presence fosters the productivity of peers thanks to learning and emulation [43] and they can act as relational pivots to foster further innovation [6]. ...
... The third possible response, cultural integration, is required when an entirely new set of norms needs to be created by the group, or for the group. Previous research suggests that cultural integration is a particularly appropriate response for cross-cultural teams, and possibly the best approach for Global Virtual Teams (GVT; Dekker et al., 2008;Earley and Mosakowski, 2000;Glikson and Erez, 2020) and interdependent teams (Miloslavic et al., 2015;Polzer et al., 2002;Swaab et al., 2014;Wildman and Bedwell., 2013). In fact, Earley and Mosakowski (2000) found that, over time, transnational teams functioned best when they established a hybrid team culture that created space for their own norms for interactions, communications, and goal setting, which helped develop a common identity and manage conflicts effectively. ...
As a review of the literature, this paper highlights how three dimensions - task performance, skill development, and cross-cultural adjustment, have been used to understand cross-cultural performance. With respect to task performance, two criteria should be measured for all those working cross-culturally, including remote or virtual cross-cultural work: (1) how accurately employees read the unique contextual demands of a cross-cultural context and (2) how effectively they respond given the contextual and strategic demands of the task. Focusing on development of cross-cultural competencies as a performance goal we highlight the dimensions most important for assessment, focusing on self- and relationship management competencies. Regarding cross-cultural adjustment, we offer a more precise and nuanced approach which accounts for the person-environment fit in the context of working in cultural novelty.
... 1-21, © 2022 The Author(s) focusing on the team level of analysis could investigate possible dynamics leading to the exceptional performance of specific teams. For example, there is a current debate on the ideal proportion of individual stars leading to team stardom (Swaab et al. 2014, Gula et al. 2021. ...
Team research typically assumes that team performance is normally distributed: teams cluster around average performance, performance variability is not substantial, and few teams inhabit the upper range of the distribution. Ironically, although most team research and methodological practices rely on the normality assumption, many theories actually imply nonnormality (e.g., performance spirals, team composition, team learning, punctuated equilibrium). Accordingly, we investigated the nature and antecedents of team performance distributions by relying on 274 performance distributions including 200,825 teams (e.g., sports, politics, firefighters, information technology, customer service) and more than 500,000 workers. First, regarding their overall nature, only 11% of the distributions were normal, star teams are much more prevalent than predicted by normality, the power law with an exponential cutoff is the most dominant distribution among nonnormal distributions (i.e., 73%), and incremental differentiation (i.e., differential performance trajectories across teams) is the best explanation for the emergence of these distributions. Second, this conclusion remained unchanged after examining theory-based boundary conditions (i.e., tournament versus nontournament contexts, performance as aggregation of individual-level performance versus performance as a team-level construct, performance assessed with versus without a hard left-tail zero, and more versus less sample homogeneity). Third, we used the team learning curve literature as a conceptual framework to test hypotheses and found that authority differentiation and lower temporal stability are associated with distributions with larger performance variability (i.e., a greater proportion of star teams). We discuss implications for existing theory, future research directions, and methodological practices (e.g., need to check for nonnormality, Bayesian analysis, outlier management).
