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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
... The inverted U-shaped relationship is a commonly observed non-linear pattern in empirical studies. Swaab (2014) [53] employed quadratic terms of the core explanatory variables in regressions to investigate inverted U-shaped in psychology research. To test the existence of the inverted U-shaped relationship, the researcher included the squared terms of the explanatory variables in the regression model and identified the inverted U-shaped relationship based on the significance of the coefficients of the quadratic terms and their heteroscedasticity with the coefficients of the primary terms. ...
... The inverted U-shaped relationship is a commonly observed non-linear pattern in empirical studies. Swaab (2014) [53] employed quadratic terms of the core explanatory variables in regressions to investigate inverted U-shaped in psychology research. To test the existence of the inverted U-shaped relationship, the researcher included the squared terms of the explanatory variables in the regression model and identified the inverted U-shaped relationship based on the significance of the coefficients of the quadratic terms and their heteroscedasticity with the coefficients of the primary terms. ...
In recent years, China’s environmental policies have continued to promote sustainable development, and listed companies have increased their environmental investment and strengthened their environmental social responsibility. Although there has been much research on the relationship between environmental performance and total factor productivity of listed companies, the impact of environmental social responsibility on total factor productivity has not yet been fully examined. In this paper, we use panel data regression to investigate the linear and non-linear relationships between environmental social responsibility and total factor productivity. These relationships are tested for robustness, analyzed for between-group differences, and validated by a machine learning model. Firstly, we find that environmental social responsibility can significantly contribute to companies’ total factor productivity within a certain range, but it varies across different categories of firms. Secondly, there is an inverted U-shape relationship between environmental social responsibility and total factor productivity, where total factor productivity initially increases with environmental social responsibility but decreases after reaching a certain threshold. Finally, we conclude that environmental social responsibility promotes total factor productivity in the early stages, but when environmental social responsibility reaches a certain threshold, it begins to exert an inhibitory effect on the development of total factor productivity.
... Continuing with our prior example, a team of paramedics (with a significant overlap of skills) may allocate and coordinate individual tasks more flexibly than a surgical team (high degrees of specialization). Furthermore, team characteristics and teaming contexts are also intertwined; teams working on complex tasks that require subject matter expertise are also more likely to require interdependent teaming strategies (Swaab et al., 2014). An example is how surgical team members tend to be highly specialized but limited to specific medical procedures, while paramedics have more general skills that allow them to perform a wider range of procedures related to their broader task contexts. ...
Teams are essential for most modern work. But who or what is a team? With today’s rapidly diverse team contexts and the diversity of research frameworks for studying them, there is no longer a definitive answer to this question. Thus, Cooke et al. introduced “teamness,” a construct through which future research can describe teamwork as a function of the many dimensions of variability between and within teams. In this multidisciplinary panel moderated by the two lead authors, we discuss the current state and future directions of team cognitive science in light of teamness as a guiding construct, along with its implications for human factors practice.
... This type of research also needs to recognise that macroand microscale time spans look different for different types of teams. For example, in professional sports (like soccer or NBA), team processes like coordination could be observed on the micro-scale by focusing on interdependent behaviours that occur over the course of a game, whereas a focus on macro-scale time span would involve focusing on an entire season (e.g., Stuart & Moore, 2017;Swaab et al., 2014). In other contexts, like opensoftware development projects, some studies have looked at team activities unfolding in projects that only involve 10 days (Riedl & Woolley, 2017). ...
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https://journals.sagepub.com/doi/10.1177/10596011241278556
Although team processes are conceptualized as temporal phenomena, our theoretical understanding of their unfolding over time is underdeveloped, particularly when “zooming in and out” into their dynamics using different temporal lenses. Team processes might unfold differently over extended project cycles (i.e., macroscale time lens) versus over brief events (microscale time lens). Our goal was to better understand temporal changes of three critical higher-order team processes (i.e., transition, action, and interpersonal processes) over both extended periods (i.e., longer project cycles) and brief time spans (i.e., recurring stand-up
meetings). Focusing on two agile software teams, we indexed team processes across these two time spans using computer-aided text analysis (CATA) of meeting transcripts. Macroscale time span processes were captured across 10 sprints (30-week project cycle). Microscale time spans were captured with data from brief stand-up meetings (i.e., using 10 equidistant time intervals from 40 meetings). From a macroscale time lens (i.e., project cycle), an increase in action processes in the early project phase was associated with increases in performance. From a microscale time lens, changes in transition and interpersonal processes around mid-meeting phases were associated with differences in performance. Qualitative analyses of meeting midpoints revealed key differences in proactive planning and interpersonal processes. We discuss how our results provide novel insights for team process dynamics in relation to micro- and macroscale time spans.
... • Collaboration: More collaborative teams generally perform better [56], implying a direct relationship between collaboration and performance. However, talent and collaboration may sometimes be inversely proportional [57]. • Leadership: Teams exhibiting superior leadership and trust usually perform better than those facing leadership crises [58]. ...
Cyber competitions are usually team activities, where team performance not only depends on the members' abilities but also on team collaboration. This seems intuitive, especially given that team formation is a well-studied discipline in competitive sports and project management, but unfortunately, team performance and team formation strategies are rarely studied in the context of cybersecurity and cyber competitions. Since cyber competitions are becoming more prevalent and organized, this gap becomes an opportunity to formalize the study of team performance in the context of cyber competitions. This work follows a cross-validating two-approach methodology. The first is the computational modeling of cyber competitions using Agent-Based Modeling. Team members are modeled, in NetLogo, as collaborating agents competing over a network in a red team/blue team match. Members' abilities, team interaction and network properties are parametrized (inputs), and the match score is reported as output. The second approach is grounded in the literature of team performance (not in the context of cyber competitions), where a theoretical framework is built in accordance with the literature. The results of the first approach are used to build a causal inference model using Structural Equation Modeling. Upon comparing the causal inference model to the theoretical model, they showed high resemblance, and this cross-validated both approaches. Two main findings are deduced: first, the body of literature studying teams remains valid and applicable in the context of cyber competitions. Second, coaches and researchers can test new team strategies computationally and achieve precise performance predictions. The targeted gap used methodology and findings which are novel to the study of cyber competitions.
... In the realm of sports, a similar phenomenon has been proposed, where having more talented team members leads to better team performance up to a certain point, after which talent becomes "too much" and detrimental to performance [9]. This notion of the too-much-talent (TMT) effect suggests that status conflicts among highly skilled members may impair coordination in teams. ...
... This notion of the too-much-talent (TMT) effect suggests that status conflicts among highly skilled members may impair coordination in teams. While this effect has been observed in sports with high coordination requirements, such as basketball and soccer [9], recent studies have questioned the appropriateness of the approach used to test the inverse-U-shaped relation [10,11]. To address this question, we conducted a study using a much larger dataset of 780 data points across 42 seasons of the best seven leagues in European football. ...
... With this clear indication, and supported evidence that talent positively impacts individual sporting success and elite performance [16], this has led to the notion, and typical belief, that the prevalence of top talented individuals within a team will also, undoubtedly, improve team success and performance. Such beliefs have been shown in research depicting that people generally believe that the relationship between top talent within a team, and team performance is linear, indicating that the effect of more top talent can only bring about positive effects, without much diminishing returns [9]. For example, Dirks [20] also found that higher levels of talent within a team was one of the greatest determinants of team success in their respective sample of basketball players. ...
Though it may appear counterintuitive, certain positive attributes can eventually have negative consequences when taken to an extreme. This concept is exemplified in sports, where an increase in talent among team members initially leads to improved success, but beyond a certain threshold, excessive talent can adversely affect the team. This occurrence is known as the Too Much Talent (TMT) effect, wherein status conflicts among highly skilled players can hinder team performance, particularly in sports that require coordination and cooperation. While early evidence supported the TMT effect in team sports, its validity has recently been challenged. In this study, we analyzed a comprehensive dataset consisting of 780 data points across 42 seasons from seven top European football (soccer) leagues to examine the TMT effect’s presence. Our findings reveal that football does not exhibit the TMT effect. Instead, we observed a consistent, positive correlation between the number of skilled players on a team and team success. Additionally, talent did not display diminishing returns, as its impact on success remained stable even at the highest concentrations of talent. We relate our results to existing theories and propose that future research comparing more individualistic and interdependent team sports could further enhance the field.
... H3 was verified. The results of Table 6 are the traditional inverted U-shaped model, but in the traditional inverted U-shaped relationship test method, the peak point of the inverted U-shaped relationship is simply regarded as the threshold point, but this treatment does not guarantee that the peak point is the threshold point, nor can it judge whether the threshold effect exists [66]. In order to further improve the accuracy of the experiment, the utest test is carried out and the regression results are illustrated (Fig. 8) [44][45][46][47]. ...
In the expanding global digital economy, the digital transformation of businesses has become a critical component of modern operations. This study investigates the relationship between executive incentives and the digital transformation in A-share-listed Chinese companies from 2011 to 2020. Using multi-period DID and linear regression models, we analyzed how equity and compensation incentives influence this transformation. We discovered an inverse U-shaped correlation between executive incentive intensity and corporate digital transformation. Additionally, the relationship between compensation incentives and digital transformation is initially non-positive but transitions to a non-linear positive association beyond a certain threshold. Our research also reveals that digital process innovation and digital business expansion mediate the relationship between executive motivation and digital transformation. These findings highlight the importance of appropriate executive rewards in fostering innovative thinking and advancing digital transformation. This study contributes to the understanding of the drivers and effects of digital transformation and the role of equity incentives in governance. It offers valuable insights for companies aiming to accelerate digital transformation, optimize industrial structure, and promote economic development. Based on this study, further research on this issue can be conducted in the future by refining the personality traits, educational background, and cognitive differences of executives.
... Our findings provide insights into the conflicting empirical studies on whether the best teams consist of the most able individuals. In baseball, offensive performance closely resembles an additive function, and (setting aside defensive positions) the best offensive team consists of the best individual hitters (Swaab et al., 2014). Replacing the worst performer likely improves performance, so a selection criterion exists. ...
In this paper, we derive necessary and sufficient conditions on team based tasks in order for a selection criterion applied to individuals to produce optimal teams. We assume only that individuals have types and that a team’s performance depends on its size and the type composition of its members. We first derive the selection principle which states that if a selection criterion exists, it must rank types by homogeneous team performance, the performance of a team consisting only of that type. We then prove that a selection criterion exists if and only if replacing the team’s lowest ranked type, as measured by homogeneous team performance, with a higher ranked type increases team performance. Finally, we show that the replace the lowest ranked property rules out most common types of team complementarities, including benefits to diverse types and types that fill structural holes.
... One of the challenges in this goal is that a good team should result in more than the sum of its constituents. For example, assembling the top players on a football team does not guarantee the best performance [6]. The concept of synergy, or team chemistry, therefore, has been debated to explain these discrepancies in diverse fields such as sports and business [7,8]. ...
Synergy, or team chemistry, is an elusive concept that explains how collaboration is able to yield outcomes beyond expectations. Here, we reveal its presence and underlying mechanisms in pairwise scientific collaboration by reconstructing the publication histories of 560,689 individual scientists and 1,026,196 pairs of scientists. We quantify pair synergy by extracting the non-additive effects of collaboration on scientific impact, which are not confounded by prior collaboration experience or luck. We employ a network inference methodology with the stochastic block model to investigate the mechanism of pair synergy and its connection to individual attributes. The inferred block structure, derived solely from the observed types of synergy, can anticipate an undetermined type of synergy between two scientists who have never collaborated. This suggests that synergy arises from a suitable combination of certain, yet unidentified, individual characteristics. Furthermore, the most relevant to pair synergy is research interest, although its diversity does not lead to complementarity across all disciplines. Our results pave the way for understanding the dynamics of collaborative success in science and unlocking the hidden potential of collaboration by matchmaking between scientists.
... 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 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.