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REVIEW 4 major objections 4 minor 52 references

What Drives Team Success? Large-Scale Evidence on the Role of the Team Player Effect

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that a consistent team player effect exists in quasi-randomly assembled temporary teams: certain individuals improve team outcomes beyond what their technical skills predict, and the effect is amplified by team…

desk verdict A large-scale, thoughtful split-sample study of the team player effect in esports; the interaction results are new, but the central residual-based measure of social skill is not fully identified. read the letter →

arxiv 2506.04475 v1 pith:Y7IDFCW2 submitted 2025-06-04 cs.SI cs.CY

Nico Elbert , Alicia von Schenk , Fabian Kosse , Victor Klockmann , Nikolai Stein , Christoph Flath This is my paper
classification cs.SIcs.CY
keywords teamplayereffectsocialskillsfamiliaritytemporaryteamsperformanceesportsquasi-randommatchmakingAgeofEmpiresII
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a stable team player effect exists in temporary teams: certain individuals consistently raise their team's chance of winning beyond what their mechanical skill, tactical skill, and prior experience predict. The evidence comes from millions of ranked Age of Empires II matches in which players are assigned to teams through quasi-random matchmaking, giving a large naturalistic test with high stakes. The paper further claims that the effect is amplified by team familiarity, so teams with prior shared experience benefit more from team players, and that the effect grows as team size increases. To isolate the effect, the authors subtract from each match outcome the win probability predicted by task proficiency alone, then average a player's residuals across matches. If correct, the result shows that social coordination skill is a distinct, measurable input into team performance, with direct implications for how organizations compose and retain temporary teams.

What carries the argument

The central object is the team player effect, defined as a player's average residual across team matches, where each residual is actual match outcome minus the win probability predicted from task-proficiency features (Solo Elo, effective actions per minute, and functional familiarity). A minimum of 25 matches per player is used to filter noisy estimates. The second central object is team familiarity, measured as the log-transformed mean of pairwise prior co-play counts among teammates. The argument proceeds in two stages: residuals from a proficiency-only model on one data split define the team player effect, and a separate split tests whether this effect, team familiarity, and their interactions predict match outcomes and how that predictive role changes with team size.

What would settle it

Recompute the player-specific residuals after adding controls for each player's recent solo-performance trend and for teammate fixed effects; if the cross-player dispersion of average residuals collapses to the level expected from noise, the team player effect is an artifact rather than a stable trait.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the team player effect is real and measurable in a large-scale, high-stakes setting. In the final model, a one-standard-deviation increase in the team player effect raises the log-odds of winning by 0.43, corresponding to a 54% increase in the odds of winning. Team familiarity by itself does not improve win prediction, but its absolute level interacts positively with the team player effect, meaning familiar teams extract more value from team players. Team size similarly amplifies the team player effect while diminishing the relative contribution of task proficiency. The authors interpret this pattern as evidence that social skills and familiarity are complementary rather than additive and that coordination demands grow with group size.

Load-bearing premise

The argument assumes that a player's average win-prediction residual is a stable individual trait rather than a byproduct of omitted variables such as skill growth, teammate quality, or selection into favorable teams.

Editorial extensions

If this is right

  • Organizations that can identify team players from interaction histories can raise team performance without changing technical hiring standards.
  • Keeping temporary teams together pays off more when team players are present, because familiarity amplifies their contribution.
  • Larger teams should weight social skill more heavily in selection, since the team player effect grows while task proficiency's relative importance declines.
  • A minimum of about 25 prior interactions offers a practical benchmark for reliably estimating an individual's team contribution.
  • Social skills and familiarity act as complements: their combined effect on winning exceeds what either would produce alone.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if the residual measure captures a portable trait, the same estimation approach could be applied to other logged team environments, such as online work platforms or open-source development, where similar quasi-random team assignments occur.
  • Editorial inference: the paper does not directly measure the mechanism; linking residual-based team player scores to communication behavior or social-cognitive tests would test whether the underlying construct is social skill.
  • Editorial inference: the negative team-size interaction with task proficiency suggests a substitution pattern worth probing further, for example by asking whether very large teams show diminishing returns to star performers.
  • Editorial inference: the quasi-random assignment claim is load-bearing; if matchmaking implicitly sorts players by coordination history, part of the residual could reflect selection rather than an individual trait.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper analyzes a large-scale dataset of ranked Age of Empires II matches to measure a 'team player effect': the extent to which certain individuals consistently improve team outcomes beyond what is predicted by their technical skills (eAPM, Solo Elo) and functional familiarity. The authors first estimate a logistic win-prediction model on one split of team matches (T1), compute player-level averages of the residuals (their team player effect), and then use this variable, along with task proficiency and team familiarity, to predict match outcomes in a second split (T2). They report that the team player effect has a substantial positive association with winning, that this effect is amplified by team familiarity and team size, and that familiar teams benefit more from team players. The paper claims to provide large-scale, quasi-randomized evidence for the team player effect previously identified in laboratory experiments by Weidmann and Deming (2021).

Significance. If the empirical strategy were fully convincing, this paper would offer valuable evidence that the 'team player effect' generalizes from small laboratory teams to large-scale, high-stakes team settings, and that it interacts meaningfully with familiarity and team size. The split-sample design (T1 for residual construction, T2 for prediction) is a genuine strength: it avoids the most direct form of in-sample circularity and provides a clean out-of-sample predictive test. The paper is also transparent about data construction and reports pre-specified models. However, the load-bearing interpretive claim—that the residual average is a stable, portable individual trait measuring social skill—is not established, and the current robustness checks do not address the main identification threats. As a result, the headline quantitative statements (e.g., the 0.43 log-odds coefficient in Table 4) cannot yet be read as causal evidence of a social-skill effect.

major comments (4)
  1. [Section 3.2 and Section 5.2] The central identification assumption—that the player-level average of win-prediction residuals in Eq. (3) measures a stable individual trait—is not defended against the most obvious threat. Section 3.2 explicitly states that ranked matchmaking allows premade groups of players to queue together, so a player's recurring teammates contribute a persistent, teammate-specific component to every residual assigned to that player. The KS-test robustness check in Section 6.2 only compares the distribution of residuals in matches with zero familiarity to the full dataset; it does not test whether the player-level averages are driven by recurring teammate pairings or other stable team compositions. To support the claim that the team player effect is a portable individual attribute, the authors should estimate the effect within players across changes in teammates, or at minimum show that the team player effect predicts outcomes in matches where no player has any prior shared experience with any teammate.
  2. [Section 5.2 (Eq. 2 and Eq. 3) and Section 4.2] The residual-based construction absorbs any unobserved player-specific trend or team-specific complementarity that is not captured by the team-level averages of eAPM, Solo Elo, and functional familiarity. For instance, a player who is improving over time, a player whose skill is nonlinearly more valuable on certain maps, or a player who consistently coordinates well with a particular partner will all produce positive residuals that are labeled 'team player effect.' The paper does not include player-specific time trends, teammate fixed effects, or interactions beyond the linear team-level means, so the residual is a composite of many omitted factors. The authors should provide evidence that the team player effect remains predictive after adding controls for player-specific trajectories and teammate composition, or discuss this as a key limitation of the interpretation.
  3. [Section 6.2 (Figure 4) and Section 5.2 (Eq. 4)] The inclusion threshold τ is selected based on the 'elbow point' of the residual-convergence pattern and is then validated by showing that predictive power on Split T2 peaks at τ = 25. This means a key parameter of the team player effect construction is chosen to maximize the out-of-sample predictive performance of the variable. This is a form of outcome-based model selection that can inflate the apparent validity of the constructed regressor. The authors should report results across a range of thresholds (or pre-register the threshold choice) to demonstrate that the qualitative findings are not an artifact of this particular tuning.
  4. [Section 6.3 (Table 4) and Section 7] The headline interpretation—'a one-standard-deviation increase in the team player effect is associated with a 0.43 increase in the log-odds of winning, corresponding to a 54% increase in the odds of winning'—treats the team player effect as a causal social-skill attribute. Given that the regressor is a generated residual from a previous model and that the assignment to teammates is not truly random (Section 3.2 allows premade teams, and matchmaking balances on Elo), the estimate is best described as an associational predictive relationship. The paper should either provide a design that identifies the causal effect (e.g., exploiting within-player variation in teammate composition, or restricting to entirely unfamiliar teams) or substantially soften the causal language in Sections 7 and 8.
minor comments (4)
  1. [Section 3.2] There is a typo: 'Teanm Elo' should be 'Team Elo'.
  2. [Figure 6 caption] The caption reads 'S.24 Feature Coefficients' but should refer to 'S2.4' to match the model numbering used elsewhere.
  3. [Equation (4)] The notation '⊮{nj ≥ τ}' is nonstandard for an indicator function; using a standard indicator notation such as 1{nj ≥ τ} would improve readability.
  4. [Section 6.3] The paper states that team familiarity delta 'lacks explanatory power' but does not discuss why the absolute level of team familiarity, which is used in the interaction models, is more informative than the delta. A brief interpretive note would help the reader understand the distinction.

Circularity Check

1 steps flagged · score 4.0 of 10

The team player effect is constructed out-of-sample on Split T1, so the main coefficient is not forced by construction; however, the inclusion threshold is selected by maximizing explanatory power on Split T2 and then the same Split T2 is used to report predictive performance, making the predictive contribution partially circular.

  1. fitted input called prediction [Section 6.2 (Figure 4) and Section 6.3 (Table 4)]
    "Figure 4 validates this choice by assessing the explanatory power of a logistic win prediction model on Split T2, using only the team player effect as a feature. The x-axis represents different inclusion thresholds τ applied in the construction of the team player effect, while the model’s explanatory power peaks at the selected threshold."

    The inclusion threshold τ is fitted to Split T2 match outcomes by maximizing the pseudo-R² of a team-player-effect-only model. The same Split T2 is then used to fit and evaluate the final S2 models in Table 4, so the reported pseudo-R² and accuracy gains for the team player effect are conditional on a threshold chosen to maximize performance on those exact outcomes. The Figure 4 peak is therefore an in-sample selected maximum rather than an independent validation, and the subsequent 'prediction' of wins on Split T2 is not out-of-sample with respect to τ. This is a fitted hyperparameter presented as predictive evidence.

full rationale

The paper's main construction is not globally circular: the team player effect in Equation (3) is computed from S1 residuals on Split T1 and then applied to players in Split T2, so the headline S2 coefficient of 0.43 is not forced by an in-sample identity. The load-bearing circularity is confined to the threshold τ: instead of being set only by the residual-convergence elbow, it is selected by maximizing explanatory power on Split T2, and then the same Split T2 is used to report predictive performance. This makes the measured predictive contribution of the team player effect partially circular. There is no self-citation chain, no imported uniqueness theorem, and no ansatz smuggled in via the authors' own prior work; the method is attributed to external sources (Weidmann and Deming; Ching et al.). Concerns that the residual may capture premade-team selection or unobserved teammate complementarities are construct-validity and omitted-variable risks rather than definitional circularity, because no equation reduces to itself by construction once the T1/T2 split is respected.

Assumptions & free parameters 1 free parameters · 4 assumptions · 1 invented entities

The central claim rests on the assumption that matchmaking is quasi-random and that residuals measure a stable social-skill trait. Both are domain assumptions without independent validation. The threshold tau=25 is a free parameter. The 'team player effect' is an invented latent construct with no external evidence.

free parameters (1)
  • Inclusion threshold tau = 25
    Chosen via elbow point of residual variability and to maximize predictive power in Split T2 (Section 6.2, Figure 4). This data-driven choice affects which players contribute to the team player effect.
assumptions (4)
  • domain assumption Matchmaking assigns players to teams quasi-randomly, so team composition is exogenous given Elo.
    Invoked in Section 3.2 and again in Section 5.3 to justify causal interpretation. Actually, players can queue in premade groups, so assignment is not fully random.
  • domain assumption The residual from the task-proficiency model captures a stable individual trait (social skill) rather than omitted variables.
    Central to defining the team player effect in Section 5.2, equations (2)-(3). No independent validation is provided.
  • domain assumption Functional familiarity (log match counts) and team familiarity (log pairwise counts) fully capture contextual experience.
    Used in S1.3 and S2.4; measurement error in these proxies could bias residual estimates.
  • standard math Logistic regression with delta features correctly specifies win probability.
    Standard modeling assumption, Section 5.1.
invented entities (1)
  • Team Player Effect
    purpose: Latent individual trait claimed to improve team outcomes beyond task proficiency.
    Measured only via residuals from the same outcome model (Eq. 3). No external validation (e.g., social skill tests) is provided; the paper itself proposes future validation.

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Pith. "Pith review of What Drives Team Success? Large-Scale Evidence on the Role of the Team Player Effect." pith.science (2026). https://pith.science/paper/Y7IDFCW2

@misc{pith2026250604475,
  author       = {Pith},
  title        = {Pith review of: What Drives Team Success? Large-Scale Evidence on the Role of the Team Player Effect},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y7IDFCW2}},
  note         = {Machine review of arXiv:2506.04475}
}
read the original abstract

Effective teamwork is essential in structured, performance-driven environments, from professional organizations to high-stakes competitive settings. As tasks grow more complex, achieving high performance requires not only technical proficiency but also strong interpersonal skills that allow individuals to coordinate effectively within teams. While prior research has identified social skills and familiarity as key drivers of team success, their joint effects -- particularly in temporary teams -- remain underexplored due to data and methodological constraints. To address this gap, we analyze a large-scale panel dataset from the real-time strategy game Age of Empires II, where players are assigned quasi-randomly to temporary teams and must coordinate under dynamic, high-pressure conditions. We isolate individual contributions by comparing observed match outcomes with predictions based on task proficiency. Our findings confirm a robust 'team player effect': certain individuals consistently improve team outcomes beyond what their technical skills predict. This effect is significantly amplified by team familiarity -- teams with prior shared experience benefit more from the presence of such individuals. Moreover, the effect grows with team size, suggesting that social skills become increasingly valuable as coordination demands rise. Our results demonstrate that social skills and familiarity interact in a complementary, rather than additive, way. These findings contribute to the literature on team performance by documenting the strength and structure of the team player effect in a quasi-randomized, high-stakes setting, with implications for teamwork in organizations and labor markets.

Figures

Figures reproduced from arXiv: 2506.04475 by the authors.

Figure 1
Figure 1. (Left) Distribution of Team Matches per Mode over Time (monthly); (Right) Standardized Mean Feature [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Pipeline for Feature Extraction, Residual Calculation, and Final Prediction [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Residuals of the team player effect over the [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Model accuracy on the test set of Split T2. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: S.24 Feature Coefficients across Familiarity Quantiles and Team Size. (Left) Coefficient trends across [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: (Left) 95th percentile of residuals for S1.1-S1.3 over match count; (Right) residual bandwidth 95th for [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]

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Works this paper leans on

52 extracted references · 52 canonical work pages

  1. [1]

    It’s only temporary: time frame and the dynamics of creative project teams.British Journal of Management, 24(3):383–397, 2013

    René M Bakker, Smaranda Boro¸ s, Patrick Kenis, and Leon AG Oerlemans. It’s only temporary: time frame and the dynamics of creative project teams.British Journal of Management, 24(3):383–397, 2013

    work page 2013

  2. [2]

    Learning in temporary teams: The varying effects of partner exposure by team member role.Organization Science, 34(1):433–455, 2023

    Song-Hee Kim, Hummy Song, and Melissa A Valentine. Learning in temporary teams: The varying effects of partner exposure by team member role.Organization Science, 34(1):433–455, 2023

    work page 2023

  3. [3]

    Skill requirements across firms and labor markets: Evidence from job postings for professionals.Journal of Labor Economics, 36(S1):S337–S369, 2018

    David Deming and Lisa B Kahn. Skill requirements across firms and labor markets: Evidence from job postings for professionals.Journal of Labor Economics, 36(S1):S337–S369, 2018

    work page 2018

  4. [4]

    The growing importance of social skills in the labor market.The quarterly journal of economics, 132(4):1593–1640, 2017

    David J Deming. The growing importance of social skills in the labor market.The quarterly journal of economics, 132(4):1593–1640, 2017

    work page 2017

  5. [5]

    Team incentives and worker heterogeneity: An empirical analysis of the impact of teams on productivity and participation.Journal of political Economy, 111(3):465–497, 2003

    Barton H Hamilton, Jack A Nickerson, and Hideo Owan. Team incentives and worker heterogeneity: An empirical analysis of the impact of teams on productivity and participation.Journal of political Economy, 111(3):465–497, 2003

    work page 2003

  6. [6]

    Opportunity counts: Teams and the effectiveness of production incentives.Journal of Labor Economics, 25(4):613–650, 2007

    Brent Boning, Casey Ichniowski, and Kathryn Shaw. Opportunity counts: Teams and the effectiveness of production incentives.Journal of Labor Economics, 25(4):613–650, 2007

    work page 2007

  7. [7]

    Teams in organizations: Recent research on performance and effectiveness.Annual review of psychology, 47(1):307–338, 1996

    Richard A Guzzo and Marcus W Dickson. Teams in organizations: Recent research on performance and effectiveness.Annual review of psychology, 47(1):307–338, 1996

    work page 1996

  8. [8]

    Team effectiveness: Beyond skills and cognitive ability.Journal of Applied psychology, 84(3):376, 1999

    George A Neuman and Julie Wright. Team effectiveness: Beyond skills and cognitive ability.Journal of Applied psychology, 84(3):376, 1999. 15

    work page 1999

Show all 52 references
  1. [9]

    Leader political skill and team performance.Journal of management, 30(3):309–327, 2004

    Kathleen K Ahearn, Gerald R Ferris, Wayne A Hochwarter, Ceasar Douglas, and Anthony P Ammeter. Leader political skill and team performance.Journal of management, 30(3):309–327, 2004

    2004

  2. [10]

    The increasing complementarity between cognitive and social skills.Review of Economics and Statistics, 96(5):849–861, 2014

    Catherine J Weinberger. The increasing complementarity between cognitive and social skills.Review of Economics and Statistics, 96(5):849–861, 2014

    2014

  3. [11]

    Human resource management and productivity

    Nicholas Bloom and John Van Reenen. Human resource management and productivity. InHandbook of labor economics, volume 4, pages 1697–1767. Elsevier, 2011

    2011

  4. [12]

    Team players: How social skills improve team performance.Econometrica, 89(6):2637–2657, 2021

    Ben Weidmann and David J Deming. Team players: How social skills improve team performance.Econometrica, 89(6):2637–2657, 2021

    2021

  5. [13]

    Extemporaneous coordination in specialist teams: The familiarity complementarity.Organization Science, 32(1):1–17, 2021

    Kenny Ching, Enrico Forti, and Evan Rawley. Extemporaneous coordination in specialist teams: The familiarity complementarity.Organization Science, 32(1):1–17, 2021

    2021

  6. [14]

    Competitive familiarity: learning to coordinate by competing

    Kenny Ching, Enrico Forti, and Evan Rawley. Competitive familiarity: learning to coordinate by competing. Organization Science, 2024

    2024

  7. [15]

    Multilevel theory of team decision making: Decision performance in teams incorporating distributed expertise

    John R Hollenbeck, Daniel R Ilgen, Douglas J Sego, Jennifer Hedlund, Debra A Major, and Jean Phillips. Multilevel theory of team decision making: Decision performance in teams incorporating distributed expertise. Journal of applied psychology, 80(2):292, 1995

    1995

  8. [16]

    Shunan Zhang, ShaoPeng Che, Dongyan Nan, Yincen Li, and Jang Hyun Kim. I know my teammates: the role of group member familiarity in computer-supported and face-to-face collaborative learning.Education and Information Technologies, 28(10):12615–12631, 2023

    2023

  9. [17]

    Time matters in team performance: Effects of member familiarity, entrainment, and task discontinuity on speed and quality

    David A Harrison, Susan Mohammed, Joseph E McGrath, Anna T Florey, and Scott W Vanderstoep. Time matters in team performance: Effects of member familiarity, entrainment, and task discontinuity on speed and quality. Personnel Psychology, 56(3):633–669, 2003

    2003

  10. [18]

    The division of labor, coordination costs, and knowledge.The Quarterly journal of economics, 107(4):1137–1160, 1992

    Gary S Becker and Kevin M Murphy. The division of labor, coordination costs, and knowledge.The Quarterly journal of economics, 107(4):1137–1160, 1992

    1992

  11. [19]

    On teams, teamwork, and team performance: Discoveries and developments.Human factors, 50(3):540–547, 2008

    Eduardo Salas, Nancy J Cooke, and Michael A Rosen. On teams, teamwork, and team performance: Discoveries and developments.Human factors, 50(3):540–547, 2008

    2008

  12. [20]

    Peer effects from friends and strangers: Evidence from random matchmaking in an online game

    Daniel Goetz and Wei Lu. Peer effects from friends and strangers: Evidence from random matchmaking in an online game. InProceedings of the 23rd ACM Conference on Economics and Computation, pages 285–286, 2022

    2022

  13. [21]

    Globally optimized matchmaking in online games

    Qilin Deng, Hao Li, Kai Wang, Zhipeng Hu, Runze Wu, Linxia Gong, Jianrong Tao, Changjie Fan, and Peng Cui. Globally optimized matchmaking in online games. InProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 2753–2763, 2021

    2021

  14. [22]

    Using sports data to advance management research: A review and a guide for future studies.Journal of Management, 49(1):325–362, 2023

    Fabio Fonti, Jan-Michael Ross, and Paolo Aversa. Using sports data to advance management research: A review and a guide for future studies.Journal of Management, 49(1):325–362, 2023

    2023

  15. [23]

    Intellectual capital and performance in temporary teams.Management Decision, 58(3):410–427, 2020

    Maurizio Massaro, Francesca Dal Mas, Nick Bontis, and Bill Gerrard. Intellectual capital and performance in temporary teams.Management Decision, 58(3):410–427, 2020

    2020

  16. [24]

    The differential effect of team members’ trust on team performance: The mediation role of team cohesion.Journal of occupational and organizational psychology, 83(3):771–794, 2010

    Merce Mach, Simon Dolan, and Shay Tzafrir. The differential effect of team members’ trust on team performance: The mediation role of team cohesion.Journal of occupational and organizational psychology, 83(3):771–794, 2010

    2010

  17. [25]

    Platform loophole exploitation, recovery measures, and user engagement: A quasi-natural experiment in online gaming.Information Systems Research, 2023

    Jianqing Chen, Shu He, and Xue Yang. Platform loophole exploitation, recovery measures, and user engagement: A quasi-natural experiment in online gaming.Information Systems Research, 2023

    2023

  18. [26]

    Digital games and beyond: What happens when players compete?MIS Quarterly, pages 111–124, 2013

    De Liu, Xun Li, and Radhika Santhanam. Digital games and beyond: What happens when players compete?MIS Quarterly, pages 111–124, 2013

    2013

  19. [27]

    level up

    Yan Huang, Stefanus Jasin, and Puneet Manchanda. “level up”: Leveraging skill and engagement to maximize player game-play in online video games.Information Systems Research, 30(3):927–947, 2019

    2019

  20. [28]

    Gaming the system: The effects of social capital as a resource for virtual team members.Information & Management, 57(6):103239, 2020

    Stacie Petter, Connie S Barber, and Diane Barber. Gaming the system: The effects of social capital as a resource for virtual team members.Information & Management, 57(6):103239, 2020

    2020

  21. [29]

    Understanding esports team formation and coordination.Computer supported cooperative work (CSCW), 28:95–126, 2019

    Guo Freeman and Donghee Yvette Wohn. Understanding esports team formation and coordination.Computer supported cooperative work (CSCW), 28:95–126, 2019

    2019

  22. [30]

    Cognitive performance in remote work: Evidence from professional chess.The Economic Journal, 132(643):1218–1232, 2022

    Steffen Künn, Christian Seel, and Dainis Zegners. Cognitive performance in remote work: Evidence from professional chess.The Economic Journal, 132(643):1218–1232, 2022

    2022

  23. [31]

    Skewness preferences: evidence from online poker

    Markus Dertwinkel-Kalt, Johannes Kasinger, and Dmitrij Schneider. Skewness preferences: evidence from online poker. 2024. 16

    2024

  24. [32]

    Using games to understand the mind.Nature Human Behaviour, pages 1–9, 2024

    Kelsey Allen, Franziska Brändle, Matthew Botvinick, Judith E Fan, Samuel J Gershman, Alison Gopnik, Thomas L Griffiths, Joshua K Hartshorne, Hauser, Tobias U, et al. Using games to understand the mind.Nature Human Behaviour, pages 1–9, 2024

    2024

  25. [33]

    Cultural imprinting, institutions, and the organization of new firms.Strategy Science, 3(2):426–438, 2018

    David M Waguespack, Eric T Dunford, and Jóhanna K Birnir. Cultural imprinting, institutions, and the organization of new firms.Strategy Science, 3(2):426–438, 2018

    2018

  26. [34]

    Missing the forest for the trees: Modular search and systemic inertia as a response to environmental change.Administrative Science Quarterly, 68(1):186–227, 2023

    Julien Clement. Missing the forest for the trees: Modular search and systemic inertia as a response to environmental change.Administrative Science Quarterly, 68(1):186–227, 2023

    2023

  27. [35]

    Do managers matter: Evidence from e-sports.Contemporary Economic Policy, 38(2):304–312, 2020

    Dennis Coates, Petr Parshakov, and Sofia Paklina. Do managers matter: Evidence from e-sports.Contemporary Economic Policy, 38(2):304–312, 2020

    2020

  28. [36]

    Learning from many: Partner exposure and team familiarity in fluid teams.Management Science, 67(2):854–874, 2021

    Zeynep Ak¸ sin, Sarang Deo, Jónas Oddur Jónasson, and Kamalini Ramdas. Learning from many: Partner exposure and team familiarity in fluid teams.Management Science, 67(2):854–874, 2021

    2021

  29. [37]

    Sanchez, and Matthew D Dean

    M Travis Maynard, John E Mathieu, Lucy L Gilson, Diana R. Sanchez, and Matthew D Dean. Do i really know you and does it matter? unpacking the relationship between familiarity and information elaboration in global virtual teams.Group & Organization Management, 44(1):3–37, 2019

    2019

  30. [38]

    Selecting individuals in team settings: The importance of social skills, personality characteristics, and teamwork knowledge.Personnel psychology, 58(3): 583–611, 2005

    Frederick P Morgeson, Matthew H Reider, and Michael A Campion. Selecting individuals in team settings: The importance of social skills, personality characteristics, and teamwork knowledge.Personnel psychology, 58(3): 583–611, 2005

    2005

  31. [39]

    reading the mind in the eyes

    Simon Baron-Cohen, Sally Wheelwright, Jacqueline Hill, Yogini Raste, and Ian Plumb. The “reading the mind in the eyes” test revised version: A study with normal adults, and adults with asperger syndrome or high-functioning autism.Journal of child psychology and psychiatry, 42(...

    2001

  32. [40]

    Esports research: A literature review.Games and Culture, 15(1):32–50, 2020

    Jason G Reitman, Maria J Anderson-Coto, Minerva Wu, Je Seok Lee, and Constance Steinkuehler. Esports research: A literature review.Games and Culture, 15(1):32–50, 2020

    2020

  33. [41]

    Playing with strangers: understanding temporary teams in league of legends

    Yubo Kou and Xinning Gui. Playing with strangers: understanding temporary teams in league of legends. In Proceedings of the first ACM SIGCHI annual symposium on Computer-human interaction in play, pages 161–169, 2014

    2014

  34. [42]

    Big data in professional sport: the perspective of practitioners in the nfl, mlb, nba, and nhl.Journal of Strategic Marketing, 31(8):1413–1433, 2023

    Gashaw Abeza, Norm O’Reilly, John Nadeau, and Yann Abdourazakou. Big data in professional sport: the perspective of practitioners in the nfl, mlb, nba, and nhl.Journal of Strategic Marketing, 31(8):1413–1433, 2023

    2023

  35. [43]

    Indoor air quality and strategic decision making.Management Science, 69(9):5354–5377, 2023

    Steffen Künn, Juan Palacios, and Nico Pestel. Indoor air quality and strategic decision making.Management Science, 69(9):5354–5377, 2023

    2023

  36. [44]

    Understanding the role of competition in video gameplay satisfaction

    Sepandar Sepehr and Milena Head. Understanding the role of competition in video gameplay satisfaction. Information & Management, 55(4):407–421, 2018

    2018

  37. [45]

    Motivations for play in online games.CyberPsychology & behavior, 9(6):772–775, 2006

    Nick Yee. Motivations for play in online games.CyberPsychology & behavior, 9(6):772–775, 2006

    2006

  38. [46]

    Motivational profiling of league of legends players.Frontiers in Psychology, 11:1307, 2020

    Florian Brühlmann, Philipp Baumgartner, Günter Wallner, Simone Kriglstein, and Elisa D Mekler. Motivational profiling of league of legends players.Frontiers in Psychology, 11:1307, 2020

    2020

  39. [47]

    The gamer motivation profile: What we learned from 250,000 gamers

    Nick Yee. The gamer motivation profile: What we learned from 250,000 gamers. InProceedings of the 2016 annual symposium on computer-human interaction in play, pages 2–2, 2016

    2016

  40. [48]

    Who plays, how much, and why? debunking the stereotypical gamer profile.Journal of computer-mediated communication, 13(4):993–1018, 2008

    Dmitri Williams, Nick Yee, and Scott E Caplan. Who plays, how much, and why? debunking the stereotypical gamer profile.Journal of computer-mediated communication, 13(4):993–1018, 2008

    2008

  41. [49]

    A strategic decision- making architecture toward hybrid teams for dynamic competitive problems.Decision Support Systems, 144: 113490, 2021

    Alparslan Emrah Bayrak, Christopher McComb, Jonathan Cagan, and Kenneth Kotovsky. A strategic decision- making architecture toward hybrid teams for dynamic competitive problems.Decision Support Systems, 144: 113490, 2021

    2021

  42. [50]

    An experimental study of team size and performance on a complex task.PloS one, 11(4):e0153048, 2016

    Andrew Mao, Winter Mason, Siddharth Suri, and Duncan J Watts. An experimental study of team size and performance on a complex task.PloS one, 11(4):e0153048, 2016

    2016

  43. [51]

    A meta-analysis of teamwork processes: Tests of a multidimensional model and relationships with team effectiveness criteria

    Jeffery A LePine, Ronald F Piccolo, Christine L Jackson, John E Mathieu, and Jessica R Saul. A meta-analysis of teamwork processes: Tests of a multidimensional model and relationships with team effectiveness criteria. Personnel psychology, 61(2):273–307, 2008

    2008

  44. [52]

    Leader network centrality and team performance: Team size as moderator and collaboration as mediator.Journal of Business and Psychology, 37(2):283–296, 2022

    Yingjie Yuan and Daan Van Knippenberg. Leader network centrality and team performance: Team size as moderator and collaboration as mediator.Journal of Business and Psychology, 37(2):283–296, 2022. 17 A Technical Appendix This appendix examines residual variability across model...

    2022

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.