Individual and Collective Performance Deteriorate in a New Team: A Case
Study of CS:GO Tournaments
- URL: http://arxiv.org/abs/2205.09693v1
- Date: Thu, 19 May 2022 16:54:49 GMT
- Title: Individual and Collective Performance Deteriorate in a New Team: A Case
Study of CS:GO Tournaments
- Authors: Weiwei Zhang, Goran Muric, Emilio Ferrara
- Abstract summary: This study aims to answer how changing a team affects individual and collective performance in eSports tournaments.
We collected data from professional tournaments of a popular first-person shooter game.
After a player switched to a new team, both the individual and the collective performance dropped initially, and then slowly recovered.
- Score: 11.86905804972623
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: How does the team formation relates to team performance in professional video
game playing? This study examined one aspect of group dynamics - team switching
- and aims to answer how changing a team affects individual and collective
performance in eSports tournaments. In this study we test the hypothesis that
switching teams can be detrimental to individual and team performance both in
short term and in a long run. We collected data from professional tournaments
of a popular first-person shooter game {\itshape Counter-Strike: Global
Offensive (CS:GO)} and perform two natural experiments. We found that the
player's performance was inversely correlated with the number of teams a player
had joined. After a player switched to a new team, both the individual and the
collective performance dropped initially, and then slowly recovered. The
findings in this study can provide insights for understanding group dynamics in
eSports team play and eventually emphasize the importance of team cohesion in
facilitating team collaboration, coordination, and knowledge sharing in
teamwork in general.
Related papers
- All by Myself: Learning Individualized Competitive Behaviour with a
Contrastive Reinforcement Learning optimization [57.615269148301515]
In a competitive game scenario, a set of agents have to learn decisions that maximize their goals and minimize their adversaries' goals at the same time.
We propose a novel model composed of three neural layers that learn a representation of a competitive game, learn how to map the strategy of specific opponents, and how to disrupt them.
Our experiments demonstrate that our model achieves better performance when playing against offline, online, and competitive-specific models, in particular when playing against the same opponent multiple times.
arXiv Detail & Related papers (2023-10-02T08:11:07Z) - Understanding why shooters shoot -- An AI-powered engine for basketball
performance profiling [70.54015529131325]
Basketball is dictated by many variables, such as playstyle and game dynamics.
It is crucial that the performance profiles can reflect the diverse playstyles.
We present a tool that can visualize player performance profiles in a timely manner.
arXiv Detail & Related papers (2023-03-17T01:13:18Z) - Informational Diversity and Affinity Bias in Team Growth Dynamics [6.729250803621849]
We show that the benefits of informational diversity are in tension with affinity bias.
Our results formalize a fundamental limitation of utility-based motivations to drive informational diversity.
arXiv Detail & Related papers (2023-01-28T05:02:40Z) - Group Activity Recognition in Basketball Tracking Data -- Neural
Embeddings in Team Sports (NETS) [10.259254824702554]
We propose a novel deep learning approach for group activity recognition (GAR) in team sports called NETS.
We used a large tracking data set from 632 NBA games to evaluate our approach.
The results show that NETS is capable of learning group activities with high accuracy, and that self- and weak-supervised training in NETS have a positive impact on GAR accuracy.
arXiv Detail & Related papers (2022-08-31T01:22:38Z) - Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
We propose a system that detects colluding behaviors in team-based multiplayer games.
The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns.
We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
arXiv Detail & Related papers (2022-03-10T02:37:39Z) - Team Power and Hierarchy: Understanding Team Success [11.09080707714613]
This research examines in depth the relationships between team power and team success in the field of Computer Science.
By analyzing 4,106,995 CS teams, we find that high power teams with flat structure have the best performance.
On the contrary, low-power teams with hierarchical structure is a facilitator of team performance.
arXiv Detail & Related papers (2021-08-09T15:10:58Z) - From Motor Control to Team Play in Simulated Humanoid Football [56.86144022071756]
We train teams of physically simulated humanoid avatars to play football in a realistic virtual environment.
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements.
They then acquire mid-level football skills such as dribbling and shooting.
Finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds.
arXiv Detail & Related papers (2021-05-25T20:17:10Z) - Who/What is My Teammate? Team Composition Considerations in Human-AI
Teaming [1.3477333339913569]
This paper investigates essential aspects of human-AI teaming such as team performance, team situation awareness, and perceived team cognition.
Perceived team cognition was highest in human-only teams, with mixed composition teams reporting perceived team cognition 58% below the all-human teams.
arXiv Detail & Related papers (2021-05-23T19:06:18Z) - Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team
Composition [88.26752130107259]
In real-world multiagent systems, agents with different capabilities may join or leave without altering the team's overarching goals.
We propose COPA, a coach-player framework to tackle this problem.
We 1) adopt the attention mechanism for both the coach and the players; 2) propose a variational objective to regularize learning; and 3) design an adaptive communication method to let the coach decide when to communicate with the players.
arXiv Detail & Related papers (2021-05-18T17:27:37Z) - Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions [103.3630903577951]
We use cooperative game theory to study teams of artificial RL agents as well as real world teams from professional sports.
We introduce a parametric model called cooperative game abstractions (CGAs) for estimating CFs from data.
We provide identification results and sample bounds complexity for CGA models as well as error bounds in the estimation of the Shapley Value using CGAs.
arXiv Detail & Related papers (2020-06-16T22:03:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.