Optimal Team Economic Decisions in Counter-Strike
- URL: http://arxiv.org/abs/2109.12990v1
- Date: Mon, 20 Sep 2021 15:16:36 GMT
- Title: Optimal Team Economic Decisions in Counter-Strike
- Authors: Peter Xenopoulos, Bruno Coelho, Claudio Silva
- Abstract summary: We introduce a game-level win probability model to predict a team's chance of winning a game at the beginning of a given round.
Using our win probability model, we investigate optimal team spending decisions for important game scenarios.
Finally, we introduce a metric, Optimal Spending Error (OSE), to rank teams by how closely their spending decisions follow our predicted optimal spending decisions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The outputs of win probability models are often used to evaluate player
actions. However, in some sports, such as the popular esport Counter-Strike,
there exist important team-level decisions. For example, at the beginning of
each round in a Counter-Strike game, teams decide how much of their in-game
dollars to spend on equipment. Because the dollars are a scarce resource,
different strategies have emerged concerning how teams should spend in
particular situations. To assess team purchasing decisions in-game, we
introduce a game-level win probability model to predict a team's chance of
winning a game at the beginning of a given round. We consider features such as
team scores, equipment, money, and spending decisions. Using our win
probability model, we investigate optimal team spending decisions for important
game scenarios. We identify a pattern of sub-optimal decision-making for CSGO
teams. Finally, we introduce a metric, Optimal Spending Error (OSE), to rank
teams by how closely their spending decisions follow our predicted optimal
spending decisions.
Related papers
- Imperfect-Recall Games: Equilibrium Concepts and Their Complexity [74.01381499760288]
We investigate optimal decision making under imperfect recall, that is, when an agent forgets information it once held before.
In the framework of extensive-form games with imperfect recall, we analyze the computational complexities of finding equilibria in multiplayer settings.
arXiv Detail & Related papers (2024-06-23T00:27:28Z) - 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) - GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win
Prediction in Esports [84.55775845090542]
We propose a semi-supervised win prediction model for esports based on graph convolutional networks.
GCN-WP integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood.
Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL.
arXiv Detail & Related papers (2022-07-26T21:38:07Z) - Sequential Item Recommendation in the MOBA Game Dota 2 [64.8963467704218]
We explore the applicability of Sequential Item Recommendation (SIR) models in the context of purchase recommendations in Dota 2.
Our results show that models that consider the order of purchases are the most effective.
In contrast to other domains, we find RNN-based models to outperform the more recent Transformer-based architectures on Dota-350k.
arXiv Detail & Related papers (2022-01-17T14:19:17Z) - Bandit Modeling of Map Selection in Counter-Strike: Global Offensive [55.41644538483948]
In Counter-Strike: Global Offensive (CSGO) matches, two teams first pick and ban maps, or virtual worlds, to play.
We introduce a contextual bandit framework to tackle the problem of map selection in CSGO and to investigate teams' pick and ban decision-making.
We find that teams have suboptimal map choice policies with respect to both picking and banning.
We also define an approach for rewarding bans, which has not been explored in the bandit setting, and find that incorporating ban rewards improves model performance.
arXiv Detail & Related papers (2021-06-14T23:47:36Z) - An analysis of Reinforcement Learning applied to Coach task in IEEE Very
Small Size Soccer [2.5400028272658144]
This paper proposes an end-to-end approach for the coaching task based on Reinforcement Learning (RL)
We trained two RL policies against three different teams in a simulated environment.
Our results were assessed against one of the top teams of the VSSS league.
arXiv Detail & Related papers (2020-11-23T23:10:06Z) - Valuing Player Actions in Counter-Strike: Global Offensive [4.621805808537653]
Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence.
We also provide use cases demonstrating high-impact play identification and uncertainty estimation.
arXiv Detail & Related papers (2020-11-02T21:11:14Z) - CRICTRS: Embeddings based Statistical and Semi Supervised Cricket Team
Recommendation System [6.628230604022489]
We propose a semi-supervised statistical approach to build a team recommendation system for cricket.
We design a qualitative and quantitative rating system which considers the strength of opposition also for evaluating player performance.
We also embark on a critical aspect of team composition, which includes the number of batsmen and bowlers in the team.
arXiv Detail & Related papers (2020-10-26T15:35:44Z) - Faster Algorithms for Optimal Ex-Ante Coordinated Collusive Strategies
in Extensive-Form Zero-Sum Games [123.76716667704625]
We focus on the problem of finding an optimal strategy for a team of two players that faces an opponent in an imperfect-information zero-sum extensive-form game.
In that setting, it is known that the best the team can do is sample a profile of potentially randomized strategies (one per player) from a joint (a.k.a. correlated) probability distribution at the beginning of the game.
We provide an algorithm that computes such an optimal distribution by only using profiles where only one of the team members gets to randomize in each profile.
arXiv Detail & Related papers (2020-09-21T17:51:57Z) - Optimising Game Tactics for Football [18.135001427294032]
We present a novel approach to optimise tactical and strategic decision making in football (soccer)
We model the game of football as a multi-stage game which is made up from a Bayesian game to model the pre-match decisions and the game to model the in-match state transitions and decisions.
Building upon this, we develop algorithms to optimise team formation and in-game tactics with different objectives.
arXiv Detail & Related papers (2020-03-23T14:24:45Z)
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.