The Experts below are selected from a list of 6165 Experts worldwide ranked by ideXlab platform
Laura Pontiggia - One of the best experts on this subject based on the ideXlab platform.
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nonconstant sum red and black games with bet dependent Win Probability function
Journal of Applied Probability, 2007Co-Authors: Laura PontiggiaAbstract:In this paper we investigate a class of N-person non-constant sum redand-black games with bet-dependent Win Probability functions. We assume that N players and a gambling house are engaged in a game played in stages, where the player’s Probability of Winning at each stage is a function f of the ratio of his bet to the sum of all players’ bets. However, at each stage of the game there is a positive Probability that all players lose and the gambling house Wins their bets. We prove that if the Win Probability function is super-additive and it satises f(s)f(t) f(st), then a bold strategy is optimal for all players.
Shoouren Hsiau - One of the best experts on this subject based on the ideXlab platform.
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two person red and black games with bet dependent Win Probability functions
Journal of Applied Probability, 2006Co-Authors: Mayru Chen, Shoouren HsiauAbstract:In this paper a two-person red-and-black game is investigated. We suppose that, at every stage of the game, player Fs Win Probability, /, is a function of the ratio of his bet to the sum of both players' bets. Two results are given: (i) if / is convex then a bold strategy is optimal for player I when player II plays timidly; and (ii) if / satisfies f(s)f(t) < f(st) then a timid strategy is optimal for player II when player I plays boldly. These two results extend two formulations of red-and-black games proposed by Pontiggia (2005), and also provide a sufficient condition to ensure that the profile (bold, timid) is the unique Nash equilibrium for players I and II. Finally, we give a counterexample to Pontiggia's conjecture about a proportional N-person red-and-black game.
Mayru Chen - One of the best experts on this subject based on the ideXlab platform.
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two person red and black games with bet dependent Win Probability functions
Journal of Applied Probability, 2006Co-Authors: Mayru Chen, Shoouren HsiauAbstract:In this paper a two-person red-and-black game is investigated. We suppose that, at every stage of the game, player Fs Win Probability, /, is a function of the ratio of his bet to the sum of both players' bets. Two results are given: (i) if / is convex then a bold strategy is optimal for player I when player II plays timidly; and (ii) if / satisfies f(s)f(t) < f(st) then a timid strategy is optimal for player II when player I plays boldly. These two results extend two formulations of red-and-black games proposed by Pontiggia (2005), and also provide a sufficient condition to ensure that the profile (bold, timid) is the unique Nash equilibrium for players I and II. Finally, we give a counterexample to Pontiggia's conjecture about a proportional N-person red-and-black game.
Jim Albert - One of the best experts on this subject based on the ideXlab platform.
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player evaluation using Win probabilities in sports competitions
Wiley Interdisciplinary Reviews: Computational Statistics, 2015Co-Authors: Jim AlbertAbstract:One type of sports competition between two teams consists of a sequence of plays where the Winner is the team with the most points scored. A Win Probability is a calculation during the game which expresses the likelihood of the home team Winning the contest. Win probabilities are helpful in understanding the ebb and flow of the game, and in assessing which plays had the largest impact on the outcome of the contest. This article reviews the definition, construction, and application of Win probabilities in a number of sports including the use of this concept in measuring the contribution of individual players. WIREs Comput Stat 2015, 7:316–325. doi: 10.1002/wics.1358 For further resources related to this article, please visit the WIREs website.
Jesse Davis - One of the best experts on this subject based on the ideXlab platform.
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who will Win it an in game Win Probability model for football
arXiv: Learning, 2019Co-Authors: Pieter Robberechts, Jan Van Haaren, Jesse DavisAbstract:In-game Win Probability is a statistical metric that provides a sports team's likelihood of Winning at any given point in a game, based on the performance of historical teams in the same situation. In-game Win-Probability models have been extensively studied in baseball, basketball and American football. These models serve as a tool to enhance the fan experience, evaluate in game-decision making and measure the risk-reward balance for coaching decisions. In contrast, they have received less attention in association football, because its low-scoring nature makes it far more challenging to analyze. In this paper, we build an in-game Win Probability model for football. Specifically, we first show that porting existing approaches, both in terms of the predictive models employed and the features considered, does not yield good in-game Win-Probability estimates for football. Second, we introduce our own Bayesian statistical model that utilizes a set of eight variables to predict the running Win, tie and loss probabilities for the home team. We train our model using event data from the last four seasons of the major European football competitions. Our results indicate that our model provides well-calibrated probabilities. Finally, we elaborate on two use cases for our Win Probability metric: enhancing the fan experience and evaluating performance in crucial situations.
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a bayesian approach to in game Win Probability in soccer
arXiv: Learning, 2019Co-Authors: Pieter Robberechts, Jan Van Haaren, Jesse DavisAbstract:In-game Win Probability models, which provide a sports team's likelihood of Winning at each point in a game based on historical observations, are becoming increasingly popular. In baseball, basketball and American football, they have become important tools to enhance fan experience, to evaluate in-game decision-making, and to inform coaching decisions. While equally relevant in soccer, the adoption of these models is held back by technical challenges arising from the low-scoring nature of the sport. In this paper, we introduce an in-game Win Probability model for soccer that addresses the shortcomings of existing models. First, we demonstrate that in-game Win Probability models for other sports struggle to provide accurate estimates for soccer, especially towards the end of a game. Second, we introduce a novel Bayesian statistical framework that estimates running Win, tie and loss probabilities by leveraging a set of contextual game state features. An empirical evaluation on eight seasons of data for the top-five soccer leagues demonstrates that our framework provides well-calibrated probabilities. Furthermore, two use cases show its ability to enhance fan experience and to evaluate performance in crucial game situations.
