Add to ORKGIn this work we confirm a Markov chain model of baseball for 2013 Major League Baseball batting data. We describe the transition matrices for individual player data and their use in generating single and nine-inning run distributions for a given lineup. The run distribution is used to calculate the expected number of runs produced by a lineup over nine innings. We discuss batting order optimization heuristics to avoid computation of distributions for the 9! = 362, 880 distinct lineups for 9 players. Finally, we describe an implementation of the algorithms and review their performance against actual game data
The stateful nature of baseball has made it a prime candidate for exploring the topics of planning a...
This manuscript details the implementation and validation of an open source probabilistic baseball e...
In this paper, we will discuss a method of building a predictive model for Major League Baseball Gam...
In this report, we present a Markov chain model for predicting the scores and the winning team of Ma...
The applications of Markov chains span a wide range of fields to which models have been designed and...
There are two fundamental notions which justify the use of Markov chains in the analysis of baseball...
Baseball is one of the most statistically analyzed sports, simply due to the substantial amount of d...
A Markov chain with a finite number of states is a probabilistic experiment where, if Xt is the outc...
There are two fundamental notions which justify the use of Markov chains in the analysis of baseball...
In this paper, we look to answer specific questions about the game of baseball in the MLB. These que...
Baseball is a very large industry that influences the lives and financial assets of millions of peop...
Abstract A baseball game between teams consisting of non-identical players is modeled using a Markov...
In previous studies for analyzing the batting order of baseball games, the order is evaluated by its...
We propose a Markov chain model of a best-of-7 game playoff series that involves game-togame depende...
We develop Bayesian techniques for modelling the evolution of entire distributions over time and app...
The stateful nature of baseball has made it a prime candidate for exploring the topics of planning a...
This manuscript details the implementation and validation of an open source probabilistic baseball e...
In this paper, we will discuss a method of building a predictive model for Major League Baseball Gam...
In this report, we present a Markov chain model for predicting the scores and the winning team of Ma...
The applications of Markov chains span a wide range of fields to which models have been designed and...
There are two fundamental notions which justify the use of Markov chains in the analysis of baseball...
Baseball is one of the most statistically analyzed sports, simply due to the substantial amount of d...
A Markov chain with a finite number of states is a probabilistic experiment where, if Xt is the outc...
There are two fundamental notions which justify the use of Markov chains in the analysis of baseball...
In this paper, we look to answer specific questions about the game of baseball in the MLB. These que...
Baseball is a very large industry that influences the lives and financial assets of millions of peop...
Abstract A baseball game between teams consisting of non-identical players is modeled using a Markov...
In previous studies for analyzing the batting order of baseball games, the order is evaluated by its...
We propose a Markov chain model of a best-of-7 game playoff series that involves game-togame depende...
We develop Bayesian techniques for modelling the evolution of entire distributions over time and app...
The stateful nature of baseball has made it a prime candidate for exploring the topics of planning a...
This manuscript details the implementation and validation of an open source probabilistic baseball e...
In this paper, we will discuss a method of building a predictive model for Major League Baseball Gam...