runexp-package {runexp} | R Documentation |
Implements two methods of estimating runs scored in a softball scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical distribution using multinomial random simulation. Scores are based on player-specific input probabilities (out, single, double, triple, walk, and homerun). Optional inputs include probability of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether a player is "fast" (e.g. the player could stretch home). These probabilities may be calculated from common player statistics that are publicly available on team's webpages. Scores are evaluated based on a nine-player lineup and may be used to compare lineups, evaluate base scenarios, and compare the offensive potential of individual players. Manuscript forthcoming. See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for implementation of discrete Markov chains.
chain
: calculates run expectancy using discrete Markov chains
sim
: estimates run expectancy using multinomial simulation
plot.chain
: S3 method for plotting chain
output objects
prob_calc
: calculates player probabilities from commonly
available stats
scrape
: scrapes player statistics from a given URL
wku_stats
: player statistics for the 2013 Western Kentucky
University softball team
wku_probs
: calculated player probabilities for the 2013
Western Kentucky University softball team
Annie Sauer anniees@vt.edu
Sierra Merkes smerkes@vt.edu
B. Bukiet, E. R. Harold, and J. L. Palacios, “A Markov Chain Approach to Baseball,” Operations Research 45, 14–23 (1997).
# see "?scrape", "?prob_calc", "?chain" and "?sim" for relevant examples