In bayesball/BayesTestStreak: Testing Streakiness in Binary Sequences by Bayes Factors

knitr::opts_chunk$set(fig.width=6, fig.height=4, fig.path='Figs/',
                      echo=TRUE, warning=FALSE, message=FALSE)

This package provides some tools for learning about the streakiness patterns in a binary (0 and 1) sequence.

Mike Trout

We illustrate this by looking at streaky patterns of the hitter Mike Trout for the 2016 season.

First load the package.

library(BayesTestStreak)

Getting the data

The package contains Retrosheet play-by-play data for the 2016 season, so we can explore streaky patterns for any player that played in this season.

The find_id function will find the Retrosheet player id for a given player.

trout_id <- find_id("Mike Trout")

The function streak_data will find the 0/1 streak data. The inputs are the Retrosheet data (the data frame pbp2016 is the play-by-play data that is preloaded), the player id, and the definition of success using the EVENT_CD variable. Here values of EVENT_CD = 20 through 23 correspond to a hit. The output is a vector of 0's and 1's where 0 is an out and 1 is a hit.

(y_PA <- streak_data(pbp2016, trout_id, 20:23))

This function will accept the non-numeric codes "H", "SO", or "HR", so the following inputs will produce the same data.

y_PA <- streak_data(pbp2016, trout_id, "H")

Maybe you want to consider hits/no hits only among the official at-bats (instead of plate appearances). Then you use the AB = TRUE option.

y_AB <- streak_data(pbp2016, trout_id, "H", AB=TRUE)

What can you do with this data?

Moving average plot

One can construct a moving average graph by the function mavg_plot -- the input is the vector of 0-1 data and the value of the width of the window where you are computing the average.

mavg_plot(y_AB, width=40)

The function find.spacings computes the lengths of the gaps between consecutive successes.

(s <- find.spacings(y_AB)$y)

Geometric plot

The geometric.plot function constructs a geometric probability plot -- if the points line along the line, a geometric constant probability model is suitable.

geometric.plot(s)

Permutation test

The permutation.test function tests (by simulation) the hypothesis that all possible ordering of the 0's and 1's are equally likely.

permutation.test(y_AB)

Computing a Bayes factor

The bayes.factor.function function implements a Bayes factor for testing for streakiness. The graph plots the log Bayes factor against the parameter log K. (See my paper for details.)

OUT <- bayes.factor.function(y_AB)
ggplot(data.frame(logK=OUT$log.K, logBF=OUT$log.BF), 
       aes(logK, logBF)) + geom_line()

bayesball/BayesTestStreak documentation built on Dec. 15, 2022, 11:36 a.m.