After using OUTS_CT as a number to remove incomplete half innings, we transform it into a category or factor for modeling. We group these data by half inning, sort observations therein by event, remove incomplete half-innings (most typically occurring in bottom of the ninth or greater inning when the home team breaks the tied score), and calculate the difference between the batting team’s score preceeding the event and their score at half-inning’s end. Where GAME_ID is a unique game Id, EVENT_ID is a unique observation (usually at bat) within each game, BAT_EVENT_FL indicates a batting event, INN_CT is the inning number, a BAT_HOME_ID of 0 indicates top of the inning and 1, bottom of the inning, OUTS_CT is the number of outs preceding the observed event (at bat), EVENT_OUTS_CT is the number of outs after the observed at bat, BASE_SCORE_CT are the away and home team scores preceeding the event.Ĭalculate runs scored after every game state of a half inningīefore modeling run expectancy with this data, we “transform” some variables and create new variables. # structure of an R object as a data frame. These variables are structured as follows, # This is simply a convenience function to display the
Select = c(GAME_ID, EVENT_ID, INN_CT, BAT_HOME_ID, OUTS_CT,ĮVENT_OUTS_CT, BASE1_RUN_ID, BASE2_RUN_ID, BASE3_RUN_ID,ĪWAY_SCORE_CT, HOME_SCORE_CT, EVENT_CD, BAT_EVENT_FL)) We begin here with data in hand, loaded in the R object named rs, which we subset below to keep only needed variables. Another source, a little more tedius to obtain, but easier to work with once gathered is available as event files from, and the website provides instructions for gathering that data. The at bat data from Major League Baseball Advanced Media (MLBAM), which can be conveniently “scraped” using the R package PitchRx provides one source. Along with game states, we need to know the batting team’s score before each at bat in each half inning and the score at the half inning’s end.Īt bat information is publicly available from multiple sources. Run expectancy is typically reported over a regular season, so we will focus here on the 2017 MLB season.
To determine run expectancy for differing “game states” in baseball, we need observations of each at bat event over a given time frame. Data and sources for estimating run expectancies
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