![\"srel_runs\"](\"http:\/\/www.decisionsciencenews.com\/wp-content\/uploads\/2014\/09\/srel_runs.png\")
<\/a><\/p>\nWe have a father-in-law who likes baseball. Occasionally, he asks us to figure out things, which we are more than happy to do. The last request was to figure out:<\/p>\n
If a team scores X runs, what’s the probability it will win the game?<\/em><\/p>\n Luckily, we had the data to solve this problem (as mentioned in past posts<\/a>). Looking back over 44 years of baseball games, we looked at how often a home team scored 1 run, and counted how often the home team won. We then looked at 2, 3, 4 runs, up to 11 runs. We stop at 11 runs because we only wanted to compute relative frequencies when there’s a decent amount of data. In all our analyses here, we cut the x-axis when there are fewer than 500 observations per bin. We analyzed the visiting team’s scores separately, to see the effect of the home team advantage.<\/p>\n The result is shown above. If you consistently score 3-4 runs a game, you’re winning about half the games. It’s simply not good enough. Going from 2 runs a game to 6 runs a game means going from winning 25% of the time to winning 75% of the time–all the difference in the world.<\/p>\n Because we had the data handy, we couldn’t help but looking at the same thing for the other key statistics: hits, walks, and errors. Results below.<\/p>\n Want to play with it yourself? The R<\/a> \/ ggplot2<\/a> code that made this plot is below. ggplot and dplyr are Hadley Wickham<\/a> creations.<\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n