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Understanding Advanced Baseball Stats: Hitting

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Understanding Advanced Baseball Stats: Hitting Understanding Advanced Baseball Stats: Hitting “Baseball is like church. Many attend few understand.” ~ Leo Durocher Durocher, a 17-year major league vet and Hall of Fame manager, sums up the game of baseball quite brilliantly in the above quote, and it’s pretty ridiculous how much fans really don’t understand about the game of baseball that they watch so much. This holds especially true when you start talking about baseball stats. Sure, most people can tell you what a home run is and that batting average is important, but once you get past the basic stats, the rest is really uncharted territory for most fans. But fear not! This is your crash course in advanced baseball stats, explained in plain English, so that even the most rudimentary of fans can become knowledgeable in the mysterious world of baseball analytics, or sabermetrics as it is called in the industry. Because there are so many different stats that can be covered, I’m just going to touch on the hitting stats in this article and we can save the pitching ones for another piece. So without further ado – baseball stats! The Slash Line The baseball “slash line” typically looks like three different numbers rounded to the thousandth decimal place that are separated by forward slashes (hence the name). We’ll use Mike Trout‘s 2014 slash line as an example; this is what a typical slash line looks like: .287/.377/.561 The first of those numbers represents batting average. While most fans know about this stat, I’ll touch on it briefly just to make sure that I have all of my bases covered (baseball pun intended). Batting average is calculated by dividing a player’s total number of hits by their total number of at bats, which gives you a number that tells you how often (on average) that player gets a hit. If you take a batting average and multiply it by 100 (or slide the decimal point over two spots to the right), it will give you a raw percentage of how often a player gets a hit. So using Mike Trout’s batting average as an example, he accumulated 172 hits in 602 at bats last year for a batting average of .287 (172/602). Multiplying .287 by 100 gives you 28.7 which tells you that in 2014, Mike Trout averaged a hit in 28.7% of his at bats. Batting Average in Context League Average Best in 2014 Worst in 2014 .251 .341 .196 The second number in a slash line represents on base percentage. This is calculated by dividing the total times a player gets on base (hits, walks, and hit-by-pitch) by a player’s total number of eligible at bats, essentially all trips to the plate minus events outside of the batters control, like reaching on error and hitting into a fielder’s choice). These “eligible at bats” are calculated by adding regular at bats with the total number of times walked, hit-by-pitch, and hit into a sacrifice fly. That gives you the following formula to calculate on-base percentage, or OBP for short. Just like with batting average, OBP can be easily turned into a raw percentage by multiplying it by 100. Going back to Mike Trout’s slash, his OBP of .377 means that in 2014, he got on base an average of 37.7% of the time. On Base Percentage in Context League Average Best in 2014 Worst in 2014 .314 .410 .256 The third and final number in a slash line represents slugging percentage. This number is very similar to batting average, but instead of treating all hits as equals, it weighs each type of hit according to its significance. Slugging percentage (or SLG) is calculated by adding singles, 2 X doubles, 3 X triples, and 4 X home runs all divided by at bats. Another way of looking at it is total bases divided by at bats. Here is the official formula that is used: The main application of slugging percentage is to go beyond just being able to tell how good a player is at getting hits, but how good they are at getting quality hits. For example, Robinson Cano and Andrew McCutchen both had a batting average of .314 last year; however Cano slugged just .454 opposed to McCutchen who finished with a .542 mark. While both players got hits just as often, McCutchen got the more valuable kinds of hits more often (he had more doubles, triples, and homers than Cano), so he was the better hitter in 2014. Slugging Percentage in Context League Average Best in 2014 Worst in 2014 .386 .581 .300 .
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