Royals Fans, Meet Norichika Aoki

There sure seem to have been a lot of players changing leagues, haven't there? That means that things will be mostly new. Ricky Nolasco, now with the Twins, had been in the National League his entire career. Justin Morneau, now with the Rockies, had been in the American League for all but one month of his career. And now, the Kansas City Royals have landed former Brewers outfielder Norichika Aoki for the endlessly-joked-about-because-of-his-name Will Smith. Other than three starts in Kansas City in 2012 during interleague play (during which, in 14 plate appearances, he got two singles, was hit twice, laid down a bunt, hit a double, and was caught stealing) he's a new name to Kansas City.

What's Good About Him? When the Brewers signed Aoki prior to the 2012 season, the consensus view was that the three-time Japanese League batting champion for the Yakult Swallows was, at age 29, over the hill. He was viewed as a fourth outfielder and played accordingly, starting only three games in April and 15 in May. His hitting and a succession of injuries to Brewers outfielders landed him a regular role that he never relinquished.

In 2013, as the team's right fielder, he led the NL in singles (140), was tenth in hits (171), and seventh in total times on base (237). His .286 batting average and .356 on base percentage compared favorably to the major league averages of .266 and .320 for right fielders. He bats left but actually hit better against left-handed pitchers last year (.781 OPS against lefties, .703 against righties), and he did better against left-handed starters (though not relievers) in 2012 as well. The average major league lefty batter lost 96 points of OPS against lefties last year. Among Brewer regulars, his on base percentage trailed only Ryan Braun and Aramis Ramirez and was 19th among the 64 players who qualified for the NL batting title. He was the toughest player to strike out in the majors. And he played strong defense, finishing sixth in the Fielding Bible Award voting for right fielders. 

What's Not So Good About Him? There's one major weakness and one minor one in Aoki's game. The major one is power. His isolated power, defined as slugging percentage minus batting average (i.e., the amount of his slugging percentage attributable to extra-base hits) was the third-lowest in the NL. His slugging percentage was tenth lowest, boosted by all those singles. He hit just 20 doubles and 8 homers in 2013 compared to 37 and 10 in 2012. He's obviously more of a singles-and-speed guy than a power guy, but that lack of power is a drawback.

The second, smaller issue is that he's gone from being a pretty good basestealer in 2012 (30 stolen, 8 caught, 79% success rate vs. major league average of 74%) to a pretty bad one in 2013 (20 stolen, 12 caught, 63% success rate vs. major league average of 73%). He turns 32 in January, so he's at an age where his speed is more likely to regress than progress.

So What Should Royals Fans Expect? Royals right fielders, primarily David Lough and Jeff Francoeur, were pretty bad last year, compiling a .258/.304/.392 slash line compared to .286/.356/370 for Aoki. Aoki's on-base skills far outweigh the prior players' superior power. Further, Royals leadoff hitters had a .309 on base percentage, sixth-worst in the majors last year, while the Brewers leadoff hitters, primarily Aoki, were third-best. The main goal of a leadoff hitter is to get on base. The Royals were bad at it and Aoki's good at it. So this trade is an upgrade for the Royals in two ways: They'll get more production from the right field position overall, and specifically, Aoki's superior on-base skills fills a major need in the leadoff position. And they'll get a strong defensive player to boot. That far outweighs the loss of all those really bad Men In Black references to the departed reliever.

Why OPS Matters

This one will be short. I've discussed how on base percentage (OBP) is more closely linked to run production than batting average, and how slugging percentage (SLG) is more closely linked to run production than batting average. The conclusion is that if you want to evaluate a batter, his batting average is OK, but his on base percentage and slugging percentage are better indicators of how good he is. The slash line--batting average/on base percentage/slugging percentage, like .319/.391/.534 for Yasiel Puig--combines all three.

But there's an even better measure. The correlation between OBP and runs is .88, which is good. The correlation between SLG and runs is .90, which is better. But if you combine the two, adding on base percentage and slugging percentage, you get a new measure, on base plus slugging, or OPS. The correlation between OPS and runs since 1995 is 0.94. That's really, really high. It means that OPS alone can explain 88% of run scoring.

OPS is not an esoteric stat. Watch ballgames, or read baseball articles, or watch Baseball Tonight, and you'll probably hear OPS mentioned. And when you do, you're hearing a single number that does a great job of describing a batter's skill.

What's a good OPS? There were 24 major leaguers who batted .300 last year. The 24th-ranked OPS was .845, Giancarlo Stanton. So let's say that an .850 OPS is like a .300 batting average.

Why Slugging Percentage Matters

If You Don't Want to Read the Whole Thing: I assume you know what batting average is. Here, I showed that on base percentage has more of an impact on run scoring than does batting average. In this post, I show that slugging percentage is even more predictive. A player's slash line (batting average/on base percentage/slugging percentage, like .280/.350/.445) is an easy way to display all three figures.

But If You Do: I've already written about on base percentage and why it's important, more important that batting average. I'm going to take the same approach in looking at slugging percentage.

Slugging percentage has been around for a long time. I remember reading a piece in a SABR publication about 30 years ago that referred to an article from around 1915 or so. The author of the older article was trying to make a point about Gavvy Cravath, a slugger (given the context of the time) for the Phillies*. He said that batting average is like asking how much money someone has in his or her pocket, and getting a response of "nine coins." It doesn't tell what the coins are worth. Slugging percentage gives you the dollar value. Batting average is hits divided by at-bats. Slugging percentage is total bases divided by at-bats. To calculate total bases, a single's worth one, a double's worth two, a triple's worth three, and a home run's worth four. A triple might not be worth three times as much as single, as batting average suggests, but it's certainly worth more than a single. The major league leaders in slugging percentage last year were Miguel Cabrera (.636), Chris Davis (.634), David Ortiz (.564), Mike Trout (.557), and Paul Goldschmidt (.551). The major league average was .396.

The difference between batting average and slugging percentage is pretty intuitive. Albert Pujols batted .258 in 391 at bats. Placido Polanco batted .260 in 377 at bats. Close, right? But Pujols hit 19 doubles and 17 homers. Polanco had 13 doubles and a homer. That gives Pujols a .437 slugging percentage compared to .302 for Polanco, illustrating who's the better hitter.

How important is slugging percentage? As I did with on base percentage, I'll check the correlation with runs scored. The higher the correlation, the more more slugging percentage is tied to scoring, which is the object of offense. As I pointed out, the correlation between batting average (BA) and runs, using data from every team since 1995, is 0.80, which is pretty good. The correlation between on base percentage (OBP) and runs is better, 0.88. The correlation between slugging percentage (SLG) and runs is higher still, 0.90. Roughly speaking, this means that BA explains about 65% of run scoring, OBP 78%, and SLG 81%.

When talking about OBP, I noted that last year 24 players batted .300 or better. Joey Votto's .491 SLG was 24th. So we'll say a .490 slugging percentage is like a .300 batting average, just as a .370 on base percentage is like hitting .300.

Enter the Slash Line

I hope I've made it clear why on base percentage and slugging percentage are more important measures than batting average. But this doesn't mean batting average is unimportant. A 0.80 correlation's a good one. So analysts talk about a batter's slash line, defined as BA/OBP/SLG. (The slash separates the figures.) Miguel Cabrera's slash line was .348/.442/.636. That's insanely good, as expected, as Cabrera led the majors in all three categories. The major league average slash line last year was .253/.318/.396. 

You can tell a lot from a slash line. Cleveland's catcher/DH/1B Carlos Santana, .268/.377/.455: Great at drawing walks, decent pop, doesn't get a ton of base hits. (Players with a low BA and high OBP usually strike out a lot, as did Santana.) White Sox shortstop Alexei Ramirez, .284/.313/.380: A lot of his value is in his batting average, i.e., he doesn't walk a lot or have a lot of power, given his below-average on base and slugging percentages. 

I'm going to use slash lines going forward (starting with the footnote below). If you forget what they are, click on the Glossary at the top of the page.




*I've seen Cravath listed as the player whose career home run record Babe Ruth topped. That's not true. Ruth hit his 139th career home run in 1921, at which time he vaulted past Roger Connor, a first baseman, primarily for the New York Giants, who hit 138 homers during a 18-year career from 1880 to 1897. Cravath was the 20th century record holder, with 119 between 1908 and 1920. He hit all but two of his home runs for the Phillies, who played at Baker Bowl, a hitter's park that was only 281 feet down the right field line, though with a Green Monster-esque 60 foot fence in right. Cravath, though, was a right-handed hitter. Baseball-reference has split data for Cravath only in the last five years of his career, past his prime, but during that time he hit .304/.392/.547 at home and .254/.351/.386 on the road--basically David Ortiz at home and something like the Cardinals' Jon Jay on the road. 

Why On Base Percentage Matters

If You Don't Want to Read the Whole Thing: I assume you know what batting average is. On base percentage, which measures a batter's ability to get on base via hits, walks, and hit by pitches (hits by pitches?) is more closely related to run scoring than batting average. So when you want to learn how good a hitter is, on base percentage does a better job than batting average.

But If You Do: I haven't been posting anything during the postseason. This is the time of year when you have many ways to enjoy baseball, from nationally televised games to wall-to-wall coverage by major media to Twitter to baseball-specific sites that are absolutely on top of their games. Easy to get lost in that shuffle. My only post has been to lament the demise of small-market teams in the postseason, though I'll concede that a World Series matching the teams with the best regular season records in their respective leagues is aesthetically, if not emotionally, pleasing.

I'm going to use this hiatus to start what I expect will be a series of posts going into the basics of baseball analysis. One of the things I've learned in a long career as a financial analyst is that you've got to constantly question your assumptions. The rules that worked a decade ago may not still be applicable. So let's start questioning.

I'm going to start with on base percentage, or OBP. It measures the frequency with which a batter gets on base. The formula is (hits + walks + hit by pitch) / (at bats + walks + hit by pitch + sacrifice flies). You've seen me use it several times in posts. Why is it important? Or, as the casual fan may ask, why should I care about it when I have batting average?

To partially answer the second question, batting average (BA) is just hits divided by at bats. It measures the percentage of time that a player gets a base hit. A good hitter hits .300. Ty Cobb, Babe Ruth, Ted Williams, Rod Carew, Tony Gwynn...all .300 hitters. A .300 batting average is good. A .200 batting average isn't. So what's wrong with that? Why do I need OBP?

Well, let's look at batting. The object on offense is to score runs. Something that helps you score runs is good. Something that impedes scoring runs is bad. We can measure how good or how bad something is by using a statistic called correlation. Correlation measures how closely related two sets of numbers are. The higher the correlation, the stronger the relationship. The correlation coefficient is the statistic that correlation yields. It runs between 0 (no correlation at all) to 1 (perfect correlation). Once you get above 0.7 or so, you're talking about a pretty decent correlation, in general. (I've put a longer discussion of correlation in the tab Statistics at the top of the blog. You can go there to read it if you want. I figure many of you already know about correlation, and many others of you didn't come here for a math lesson.)

In order to measure how much a statistic like BA or OBP contributes to run production, I ran correlations. I used every season in the three-division era that began in 1995. That works out to 564 team seasons: 28 teams for each of 1995-97, and 30 teams starting in 1998, when Arizona and Florida joined the league.

The correlation coefficient between runs and batting average is 0.80. That's pretty high. What it means is that batting average explains a lot of how runs are scored (technically, about 65%).

Again, that's pretty high correlation. If we're trying to measure how runs are scored, batting average does a nice job, and that's based on 564 datapoints, so it's not random.

The thing is: on base percentage does better. The correlation between runs and OBP is 0.88. That's a good bit higher than for BA. If batting average provides 65% of the explanation of how runs are scored, on base percentage explains 78%. That's a big difference, big enough to make OBP much more useful than BA. Simply stated, on base percentage contributes more to scoring runs than batting average. 

We're talking about teams here, but this relationship applies to individuals as well. Consider, for example, two catchers: The Tigers' Alex Avila and the Angels' Chris Ianetta. They played about the same amount this year (379 plate appearances for Avila, 399 for Ianetta) with similar power (14 doubles, 1 triple, 11 homers for Avila; 15 doubles, no triples, 11 homers for Ianetta). Their batting averages were similar: .227 for Avila, .225 for Ianetta. Identical players? Not at all: Ianetta drew 68 walks and was hit twice, while Avila walked 44 times and was hit once. That difference gives Ianetta a .358 on base percentage, fifth best among the 24 catchers who played in 100 or more games, compared to Avila's .317, which ranks 16th. That makes Ianetta a more valuable offensive performer, given the importance of OBP. 

Or compare two second basemen, the Mets' Daniel Murphy and Tampa Bay's Ben Zobrist. In an almost identical number of plate appearances (697 for Murphy, 698 for Zobrist) they displayed similar power (38 doubles, 4 triples, 13 home runs for Murphy; 36, 3, and 12 for Zobrist) and Murphy had a better batting average, .286-.275. So was Murphy better? Nope. Zobrist got on base an additional 45 times that don't show up in batting average compared to Murphy (72 walks and 7 hit by pitch for Zobrist, 32 and 2 for Murphy). That gives Zobrist a much bigger edge in OBP (.354-.319) compared to Murphy's edge in the less important BA.

As you can tell, the biggest difference between BA and OBP is walks. A player who walks a lot boosts his OBP more than one who walks infrequently. You know that saying from the playground when you were a kid, "a walk's as good as a hit?" From the perspective of OBP (and from the perspective of scoring runs), that's exactly right.

What's a good OBP? This year the major league average OBP was .320 compared to .256 for batting average. The top three were Miguel Cabrera (.442), Joey Votto (.435), and Mike Trout (.432). The bottom three were Alcides Escobar (.259), Darwin Barney (.266), and Adeiny Hechavarria (.267). The 75th percentile was Adam Lind's .357, and the 25th percentile was Ryan Doumit's .314. If we're looking for a rule of thumb, like for a .300 hitter, well, there were 24 hitters who batted .300 or better in 2013. Chris Davis was 24th in OBP with .370. So let's say a .370 on base percentage is like a .300 batting average.

When you're watching the Series, you might see that Mike Napoli hit .259 this season compared to his likely first base counterpart, Matt Adams, who batted .284. Here's two other numbers: Napoli had a .360 on base percentage, while Adams' was .335. So you'll know that Napoli was actually the better offensive performer, and that's before you start comparing beards.