In September, an old man’s fancy turns to the Greatest American Sport – greatest, that is, if you’re addicted to statistics. Mavens of Major League Baseball are poring over the numbers and chicken entrails to predict the playoffs.
How to size up hitters? The popular yardstick is the batting average – the share of times at bat that the player gets a hit, discounting an error or a fielder’s choice (when the other team lets the batter go to first while it throws out another player). The batting average is normally about one in four. As I write, the hottest hand is attached to Yordan Alvarez, designated hitter for Houston, who is merrily clicking along at .312. The Astros aren’t complaining: They’re leading the American League West, with 75 games won and 72 lost.
The batting average is easy to understand, but it misses the point. To win the game, you must cross home base more often than the rival. First base is just the first step.
What other yardsticks can we use? Well, the number of home runs is spectacular – Kyle Schwarber, a designated hitter for Philadelphia, has hit 44 so far, although the Phillies could use a few more: With a record of 82-65, they’re only second in the National League East, 4 games behind the Atlanta Braves. Maybe that’s because this statistic counts only some of the runs that the batter has set into motion. Even his single can be batted in later. So we should tally all his hits – singles, doubles, triples, and home runs, giving more weight to the hit that is closer to home, since it is more likely to become a run. The slugging percentage fills the bill. It rewards a point for a single, two points for a double, three points for a triple, and four points for a home run. Thus weighted, the sum of hits can be compared to the number of at bats. In short, the slugging percentage is the hitter’s number of bases per at-bat. Alvarez of the Astros is slugging away this season at .593, or nearly three-fifths of a base per at-bat.
Slugger’s choice
Already you see the problem. In reality, the impact on the team’s score of a homer rather than a triple is greater than the impact of a double rather than a single. And yet the slugging percentage awards just one more point in either case. It also ignores walks and pitches that hit the batter, although these put him on base just as surely as a single.
Let’s add to the brew the on-base percentage. This is how often the player gets to base in a typical at-bat. Hits, walks, beanies, whatever works. Houston’s Alvarez sizzles at .428; 43% of his at-bats wind up on a base. As always, we will exclude base trips due to an error or a fielder’s choice. The idea is to gauge the batter’s skill, not his luck. (So why do we count beanies? Search me.) However, the on-base percentage strangely weights all base trips equally. A homer is worth no more than a single.
Back to the drawing board. Since the slugging percentage and the on-base percentage reflect different aspects of the batter’s performance, let’s add them together. That’s the On-Base Plus Slugging statistic. King of the stat is Babe Ruth, with a career value of 1.164. Breathing down his neck are Ted Williams (1.116) of the Boston Red Sox (the Yanks’ historical nemesis, though not this year, yet) and Damn Yankee Lou Gehrig (1.079). The OPS may be the favorite batting statistic of aficionados of inside baseball. Unfortunately, just adding two statistics together makes no sense here. Why should the on-base percentage get the same weight as the slugging percentage? And what does the sum really mean?
OMG, another statistic
Maybe we should take another whack at the question. Our meta is a statistic that shows how much a batter contributes to the team’s runs. It considers two possibilities: He batted in runs himself; or he will be batted in later in the inning. Let’s look at the formula, then demystify it:
Chance of Runs = [Player’s runs batted in / Player’s at-bats] + [Team runs batted in / Team at-bats] * Player’s slugging percentage
Chance of Runs is the probability that the player generates a run. On the right-hand side of the equation, the first term is the probability that the player bats in a run. The second term is the probability that the player himself is batted in. This second term is the number of bases that the player reaches in a typical at bat, times the probability that someone on the team will bat him in, in a typical at bat. In this first pass at the problem, I have not considered stolen bases. In the next millennium, maybe. Neither does the formula consider walks or beanies; it focuses on what the player achieves by his own bootstraps.
The table below lists the top 25 players in Major League Baseball in the current season up to September 7, using MLB’s official statistics. The standouts are Alvarez of the Astros and Luis Garcia, first baseman of the New York Yankees. But Pete Crow-Armstrong, center fielder for the Chicago Cubs, C.J. Abrams, shortstop of the Washington Nationals, and Sal Stewart, first baseman of the Cincinnati Reds, also exceeded a chance of one in four of sparking a run in an at-bat. All 25 players exceeded a chance of one in five of an eventual run per in a typical at-bat.
Of the 25 players, 9 are first basemen. This may reflect the fact that first base is one of the easier infield positions, leaving the player free time to hone his batting chops. On the other hand, shortstop may be the most demanding position on the diamond, and only two shortstops made the list of the top 25 hitters. CJ, of the bottom-feeding Nats, really is a magician.
The table also suggests that home runs are not the alpha and omega of baseball. Schwarber, king of homers, ranks only 11th on the Chance statistic. – Leon Taylor, Seymour, Indiana tayloralmaty@gmail.com
Rank Player Team Chance Position
1 Yordan Alvarez HOU 0.263 DH
2 Luis Garcia NYY 0.262 1B
3 Pete Crow-Armstrong CHC 0.258 CF
4 CJ Abrams WASH 0.253 SS
5 Sal Stewart CINCY 0.252 1B
6 Willson Contreras BOS 0.247 1B
7 Jordan Walker STL 0.245 RF
8 Ben Rice NYY 0.240 DH
9 Pete Alonso BALT 0.240 1B
10 James Wood WASH 0.238 RF
11 Kyle Schwarber PHI 0.237 DH
12 Hunter Goodman COL 0.236 C
13 Junior Caminero TB 0.235 3B
14 Shohei Ohtani LAD 0.231 1B
15 Rafael Devers SF 0.231 1B
16 Max Muncy LAD 0.227 3B
17 Muneta Murakami CWS 0.224 1B
18 Bryce Harper PHI 0.223 1B
19 Brandon Lowe PIT 0.223 2B
20 Dilson Dingler DET 0.220 C
21 Colson Montgomery CWS 0.220 SS
22 Miguel Vargas CWS 0.216 3B
23 Kazuma Okamoto TOR 0.212 3B
24 Matt Olson ATL 0.208 1B
25 Manny Machado SD 0.202 3B
