Baseball Scoreboard, Part 2

Scoreboard at Chase Field, Phoenix, A

In the previous discussion (Part 1) on the measures seen at a Baseball park, I covered the pitching metrics seen here fairly heavily. It is possible that hitting metrics are reasonably well-known in many places, but there is at least one here on the scoreboard that some explanation may be required.

The Triple Slash Line

Review of the “Familiar” hitting statistics would start with what is sometimes known as the “triple slash line“. This is simply three statistics that are frequently seen shown in order separated by slashes, like this: AVG/OBP/SLG. This refers to, in order, Batting Average, On-Base Percentage, and Slugging percentage. The Batting Average definition is the percentage of at-bats ending in a hit. An at-bat is defined as a plate appearance that ends in an out (excluding sacrifice flies), a hit, a fielder’s choice, or an error. For years, batting average was the preferred statistic for comparing player performance, but in recent years, the other metrics in the triple slash line have increased in prominence due to their impact on scores (and thus, wins). On-Base Percentage is more simply defined… it is the percentage of plate appearances where a batter reaches safely (could be a hit, walk, or getting hit by a pitch), excluding reaching by error, fielder’s choice, or a dropped third strike. This metric goes back to the Hall of Fame manager of the Brooklyn Dodgers, Branch Rickey, who is still beloved for his innovations in baseball (including signing the great Jackie Robinson and breaking baseball’s color barrier). One of the breakthroughs of the Oakland General Manager, Billy Beane, that became famous in the movie “Moneyball” was a stronger reliance on OBP when signing free agents. FInally, Slugging Percentage is a metric designed to give weight to a batter’s power. The formula is (#Singles + 2*#Doubles + 3*#Triples + 4*#Home Runs)/Plate Appearances. This makes slugging percentage useful, but not necessarily perfectly correlated with runs and therefore wins.

As an example of how the triple slash line can aid in evaluating player value, consider these two players (2024 stats as of 6/20/2024).

Aaron Judge (Center Field, NY Yankees): .303/.429/.697

William Contreras (Catcher, Milwaukee): .304/.364/.461

Though these two players (both having very nice seasons) have almost identical batting averages, that doesn’t tell the full story. Aaron Judge has batted in 66 runs this season whereas Contreras has only batted in 48 (on very similar numbers of games played). Judge has 27 home runs to Contreras’ 9. Amazingly, Judge has been walked 30 more times (57 to 27) than Contreras. Obviously this means that in walks alone, Judge has had 30 more scoring opportunities than Contreras. This has translated to Judge scoring 5 more runs this season. But this is at the cost of 20 more strikeouts for Judge. Lots to think about! First, let’s discuss the impact of RBI and HR to wins .

The RBI, short for Runs Batted in, has always been seen as a fairly critical metric in baseball, as it recognizes a hitter’s role in a run being scored for their team. It can result from a hit, a sacrifice fly, or even a walk, but not an error. In a sense it is a really valuable metric because it shows impact on the most important measure, the runs a team scores in a game. In another sense, one may over-reach when comparing players by their RBI accomplishments, because a player who is preceded in the batting order by a player with a stratospheric on-base percentage has a much higher chance of having a hit bat in a run. So RBI isn’t comparing apples and apples. There is a big controversy over the RBI metric amongst baseball nerds due to this. If you want to go deep down this rabbit hole, here is a good article from Bleacher Report back in 2012.

The appearance is that more home runs lead to a greater number of wins for one’s team. The home run (especially one that ends the game!) is exciting and draws fans more than anything else. The modern era of baseball is often referred to as the “Long Ball Era” due to the prevalence of home runs in the game. A method to identify the value of the home run called regression shows that home runs tend to be highly correlated with win percentages. Conceding that home runs are correlated with wins, the next question would be if home runs CAUSE wins. These are two very different things. Ice Cream sales are highly correlated with higher temperatures, but we cannot say that the temperatures cause the sales. The answer to the question about home runs causing wins is a hard one, and there are plenty of scientific papers analyzing this (and doctoral dissertations!). What seems obvious is that teams value the home run highly — even in the face of the higher numbers of strikeouts that power hitters tend to rack up. One thing that we know, though, is that teams express value through the salary they give a player. In this respect, Aaron Judge stands out with his $40M annual salary compared to the $760K that the Brewers are paying Mr. Contreras! (I think he’ll be getting a raise after this season!) Here’s where I found these salaries

The Mystery Metric, OPS

All of this builds up to the final metric on the scoreboard that is less known, OPS. This stands for “On-base plus Slugging” and is actually a combination of two metrics from the triple slash, OBP and SLG. They’re just simply added together. I suppose this metric saves fans time (or the mathematical embarrassment) of adding the last two numbers in the triple slash together. The intent of the OPS is to provide a view into overall effectiveness of a hitter and their potential value for scoring runs. The historical record for OPS was rung up by Babe Ruth (1.16), followed closely by Ted Williams and Lou Gehrig. So clearly it is a measure of the historical greatness of a player. By the way, keep an eye on Aaron Judge’s OPS in 2024 (currently at 1.126), as he is threatening the Babe’s record!

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