Related: MLB Runs Prevented Spreadsheet (May 31), by Baseball Prospectus Milwaukee
https://docs.google.com/spreadsheets/d/1VpFojsjq2PZMfcHQmTvXxGeMxirSqyMACnKQ7oxjx3M/edit?usp=sharing

Batting Inefficiencies

For the season, however, a few nearly inexplicable inefficiencies exist among Brewers batting splits. Overall, the offense remains moderately below average, but the batting performances by position are worse than those assessed by spot in the order. This, on the whole, is a good thing, as it suggests that even though the club is not hitting well, Manager Craig Counsell is using the batting order to create efficient production.

Yet, the production does not occur at the positions in the order one would expect. The following table uses “RRBI,” my favorite “quick and dirty” at-a-glance statistic that assesses the harmonic mean between Runs Scored and Runs Batted In (to very quickly assess team run distribution within their specific environment), and simple OPS. Both statistics are corrected for 2018 National League and multi-year park factor (Baseball Reference). While RBI is not necessarily a valuable stat in terms of predictive function, or even a descriptive metric of actual value, taking the harmonic mean between R and RBI allows one to understand the development of run production throughout a batting order in terms that are scaled to the team. Think of “RRBI” as a statistic for “uneven development” translated into baseball terms: uneven terrain between expected value, peripheral and predictive metrics, and actual run distribution matters in the context of attempting to win baseball games by scoring runs.

By isolating individual players, instead of looking at each position and batting order on a teamwide scale, two key inefficiencies emerge: First, the time-share in the outfield between Ryan Braun, Lorenzo Cain, Domingo Santana, and Christian Yelich appears to be yielding inefficient results; Second, the frigid starts for many Brewers depth players created a wider chasm between “starting” and “bench” players than would have been expected. One might raise a third issue with batting Lorenzo Cain in the lead-off position, as although the center fielder’s performance is fantastic, the lead-off spot typically saps RBI opportunities and therefore may unduly discredit Cain for aspects of his run performance.

What is interesting to note here is that some of the coldest bats in the order are not hurting the team as much as fans might typically expect. For example, the slow start by Santana and the veritable black hole that is the sixth batting order spot have hurt the Brewers production much more so than Orlando Arcia (who actually gains “efficiency” by batting eighth; by position, his bat is second worst, but the club gained almost two runs by batting Arcia, Saladino, and Sogard eighth). Blending position and batting order strength against the National League expectations, however, a sample “maximum efficiency” batting order does not appear feasible whatsoever:

The Inevitable Pitching Correction?

In terms of pitching, the staff is much less puzzling than the bats, which seem to be a group prone to wild variations of extreme production. Available for sharing above, the “Runs Prevented” spreadsheet showcases that the Brewers bullpen is so elite that Jeremy Jeffress and Josh Hader rank in the Top 25 for average runs prevented among all pitchers (not simply relief pitchers). As mentioned in my first Runs Prevented Ranking post, however, the direction of change expected for Brewers pitchers suggests that this performance may not continue overall.

What is intriguing is that when one aggregates DRA Runs Prevented by team, the major contenders in the NL Central are each due rather large corrections. The Cardinals, Brewers, and Cubs are each “off course” by more than 30 Runs Prevented, according to DRA metrics, which raises an interesting question about pitching staff construction.

While the Brewers’ main concerns are in the starting rotation, any course correction for the Cardinals suggests that the bullpen is of concern. For the Cubs, the rotation is due a course correction, but the bullpen is also the major concern for the Lakeview Nine.

The Brewers’ pitching situation is complicated by the club’s elite fielding performance. According to Baseball Prospectus Defensive Efficiency metrics, the Brewers’ flyball defense is third best in the MLB, while their groundball defense is seventh best. Perhaps most importantly, the Brewers have the most efficient defense in terms of converting line drives into outs, as well. So while DRA suggests that the Brewers starting pitchers might not be expected to be as good as their runs prevented numbers, it is worth questioning whether the strength of the staff lies in their relationship to the fielding performance of the club. In this regard, the Brewers must carefully weigh their midseason moves: while the club may like to upgrade certain positions in terms of offensive performance, an offensive upgrade at the expense of defensive performance could be devastating to the pitching staff. One must also consider the extent to which Chase Anderson and Zach Davies can adjust, or Corbin Burnes, Alec Asher, and Brandon Woodruff could contribute quality innings.

GM David Stearns has his midseason work cut out for him. The GM has already proven his ability to design and construct a systemic ballclub that works almost as an ecosystem might, translated into baseball performance. This systemic design for the club should impact midseason moves. Perhaps this Brewers squad is not one that requires an elite, impact trade, but rather more marginal moves to continually improve the margins of the club. After all, with a club that is driven by defensive efficiency and bullpen, the Brewers are already a sufficiently marginal club to begin with: what would the 2018 Brewers look like if they lose their balance at the margins of the roster?

Photo Credit: Benny Sieu, USA Today Sports Images

Years before Deserved Runs Average (DRA), and the expansion of Fielding Independent Pitching (FIP) into popular language, I was personally interested in investigating ways to discuss pitching performance in a language other than Earned Runs Average (ERA). The typically cited shortcomings with ERA are its dependence on the official scorer’s discretion (due to errors), and its dependence on defensive ability. Another more straight forward issue with ERA is that it simply does not equal Runs Scored / Runs Allowed (RS / RA). This is unsatisfactory because RS / RA is the primary statistic in all of baseball: teams win based on outscoring their opponents, plain and simple.

Runs Prevented is a useful stat in order to place pitching performance on the same level as the run environment, or leaguewide RS / RA. In this way, it gets a little bit closer to the language of wins and losses than ERA. Granted, by assessing a pitcher’s runs allowed, the defense is still in the mix, but there’s always going to be a shortcoming with a statistic; in this case, the issues with the official scorer’s discretion is removed.

So Runs Prevented is simply the following:

In a given workload (Innings Pitched), league (Runs Allowed per 9 IP), and park (Park Factor — I typically use Baseball Reference Three-Year Factors), how many runs would a pitcher be expected to allow? How many runs did they actually allow?

These questions can be answered in a really simple table. Here’s the 2017 Brewers, who were quite good:

This table reads as follows: the pitcher is followed by workload (Innings Pitched), actual runs allowed, expected league and park runs allowed, and the difference between the two figures (Expected RA – Actual RA = Runs Prevented). Runs Prevented is the difference between a pitcher’s actual runs allowed and the runs they would have been reasonably expected to allow given their park and league.

Unfortunately, this is quite abstract, but it’s tough to measure pitching quality. Batting quality is a bit more intuitive, I think, because Runs Scored count “upward;” but Runs Allowed should be kept low (theoretically) in order to increase the chances of winning, so “Runs Prevented” do not technically “exist.” If a pitcher works a Complete Game Shutout, that’s an easy way to understand that someone prevented runs, but it’s less useful or intuitive to say that when Chase Anderson or Zach Davies work 6 IP / 2 R, they “prevented” one run. But, in 2017 Miller Park, that’s exactly what a 6 IP / 2 R start was worth compared to the National League and park environment. A 6 IP / 2 R start equals roughly one run prevented.

If you’re interested in the basics, this is a good place to stop. The basic concept of Runs Prevented is to assess a pitcher’s performance, and a team’s performance, against the context of their league and park. The underlying goal, of course, is to assume that if a team can prevent more runs from scoring, they can increase their likliehood of winning.

One advantage of this type of focus on Runs Scored and Runs Allowed is that the stat can easily be expressed across the league in a very straightforward manner. Park-adjusted each 2017 National League team, for example, and ranking them by Runs Prevented shows that (once again) the Brewers were quite strong with their pitching staff:

A benefit of using this simple, straightforward number to express Runs Prevented is that an individual pitcher’s performance can be inserted onto any team. For example, if everything else was held steady on a .500 club, but they added Zach Davies, that club would be likely to improve by at least 9 runs (or approximately one win, holding the approximate scale of 10 Runs = 1 Win). What is especially significant about this stat is that it takes baseball out of the “Replacement Theory”; a “win” is not necessarily a marginal win in the sense that you’re assessing a player against a theoretical minor league replacement level. Instead, you’re judging a player against the park-adjusted league average, which is a much harsher standard than replacement level (in fact, something like Matt Garza’s -13 runs prevented season last year is not yet “replacement level”). So here, adding “one win” is really, really important; it’s quite different than adding one win above replacement (which raises its own questions).

What’s difficult about using Runs Prevented to assess a pitcher’s performance is that there are many contingencies in each league and team (including injuries, free agency and trades, other transactions, promotions, and demotions). This means that there is not necessarily a clear way to track future performance by using Runs Prevented. So, let’s look at shortcomings and contextual issues:

• Runs Prevented is a “one-year” statistic that captures a snapshot of the league run environment at one point in time, with the focus on the distribution of runs prevented across each individual pitcher. But, constructing runs prevented rankings over years has taught me the value in embracing the variance in the game; last season I tracked National League pitchers who worked consecutive years between 2011-2017 and found that the typical pitcher working consecutive years varies by +/- 57.0 IP and +/- 12.1 runs prevented.

• Using this context can also be helpful to understand the league’s pitching environment, especially assessing team’s by rotation spot. For example, the typical MLB team uses approximately 240 innings of -24 Runs Prevented pitchers in their starting rotation, which is quite bad; keeping this type of fact in mind will help Brewers fans understand why the 2018 depth-first rotation is actually quite solid (for they are mitigating this type of issue by seeking quality depth right out of the gate, rather than waiting for replacements to “arise” naturally).

• These are just some of the ways that Runs Prevented can be used. Additionally, I’ve been using it lately to expressed DRA figures on a different scale (i.e., to say whether one’s DRA is better or worse than average in a particular environment, and what that means in terms of improving a team’s RS / RA). Most recently, I conducted this type of analysis to emphasize the strengths of the Brewers’ 2018 rotation.

• One could also construct a FIP Runs Prevented stat as well, by scaling FIP to RA9 rather than ERA, or even a cFIP (a contextual FIP stat) Runs Prevented figure by using cFIP to index RA9. I attempted such a cFIP expression while arguing in favor of the pitching staff during a tough stretch last year.

• These are just some of the ways Runs Prevented can be used. I’m sure there’s even more ways to use it; one could design a theoretical compensation system, or even attempt to track Runs Prevented by other designations (for instance, wouldn’t it be interested to track MLB Runs Prevented based on Prospect Scouting Grades?).

Just remember with Runs Prevented to keep context in mind: there are many shortcomings with the statistic, and it should not be used as a “predictive” metric (but that’s okay; not every stat should be predictive, as descriptive statistics are also very important). By exploring the context of a run environment, and the distribution of pitching success across teams, one can come to understand a lot about roster construction, pitching rotations, and mitigating the circumstances of a 162-game season.