Momentum-Controlled and Season-Adjusted Controlled Runs Averages

                There are no current baseball statistics that truly measure a pitcher’s value. There is nothing inherently wrong with the statistics that have stood the test of time. They tell us part of the story. They are comfortable. They are easily understood. The newer sabermetrics that rely on advanced statistics or a derivative understanding of the game’s events are also good indicators of success and failure of particular aspects of baseball. This is exciting for some; unnecessary window dressing for others. They tell us our understanding of the game is evolving—even today. Perhaps most importantly, these new statistical interpretations have come from contemporaries within our lifetime. We are living in a time where we are changing the understanding of baseball for future generations. While the sport may simply be—to paraphrase Leo Durocher—running and throwing and catching and hitting, how we understand baseball is constantly changing.

I ended up developing two related metrics that have changed how I look at pitchers. What I contend is that they will make the game more accessible for you. They will help you evaluate pitchers using new metrics that focus only on what really matters, instead of being distracted by fancy peripheral numbers that do not translate into game control and seasonal dominance.

Background

Last summer, while flipping between channels to watch a couple of different ballgames, I was both frustrated and intrigued by how the broadcast crews focused on different peripheral stats—such as strikeouts-per-nine-innings, or first-pitch strike percentage—that this pitcher or that one dominated against the league. Needless to say, this was clearly an effort by each broadcast crew to show why the incoming pitcher was the obvious and correct choice for his team.

The more I thought about what I was seeing, the more I realized that everything we talk about and measure to determine a pitcher’s mettle or worth dances around the issue without addressing their primary job—controlling runs. Pitchers get paid varying degrees of fortune to control the momentum of an inning, of a game, and of a season. Controlling momentum means limiting the damage from the opposing offense. Periphery numbers are a good barometer or benchmark of being on the road to success; and having good numbers tend to accompany good game-control. However, this is nothing more than the dashboard gauges in your car telling you that it’s running properly. And anyone who has owned more than one car can tell you, each runs a bit differently, despite them all performing the same basic functions.

It quickly became apparent that most traditional methods of evaluating pitchers simply could not be correlated to measuring their ability to control the momentum of a game.

Methodology

The process of selecting which variables—raw statistics—should be included started with defining what the two hypothesized metrics were intended to measure. The first—Momentum-controlled runs—is concerned with identifying the extent to which a pitcher limits the amount of runs the opposition scores. Because this metric is focused solely on shutting down the opposition once the pitcher-in-question enters the game, we would hypothesize that relief pitchers will be overrepresented in this data set. For example, among qualified pitchers (minimum of 45 innings), Craig Kimbrel and Kenley Jansen led the league in 2017 with an MRA of 1.43 and 1.45, respectively. At the bottom of this category is Tyler Glasnow, who had a league worst MRA of 8.85, among qualified pitchers.

The second—Season-adjusted controlled runs—is concerned with quantifying how much a pitcher’s MRA helped his team over the course of a season. Because this metric is focused on a season-long effort—which suggests that a pitcher’s ability to control momentum needs to be balanced against how many innings he actually pitched for his team in a given season—we would hypothesize that starting pitchers will be overrepresented in this data set. For example, among qualified pitchers (minimum of 45 innings), Corey Kluber and Max Scherzer led the league in 2017 with an SRA of 1.97 and 2.24, respectively. At the bottom of this category is Tyson Ross, with an SRA of 27.93, among qualified pitchers.

The population sample of pitchers included everyone with at least 45 innings pitched. This was chosen to eliminate statistical outliers who did not have enough sample size to validate their inclusion in either data set. For the 2017 season, this sample included 382 pitchers. It should be noted that the size of the population sample of pitchers with at least 45 innings pitched has grown since 1995, which is the first year I included in this study. For the record, the sample in 1995 included 292 pitchers.

The next step was to determine which raw statistics would correlate to controlling the momentum of a game through limiting runs allowed. Comparing the correlation of typical or popular raw statistics—strikeouts, walks, hits, homeruns, runs allowed, etc.—to the possible formulas used to define MRA, it was determined that any raw statistics that did not show a strong correlation would not, and should not, be recognized as a major contributing factor towards controlling the momentum of a baseball game. The minimum threshold for an acceptable correlation coefficient was +/- 0.8. This was selected as the threshold because it is above the widely-accepted +/- 0.7 threshold needed to establish a strong linear relationship.

The raw statistics that met the minimum threshold of +/- 0.8, when correlated to the final formula used to calculate Momentum-controlled Runs Average (MRA), were baserunners-per-run, WHIP, and runs-allowed-per-inning. The correlation coefficient of these were -0.81, 0.83, and 1.0, respectively over a 23-year average of the seasonal correlation coefficients for the 1995 – 2017 MLB seasons. It should be noted that no other raw statistic established anything close to a strong linear relationship with the final formula.

To calculate the formula for Momentum-controlled Runs Average, a pitcher’s WHIP (walks-plus-hits divided by innings-pitched) was divided by his baserunners-per-run, with the corresponding value multiplied by 9. This yields a final MRA value that demonstrates how many momentum-controlled runs each pitcher allowed for a 9-inning game. This follows the same basic conception used to determine a pitcher’s Earned-Runs-Average (ERA), his Deserved-Runs-Average (DRA, a metric compiled by Baseball Prospectus), and his Fielding-Independent-Pitching (FIP). It was decided that calculating MRA to mirror metrics already in existence (ERA, DRA, FIP) would offer the easiest understanding for the average baseball fan.

 

(WHIP/(BR/R)) * 9 = MRA

 

It is important to establish that pitchers were held accountable for all runs allowed because their primary responsibility is to keep the opposition from scoring, whether those runs are deserved, earned, or not.

The next step was to determine how to translate MRA into a season-adjusted metric that took into consideration how many innings a pitcher controlled the momentum of each game. The obvious reason for expanding on MRA was that pitchers who control the momentum of 45 individual innings should be compared to pitchers who control the momentum of 200+ individual innings. This metric was intended to enumerate the value of a top-end closer as he compares to a starting pitcher.

To calculate the formula for Season-adjusted controlled Runs Average, a pitcher’s MRA was divided by the value of his innings-pitched divided by 162. This yields a final SRA value that demonstrates how effective that pitcher was at controlling momentum over an entire season of baseball. This also follows the same conception of ERA, DRA, and FIP; and SRA likewise mirrors those metrics in how it is scored.

 

(MRA/(Inn/162)) = SRA

 

After creating these metrics, it was determined that an adjusted version of each would provide the benefit of comparing a pitcher’s MRA or SRA from one season to that of a different season. This allows us to compare pitchers relative to their own seasonal performance from different seasons, but also to compare pitcher A from one season to pitcher B from another season. These metrics are reported as MRA+ and SRA+, respectively.

 

100 * ((2-(MRA/Lg. Avg. MRA))*(1/(PPF*.01))) = MRA+

 

100 * ((2-(SRA/Lg. Avg. SRA))*(1/(PPF*.01))) = SRA+

 

Results

                For the 2017 MLB season, the pitchers who led the league in MRA and SRA were household names that could be expected to demonstrate this type of dominance. What stood out were the number of pitchers who had not been properly evaluated using other traditional metrics—be they ERA or WAR. For example, Josh Hader posted the 11^th best MRA in 2017 at 2.08, despite posting a WAR of 0.73 wins—which tied him for 197^th best among qualified pitchers. Furthermore, Lance Lynn posted the 18^th best SRA in 2017 at 3.36, despite posting a WAR of 2.15 wins—which tied him for 67^th best among qualified pitchers. These are not just anecdotal examples. Instead, they are exemplary of how traditional metrics have incorrectly valued pitchers’ worth. In fact, what is most telling is not the names above these examples, but the names below them.

As mentioned previously, the raw statistics that were most closely correlated with MRA were baserunners-per-run (BR/R), WHIP, and runs-allowed-per-inning (R/Inn), using the 23-year averages of these correlations. The correlation between BR/R and MRA was -0.81. This is evidence of a strong negative relationship between the two. Basically, as the number of baserunners-per-run-scored increased for a pitcher, his MRA demonstrated a corresponding decline. A correlation coefficient of this magnitude indicates that nearly 66% of the variance in MRA is explained or predicted by BR/R.

The correlation between WHIP and MRA was 0.83. This is evidence of a strong positive relationship between the two. As a pitcher allowed fewer hits or walks per inning, his MRA demonstrated a similar decline. A correlation coefficient of this magnitude indicates that nearly 69% of the variance in MRA is explained or predicted by WHIP.

The correlation between R/Inn and MRA was 1.0. This is evidence of the strongest possible positive relationship between the two. As the number of runs-allowed-per-inning decreases, a pitcher will see an equivalent decline in his MRA. A correlation coefficient of this magnitude indicates that 100% of the variance in MRA is explained or predicted by R/Inn.

By comparison, home runs-allowed, strikeouts, and total-bases-per-run all had weak correlations with MRA. They were 0.22, -0.25, and -0.53, respectively. The strongest of these unfounded variables—TB/R—could only explain or predict 28% of the variance in MRA.

It should be noted that the correlation between MRA and SRA was 0.58. This was not unexpected as a pitcher’s SRA could not be accurately described by only his MRA, since we know that SRA is a function of controlling momentum over an entire season, whereas MRA is designed to focus on a smaller sample size.

Perhaps the most important results were found in the correlations between the adjusted MRA+ and WAR, and the adjusted SRA+ and WAR. The correlations were 0.47 and 0.56, respectively. These correlation coefficients indicate that WAR is not explained or predicted by MRA+ or SRA+. More to the point, this indicates that adjusted MRA+ and SRA+ are able to find and explain value in pitchers that is not being explained by a myriad of other metrics.

Reference for correlations of unfounded variables

Baserunners/Run and HR allowed: -.27

Baserunners/Run and Strikeouts: .09

MRA and DRA: .70

MRA and FIP: .66

MRA and HR allowed: .22

MRA and Strikeouts: -.25

MRA and TB/Run: -.53

Runs/Inn and HR allowed: -.27

Runs/Inn and SRA: .60

Runs/Strikeouts: -.25

Conclusions

The data sets that produced the metrics MRA and SRA were, quite simply, game-changing. I expected to find that I would need to incorporate complex calculations to measure a pitcher’s ability to control the momentum of a game and of season. I was wrong. The formulas used to calculate these metrics were not complex. Instead, I discovered that I needed to approach the raw statistics from a complex point-of-view that forced me to challenge my own misconceptions about what counted towards a pitcher’s success. I wanted to believe that strikeouts mattered. I wanted to chastise my team for allowing opponents to hit too many homeruns (I’m looking at you Ian Kennedy). Mostly, I wanted to criticize the Royals for signings—such as the Kennedy deal—that I wrongly believed were not working out.

For the record, Ian Kennedy posted the 31^st highest adjusted SRA+ in 2016, at 162.22 (which means he was 62% above average); and he posted the 125^th highest adjusted SRA+ in 2017, at 137.16 (37% above average). Maybe this doesn’t live up to what the Royals spent on him, but it changed how I internalized his value.

Moreover, I realized that when were told that Pedro Martinez posted a banner year in 2000—one that we were told redefined pitching dominance—we were previously unable to accurately measure where this season belonged in the pantheon of baseball lore. While it was an amazing season, it ranks as the 3^rd best season of the Wild Card Era—just behind Greg Maddux’ 1995 season and Roger Clemens’ 1997 season. In fact, Maddux adjusted SRA+ in 1995 was 185.66 (85% above average), while Clemens posted a 184.37 in 1997, and Martinez posted a 183.78 in 2000.

We could be forgiven for recency bias when we rank the best pitchers of the Wild Card Era, as we tend to forget those performances that came in the years just following MLB’s fractious strike in 1994. However, the data simply doesn’t support that modern pitchers currently stack up to the best of the era. Randy Johnson has recorded five of the top twenty best individual seasons in adjusted SRA+ and six of the top fifty seasons of the Wild Card Era. Adding the total scores of his top-50 seasons places him nearly two seasons better than his closest competitor. Scoring the top-3 most dominant pitchers in adjusted SRA+ of the Wild Card Era, we find Randy Johnson ranked first (total score of 1078.02), Greg Maddux ranked second (total score of 723.36), and Kevin Brown ranked third (total score of 712.99). This might force you to reconsider your opinion of the Kevin Brown deal when the Dodgers signed him to the first $100-million-dollar-plus contract.

The findings were equally demonstrative in adjusted MRA+ as the top-50 individual seasons of the Wild Card Era yielded appearances by forty-four different pitchers. The most dominant adjusted MRA+ individual season was in 2016, from Zach Britton, when he posted a 179.92 (79% above average). The top-3 individual seasons in adjusted MRA+ were Britton in 2016, Jonathan Papelbon in 2006, when he posted a 179.14, and Craig Kimbrel in 2012, when he posted a 176.14.

Again, the recency bias may be misleading when we rank the most dominant adjusted MRA+ pitchers of the Wild Card Era. Remember that forty-four different pitchers graced the top-50 individual seasons list; only four pitchers posted multiple seasons on this list. Scoring the top-3 most dominant pitchers in adjusted MRA+ of the Wild Card Era, we find Craig Kimbrel ranked first (total score of 512.23), Joe Nathan ranked second (total score of 496.17), and Wade Davis ranked third (total score of 349.10). With great respect for Kimbrel, were it not for his 2017 season, Joe Nathan would be the clear favorite here.

What I hope you find when you use these metrics and you review the data I’ve compiled is that we should be looking at pitchers based ultimately on their ability to control the momentum of any given game AND their ability to exact this control over an entire season. While other raw statistics are nice and make for good drama, they simply don’t measure up to MRA and SRA. We all want to see strikeouts. They are sexy. They are the equivalent of homeruns for pitchers. We value them as the pinnacle of individual pitcher stats. We include them in the pitching Triple Crown. This shouldn’t elevate them to a level beyond that of a triple for a batter—something exciting for us to watch and recall later with our friends. To quote John Adams, facts are stubborn things. The facts, when properly correlated using appropriate data sets, show us that controlling the momentum of a game—limiting the opposition’s scoring—is the ultimate measure that adds up to wins and losses.

Enjoy these metrics. Use them to enhance your appreciation of the game we love. As always, constructive feedback is welcome.

Afterthought

I would be happy to share my data with interested parties for review. Message me and I will share a read-only version.