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Amplified Run Average (ARA): An ERA Comparison

Fangraphs and Baseball Savant are two of the titans of baseball data and research. Obviously, most normal people don’t know the first thing about either site. However, fantasy baseball analysts and players alike browse both religiously. The seemingly infinite amount of data presented gets the heart rate elevated with excitement for us addicts.

Some people use face value stats to get their opinions on players. Others combine stats to make metrics that they feel matter. Being the latter, things get quite time-consuming. After spending hours upon hours researching, comparing, contrasting, and creating data, this junkie has created countless “metrics” and statistics for personal use.

While personal research is fun, nothing ever seems to be sticky with any real-life numbers that matter. However, through multiple trials, multiple data points, and a basic knowledge of pitching, a metric was discovered that looks to be nearly 100% correlated to ERA. In this article, that metric will be discussed in detail, along with results between 2018-2020.

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Amplified Run Average (ARA)

Amplified Run Average, or ARA for short, attempts to emulate Earned Run Average (ERA) using six different variables. These variables are common, everyday numbers seen at the forefront of a player’s profile. One of them is altered to better adjust to ERA, but nonetheless, they are simple numbers.

Below, the variables will be explained, as well as arriving at the final result. That final result shows a near-direct correlation to ERA, as the R² value is a strong 0.9357. In case the reader does not understand how R² values work, it is on a scale of zero to one. Zero meaning there is absolutely no correlation between the two objects being compared. One meaning there is a direct correlation, whether positive or negative. Perhaps an easier way to look at it is like a percentage. The correlation between ARA and ERA is 93.57% correlated between the 2018-2020 seasons. Without further ado, here are the variables used in ARA.


The first statistic used was strikeout rate (K%). There perhaps isn’t a more dominant number a pitcher can show than his strikeout rate. It shows the pitcher’s ability to fool a hitter consistently and effectively. The latter being that it directly results in an out, therefore lowering ERA. Here is the starting point, and here is the current correlation to ERA:

K% vs ERA

A 0.2437 R² value is decent for such a simple number as strikeout rate. We are off to a good start. Now it is time to include its counterpart to get one of the best metrics we use in today’s game.


Walk rate is an extremely important metric in that it is highly correlated to a pitcher’s ability to control his pitches. The better a pitcher is at controlling his pitches, the more likely the pitcher is to locate the pitch where they want. However, walk rate by itself has almost no correlation to ERA as can be seen below:

BB% vs ERA

Subtracted from strikeout rate, now we have K-BB%, which is considered a metric itself. K-BB% shows a key trait to a pitcher, the ability to not only fool a hitter, but to fool a hitter while showing control.  This is the first spike in correlation to ERA that we see. K-BB% shows why it is such a highly thought of metric:

K-BB% vs ERA


With the core statistic set, another metric quickly came to mind when it came to suppressing runs. That was groundball rate. While it doesn’t have a direct correlation to ERA, it still has supreme value for the final formula. Since we already have K-BB%, we can look at the formula now as (K-BB)+GB%:

For those doubting the inclusion of groundball rate, not only does the above graphic show a strengthened correlation, but the final result is strongly supported by groundball rate. Despite groundball rate alone having a small direct correlation to ERA, when it is subtracted out of the final formula for ARA, the correlation drops from 0.9357 to 0.9011. Groundball rate has meaning in ARA.


And now we get into the three variables that are directly tied to runs allowed and hits allowed. The first of that bunch is home-run-to-fly-ball ratio. It is a self-explanatory metric, as it is simply the percentage of home runs hit on fly balls. As we all know, home runs lead to runs, which directly affects ERA. Simply subtracting HR/FB rate from our current formula (K-BB+GB%), and our correlation continues to strengthen, now up to 0.4748.

This metric was included to help support fly ball pitchers who are skilled at suppressing home runs. This includes Justin Verlander, Trevor Bauer, and Max Scherzer, among others. HR/FB rate is known to fluctuate from season to season and is a large reason why ERA fluctuates season to season. For this article, we will stick to it at face value since we are simply showing the ERA correlation. However in another article to come, HR/FB rate will be adjusted. Those changes to HR/FB rate (along with the other variables to come) will show an adjusted ARA score. This adjusted ARA score will show an expected outcome given past performance, and will hopefully yield predictiveness to future seasons.


Weighted-On-Base-Average on contact (wOBAcon) measures not only hits allowed by a pitcher when the ball is put in play, but it weighs the types of hits differently. A single is worth less than a double, doubles less than a triple, and triple less than a home run. This is one of the more underrated stats in all of baseball. If a pitcher is giving up singles instead of home runs, they deserve more credit, even if they are both hits. All hits are not created equal.

wOBAcon at it’s core is already fairly strongly connected to ERA, with an R² value of 0.5767 between 2018-2020. Added into the current ARA formula, and our correlation continues to grow:

Score+wOBAcon vs ERA


You probably haven’t seen this statistic because it doesn’t exist. This is an alteration to left-on-base percentage (LOB%) that you can find at the top of a pitchers Fangraphs page. The only difference is that instead of using runs allowed in the formula, it was changed to earned runs allowed. Obviously, adding any metric that includes earned runs will strengthen the correlation to ERA. Using LOB% still strengthens the correlation significantly, as it is strongly related to ERA. However when using earned runs instead of runs, the correlation jumps.

Also of note, erLOB% is the only metric that has a coefficient. Multiplying by two takes the correlation from 0.8791 to the end result, 0.9357.

Final Formula & Converting to ERA Scale

Here is the final formula:

ARA Score = (K%-BB%+GB%-(HR/FB)-wOBAcon+(2*erLOB%))*100


Surprisingly not overcomplicated, none of the variables had to be adjusted outside of erLOB%. They were taken at face value, and they produced a strong result. Among 234 samples, the tightness of the circles on the trendline show this is a near-perfect correlation. Success!

With the correlation shown, the ARA score needs to be converted so we see it on an ERA scale. That is simply done by plugging in numbers into the formula seen at the top right of the correlation graph above. That formula is:

ARA = (ARA Score – 234.43) / -18.036

The final ARA metric is complete. Now that the number has been reached, how about some actual results? Without further ado, here are the best ARA’s since 2018, along with ERA, and the difference between the two. For any positive numbers in the “ERA-ARA” column, this suggests the pitcher was as good or better than his ERA. Negative numbers in the column indicate the pitcher overperformed their ERA.

Shane Bieber20201.630.990.64
Trevor Bauer20201.731.020.71
Clayton Kershaw20202.161.290.87
Jacob deGrom20181.701.430.27
Blake Snell20181.891.660.23
Chris Sale20182.111.810.30
Corbin Burnes20202.111.850.26
Zach Plesac20202.281.910.37
Dallas Keuchel20201.992.04-0.05
Yu Darvish20202.012.07-0.06
Dustin May20202.572.100.47
Max Fried20202.252.100.15
Dinelson Lamet20202.092.14-0.05
Trevor Bauer20182.212.150.06
Aaron Nola20182.372.170.20
Hyun Jin Ryu20192.322.170.15
Justin Verlander20192.582.230.35
Gerrit Cole20192.502.240.26
Jacob deGrom20192.432.250.18
Jacob deGrom20202.382.280.10
Chris Bassitt20202.292.30-0.01
Gerrit Cole20202.842.380.46
Hyun Jin Ryu20202.692.410.28
Sonny Gray20192.872.580.29
Mike Soroka20192.682.580.10
Brad Keller20202.472.60-0.13
Brandon Woodruff20203.052.610.44
Clayton Kershaw20182.732.620.11
Kenta Maeda20202.702.670.03
Clayton Kershaw20193.032.670.36
Justin Verlander20182.522.70-0.18
Zac Gallen20202.752.710.04
Jack Flaherty20192.752.720.03
Zack Wheeler20202.922.730.19
Max Scherzer20182.532.75-0.22
Zach Davies20202.732.77-0.04
Taijuan Walker20202.702.79-0.09
Miles Mikolas20182.832.84-0.01
Kyle Freeland20182.852.86-0.01
Luis Castillo20203.212.890.32
Noah Syndergaard20183.032.910.12
Kyle Hendricks20202.882.93-0.05
Mike Foltynewicz20182.852.94-0.09
Marcus Stroman20193.222.980.24
Corey Kluber20182.892.98-0.09
Charlie Morton20193.052.990.06
Charlie Morton20183.132.990.14
Max Scherzer20192.923.01-0.09
Luis Castillo20193.403.020.38
Patrick Corbin20193.253.060.19
Carlos Carrasco20202.913.09-0.18
Zack Greinke20192.933.16-0.23
Stephen Strasburg20193.323.180.14
Jameson Taillon20183.203.22-0.02
Gerrit Cole20182.883.22-0.34
Shane Bieber20193.283.220.06
Dakota Hudson20193.353.230.12
Zack Greinke20183.213.24-0.03
Patrick Corbin20183.153.25-0.10
Adam Wainwright20203.153.28-0.13
Mike Clevinger20183.023.28-0.26
Carlos Carrasco20183.383.290.09
Jack Flaherty20183.343.310.03
Justus Sheffield20203.583.380.20
Mike Fiers20183.563.400.16
Walker Buehler20193.263.40-0.14
Aaron Nola20203.283.40-0.12
Zack Wheeler20183.313.41-0.10
Pablo Lopez20203.613.430.18
Antonio Senzatela20203.443.440.00
Blake Snell20203.243.46-0.22
Lance Lynn20203.323.47-0.15
CC Sabathia20183.653.490.16
Eduardo Rodriguez20193.813.510.30
Marco Gonzales20203.103.52-0.42
Trevor Williams20183.113.52-0.41
Mike Minor20193.593.540.05
Kyle Hendricks20183.443.54-0.10
Cristian Javier20203.483.57-0.09
Kyle Gibson20183.623.580.04
Masahiro Tanaka20183.753.580.17
Alex Wood20183.683.600.08
Sonny Gray20203.703.620.08
Dallas Keuchel20183.743.620.12
Kyle Hendricks20193.463.63-0.17
J.A. Happ20183.653.630.02
Dylan Bundy20203.293.64-0.35
Jose Berrios20193.683.650.03
Jon Lester20183.323.65-0.33
Aaron Nola20193.873.650.22
Cole Hamels20183.783.660.12
Lucas Giolito20203.483.67-0.19
Steven Matz20183.973.690.28
David Price20183.583.70-0.12
Jon Gray20193.843.700.14
Framber Valdez20203.573.70-0.13
Anthony DeSclafani20193.893.720.17
Luis Severino20183.393.72-0.33
Sean Manaea20183.593.72-0.13
Wade Miley20193.983.730.25
Brett Anderson20193.893.780.11
German Marquez20183.773.79-0.02
James Paxton20193.823.810.01
Ryan Yarbrough20203.563.81-0.25
Jhoulys Chacin20183.503.81-0.31
Lucas Giolito20193.413.82-0.41
Jeff Samardzija20193.523.83-0.31
Derek Holland20183.573.83-0.26
Max Fried20194.023.840.18
Lance Lynn20193.673.86-0.19
Kevin Gausman20183.923.860.06
Yu Darvish20193.983.860.12
German Marquez20203.753.86-0.11
Jake Odorizzi20193.513.89-0.38
Jake Arrieta20183.963.890.07
Zach Eflin20194.133.890.24
Spencer Turnbull20203.973.890.08
Zach Davies20193.553.89-0.34
Jose Berrios20183.843.90-0.06
Steven Matz20194.213.900.31
Mike Leake20194.293.900.39
Julio Teheran20193.813.90-0.09
Sandy Alcantara20193.883.92-0.04
Zach Eflin20203.973.920.05
Lance McCullers Jr.20203.933.94-0.01
Brady Singer20204.063.950.11
Jose Urena20183.983.970.01
Zack Wheeler20193.963.98-0.02
James Paxton20183.763.98-0.22
Max Scherzer20203.743.99-0.25
Adam Wainwright20194.194.000.19
Sean Newcomb20183.904.01-0.11
Kevin Gausman20203.624.02-0.40
Julio Urias20203.274.03-0.76
Joey Lucchesi20194.184.060.12
Mike Fiers20193.904.06-0.16
Miles Mikolas20194.164.060.10
Jose Berrios20204.004.07-0.07
Wade LeBlanc20183.724.10-0.38
Brad Keller20194.194.110.08
Jose Quintana20184.034.12-0.09
Anibal Sanchez20193.854.12-0.27
Jakob Junis20184.374.150.22
Kris Bubic20204.324.150.17
Jesus Luzardo20204.124.16-0.04
Ivan Nova20184.194.160.03
John Means20193.604.16-0.56
Gio Gonzalez20184.214.170.04
Chase Anderson20183.934.17-0.24
Marco Gonzales20193.994.18-0.19
Erick Fedde20204.294.180.11
Andrew Heaney20184.154.19-0.04
Noah Syndergaard20194.284.200.08
Dylan Cease20204.014.21-0.20
Marco Gonzales20184.004.24-0.24
Julio Teheran20183.944.24-0.30
Rick Porcello20184.284.250.03
Kyle Freeland20204.334.250.08
Madison Bumgarner20193.904.27-0.37
Tyler Glasnow20204.084.27-0.19
Luis Castillo20184.304.270.03
Griffin Canning20203.994.28-0.29
Jon Lester20194.464.350.11
Lance Lynn20184.774.380.39
Patrick Corbin20204.664.390.27
Mike Leake20184.364.40-0.04
Kenta Maeda20194.044.40-0.36
Garrett Richards20204.034.41-0.38
Masahiro Tanaka20194.454.420.03
Reynaldo Lopez20183.914.43-0.52
Tanner Roark20194.354.44-0.09
Zack Godley20184.744.440.30
Tanner Roark20184.344.45-0.11
Kyle Gibson20194.844.450.39
Andrew Suarez20184.494.450.04
Merrill Kelly20194.424.48-0.06
Zack Greinke20204.034.51-0.48
Matthew Boyd20194.564.510.05
Trevor Bauer20194.484.51-0.03
Robbie Ray20194.344.53-0.19
Joe Musgrove20194.444.53-0.09
Homer Bailey20194.574.540.03
Andrew Cashner20194.684.580.10
Sean Manaea20204.504.58-0.08
Ivan Nova20194.724.610.11
Nick Pivetta20184.774.620.15
Alec Mills20204.484.63-0.15
Alex Cobb20204.304.64-0.34
Chris Paddack20204.734.690.04
Alex Cobb20184.904.700.20
Jose Quintana20194.684.71-0.03
Dylan Bundy20194.794.720.07
Martin Perez20195.124.760.36
Mike Fiers20204.584.77-0.19
Caleb Smith20194.524.77-0.25
Mike Minor20184.184.77-0.59
Andrew Heaney20204.464.78-0.32
Matt Harvey20184.944.820.12
J.A. Happ20194.914.830.08
James Shields20184.534.85-0.32
Logan Webb20205.474.860.61
Aaron Civale20204.744.86-0.12
Martin Perez20204.504.87-0.37
Adrian Houser20205.304.880.42
German Marquez20194.764.89-0.13
Tyler Anderson20184.554.91-0.36
Jakob Junis20195.244.940.30
Matthew Boyd20184.394.96-0.57
Jon Lester20205.164.960.20
Jon Gray20185.124.970.15
Danny Duffy20184.885.00-0.12
Yusei Kikuchi20195.465.040.42
Jake Odorizzi20184.495.10-0.61
Clayton Richard20185.335.110.22
Trent Thornton20194.845.14-0.30
Tyler Anderson20204.375.15-0.78
Kyle Gibson20205.355.230.12
Felix Hernandez20185.555.250.30
Andrew Cashner20185.295.280.01
Danny Duffy20204.955.34-0.39
Reynaldo Lopez20195.385.43-0.05
Dylan Bundy20185.455.450.00
Rick Porcello20205.645.480.16
Johnny Cueto20205.405.49-0.09
Rick Porcello20195.525.510.01
Trevor Williams20206.185.570.61
Frankie Montas20205.605.580.02
Lucas Giolito20186.135.800.33
Mike Minor20205.566.01-0.45
Anibal Sanchez20206.626.130.49
Luke Weaver20206.586.560.02
Jordan Lyles20207.026.580.44
Robbie Ray20206.626.69-0.07
Matthew Boyd20206.716.74-0.03

How Interpreting ARA Can Help

Plenty of pundits out there are likely wondering why does this matter? Great that it’s strongly connected to ERA, but why does it matter? Is it predictive, or simply descriptive? After all, that’s what we want, a predictive metric. Bluntly put, it isn’t predictive since it is so correlated to ERA. Typically, when a pitcher’s ERA goes up and down, so does the ARA. And as we all know, outside of the elite pitchers, ERA itself fluctuates from pitcher to pitcher every season. They try new pitch mixes, have increased and decreased velocity, try different arm angles, release points, etc. If you are looking for the perfect predictive metric to ERA, you won’t find it. But ARA can be useful if you understand it at its core.

Examining the Volatile Variables

Examining each of the six variables, and figuring out the percentage (on average) each makes up of the entire formula is a good starting point. Here are those league average numbers for the six aforementioned variables, and the final percentage each makes up of the final formula:

LG AVG 2018-2020

Weighted Variables


erLOB% is the dominant statistic in the formula, making up (on average) 56% of the entire score. It was already the highest percentage used before doubling it, which only further strengthened the weight. Understanding that erLOB% fluctuates from season to season is key, and this is one of the primary reasons ARA will fluctuate. Using a population of 68 starting pitchers who pitched over 150 innings in 2018 and 2019, and over 50 innings in 2020, the standard deviation of erLOB% across the population was 6.3%. In other words, between these 68 pitchers, erLOB% could be expected to fluctuate 6.3% above or below the seasons prior. Doing a quick conversion between ERA and erLOB%, and that roughly comes out to be a full point higher or lower on the ERA scale.

erLOB% ERA conversion

This knowledge will be used in an article to come. erLOB% will be adjusted to league average for small sample pitchers, and a pitchers average for large samples in order to come up with an adjusted ARA metric. But for now, we need to understand that pitchers with high erLOB% will almost assuredly see a rise in their ERA the following season, and vice versa. There is an exception with good pitchers who have high strikeout rates, as they can continuously strand runners at a higher rate. Below you can see erLOB% since 2018:

Shane Bieber202092.8
Trevor Bauer202097.1
Clayton Kershaw202092.8
Jacob deGrom201885.7
Blake Snell201889.9
Chris Sale201884.0
Corbin Burnes202081.7
Zach Plesac202091.7
Dallas Keuchel202083.1
Yu Darvish202085.3
Dustin May202093.1
Max Fried202082.0
Dinelson Lamet202083.9
Trevor Bauer201883.8
Aaron Nola201883.1
Hyun Jin Ryu201985.9
Justin Verlander201989.9
Gerrit Cole201987.9
Jacob deGrom201984.1
Jacob deGrom202085.1
Chris Bassitt202088.6
Gerrit Cole202093.5
Hyun Jin Ryu202083.3
Sonny Gray201981.4
Mike Soroka201982.1
Brad Keller202077.9
Brandon Woodruff202080.7
Clayton Kershaw201882.8
Kenta Maeda202080.2
Clayton Kershaw201986.0
Justin Verlander201887.1
Zac Gallen202086.5
Jack Flaherty201984.6
Zack Wheeler202078.1
Max Scherzer201883.5
Zach Davies202086.3
Taijuan Walker202091.2
Miles Mikolas201879.7
Kyle Freeland201882.8
Luis Castillo202077.5
Noah Syndergaard201878.3
Kyle Hendricks202082.4
Mike Foltynewicz201881.0
Marcus Stroman201981.2
Corey Kluber201881.2
Charlie Morton201977.7
Charlie Morton201882.3
Max Scherzer201980.6
Luis Castillo201978.8
Patrick Corbin201981.1
Carlos Carrasco202085.2
Zack Greinke201978.5
Stephen Strasburg201977.6
Jameson Taillon201880.3
Gerrit Cole201880.0
Shane Bieber201981.7
Dakota Hudson201984.7
Zack Greinke201881.8
Patrick Corbin201874.7
Adam Wainwright202082.2
Mike Clevinger201881.7
Carlos Carrasco201877.9
Jack Flaherty201881.3
Justus Sheffield202073.4
Mike Fiers201886.0
Walker Buehler201977.5
Aaron Nola202079.8
Zack Wheeler201875.6
Pablo Lopez202073.0
Antonio Senzatela202080.8
Blake Snell202091.3
Lance Lynn202083.3
CC Sabathia201880.9
Eduardo Rodriguez201978.5
Marco Gonzales202078.2
Trevor Williams201879.3
Mike Minor201981.6
Kyle Hendricks201878.1
Cristian Javier202086.2
Kyle Gibson201879.5
Masahiro Tanaka201879.7
Alex Wood201875.3
Sonny Gray202073.0
Dallas Keuchel201875.7
Kyle Hendricks201977.3
J.A. Happ201880.1
Dylan Bundy202073.8
Jose Berrios201979.0
Jon Lester201884.1
Aaron Nola201978.5
Cole Hamels201882.0
Lucas Giolito202074.5
Steven Matz201880.2
David Price201880.1
Jon Gray201979.3
Framber Valdez202072.7
Anthony DeSclafani201980.8
Luis Severino201877.0
Sean Manaea201877.2
Wade Miley201978.9
Brett Anderson201976.7
German Marquez201877.0
James Paxton201980.5
Ryan Yarbrough202077.3
Jhoulys Chacin201876.3
Lucas Giolito201978.9
Jeff Samardzija201981.0
Derek Holland201879.1
Max Fried201977.3
Lance Lynn201976.1
Kevin Gausman201879.2
Yu Darvish201979.6
German Marquez202072.9
Jake Odorizzi201977.2
Jake Arrieta201876.7
Zach Eflin201980.8
Spencer Turnbull202070.5
Zach Davies201980.6
Jose Berrios201876.3
Steven Matz201979.8
Mike Leake201982.3
Julio Teheran201979.8
Sandy Alcantara201977.6
Zach Eflin202077.2
Lance McCullers Jr.202072.6
Brady Singer202072.9
Jose Urena201873.7
Zack Wheeler201974.6
James Paxton201876.0
Max Scherzer202082.5
Adam Wainwright201977.9
Sean Newcomb201876.4
Kevin Gausman202076.6
Julio Urias202076.8
Joey Lucchesi201974.2
Mike Fiers201979.6
Miles Mikolas201976.3
Jose Berrios202077.5
Wade LeBlanc201879.2
Brad Keller201973.6
Jose Quintana201878.3
Anibal Sanchez201978.2
Jakob Junis201878.9
Kris Bubic202080.2
Jesus Luzardo202078.0
Ivan Nova201877.8
John Means201980.0
Gio Gonzalez201875.0
Chase Anderson201882.4
Marco Gonzales201975.9
Erick Fedde202082.8
Andrew Heaney201875.7
Noah Syndergaard201972.1
Dylan Cease202087.3
Marco Gonzales201873.0
Julio Teheran201877.3
Rick Porcello201873.8
Kyle Freeland202076.3
Madison Bumgarner201976.2
Tyler Glasnow202078.6
Luis Castillo201875.8
Griffin Canning202079.3
Jon Lester201978.5
Lance Lynn201871.9
Patrick Corbin202077.5
Mike Leake201873.1
Kenta Maeda201972.4
Garrett Richards202076.5
Masahiro Tanaka201973.1
Reynaldo Lopez201878.1
Tanner Roark201980.1
Zack Godley201871.1
Tanner Roark201874.3
Kyle Gibson201973.9
Andrew Suarez201873.3
Merrill Kelly201975.6
Zack Greinke202068.5
Matthew Boyd201978.3
Trevor Bauer201975.4
Robbie Ray201978.7
Joe Musgrove201970.7
Homer Bailey201971.7
Andrew Cashner201971.3
Sean Manaea202069.4
Ivan Nova201976.6
Nick Pivetta201871.2
Alec Mills202077.1
Alex Cobb202077.3
Chris Paddack202079.0
Alex Cobb201873.4
Jose Quintana201971.1
Dylan Bundy201975.4
Martin Perez201972.1
Mike Fiers202076.0
Caleb Smith201979.2
Mike Minor201874.5
Andrew Heaney202071.4
Matt Harvey201872.3
J.A. Happ201975.7
James Shields201875.9
Logan Webb202068.3
Aaron Civale202072.4
Martin Perez202073.5
Adrian Houser202071.6
German Marquez201970.4
Tyler Anderson201874.6
Jakob Junis201973.1
Matthew Boyd201873.4
Jon Lester202071.4
Jon Gray201869.9
Danny Duffy201874.5
Yusei Kikuchi201976.3
Jake Odorizzi201873.1
Clayton Richard201866.7
Trent Thornton201973.8
Tyler Anderson202072.5
Kyle Gibson202074.8
Felix Hernandez201869.7
Andrew Cashner201873.8
Danny Duffy202073.0
Reynaldo Lopez201973.1
Dylan Bundy201875.6
Rick Porcello202064.3
Johnny Cueto202067.2
Rick Porcello201969.1
Trevor Williams202075.7
Frankie Montas202071.6
Lucas Giolito201865.6
Mike Minor202064.7
Anibal Sanchez202068.8
Luke Weaver202064.7
Jordan Lyles202062.5
Robbie Ray202075.5
Matthew Boyd202067.1

HR/FB & wOBAcon

Home run per fly ball is a more known statistic, so less time is necessary to be spent talking about it. HR/FB is another statistic that fluctuates and has near-identical standard deviation numbers to erLOB%. With that being the case, HR/FB rate can be looked at the same way as erLOB%. If a pitcher hasn’t shown the tendency to suppress home runs, and yet was below an 8% HR/FB rate on the season, then we know that could be a prime candidate for ERA regression and vice versa. All of this is fairly common knowledge in the fantasy industry.

wOBAcon is using a slightly tighter scale, so it doesn’t technically fluctuate as much as the aforementioned two. However, it still needs to be adjusted for potential positive or negative regression. Use the same logic applied to erLOB% and HR/FB rate and find the potential outliers.

As mentioned previously, adjusting these variables will be a major topic of the next article to come. Using an adjusted ARA score will hopefully give us more predictability than the ARA at face value.

K%, BB%, and GB%

These three are more skill than luck. While these numbers can change when pitchers make significant changes, there isn’t much logic in trying to adjust these numbers. Take these at face value and give the pitchers credit where credit is due.


In sum, ARA is not meant to be a brand new metric used on the front page of websites. It should be used as another way of understanding ERA through the six metrics talked about throughout the article. While analysts and players alike may already use these numbers every day in their research, the combination of the six gives us an undeniable correlation to ERA.

Understanding that ARA is nearly directly correlated to ERA is vital for this article. With this understanding, we can go further into pitchers who were perhaps lucky or unlucky using erLOB%, HR/FB, and wOBAcon. Ultimately, an adjusted ARA score will be attempted in a succeeding article, with hopes that it will more accurately project whether or not a pitcher performs better or worse with less luck (and potentially more skill) factored in.

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