The Baltimore Orioles and Home Runs

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A Simple Question

 

The Baltimore Orioles give up a lot of dingers. To clarify, in this instance, “a lot” means “an ungodly amount that smashes all previous historical context for home runs allowed.” With 94 HR allowed through 46 games (as of May 20) – a rate of 2.04 per game – the Orioles are on pace to allow 331 HR in 2019. This is more than their league-leading 234 HR allowed in 2018 and, incredibly, a massive 73 bombs more than the record-setting 258 allowed by the 2016 Cincinnati Reds.

The rate of 2.04 actually exceeds the rate of home runs hit in the notoriously hitter-friendly Pacific Coast (AAA) and California (A+) leagues and would even place the Orioles second in the Mexican League (which, as near as I can tell, is having its most home run-friendly season ever, as well) behind Rieleros de Aguascalientes. The team plays in a stadium at 6,190 feet above sea level with a similar climate profile as Denver from April to September.

The amount of home runs Baltimore gives up is naturally a topic of conversation among Orioles fans. One such conversation at Camden Chat led to the question, “What would the Orioles’ Pythagorean record be if they gave up a league average number of home runs?”

 

Data and Analysis

 

There are a few steps to answering this question. It does not seem reasonable to assume that every HR not hit is converted into an out. As such, we will need to understand at what rate various plate appearance results occur. Note: BB includes HBP and IBB, as they have the same net impact and Sac includes SH and SF. The following data is from Baseball-Reference. Rates are determined by dividing the number of events by the number of plate appearances.

Runners	PA	AB	R	BB	H	1B	2B	3B	HR	Sac
—	989	880	54	109	247	137	53	3	54	0
1–	364	329	43	34	76	44	14	1	17	0
-2-	153	135	32	19	32	21	4	1	6	0
–3	48	41	20	4	13	6	3	0	4	3
12-	121	105	36	14	23	13	4	0	6	2
1-3	43	37	31	3	12	4	2	0	6	2
-23	37	27	27	7	10	6	3	0	1	5
123	35	25	28	6	6	6	0	0	0	4

Additionally, because these events have different runs scored results than a home run, that will have to be taken into account, too. I have used the following runs scored per event values based on historical data. They are somewhat simplified, but are reasonable expectations for our purposes here.

Runners	R/1B	R/2B	R/3B	R/HR	R/BB	R/Sac
—	0	0	0	1	0	0
1–	0.01	0.41	0.99	2	0	0
-2-	0.59	0.99	0.99	2	0	0
–3	0.99	0.99	1.00	2	0	1
12-	0.60	1.40	1.98	3	0	0
1-3	1.00	1.40	1.99	3	0	1
-23	1.58	1.98	1.99	3	0	1
123	1.59	2.39	2.98	4	1	1

The next step is to determine the number of adjusted events that would occur if HR were reduced by a certain rate. This is calculated by (for example): ((HR * HR Reduction Rate) * 1B/PA) + 1B. In this example, the Orioles allow 75% as many HR as actual.

Runners	adj_1B	adj_2B	adj_3B	adj_HR	adj_BB	adj_Sac
—	142.6	55.2	3.1	40.5	113.5	0.0
1–	45.5	14.5	1.0	12.8	35.2	0.0
-2-	21.6	4.1	1.0	4.5	19.6	0.0
–3	6.4	3.2	0.0	3.0	4.3	3.2
12-	13.5	4.1	0.0	4.5	14.5	2.1
1-3	4.4	2.2	0.0	4.5	3.3	2.2
-23	6.1	3.1	0.0	0.8	7.1	5.1
123	6.0	0.0	0.0	0.0	6.0	4.0

By multiplying the number of R/event by the number of adjusted events, a new team Runs Allowed can be calculated or each event and situation. Again, the following table is for a 75% HR rate.

Runners	R_adj_1B	R_adj_2B	R_adj_3B	R_adj_HR	R_adj_BB	R_adj_Sac
—	0.00	0.00	0.00	40.50	0.00	0.00
1–	0.46	5.94	1.02	25.50	0.00	0.00
-2-	12.71	4.08	1.02	9.00	0.00	0.00
–3	6.31	3.16	0.00	6.00	0.00	3.19
12-	8.06	5.81	0.00	13.50	0.00	0.00
1-3	4.42	3.09	0.00	13.50	0.00	2.21
-23	9.66	6.06	0.00	2.25	0.00	5.10
123	9.53	0.00	0.00	0.00	6.00	4.00

If the Orioles allowed 75% of their actual HR, which would be 70.5 instead of 94, they would have been expected to give up 212.1 runs (RA) instead of 271. With 176 runs scored (RS) on the season, that would translate to a Pythagorean win% of .408 or a 19 – 27 record. That’s not exactly competitive, but certainly an improvement over the Orioles’ actual .326 and 15 – 31 numbers. Here is a look at various reduced HR rates and the impact on the Orioles’ Pythagorean record.

HR	94	94	94	94	94
HR Rate	90%	75%	64%	50%	25%
adj_HR	84.6	70.5	60.0	47.0	23.5
adj_RA	234.8	212.1	195.2	174.2	136.4
RS	176	176	176	176	176
Pyth Win%	.360	.408	.449	.505	.625
Pyth Rec	17 – 29	19 – 27	21 – 25	23 – 23	29 – 17

 

Conclusion

 

The Baltimore Orioles, regardless of how many home runs they allow, are not a particularly good team. It would take being historically good at preventing HR for the Orioles to approach first in the AL East, all else being equal. However, if the team could keep balls in the park at even a league average rate, they would be much closer to .500.

It is probably unlikely that the current crop of pitchers in Baltimore will suddenly start reducing the rate at which they give up dingers over the rest of the season, especially with more hot and humid air headed into Camden Yards soon. Dylan Bundy is giving up long balls at exactly the same 2.1 HR/9 rate as he did in 2018. The same is true for Andrew Cashner and his more reasonable 1.5 HR/9 rate. David Hess and Dan Straily are allowing home runs at significantly higher rates than last year or for their careers, so there may be room for improvement there.

To avoid smashing the single season HR allowed record, the Orioles would have to reduce their HR allowed rate from 2.04 to 1.41 per game over the rest of the year. That would put them squarely between Colorado and Philadelphia in terms of HR/G so far. Even if that happens, and it seems unlikely, the Orioles will continue to be one of the worst teams in MLB as the franchise is currently facing a number of issues standing between it and contention.

(Photo by Tony Quinn/Icon Sportswire)