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Does the Orioles on-field performance match the results?

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The Orioles seem to get a lot of hits, but not a lot of runs. Is that perception wrong or is there something to it?

Winslow Townson-USA TODAY Sports

I think it was the first game of the series against the Yankees that I started thinking that the Orioles seemed to be getting a lot of hits, but had more trouble putting up runs then their opponent. They lost that game after just losing two of three to the Blue Jays, so perhaps there was some "glass half empty" aspect to the idea, but still I thought I'd look into if maybe this was the case.

I remember reading a little bit about sequencing and cluster luck last year and I immediately thought of that. Basically, one part of this idea says that two teams could put up almost the exact same offensive numbers but produce two totally different scores based on the sequencing of the events. A team that hits single, single, and home run in one inning could score three runs (assuming no outs in between). A team that hits single, home run, single in one inning may only score two runs. A team that hits single, single, and home run spread across multiple innings may score only one run. This is one of those things (like countless others) subject to random variation in baseball...a team or player probably doesn't have any kind of repeatable skill that may help cluster hits together to score runs, it just happens or it doesn't.

There's a good article by Jonah Keri on FiveThirtyEight you can read more for information, as well as this one by Dave Cameron on Fangraphs. It's this second one that gave me a way to measure whether or not the Orioles this year were in fact suffering from a lack of cluster luck, which as I described above is what it seemed like to me at first. By simply comparing two stats on Fangraphs, Batting and RE24, you can tell if a team has been lucky or not. Here's what this looks like as of Monday afternoon (NOTE: I don't think these include numbers from the rain-shortened loss to the Red Sox, but the timing of it made it hard to be sure):

Team

Bat

RE24

Difference

Giants

-6.9

-17.84

-10.94

Dodgers

26.2

17.7

-8.5

Astros

-10.8

-18.27

-7.47

Rays

1.6

-1.91

-3.51

White Sox

-6.5

-8.43

-1.93

Tigers

19.2

18.85

-0.35

Twins

-16.6

-16.67

-0.07

Indians

-13.1

-12.75

0.35

Royals

18.6

19.36

0.76

Rockies

-3.4

-1.99

1.41

Mariners

0.7

2.37

1.67

Cubs

-3

-1.29

1.71

Phillies

-18.3

-16.39

1.91

Brewers

-20.6

-18.3

2.3

Orioles

12.1

14.74

2.64

Angels

-9

-5.52

3.48

Athletics

10

13.51

3.51

Reds

-13.3

-8.65

4.65

Cardinals

-3.6

1.85

5.45

Braves

1.5

7.89

6.39

Nationals

-4.8

2.07

6.87

Padres

6.7

13.7

7

Pirates

-9.1

-0.29

8.81

Marlins

-9.4

0.84

10.24

Diamondbacks

-2

8.33

10.33

Rangers

-15.4

-4.07

11.33

Yankees

1.7

13.96

12.26

Mets

0.2

12.58

12.38

Blue Jays

1.6

15.87

14.27

Red Sox

-3.4

11.15

14.55

A positive number indicates a team has scored more runs than they might have been expected to thus far, a negative value indicates they've scored less. As you can see, the Orioles have been slightly lucky scoring 2.64 more runs than may have been otherwise expected based on their context-neutral offensive performance. So my initial observation was wrong, the Orioles haven't had bad luck, they've had good. So why did I think that wasn't the case?

Take a look at where some of their opponents are on that table. Sure, the Rays are in the negative, but the Yankees, Red Sox, and Blue Jays have been some of the luckiest teams in the majors so far. So in a way, the Orioles have been unlucky in their contests to date - they've had to play some very lucky teams.

In that same Fangraphs article, Dave Cameron discusses how you have to adjust a team's actual record not just based on its pythagorean expectation, but to use the expected runs scored (as opposed to the actual) when doing so. I decided to try this for all the teams in the AL East (as of results before any games on Monday):


Actual Wins

Runs Scored

Runs Allowed

Adjusted Runs Scored

Pythagorean Wins

Adj. Pythagorean Wins

Orioles

7

61

53

58.36

7

7

Red Sox

7

63

64

48.45

6

5

Yankees

6

64

56

51.74

7

6

Blue Jays

6

70

61

55.73

7

6

Rays

6

51

63

54.51

5

6

There's not a lot of variation here, which is probably what we'd expect this early in the season. The Red Sox certainly look to be getting lucky early, but even two wins is not that much. What I like most is the consistency from the Orioles, who appear to be performing exactly as expected. But if this is true, why did it seem like the team seemed to be putting so many guys on base and then not being able to drive them home? I wanted to try and look at it one more way.


BR

H

R

R/BR

R/H

Orioles

157

113

61

.38

.54

Red Sox

170

106

63

.37

.59

Yankees

157

99

64

.40

.64

Blue Jays

157

107

70

.44

.65

Rays

155

97

51

.33

.52

BR (baserunners) = H + BB + HBP + IBB

I was surprised to see the Orioles leading the division in hits. But I thought if I added those hits to other baserunners from walks and HBPs, I'd find they have a poor rate of producing runs from them. This wasn't really true either...they're about even with the Red Sox and Yankees, and are pretty far above the Rays. Then I realized I was talking about the Orioles and I needed to strip out walks. I think the team has done a better job at this this year, but still....So I took the walks and HBPs out and sure enough, there it is: The Orioles get runs at a lower rate per hit than everyone except the Rays, who they're now much closer to. This makes sense when you think of a typical box score, that displays R/H/E. I was seeing that and thinking "seems like a lot of hits and not a lot of runs", but what the box score was not showing me was how many more walks the other teams had. If I had processed that data, I may have thought the Orioles were about even with their opponents. All of this of course is a long way of saying the Orioles really need to continue improvement with their Bases on Balls.

Sometimes there really isn't an explanation for everything and we can only attribute it to luck and random variation. In this case though, I was able to reconcile this thought in my head with actual data. Have you ever wondered about something while watching a game and couldn't see how the data matches what you saw on the field? Tweet me @cebacon and if I can, I'll try to analyze it and put it into a future post.