In 2007, the Angels won 94 games despite a third order win total of 86. In 2008, the Angels won 100 games despite a third order win total of just 84. In 2009, the Angels won 97 games with just a third order win total of 87. Going into 2010, we were left wondering whether the Angels were the luckiest team in the world or whether they were doing something that made them appear to be a slightly above average team but actually among the best in the league. A clue was thrown our way in 2010 when the Angels came back to earth, dipping to a record of 8082. This might have resolved the issue if the Angels third order win total had been more than 72, because even a mediocre team beat the stuffing out of its third order record! The Adjusted Standings that we publish under our Statistics tab here at Baseball Prospectus are designed to give fans a clue about how lucky teams have been, but the Angels’ performances have thrown some major question marks our way in recent years about the methodology of those standings.
So what is going on here? To understand this, let’s first walk through what the third order standings are trying to tell us. There are three orders of luck that are gradually stripped away, and while the math is tough, the intuition is simple. In order of presentation in the Adjusted Standings:
 WL: Actual Record, how many games did teams win?
 W1L1: First Order Standings, how many games would a normal team win given their runs scored and runs allowed?
 W2L2: Second Order Standings, how many runs would a normal team score and allow given their hits, hits allowed, walks, walks allowed, etc., and how many games would a normal team win given that run differential?
 W3L3: Third Order Standings, what if the Second Order Standings were adjusted for difficulty of opponents?
Knowing how to calculate them would be a challenge, but understanding the intent is simple. The goal is to infer which teams were good, and which teams just won a lot of close games, and also which teams just got a lot of timely hits. Looking at third order records as more valuable implies that teams that win a lot of close games are getting lucky, and that teams that get five runs on five hits and a walk are getting lucky too. Is that really true?
I figured that one of the best ways to answer that question would be to look at which “standings” actually correlated the most with the following year’s actual standings. Keep in mind that although players switch teams and some teams actually get better or worse from year to year, that will all wash out. For example, losing John Lackey to the Red Sox costs the Angels in both real record and adjusted record because his effect on runs allowed and wins depart.
So, while the Angels in 2009 clearly were much closer to their 2008 actual record than their 2008 third order record, is that true for most teams? And if so, which standings correlate the best with actual record the following year? I used the standings for 200510, giving me a solid 150 pairs of consecutive years to consider.
Standings version 
Correlation with next year’s standings 
Actual Standings 
.487 
First Order Standings 
.493 
Second Order Standings 
.504 
Third Order Standings 
.485 
Do not be disappointed in the third order standings. The difficulty of opponents is something that is persistent, so the constant bonus added to the Orioles third order win total due to playing the Yankees, Red Sox, and Rays is not supposed to predict an Orioles resurgence the following year, because those teams are just going to keep on beating the Orioles next year. However, we see solid evidence that each adjustment adds a small something to our estimate of team skill, so even though the Angels are beating their first, second, and third order records every year, on average we are still better off looking at a team’s second order record if we want to guess how well it will do the following year.
That does not mean the Angels do not have a knack for beating their first and second order records. To prove that, we would need to look at how well teams that beat their first and second order records repeat that feat the following year. If there is no yeartoyear correlation of teams’ abilities to beat their adjusted records, then we are left with a conclusion that the Angels are simply very lucky. That might sound like a copout, but it is not at all. There has to be one team that gets the title of “the luckiest team ever” just as someone out there needs to be flipping a coin and calling it in the air correctly 10 times in a row. One of every 1,000 people will accidentally call a coin correctly 10 times in a row without any skill (and one out of every 1,000 will guess wrong 10 times in a row). Statistically, some team needs to be the luckiest.
However, we see some pretty clear evidence that the Angels might have some skill. There is a small but real skill level in beating one’s first order record. The difference between first order wins and actual wins for teams had a .079 correlation from year to year. So while almost all of the fluctuation around one’s first order record is luck, about 8 percent of that fluctuation is actual skill level.
Not only that, there is a .193 correlation in the difference between second order wins and real wins, though only a .103 correlation in the difference between second order wins and first order wins.
The .193 difference comes from simply aggregating the two effects: that some teams are good at winning close games, and that some teams are good at generating a bigger run differential than their total base, hit, and walk differentials suggest.
The ability to generate that bigger run differential than hit and walk differentials suggest is actually made up of two parts which should be looked at separately. Firstly, do some teams have the ability to sequence their total bases, hits, walks, and outs in such a way that they score more runs than other teams? Secondly, do some pitching staffs have the ability to sequence their total bases, hits, walks, and outs in such a way that they allow fewer runs? The answer to the first question is more likely to be yes than the second.
The correlation from year to year of the difference between runs and EQR is .104, while the correlation from year to year of the difference between runs allowed and EQR allowed is just .055. Both suggest some evidence of a skill, while also highlighting that the majority of this comes from luck.
So, can we do any better than looking at second order standings if we want to predict next year’s standings?
Standings version averaged 
Correlation with next year’s standings 
Actual & 1^{st} Order 
.501 
Actual & 2^{nd} Order 
.511 
Actual & 3^{rd} Order 
.504 
Actual, 1^{st}, & 2^{nd} Order 
.510 
Actual, 1^{st}, 2^{nd}, & 3^{rd} Order 
.509 
1^{st} & 2^{nd} Order 
.507 
1^{st}, 2^{nd}, & 3^{rd} Order 
.503 
2^{nd} & 3^{rd} Order 
.496 
It appears that the most information comes from averaging the actual standings with the second order standings. There are obviously more things that could be done such as weighted averages, but these will lead to only small gains. The lesson to be learned is that although you are better off looking only at the second order standings if asked to pick just one column, there is something added by looking at the real standings, too. Adding in which teams are likely to win close games might do something. too, but this information is probably already contained when adjusting for second order standings.
For readers’ information, and also to fuel discussion about the natural followup question of “which teams win more games than their run differentials or batting lines suggest?”, I leave you with a few lists: one that answers the question of which teams have won more games than their run differentials suggest over the last six years, one that tells you how much each team has outscored their expected runs over the last six years, and another tells you how much each team has beaten their expected runs allowed over the last six years.
Team 

Angels 
28.2 
Astros 
22.5 
White Sox 
15.7 
Diamondbacks 
10.4 
Padres 
9.5 
Marlins 
9.1 
Mariners 
8.6 
Yankees 
8.2 
Brewers 
7.1 
Red Sox 
5.7 
Reds 
4.6 
Cardinals 
4.2 
Rays 
2.3 
Giants 
2.0 
Phillies 
1.5 
Twins 
0.9 
Royals 
1.4 
Nationals 
2.7 
Mets 
4.1 
Orioles 
4.4 
Tigers 
5.0 
Dodgers 
7.1 
Cubs 
7.1 
Rangers 
8.0 
Pirates 
8.8 
Rockies 
9.4 
Athletics 
10.6 
Indians 
22.6 
Blue Jays 
24.5 
Braves 
25.1 
Team 
Runs – EQR (2005 to 2010) 
Angels 
111 
Twins 
83 
Cardinals 
79 
Rangers 
77 
Braves 
61 
Pirates 
51 
Royals 
46 
Giants 
42 
Athletics 
38 
White Sox 
25 
Rockies 
15 
Blue Jays 
14 
Tigers 
11 
Astros 
10 
Phillies 
9 
Marlins 
6 
Dodgers 
1 
Padres 
2 
Indians 
19 
Mariners 
27 
Brewers 
28 
Nationals 
39 
Yankees 
39 
Reds 
41 
Cubs 
43 
Mets 
46 
Diamondbacks 
51 
Rays 
90 
Orioles 
113 
Red Sox 
138 
Team 
Runs Allowed – EQR Allowed (2005 to 2010) 
Phillies 
170 
Angels 
100 
Pirates 
95 
Twins 
86 
Reds 
72 
Braves 
67 
Red Sox 
67 
Blue Jays 
46 
Astros 
36 
Mets 
18 
Indians 
11 
Giants 
8 
White Sox 
8 
Orioles 
5 
Cardinals 
3 
Athletics 
3 
Padres 
15 
Tigers 
15 
Mariners 
20 
Rockies 
37 
Rays 
38 
Nationals 
40 
Brewers 
45 
Yankees 
51 
Cubs 
52 
Marlins 
60 
Rangers 
79 
Diamondbacks 
89 
Royals 
111 
Dodgers 
145 
Thank you for reading
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That's a pretty significant average difference between projected and actual.
Evidence suggests that the variability of runs scored (and runs allowed) can have a significant impact on a team outperforming their Pythag expected Win Pct.
A team who has a higher than normal variability on Runs Allowed perform better, (and the corrollary that a team who has a lower than normal variability on Runs Scored) seem to consistently outperform their Pythag.
With that said, are the Angels more consistent in Runs Scored and "less consistent" in Runs Allowed than most teams?
I don't think there's any particular genius or "luck" involved, beyond noting they were initially trying to play guys like Towles and Feliz and Manzella, and coming to the belated recognition that these weren't just bad answers, they were horrific. Because they were *so* bad early, that contributed to a huge deficit in their eventual RS that helps makes them seem like massive overachievers.
Or to put it another way, Brad Mills seems like a decent skipper, but I wouldn't fire up the "certified genius" bandwagon just yet.
For example: Feliz started 2010 as the 3B starter projected for 500+ PA, but most pundits would agree that if the Astros were to make a change at that base, the most likely outcome would be a small improvement in Runs Scored. Let's say a 60% chance that any change at 3B would mean more Runs Scored, based on his own projected VORP.
His own history and projections should also imply that a change at that base during the course of 2010 would be more likely than not  let's say a 70% chance, whereas a player like Hunter Pence would have a 5% chance.
Quantifying or projecting the likelihood of player and personnel changes would be an interesting experiment to see how it would affect the projections. 4th Order?
P.S. You forgot that Kazuo Matsui started at 2nd base. That surely didn't help.