This is being discussed at Beyond The Boxscore and Baseball Think Factory. With 685 runs scored and 605 allowed, their Pyth pct is .561, good for 91 wins in a season. But they actually have a .613 pct, good for about 99 wins. So the gap is projected to be about 8 (this comes from the Bill James idea that a team's winning percentage is going to be close to their runs scored squared/(runs scored squared + runs scored allowed)).
The big reason why the Angels are doing better than expected is how well they are doing in close and late situations (situations "in the 7th or later with the batting team tied, ahead by one, or the tying run at least on deck" according to Baseball Reference, where I got my data from along with ESPN). Then they also hit extremely well with runners on base (as I explain in Part 2). Part 3 discusses how well the Angels are doing by a sophisticated measure called Win Probability Added.
I did a study a few years ago called Does Team Clutch Matter in Baseball?. The equation for pct was
(1) PCT = 0.49 + 1.27*OPS - 1.26*OPPOPS
Where OPPOPS is the OPS allowed by a team's pithcers. Using only walks and hits in OBP, the Angels have an .804 OPS and a .787 OPS allowed. It predicts they will have a pct of a .519.
But if you break it down by close and late situations and non close and late situations it was
(2) PCT = 0.501 + 0.918*NONCLOPS + 0.345*CLOPS - 0.845*OPPNONCLOPS - 0.421*OPPCLOPS
For hitting, their OPS was .796 in NONCL and .852 in CL. For pitching, it was .799 & .707. So they get predicted to have a .553 pct. That .033 gain over 162 games is 5.41 wins.
With 685 runs scored and 605 allowed, their Pyth pct is .561, good for 91 wins in a seasons. But they actually have a .613 pct, good for about 99 wins. So the gap is projected to be about 8. But 5.4 (or about 2/3) of that is due to their close and late performance. And as the next part shows, some of the rest of the gap will be explained by how well they hit with runners on base (they don't pitch any better with runners on base than they do normally).
But the more important comparison is between the .519 predicted by equation (1) and and the Angel's actual pct of .613. The .519 says they should win about 84 games. So the gap is 15. The 5.4 explains about 1/3 of it. Still, a big chunk.
One more thing, and to make another long story short, and using formulas from my team clutch study linked above, taking RISP performance into account would add another .023 to the Angel's pct (similar to what I did with equation to and close and late situations). That amounts to 3.7 additional wins. It is probably not that much, since some RISP situations happen when it is close and late and I already did a calculation that took that into account. But if about 25% of PAs are with RISP and about 15% when it is C&L, then maybe 3.75% are both. Not sure if is that simple, but I think most of that 3.7 could be added to the 5.4 I got before and get us close to 9 wins, 60% of the differential of 15.
The Angels have scored about about .25 more runs per game than expected. From a regression, the runs per game in the AL this year can be estimated by
R/G = 5.96*SLG + 24.96*OBP - 6.01
It says the Angels should score 5.50 R/G but they actaully have 5.76. So over the whole season, that is about an extra 40 runs. But they are doing it because of their phenomenal hitting with runners on base (ROB). The overall AVG-OBP-SLG this year are 0.290-0.354-0.451. But with ROB, they are 0.307-0.378-0.464. So their differential in all three with ROB are .019-.024-.013.
The AL league averages for AVG-OBP-SLG are .266-.335-.430. With ROB, they are .270-.345-.430. The differences are .004-.010-.000. So the Angels ramp it up alot more with ROB than most other teams.
Then I ran a regression with SLG and OBP broken down by ROB & NONROB. The equation was
R/G = 6.94*NONROBOBP + 4.46*NONROBSLG +17.18*ROBOBP + 1.72*ROBSLG -5.98
This predicts that the Angels would score about 5.63 runs per game. Over a whole season, it means they are scoring about 21 more runs than expected. So taking their ROB hitting into account, we reduce their differential by about half. When I did OBP for both regressions, I only included walks and hits. So the OBPs used are slightly different than what I report (from ESPN). Now their will be some overlap between close and late situations and ROB situations, but my guess is that the gap between actual and predicted wins from Part 1 will be even smaller once ROB is taken into account.
The Fangraphs website has a more sophisticated measure. It uses WPA, or Win Probability Added. It is a stat which credits a player with how much he changes his team's probability of winning after a given plate appearance. A guy might get a hit, raising the prob. by .1 or make an out lowering it by .1. These probabilities are based on historical data of how often teams win games in certain situations (leading by 2 in the seventh inning, trailing by 4 in the 8th, etc.) It also takes into account how the base-out situation changes, which affects runs and the chances of winning.
So far this year, Fangraphs has the Angels as +4.79 clutch for their hitters and +4.18 for their pitchers. So that is 8.92 extra wins due to clutch performance (that is, doing better in clutch situations than they normally do). What it means is that the Angels, whether the batters or pitchers, are really coming through the closer and later the game and the more runners on base there are. You can see this data at
Fangraphs Win Probabilities for batters
Fangraphs Win Probabilities for pitchers