BtB Power Rankings Season Review: AL East
To round out this inaugural year of the BtB Power Rankings, I'm going to do team-by-team reviews of each division, as seen through the lens of the BtB Power Rankings. First up, the division that dominated the rankings all year: The American League East. Below, W% = true winning percentage, pW% = pythagenpat winning percentage, and cW% = component W% (the basis of these rankings). You can see all of the data I will reference in the final Power Rankings post of the year.
1. New York Yankees. TQI = 0.643
I'm a dedicated Yankees hater, but that doesn't mean I can't admire this year'd edition of the Bronx Bombers. Their expected runs scored total is 100 higher than any other team in baseball. And their defense (pitching & fielding) is solidly average. The result is a extremely powerful club that dominated our power rankings over the final months of the season. The difference between the first first and second place teams in TQI is more than the difference between second and fifth. Overall, our estimated cW% matches up well to their true W%, despite the fact that pW% is a fair bit lower (0.594 vs. 0.620 cW% & 0.634 W%). They're not unstoppable, but they have to be favored to go all the way this year. Last night, they took the first step toward that goal.
See the rest of the teams below the jump!
2. Tampa Bay Rays. TQI = 0.601
The Rays were consistently at or near the top of the power rankings all season long, which is something that caused more than a few raised eyebrows. Early in the season, this occurred despite their having a sub-0.500 record! That they surged mid-season and became, at least for a time, a legitimate threat in the East was a modest vindication--but even so, we have a 0.519 W% team as our #2 overall team. Why? We're estimating that a team with the Rays' component stats should score 24 more runs than the Rays did as well as allow a whopping 53 fewer runs. Offensively, the Rays do rate as a below-average "clutchy" team by FanGraphs' clutch measures (-23 runs), which makes up to the offensive shortfall very well. On defense, though, I think they were just unlucky. ERA was very much in line with FIP & tERA, despite the Rays having an extremely good fielding team. Somehow, despite the good fielding, those extra runs were still scoring.
3. Boston Red Sox. TQI = 0.593
Despite missing out in our "on paper" wild card berth, the Red Sox are clear ly a deserving playoff team. Their sweep at the hands of the Angels is only surprising when you remember that anything can happen in a 5-game series. Our cW% is a tad below their actual winning percentage due to a 23 run disparity between actual and estimated runs allowed. tERA is actually substantially below the team's true ERA, which speaks to the Red Sox main weakness: only the Royals rated as a poorer-fielding team. FWIW, there's a large spread between the two fielding measures I use for non-catchers: -17 runs by bUZR, but -42 runs by THT's team stat. When there's a dispute, I tend to lean towards bUZR, as it has more info to work with...so perhaps we're underrating them?
6. Toronto Blue Jays. TQI = 0.554
The Blue Jays had an early lead in the BtB Power Rankings as well as their own division, but began drifting downward by mid-June. Ultimately, the team fell well short of 0.500, leading to the ouster of their general manager. But they never quite fell that far according to either their pythagorean record (pW%) or our cW%. As pW% and cW% are both PythagenPat-based methods, I wonder if fairly severe changes in team quality from one part of the season to the next aren't handled appropriately when you're just looking at aggregate runs scored & allowed. Or, perhaps, the Blue Jays just got unlucky in the end. I'm not sure what to think of them looking to next year. They're not an especially young team, and I'm skeptical about whether break-out players like Scutaro or Hill will repeat.
24. Baltimore Orioles. TQI = 0.439
The power rankings rate the Orioles as the worst team in the American League. There's good agreement across all of our different win "measures," from reality to cW%. Breaking them down is simple too: the Orioles were below average in hitting, pitching, and fielding. But I'll say this: if there's one team that stands to benefit the most from a proper strength of schedule adjustment, it's the Orioles. The Power Rankings think that the AL East contained the three best teams in baseball this year, and four of the top six. That's absurd, but those are the teams that the Orioles have to face in...what, half of their games thanks to the unbalanced schedule? Since their opposition is the toughest in baseball, their players' numbers suffer more than any team in baseball. So cut the poor birdies some slack.
BtB Power Rankings 2009 End of Season Data
Team by Team reviews: AL East | AL Central | AL West | NL East | NL Central | NL West
BtB Power Rankings 2009 End of Season Data
Team by Team reviews: AL East | AL Central | AL West | NL East | NL Central | NL West
BtB Power Rankings 2009 End of Season Data
Team by Team reviews: AL East | AL Central | AL West | NL East | NL Central | NL West
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Comments
I really liked these blurbs, Justin.
They’re especially useful for when people go “why was a team ranked HERE”? Instead of looking up all the component data myself, you’ve already aggregated it for me. Thanks!
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by Sky Kalkman on Oct 12, 2009 2:41 PM EDT reply actions 0 recs
thanks
I originally was going to stick all of these in the final BtB post, but I figured they represented a “minimum publishable unit” and could stand alone as a nice mini-series.
I have about half of the teams written already, so I should be able to do one per day this week (barring real life interrupting).
-j
by JinAZ on Oct 12, 2009 2:45 PM EDT up reply actions 0 recs
Can we get a full article on the Royals ranked above the Giants?
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by Sky Kalkman on Oct 12, 2009 2:49 PM EDT up reply actions 0 recs
I'll think on it
But it’s basically all going to be about the league adjustment, which I’m frankly tired of fighting about. :)
-j
by JinAZ on Oct 12, 2009 2:50 PM EDT up reply actions 0 recs
I was mostly kidding -- was just reading reactions at some other sites.
A nice run through of why the Giants are so low should fit in the NL West article.
- league adjustment
- scored 50 runs more than estimated (clutch = 30, the rest = ?)
- allowed 40 runs fewer than estimated (?)
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by Sky Kalkman on Oct 12, 2009 3:01 PM EDT up reply actions 0 recs
And maybe we could get a full article on why you’re very, very wrong about ranking the Royals ahead of the Giants. Greinke was great, and Butler was okay, but that’s about it; if the Giants were in the AL Central they would be playing postseason baseball. Going from baseball’s 2nd best division to its weakest, and adding a DH to alleviate some offensive struggles? No question.
by quincy0191 on Oct 18, 2009 5:12 AM EDT up reply actions 0 recs
Great work, Justin. This is one of my favorite parts of our entire site.
See Data Differently.
beyondtheboxscore.com | Twitter: @ justinbopp
by Justin Bopp on Oct 12, 2009 8:17 PM EDT reply actions 0 recs
2009 Rays
Analize this: How many more runs would the Rays have scored, how many more RBI would Pena and Longoria have had, how many wins the team would have achieved by having a different more productive lineup with no Upton leadoff, no Burrell, Navy and Kapler playing and Zobrist/Aybar seating because Maddon had a “better idea” of what to do with the lineup? I think this(along with the bullpen missmanagement) is the key of the underachieving Rays this season: an stubborn (baseball?) manager with more interest in some individual players than the whole TEAM. Clutchy? Too many rally killers competing for Team LVP.
by Luis D on Oct 14, 2009 1:32 AM EDT reply actions 0 recs
Lineups do matter
But usually by less than a win or two per year between the best possible and worst possible lineups (worst=pitcher hitting 4th or leadoff, etc). That would be the kind of thing that would be among the reasons that a team might underperform their expected offensive levels, though it would have to be a pretty gross divergence from a typical team lineup.
John Beamer put together a nice Markov model a few years ago for the THT Annual that is one of the better tools to analyze lineup effects. At some point, i’d like to revisit it—lots of cool stuff that you can do with that model. Unfortunately, last time I “used” it, I was using his spreadsheet incorrectly, and as a result put out a horrifically flawed study. I know how to use it now, but I sort of lost all credibility on lineup issues there. :}
-j
-j
by JinAZ on Oct 14, 2009 12:26 PM EDT up reply actions 0 recs













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