Measuring team efficiency
By Tom Tippett
December 9, 2003
It goes without saying that wins and losses are the most important things
to consider when judging a team's performance. They are, after all, what
the game is all about and what determines who gets to keep playing until
there's only one winner left.
The next most important things are runs scored and runs allowed. You
win games by outscoring your opponents, so the connection between runs
and wins is very strong. It's not perfect, though, and every season produces
a few teams that win more or less than you'd expect given their run differential.
If runs are one step removed from wins, then the events that produce
runs are two steps removed from wins. You score runs by putting together
singles and walks and doubles and steals and homers, and you prevent runs
by holding the other team to a minimum of those things.
In most cases, there's a very direct relationship between wins and runs
and the underlying events that produce runs. But that's not always the
case, and in this review of the 2003 season, we'll identify teams where
those relationships didn't hold up. If the past is any guide, this will
give us some very strong hints about what is likely to happen with those
teams in the future.
To explore the relationship between runs and wins, we'll use the pythagorean
method that was developed by Bill James. To explore the relationship between
offensive events and runs, we'll compare measures that translate batting
events into estimated runs and then compare those estimated runs to actual
runs. This will tell us which teams were unusually good at turning offensive
events into runs and unusually good at keeping the other team from doing
the same.
In the 2002 edition of this article, we presented
the results of a study showing that teams that are unusually efficient
(or inefficient) have exhibited a very strong tendency to revert back
to the norm the next year. That's good news for some teams and bad news
for others. If you'd like to find out who falls into which category, read
on.
Converting runs into wins
Others, notably Rob Neyer of ESPN.com, have written extensively about
the Bill James pythagorean method, a well-established formula based on
the idea that a team's winning percentage is tightly coupled with runs
scored and runs allowed. The expanded standings on ESPN.com include run
margins and expected win-loss records that are derived using this formula,
and Rob's home page showed pythagorean standings every day.
The formula is quite simple ... take the square of runs scored and divide
it by the sum of the squares of runs scored and runs allowed (RF = runs
for, RA = runs allowed):
RF ** 2
Projected winning pct = -----------------
RF ** 2 + RA ** 2
In 2002, for instance, the Red Sox and Cubs won 8 fewer games than their
run margin would normally produce, while three teams (Minnesota +7, Oakland
+6, and Detroit +6) won at least six more than expected. By looking at
40 years of baseball history, we showed that large deviations are unusual
and tend not to be repeated the following year.
How did those 2002 teams fare in 2003? The Red Sox matched their pythagorean
projection, the Cubs exceeded theirs by two games, Minnesota (+5) had
another good result, Oakland (+1) was normal, and Detroit (-4) flipped
to the negative side. There's almost no correlation between the 2002 and
2003 results for these teams, as expected.
In 2003, 26 of 30 teams produced win totals that were within five games
of their pythagorean projection. Two of the other four won more games
than expected. Cincinnati picked up seven more wins and San Francisco
was plus six. The underachievers were Houston (-8) and Seattle (-6), two
teams that would have won their respective divisions had they managed
to win as many games as their run margins would normally produce.
Converting offensive events into runs
Runs don't appear out of nothing, so in this section we'll take another
step back and look at the offensive events -- the hits and walks that
led to the runs that generated the wins -- produced and allowed by each
team.
Just as there is a strong relationship between runs and wins, it's almost
always true that the more hits and walks you produce, the more runs you'll
score. Sometimes, of course, a productive team comes up short on the scoreboard
because they didn't hit in the clutch or were unlucky enough to hit line
drives right at people in key situations. But this relationship holds
up most of the time.
To shed some light on this relationship, we need a way to take batting
stats and turn them into a measure of overall offensive production. There
are several good options here, including Runs Created (Bill James), Batting
Runs (Pete Palmer), Equivalent Average (Clay Davenport), and OPS (on-base
average plus slugging average). But many of them require a computer, and
although we do computer analysis all the time, we also like to use simpler
measures that anyone can use whenever they have a page of stats in front
of them. The best of these simple methods give up very little accuracy
in return for a big gain in usability.
For this exercise, we'll use the sum of total bases and walks, or TBW
for short. TBW is not a perfect measure, but it does have a few things
going for it. It captures the most important things a team does to produce
runs -- singles, extra-base hits, and walks. It's easy to figure without
a computer. In the past, we've used both TBW and OPS for this type of
analysis, and the results are almost exactly the same, so the accuracy
is more than acceptable.
And sometimes it just seems to tell a story more clearly. For instance,
the 2003 Red Sox had a team OPS of .851 compared to the .809 mark of the
Yankees. Even though we've been working with OPS figures for many years,
we still need to stop and think about what a 42-point advantage means.
But if you tell us that Boston produced 228 more total bases and walks
than the Yankees, that's something we can grasp.
As with other statistics, a team's TBW total can be significantly influenced
by its home park. For that reason, we generally look at the difference
between the TBW produced by a team's hitters and the TBW allowed by its
pitchers. This effectively removes the park from the equation and helps
us focus on whether the team outproduced its opponents.
The following table shows the offensive and defensive TBW figures for
the 2003 American League, along with the difference between these two
figures and each team's league rank based on those differences. It also
shows runs for and against, the run differential, and the rankings based
on run differential. Finally, because we're trying to trace a path from
TBW to runs to wins, it lists the team's win total and league rank for
the year.
---------- TBW ---------- ------- Runs -------- - Wins -
AL Off Def Diff Rank Off Def Diff Rank Num Rank
NY 3224 2695 +529 2 877 716 +161 1 101 1
Bos 3452 2858 +594 1 962 809 +153 3 95 3
Tor 3119 2981 +138 6 895 826 + 69 6 86 6t
Bal 2728 3062 -334 12 743 820 - 77 10 71 10t
Tam 2706 3062 -356 13 714 853 -139 12 63 13
Min 2952 2789 +113 7 801 758 + 43 7 90 5
Chi 2965 2839 +270 5 791 715 + 76 5 86 6t
KC 2854 3189 -248 11 836 867 - 31 9 83 8
Cle 2701 2930 -182 9 699 778 - 79 11 68 12
Det 2493 3006 -669 14 591 928 -337 14 43 14
Oak 2847 2595 +304 3 768 643 +125 4 96 2
Sea 2868 2710 +295 4 795 743 +158 2 93 4
Ana 2741 2647 - 73 8 736 637 - 7 8 77 9
Tex 3057 3205 -220 10 826 969 -143 13 71 10t
As you can see, the team rankings using TBW and those using run differentials
are similar. The similarities are to be expected given the strong relationships
between batting events and runs and between runs and wins. But it's the
differences that tell some of the most important stories of the 2003 season
and provide the most insight into what's likely to happen next year.
Boston's TBW differential of +594 was the fourth best in the past 30
years. So why didn't they run away with the division? Because they were
highly inefficient on both sides of the ball. The Runs Created (RC) formula
predicts 1014 runs for a team with their offensive stats, but Boston scored
52 fewer runs than that. That was the biggest RC shortfall in the majors.
If you apply the RC formula to the stats compiled by Boston's opponents,
you'd predict a total of 761 runs allowed. The Red Sox actually allowed
48 more runs than that. This was also the worst mark in the majors. (This
won't come as a surprise to anyone who read our stolen
games article in September.)
Some people have questioned the decision to fire Grady Little as Boston
manager after he won 93 and 95 games in his two seasons at the helm. We're
not saying that he was the reason the Red Sox tied the Cubs for 2002's
largest pythagorean shortfall or that he was responsible for Boston having
the least efficient offense and defense in 2003. Little may have been
a big part of the reason why both teams were so successful at the TBW
level in the first place. But when a team fails to translate those achievements
into wins two years in a row, somebody's going to start wondering how
to change that.
Another interesting story is developing at the bottom of the AL East.
Baltimore won eight more games than Tampa Bay, but their advantage in
TBW was only 22 bases. If Baltimore doesn't follow through on their widely-rumored
plans to make some big moves this winter, they'd better start looking
in their rear-view mirror. From 2002 to 2003, Baltimore's TBW differential
worsened by 18 bases while Tampa Bay's improved by 223.
In the AL Central, Chicago outdid Minnesota by a big margin in TBW differential
and edged the Twins in run differential, but handed the division to the
Twins when they lost five straight head-to-head meetings in September.
The Royals boasted baseball's most efficient offense, exceeding their
Runs Created figure by 48 runs. In fact, their TBW total was about 100
bases shy of both Chicago's and Minnesota's, yet they outscored both of
their division rivals by a wide margin. In fact, Cleveland's TBW differential
was quite a bit better than Kansas City's. This is not a good sign for
next year's Royals.
The Tigers are worth mentioning only because they were so bad. Their
TBW differential of -669 was the fourth-worst we've seen since 1974, the
first year for which we have all the stats we need to compute these figures.
Unfortunately for fans of this once-proud franchise, this club joins the
1996 Tigers (-727) and the 2002 Tigers (-592) among the six worst teams
of the past thirty years. We noted above that Tampa Bay improved by 223
TBW this year. That's a very big jump, but the Tigers would have to accomplish
this feat three years in a row just to get back to .500 and another year
or two after that to be in position to challenge for a division title.
In the AL West, Oakland topped Seattle in TBW differential but trailed
the Mariners in run margin. In other words, Seattle was more efficient
in the events-to-runs transition and much less efficient in the runs-to-wins
transition. With two teams so evenly matched, the division could have
gone either way.
In last year's edition of this article, we pointed out that (a) Anaheim
had the most efficient offense in the majors, (b) Anaheim had the second-most
efficient defense in the majors, and (c) teams that are highly efficient
or inefficient tend to settle back toward the league average the next
year. In 2003, the defending world champs were near the league average
in both offensive and defensive efficiency, and that, coupled with a rash
of injuries to key players, was a big reason why they were not a factor
this time around.
Moving on to the National League:
---------- TBW ---------- ------- Runs -------- - Wins -
NL Off Def Diff Rank Off Def Diff Rank Num Rank
Atl 3241 2780 +461 1 907 740 +167 1 101 1
Flo 2825 2703 +122 7 751 692 + 59 6 91 3
Phi 2976 2729 +247 3 790 697 + 93 4 86 6
Mon 2702 2813 -111 11 711 716 - 5 10 83 10
NY 2488 2976 -488 15 641 753 -112 13 66 15
Chi 2789 2636 +153 6 725 683 + 42 7 88 4
Hou 2964 2722 +242 4 805 677 +128 2 87 5
StL 3154 3077 + 77 8 876 796 + 80 5 85 7t
Pit 2875 2918 - 43 10 753 801 - 48 12 75 11
Cin 2700 3217 -517 16 694 885 -191 16 69 13
Mil 2872 3212 -340 14 714 873 -159 15 68 14
SF 2912 2624 +288 2 755 638 +117 3 100 2
LA 2417 2420 - 3 9 574 556 + 18 9 85 7t
Ari 2851 2669 +182 5 717 685 + 32 8 84 9
Col 3077 3194 -117 12 853 892 - 39 11 74 12
SD 2712 3026 -314 13 678 832 -154 14 64 16
Atlanta was the league's top team, ranking first in TBW differential,
run differential, and wins. Houston and San Francisco were a good match
statistically, with the Giants second in TBW differential and Houston
second in run margin, but that's where the similarity ended. Houston was
eight wins shy of its pythagorean projection while San Francisco was plus
six, so the Giants won 13 more games. Philadelphia was number three in
TBW differential and number four in run margin, but like the Astros fell
a few wins short of their pythagorean record.
That's an interesting rundown. We identified four NL teams that outproduced
and outscored their opponents by the largest margin during the regular
season, yet two of them missed the playoffs altogether and the other two
were eliminated in the first round. Contrast that with 2002, when the
World Series featured Anaheim and San Francisco, teams that led their
respective leagues in run margin (even though neither won its division).
The big story in the NL East was the battle for second place. Philly
was the better team statistically, but Florida won the season series 13-6
and took second place (and the wildcard) by five games. The rest, as they
say, is history. In fact, Florida's ability in the regular season to overcome
a statistical deficit and "just win, baby" was repeated in the
World Series, when the Yankees topped the Marlins in every statistical
category except the all-important win column.
The Cubs and Astros played a similar tune in the NL Central. Houston
piled up the TBW and the runs but fell eight wins short of their pythagorean
record. Chicago won some big September games and eked out a one-game victory
despite some good-but-not-great underlying stats.
You may recall that we singled out the 2002 Cubs as a team that was highly
inefficient and likely to bounce back in 2003. That 2002 team was very
inefficient on offense and on defense and still managed to fall eight
games shy of their pythagorean record. As efficient as the Angels were
that year, the Cubs were far worse in a negative way.
Just as history told us that Anaheim wouldn't be able to sustain their
success, it told us that Chicago could be expected to contend in 2003
even though they were coming off a 65-win season. A return to normal levels
of efficiency would get them two-thirds of the way there. As it turned
out, their offensive and defensive efficiency in 2003 was right around
the league average and they were two games over their pythagorean record.
The rest of their success can be chalked up to a fundamental improvement
in the team, as indicated by a 114-base improvement in their TBW differential.
For Reds fans, this table contains bad news. Cincinnati won 69 games,
but their underlying numbers were even worse. Only the Tigers had a larger
TBW deficit, and several of the game's recent doormats -- Tampa Bay, Baltimore,
Milwaukee, San Diego -- outperformed the Reds by 170 TBW or more. In fact,
Cincinnati's deficit of 517 TBW is the 13th worst mark since 1974.
The Giants ran away with the NL West, but the Diamondbacks could have,
and perhaps should have, mounted a serious challenge. Arizona's TBW differential
was a highly-respectable +182, good for fifth in the NL. With the Giants
at +288, Arizona could have hung within a few games of the leaders and
maybe stolen the division with a few clutch wins down the stretch, much
like the Twins, Marlins and Cubs did in their races. But the Diamondbacks
lineup was next to last in offensive efficiency, scoring only 717 runs
with an offense that created 760 runs according to the Bill James formula.
Staying at the top
It's one thing to have a single winning season and quite another to put
together a club that lives in the upper echelon year after year. Just
for fun, we compiled the following table of TBW differentials for the
period from the last expansion to the present, 1998 to 2003. The last
six columns show the number of division titles, wild cards, times in the
postseason, Division Series wins, League Championship Series wins, and
World Series wins in that span.
Qualifying -Advancing-
Rk Team TBWD Dv WC PS LDS LCS WS
1 Yankees +2683 6 6 5 5 3
2 Red Sox +2409 3 3 2
3 Braves +2233 6 6 3 1
4 Giants +1884 2 1 3 1 1
5 Athletics +1625 3 1 4
6 Mariners +1474 1 1 2 2
7 Astros +1269 3* 3
8 Diamondbacks +1217 3 3 1 1 1
9 Cardinals + 859 3* 3 2
10 Indians + 658 3 3 1
11 White Sox + 459 1 1
12 Dodgers + 256
13 Blue Jays + 229
14 Cubs - 159 1 1 2 1
15 Phillies - 181
16 Mets - 185 2 2 2 1
17 Angels - 207 1 1 1 1 1
18 Rangers - 436 2 2
19 Reds - 686
20 Rockies - 862
21 Padres - 868 1 1 1 1
22 Twins - 875 2 2 1
23 Expos - 968
24 Orioles - 975
25 Pirates -1224
26 Marlins -1334 1 1 1 1 1
27 Brewers -1758
28 Tigers -2084
29 Royals -2091
30 Devil Rays -2362
* Houston and St. Louis tied for the division title in 2002
Run Efficiency Average
In last year's team efficiency article, we introduced a new statistic
called Run Efficiency Average, or REA for short. The purpose of REA was
to quantify the relationship between TBW and runs in a way that was (a)
simple enough to compute without having to write a computer program, (b)
accurate enough to be useful, and (c) had some predictive value.
REA is simply runs divided by TBW, and it turns out that run efficiency
averages look an awful lot like team batting averages. From 1974 to 2003,
team batting averages ranged from a low of .229 to a high of .294 with
a midpoint of .261. Baseball fans know from experience that a team batting
average of .280 or higher is very good, and one below the .245 mark is
woeful.
In this time period, run efficiency averages have ranged from .225 to
.305 with a midpoint of .264. The midpoint and the spread are slightly
higher than for team batting averages, but the benchmarks are basically
the same. Anything over .280 indicates a very efficient offense, while
anything under .245 indicates a team that squandered a lot of its chances.
A year later, our feelings about REA are quite mixed. In some ways, it
has proved to be a very useful tool, but we also found it to have some
limitations. The measure does have some predictive value, but it appears
that we asked it to do more than it could.
Let's start with predictive value. In 2002, we pointed out that teams
with extreme REA values tend to retreat toward the league average the
following season, though it wasn't as strong a tendency as we saw for
the pythagorean differentials, which tend to disappear completely the
next year. Here's how things look when we compare 2002 to 2003:
- of the top five AL teams in offensive REA, four moved toward the league
average; of the bottom five, four moved toward the league average
- of the top five NL teams in offensive REA, three moved toward the league
average; of the bottom five, five moved toward the league average
- of the top five AL teams in defensive REA, four moved toward the league
average; of the bottom five, three moved toward the league average and
one stayed the same
- of the top five NL teams in defensive REA, two moved toward the league
average and one stayed the same; of the bottom five, two moved toward
the league average
As you can see, most of these teams moved in the direction we expected,
so REA was a pretty good leading indicator for the 2003 season. That makes
sense. We reached the conclusion that it would work after studying 30
years of data, and we couldn't think of any reason why it would stop working
all of a sudden.
During the 2003 season, we decided to start tracking offensive and defensive
REA on a weekly basis to see if we could gain some insight into what might
happen during the remainder of the season. We wondered whether teams that
began the year at a very high or low level of efficiency would be able
to sustain that level, or whether they would be drawn toward the league
average as the year progressed.
It's tempting to say that it worked here, too. Consider the following
examples, each of which compares team efficiency as of May 18th to that
of the end of the season:
- Boston and Toronto were leading the AL with offensive REAs of .294
and .293, respectively ... by the end of the year, Toronto was down to
.287 and Boston had fallen to .279
- the Dodgers and Expos were leading the NL with defensive REAs of .215
and.228 ... by season's end, they were up to .230 and .255
- the Padres and Marlins trailed the NL with offensive REAs of .230 and
.234 ... they finished at .250 and .266
- roughly 80% of the offensive and defensive REAs moved closer to the
league average between May 18th and the end of the year
So why aren't we getting more excited about REA? Well, for one thing,
many statistics tend toward the league average as the season goes along,
so the fact that REA does this doesn't necessarily mean that it has more
predictive value than other stats. It might be a better predictor
than other stats, but we haven't studied this enough to know for sure.
In 2003, at least, REA was more likely to move toward the league average
than were runs per game and ERA. That's something, but not enough to prove
the worth of REA.
Another concern is that we fell into a trap early in the season and tried
to do too much with REA. Here's an example that makes the point pretty
well. As of May 18th, the Dodgers defensive REA was .215, an exceptional
figure -- 44 points lower than the NL average and on pace to be the best
in thirty years.
While we were right to believe that they would tend toward the
league average from this point, we made the mistake of assuming that it
was normal for all pitching staffs to be at or near the league
average by the end of the year, that any deviation was transient and due
to favorable luck.
But it wasn't. The deviation had something to do with luck, to be sure,
but it also had something to do with the incredible quality of the Dodgers
pitching and defense. These guys were really, really good, and they would
go on to post better-than-average defensive REA numbers all season.
Here's the problem. We knew last year that REA was an approximation,
not an exact measure of how TBW and runs are related. We've always known
that run creation is not linear -- that adding four TBW doesn't always
produce one more run. Four TBW in a run-scarce environment (such as the
2003 Dodgers batting at home) may be squandered. Four TBW in a run-rich
environment (the 2003 Red Sox in Fenway) may produce more than a run because
the team is constantly threatening to score. That's why non-linear formulas
like Runs Created have proven to be among the most accurate estimators.
If we knew that REA was an approximation, why did we use it instead of
Runs Created in the first place? Because a non-linear relationship can
be close enough to linear to allow for a favorable trade-off of accuracy
for simplicity.
For example, Bill James developed the log5 method for predicting the
likely outcome of a game involving two teams with known winning percentages.
That method is non-linear, as it should be. But it turns out there's a
linear approximation that is simple enough to do in your head and gives
almost identical results for any matchup involving teams with winning
percentages in the .400 to .600 range. Because this range includes the
vast majority of real teams, the approximation provides a big gain in
simplicity in return for a very small loss of accuracy, as long as you
remember that it doesn't work outside that range of winning percentages.
Hoping to find something similar for run efficiency, we plotted runs
against REA for every team-season since 1974 and saw a very strong linear
relationship. It wasn't perfect, but these things never are, and it seemed
close enough. REA values also had the virtue of looking a lot like team
batting averages, so we could tell at a glance whether a team was doing
well or doing poorly.
Then, in 2003, we witnessed some historically good and bad performances.
The Red Sox set a new all-time record for slugging percentage. The Braves
offense was just as impressive, perhaps even more so, falling only a little
short of the Red Sox even without the DH. And the Dodgers allowed runs
at only 70% of the NL average rate, one of the five best percentages ever.
These extreme teams, and some others that weren't quite as far from the
league averages, pushed the boundaries of the linear approximation that
was the basis for the REA figures.
For example, the Dodgers allowed a total of 2420 TBW on the season, and
if those TBW were converted at the league average REA of .261, they would
have conceded 632 runs. They actually allowed only 556, so one might argue
that their efficiency was so good that they saved 76 runs relative to
the norm. By extension, if you believe this sort of efficiency is transient,
as we argued last year, you'd conclude that they could pitch just as well
in 2004 and still allow a lot more runs.
But the Runs Created formula tells us that a team with these stats normally
allows 573 runs, a figure that is much closer to their actual runs allowed.
Or, to put it another way, the normal REA for a team as good as LA would
be 573 / 2420 = .237. This puts their May 18th figure of .215 in a whole
different light. We would still conclude that they'd been more efficient
than expected, but the target for a correction would be somewhere in the
.230s, not somewhere in the .260 range. In fact, their defensive REA after
May 18th was .235, so the Runs Created formula was a better predictor
of future performance than was the notion that REAs tend to retreat to
the league average.
If each team has a different norm for REA that depends on the quality
of the team, we cannot glance at an REA number and know whether it's like
to rise or fall in the future. And if we have to check a second option,
like Runs Created, to find what normal is, why not just start with Runs
Created in the first place?
Regular readers of our weblog
may have noticed that we mentioned REA a fair amount early on and then
stopped talking about it. This is why. We realized that the normal REA
for some teams isn't the league average. We're not ready to dump REA altogether,
because it may still have some value as a quick-and-dirty filter for teams
that deserve a closer look, but we no longer believe that it tells enough
of the story to stand on its own.
Looking ahead
We'll end with a quick list of teams that might see an efficiency-related
change in performance:
Offenses likely to be more efficient in 2004: Red Sox, Diamondbacks,
Brewers, Padres, Twins.
Offenses likely to be less efficient in 2004: Royals, Athletics,
Reds, Rockies, Mariners.
Defenses likely to be more efficient in 2004: Red Sox, Cubs, Devil
Rays, Braves, White Sox.
Defenses likely to be less efficient in 2004: Mets, Cardinals,
Orioles, Expos, Brewers.
This is based on a comparison of actual runs versus Runs Created in 2003,
and by "defenses", we mean the combined ability of the pitchers
and fielders to prevent runs.
By the way, don't confuse a change in efficiency with being better or
worse next year. Just because the Red Sox were inefficient on offense
this year, it doesn't mean they're likely to score more than the 962 runs
they brought home this past season. Even if efficiency does improve, they
may not produce as many TBW in the first place because of changes in personnel
or the inability of current players to repeat their strong 2003 performances.
A lot of things will change between now and opening day. This process
of looking at TBW differentials and run margins doesn't tell us how the
2004 season will unfold, but it can identify some teams that might have
more or less work to this winter than you may have thought.
For instance, the Royals had a winning record despite finishing with
the league's 11th best TBW differential, so they could easily fall into
the low-to-mid 70s in wins next year. Even if they have better luck on
the injury front in 2004, they may struggle to stay at or above the .500
mark. The Orioles, Expos, and Reds are three other teams whose stats don't
fully support their 2003 win-loss record.
Last year, we used this sort of analysis to identify the Cubs as a team
that could rebound in a big way in 2003:
"I wouldn't mind being Dusty Baker right now ...
The Cubs are the team most likely to get a large efficiency-related
bounce, and with one of baseball's best-regarded farm systems, they
are poised for a strong run in the NL Central."
It's much harder to find teams like that this time.
The Tigers fit the pattern of being inefficient on offense, inefficient
on defense, and coming up short of their pythagorean record, but they're
too far down to make any serious noise, even with a big bounce.
Tampa Bay has a good chance to be better, but they're in a division with
two big spenders (Yankees, Red Sox), a very interesting Toronto team,
and an Orioles club that may add a couple of big-time free agents, so
the Devil Rays could improve and still find themselves struggling to gain
ground.
What about the San Diego Padres? With a new stadium, a healthy Phil Nevin,
a full season from Brian Giles, Ramon Hernandez behind the plate, some
interesting young pitchers, and a new stadium to help fund some additional
moves, the Padres are well positioned to advance. But they're starting
from a season where their TBW differential was -314, whereas the Cubs
were in positive territory a year ago, so it's probably asking too much
for San Diego to go from worst to first in one year.
If any team has a chance to come out of the pack and challenge for a
division title, it might be the Indians. Because the Tribe was near average
in efficiency and only four games shy of their pythagorean record, they're
not all that similar to the '02 Cubs. But Cleveland's TBW and run differentials
weren't all that bad for a team that was giving some young players a lot
of valuable experience, and there are no powerhouse teams in the division
to contend with.
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