Magic Numbers (NL)

Philadelphia (97-65) #1 SEED – Will Host CIN
San Francisco (92-70) #2 SEED – Will Host ATL
Cincinnati (91-71) #3 SEED – @PHI
Atlanta (91-71) WILDCARD – @SF

New York

Leading Off

Denard Span has without question been the Twins best leadoff hitter and one of the reasons often cited for that is the fact that he sees a lot of pitches early in the game and forces the pitcher to show all of their pitches so the other hitters know what they are up against.  The fact that Span is more patient when leading off the game is pretty indisputable.

Stat Leading Off Other Total
PA 66 243 309
Pitches/PA 4.38 3.86 3.97
0-strike 2 (3%) 54* (22%) 56* (18%)
2-strike 35 (53%) 98 (40%) 133 (43%)
Full 11 (17%) 32 (13%) 43 (14%)
* – includes 4 plate appearances without a strike thrown (3 four-pitch walks and a HBP)

He sees a significantly larger number of pitches in the first inning than his other at-bats (of course a small sample caveat has to be applied to this whole exercise).  He waits for the first strike almost every time when leading off (as opposed to putting the first strike in play more than 1 in 5 times otherwise) and gets to two-strike counts in over half of his first inning plate appearances.

But it seems like he may be taking the patient approach to the point where it is detrimental to his production.

Stat Leading Off Other Total
PA 66 243 309
BA .211 .312 .291
OBP .318 .395 .379
SLG .228 .427 .384
OPS .546 .822 .763

A .546 OPS is nearing Punto (2007 version) territory and it’s clearly not reflective of the hitter that Span is.  It’s nigh impossible to calculate the effect the extra pitches seen have on the rest of the lineup, but it is at least a disturbing pattern for the Twins leadoff man.  More 2-strike counts will depress anyone’s numbers and it seems to me, the Twins would be better served if Span focused less on maximizing the number of pitches he sees, and more on putting the ball in play when he has the best chance for success, which seems to be earlier in the at-bat for him.

After One Third of the Season: RISP

Here are the numbers for runners in scoring position (and how often they drive them in).  See the post below for the 2-out numbers.

Player Chances Driven In %
Mauer 37 19 51
Morneau 77 27 35
Young 42 13 31
Kubel 67 18 27
Cuddyer 81 21 26
Punto 55 14 26
Span 72 17 24
Buscher 24 5 21
Harris 38 7 18
Crede 55 10 18
Tolbert 28 5 18
Morales 17 3 18
Redmond 19 3 16
Casilla 45 7 16
Gomez 33 4 12

After One Third of the Season: 2-out Hitters

Here are the numbers for the Twins with runners in scoring position and two out through the first 54 games of the 2009 season.

Player Chances Driven In %
Mauer 14 6 43
Crede 17 6 35
Young 18 5 28
Morneau 26 7 27
Punto 16 4 25
Morales 9 2 22
Span 32 7 22
Kubel 36 7 19
Cuddyer 37 7 19
Casilla 11 2 11
Harris 19 2 11
Gomez 14 1 7
Tolbert 14 1 7
Buscher 14 1 7
Redmond 10 0 0

After One Third of the Season: Advancing Runners

Looking at players’ ability to move runners up in situations with none out.

The last column is the number of bases the runners advanced per 100 plate appearances.

Player Chances Advanced BA/100
Punto 28 24 86
Cuddyer 32 27 84
Redmond 13 10 77
Mauer 11 8 73
Casilla 23 15 65
Tolbert 12 7 58
Buscher 14 8 57
Harris 20 11 55
Morneau 31 16 52
Kubel 22 11 50
Crede 24 11 46
Span 17 7 41
Young 15 6 40
Morales 6 2 33
Gomez 10 3 30

Get ’em Over, Get ’em In – Individuals

Breaking down the stuff I posted on Monday a little bit further.


Coming up with a runner on first or second and nobody out, hitters are looking to advance the runner into scoring position or to third base, where any number of outcomes will bring them home.  That is of course assuming the hitter doesn’t just drive in the baserunner themselves.

Through the first 27 games, the players with the most opportunities of this kind (runner on 1st or 2nd, 0 out) are:

Casilla – 23 (hitting behind Span has it’s perks)
Punto – 19
Morneau – 14
Cuddyer – 13

Those who have advanced runners from 1st most efficiently (0 out):

Punto – 9 bases advanced in 12 opportunities (0.75 B/Op)
Cuddyer – 5 bases advanced in 7 opportunities (0.714 B/Op)
Casilla – 10 bases advanced in 16 opportunities (0.625 B/Op)

Those who have advanced runners from 2nd most efficiently (0 out):

Punto – 7 bases advanced in 7 opps. (1.00 B/Op)
Kubel / Harris – 3 bases advanced in 4 opps. (0.75 B/Op)
Casilla – 5 bases advanced in 7 opps. (0.714 B/Op)


First off, scoring runners from third with less than two out.

Redmond 100% (2 for 2)
Gomez 100% (1 for 1)
Kubel 80% (4 for 5)
Morneau 73% (8 for 11)
Cuddyer 64% (7 for 11)
Span 42% (5 for 12)
Young 42% (5 for 12)
Harris 40% (2 for 5)
Punto 33% (2 for 6)
Casilla 33% (2 for 6)
Morales 0% (0 for 2)
Crede 0% (0 for 8)

Scoring runners in scoring position:

Young – 37% – 11 of 30
Span – 33% – 11 of 33
Mauer – 33% – 1 of 3
Kubel – 31% – 11 of 36
Morneau – 30% – 14 of 47
Cuddyer – 25% – 11 of 44
Redmond – 25% – 3 of 12
Punto – 24% – 7 of 29
Buscher – 23% – 3 of 13
Harris – 19% -3 of 16
Morales – 17% – 2 of 12
Casilla – 15% – 6 of 39
Crede -12% – 3 of 25
Gomez – 8% – 1 of 12

There’s a lot of pretty small samples in there, so it’s tough to pull anything out of here just yet, but we’ll give it another 27 games and see where we’re at.

Get ’em Over, Get ’em In

27 games represents the first 1/6th of the season.  With that passing last week, here’s an update on a few stats I have been kinda/sorta keeping track of this season. (All of these numbers are through last Tuesday, game 27).

First up is where the Twins are scoring their runs.  The number of times the runner started the run-scoring play in each situation divided by the number of opportunities in those situations gives the following numbers:

Runners on 1st Runners on 2nd Runners on 3rd
0 out 0% (0 for 83) 17% (9 of 52) 54% (20 of 37)
1 out 3% (3 for 114) 13% (10 of 77) 41% (18 of 44)
2 out 4% (5 for 122) 18% (16 of 89) 27% (14 of 52)

So, the Twins don’t drive in hardly anyone from first base. Why more score from first with two outs? Maybe more agressive baserunning (running on contact), maybe less of a focus on getting the runner into scoring position (vide infra). They are pretty good at getting baserunners in from third with less than 2 out (38 for 81, 47%). Scoring runners from second doesn’t really depend on the number of outs. Which makes sense, because it’s going to take a hit to score them regardless of situation.

Now to focus on getting the runner over. This table is “bases advanced” per plate appearance.

Runners on 1st Runners on 2nd
0 out 0.59 (49 in 83) 0.73 (38 in 52)
1 out 0.41 (47 in 114) 051 (39 in 77)
2 out 0.45 (55 in 122) 0.44 (39 in 89)

The good news here is the twins are advancing the runner on second with less than two out.  In 19 of the 129 opportunities the runner was driven in.  In 39 of the 110 remaining opportunities the runner was moved up to third base.

I’ll keep an eye on this stuff and post periodic updates throughout the season.

Stupid Stats, Vol. 1


I’m warning you: these will be stupid.

Over the last four years in MLB (2005-2008), the first hitter of the season for each team is hitting .306/.375/.454 (120 plate appearances)

Teams who get the first batter of the season on base (46 of 120) average 79.5 wins.

Teams whose first batter records an out (74 of 120) average 81.9 wins.

Denard Span led off the Twins 2009 season with a walk.  Teams that do that (12 of 120) average 80.7 wins. 4 of the 12 made the postseason.

Teams starting the season with a triple have not made the postseason in this time frame.  (the only example – 2006 Cubs – 66-96)

Every team whose first batter reaches on an error has made the postseason in this time frame (the only example – 2006 Dodgers – 88-74, NL West Champs)

7 of 29 teams whose season started with a strikeout made the postseason (which is pretty close to the percentage of teams overall – 7 of 32 = 21.9% while 29 of 120 = 24.2%)

The first hitter of the season for each team that eventually makes the postseason is hitting .286/.375/.464 (which mirrors almost exactly the overall numbers above)

In conclusion, none of this tells us anything, you just wasted all the time it took you to read this.


Bullpen Usage

At the WGOM, ubelmann posited:

It would be interesting to chart something like winning percentage for home teams with an R run lead going into the 9th inning vs. season. I feel like I haven’t seen that anywhere? That would seem to be a strong indicator of whether or not modern bullpen usage is better than old-school bullpen usage.

That seemed like something I could jump on.

From 1977 to 2006, WE for th home team with a 1-run lead entering the 8th or 9th inning.

we-relieversNumbers from here.  Compare these numbers to this graph of bullpen usage to get an idea of when the philosophy changed concerning closers, and you get some surprising results.

1977 – 1989 (over 40% SV = 1+ IP)  –  9 of 14 seasons above average at preserving 1-run leads in the ninth. (2561 out of 2925 – 87.56% of leads preserved)

1990-2006 (over 40% SV = 1 IP)  –  4 of 16 seasons above average at preserving 1-run leads in the ninth. (3133 out of 3651 – 85.81% of leads preserved)

An artifact of a more offensive era, or a refutation of the current bullpen template of the closer as a “9th inning only” guy?

I lean toward the former, but it is pretty interesting that there’s a lack of a convincing argument for the modern bullpen.

Bullpen WPA

In the offseason, I think most Twins fans would agree that the bullpen is an area that the front office should be looking to improve. After Pat Neshek’s injury last season, the bullpen became a bit shorthanded, and everyone had to step up and pitch in situations that were a step up from what we had expected at the beginning of the year.

In an effort to grade how our relievers compared to others in the league, I looked at about 40 relievers, chosen by leverage index (i.e., how high-pressure were the situations they pitched in) and number of appearances. Thus I got a few comparison points for the six relievers used most often out of the Twins bullpen, these are all pitchers used in similar situations a similar amount of time.

To examine their effectiveness, I also tallied the number of appearances that resulted in a negative WPA. If they made it any more difficult for their team to win, I counted it as a negative outing. I also looked at “real bad” performances, which I defined as -0.80 WPA or lower. The -0.8 number is somewhat arbitrary, but 0.8 WPA is credited for closing a game pitching an entire inning with a 2-run lead. So I went with the negative of that value for symmetry’s sake.

Here’s the raw data, color coded to keep those used similarly together.


To represent this graphically, everything was converted to percentage of negative WPA appearances.


Also, a plot of the “real bad” performances as percentage of appearances with WPA < -0.8.


Joe Nathan is awesome, Mariano Rivera is the only pitcher close to him in these representations.  Dennys Reyes had a lot of negative WPA appearances, although he was good at avoiding the blow-up outings (probably at least partly due to Gardenhire’s propensity to remove Reyes after a batter or two).  Guerrier and Crain both look pretty bad, while Bass is the middle of the road for relievers with his usage pattern (That usage pattern is pretty easy to replace, so not really the area we need outside help).  Breslow also had a pretty good year as far as preventing big, bad outings.

It is obvious that the loss of Neshek created some strain on the remaining members of the bullpen.  Coming into this year, however, Breslow had perhaps the best year last year of those analyzed here, and with Reyes gone, the Twins will need Breslow or Mijares or Crain/Guerrier to step up and improve the somewhat disappointing performance of last season.

Levale Speigner Report

Here’s something that I compiled over the season, that I haven’t even looked at until now. I thought it was somewhat interesting (obviously, since I spent the time to compile the numbers), but we’ll see whether it was worth it.

Levale Speigner made 6 starts this season for the Washington Nationals. In the five starts against a team other than the Twins, he never lasted more than 4 innings, he allowed 30 earned runs, and 49 baserunners (hits+walks) in 17 2/3 IP.

Of course, against the Twins, this was his line: 6.0 IP, 1 ER, 2 H, 1 BB, 3 SO for his only victory as a starter.

Anecdotally, this seemed to be a pattern for the Twins this season. When they faced a struggling pitcher (probably young, especially left-handed) they seem to have enormous struggles putting up any kind of offense. I know it’s easy to remember getting only 2 hits off of a pitcher like Speigner, but if Minnesota had knocked out 7 or 8 hits in five innings and put some runs on the board, the game would have been quickly forgotten. Do the Twins actually struggle more against below average pitchers? Or is this a case of selective memory making a problem seem worse than it actually is?

To investigate that question, I looked at the 93 different opposing starting pitchers the Twins faced in 2007. I looked at the 5 starts immediately preceding their start against the Twins for each opposing starter (if they didn’t have 5 starts under their belt, I just took the 5 starts closest to their start against the Twins) to get an idea of how the pitchers were doing about the time they faced Minnesota. I calculated FIP (Fielding Independent Pitching) as well as the more conventional ERA statistic for the 5 starts. Then I calculated runs per nine innings and WHIP for the start against the Twins.

Data and all that after the jump.

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Wins, Losses, and Saves (A New Look)

Is the save a misleading statistic? It is certainly governed by a strange set of rules. Most simply stated, a save is awarded to the pitcher who finishes a game (who isn’t in line for the win) who entered the game with a lead of three runs or less unless there are less than three outs remaining when the pitcher entered. Then you have to consider runners on base and outs in the inning to determine whether a save should be awarded. This doesn’t even bring up the elusive three-inning save, but the point is that the save could be considered a somewhat overengineered statistic. Personally, I’m in favor of overengineered statistics (just wait, you’ll see) but given that current bullpen usage is designed to maximize save opportunities for the designated “closer”, the number of saves across the board has become inflated and almost the sole property of designated closers.

There is a growing sentiment which holds that the save, in the current era, doesn’t necessarily measure what it claims to. For example, consider the Twins game of July 29, 2007:

Neshek enters with one out in the 8th inning, Twins lead 3-1, one runner on, one out. He gets Ryan Garko to ground into a double play to get out of the inning.

Lew Ford hits a homer before Nathan enters in the ninth with a three-run lead. He doesn’t allow a run and is awarded a save for his efforts.

Now, both pitchers were effective, but one could argue that the situation that Neshek stepped into was more crucial to the outcome of the game, with the tying run at the plate Neshek ended the threat. That seems deserving of a save as well.

Inspired by this situation, and borrowing a bit from a Baseball Prospectus article, I set out to generate some new criteria for bullpen performance making use of Win Probability Added (WPA, which can be found at Going forward, these criteria will be used to award a save (remember, I promised overengineering):

  • WPA of 10.0 or higher.
  • Highest WPA of relief pitchers not earning a win.
  • If starter has WPA greater than or equal to 10.0, the win goes to the starter, otherwise, highest WPA (over 10.0) gets the win.

You may notice that for relief pitchers, there’s not much difference betwixt a win and a save, so I’ve combined the two under the heading Relief Win in the table at the end of this article.

This method makes it a little more difficult to determine save opportunities so its difficult to see if someone is converting opportunities at a high rate, or just receiving a lot of chances. So, let’s check out the opposite of a Relief Win, the Relief Loss:

  • Loss goes to the lowest WPA less than or equal to -10.0
  • Blown save awarded if WPA less than or equal to -10.0 and lead is lost or goes from tied to behind when the pitcher is in the game.

Using these two stats (Relief Wins and Relief Losses) we can get a rough percentage of how a relief pitcher has affected a teams wins/losses. Collecting all this data for the 2007 Minnesota Twins bullpen gives the data below (+G = WPA over 0.0, ++G = WPA over 9.9, –G = WPA under -9.9). The “Score” column gives more positive weight to higher WPA and Relief Wins, while also punishing lower WPA and Relief Losses.

Starter G +G ++G –G RW-RL Score
Joe Nathan 62 54 23 7 18-7 16.5
Pat Neshek 74 56 20 5 9-4 12.3
Matt Guerrier 69 48 13 6 6-6 5.5
Nick Blackburn 3 3 2 0 1-0 1.6
Ramon Ortiz 18 12 1 0 1-0 1.5
Carmen Cali 24 17 2 1 1-1 1.4
Glen Perkins 13 8 1 0 0-0 0.7
Kevin Slowey 2 1 1 0 0-0 0.4
Jason Miller 4 3 0 0 0-0 0.2
Matt Garza 1 1 0 0 0-0 0.1
Jesse Crain 18 12 3 3 1-3 -0.4
Juan Rincon 59 38 4 6 1-6 -1.6
Julio DePaula 12 8 0 2 0-1 -0.9
Dennys Reyes 50 30 1 4 0-2 -1.2

No surprise that Nathan, Neshek, and Guerrier are the top three, but it’s pretty surprising that there is such a dropoff after that. Nick Blackburn has only pitched in three games, but well enough to earn one Relief Win (a save) and lead the middling pack of relievers (Miller, Perkins, Cali, Ortiz). Rincon and Reyes have been pretty bad, and they’ve gotten lots of chances to prove it (again, not groundbreaking, but it shows that the method holds some water). Recently it seems that Neshek and Guerrier have been getting used a lot, but looking at these numbers, there aren’t many effective options in an important situation.

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Justin Morneau and the Twins Offense

Over the last two seasons Justin Morneau has been one of the best run producers in the American League with 62 HR and 219 RBI. What makes this fact more impressive is that on the surface he has been producing all those runs on a team who does not possess one of the elite AL offenses. It would seem intuitive to conclude that Morneau has to be more efficient in the opportunities that he gets, since the more potent offenses are going to yield more opportunities for hitters in their lineups. While there may be some truth to that way of thinking, it’s actually Morneau’s ability to go deep that is keeping him on the RBI leaderboard this season, not his performance with runners on base.

A simple way to approximate the chances a hitter gets to drive in baserunners is baserunners per PA, which is shown in the table below for the top 5 AL hitters in Runners Driven In (RDI = RBI-HR).

M. Ordonez 72 346 0.77
A. Rodriguez 68 356 0.76
V. Guerrero 67 324 0.74
J. Morneau 61 333 0.77
V. Martinez 61 296 0.70

Morneau is in the middle of the pack here, so at first glance it doesn’t seem he’s suffering from a lack of opportunities. But one of the hallmarks of the Twins offense is its lack of power, so is it reasonable to expect a larger percentage of those runners to be on first base? It turns out that is not the case either. In fact, Morneau has had the most chances with a runner on third (the easiest RDI opportunity) and isn’t getting less chances with RISP than any of the top 5. If you’ve noticed that the number of chances (the numbers in parentheses) don’t add up to the number of baserunners in the previous table, I discounted plate appearances in which the hitter was intentionally walked because that doesn’t represent an opportunity to drive in any runners. Back to the numbers with RISP, a caveat to that observation is that a larger percentage of those RISP chances come with two outs for Morneau. This is most likely the result of “productive” outs moving runners into scoring position (or “non-productive” outs keeping them there until Morneau comes to bat). What struck me is the fact that Morneau doesn’t stand out in driving runners in from third base (more two out situations mean less RBI groundouts or sac flies) and he is merely average bringing runners in from second and first. In general the Twins offense has had difficulties scoring runners from first, as detailed previously here, and it appears that Morneau is not an exception to that rule.

Player from 1st from 2nd from 3rd RISP % of PARISP
w/ 2 out
M. Ordonez 0.09 (171) 0.22 (119) 0.60 (50) 0.33 0.43
A. Rodriguez 0.13 (181) 0.15 (115) 0.50 (54) 0.26 0.41
V. Guerrero 0.12 (150) 0.22 (88) 0.51 (58) 0.34 0.33
J. Morneau 0.08 (151) 0.20 (98) 0.41 (70) 0.29 0.47
V. Martinez 0.07 (159) 0.22 (86) 0.65 (48) 0.37 0.38

While Morneau has performed well in his role, driving in plenty of runs, his place on the RBI leaderboard has come abgout differently than the other hitters. Morneau has been able to remain on this short list mostly due ot his ability to hit HR. Only A-Rod has more home runs in the AL and the highest percentage of RBI from HR. Looking at the runs per HR for these hitters, it could be used as an argument that not enough Twins are getting on base in front of Morneau. If you’ve been paying attention, you know that Morneau is second in the AL in solo HR (Morneau-16, Carlos Pena-17) not because of a lack of opportunities. He’s been able to hit a lot of solo HR to make up for a performance with runners on which isn’t quite in line with the other top AL hitters this season.

Player HR RBI% from HR R/HR
M. Ordonez 16 0.28 1.56
A. Rodriguez 35 0.66 1.94
V. Guerrero 14 0.36 2.07
J. Morneau 28 0.49 1.57
V. Martinez 17 0.37 1.71

With all of this considered, Morneau is having another monster year in which he is currently on pace for 43 HR and 137 RBI. Those numbers are impressive enough that the footnote that he’s doing it all within a subpar offense needn’t be applied. Especially since the surrounding offense is actually giving him a reasonable amount of opportunities to add to those numbers.

Inconsistent Offense?

The Twins just completed a series in which they scored five runs in three games while being swept by the Blue Jays. In my searches for the Series Preview in Blog, I came across this article about the inconsistency of the Jays offense in comparison to some other teams. The Twins were one of the more inconsistent offenses mentioned in the study. The author doesn’t delve into cause and effect with his numbers (except for a rough characterization of teams as ‘power’ or ‘speed’ reliant offenses), but the short answer has little to do with a reliance on speed in the Twins offense. Rather the Twins offense goes mostly as the 3-6 hitters go. Unfortunately those hitters are the least consistently productive of the Twins regular lineup.

Player OPS St. Dev. High Low
Mike Redmond .680 .224 1.173 .343
Jason Tyner .649 .221 1.026 .266
Jeff Cirillo .741 .202 1.019 .449
Michael Cuddyer .923 .184 1.156 .508
Joe Mauer .849 .182 1.249 .547
Torii Hunter .901 .174 1.180 .623
Justin Morneau .949 .160 1.260 .689
Luis Castillo .702 .156 .901 .372
Jason Bartlett .666 .155 .867 .247
Nick Punto .594 .137 .816 .370
Jason Kubel .709 .120 .919 .512

I looked at the eleven Twins players that have the most plate appearances so far this season. For each player, I divided the 100 games of the 2007 season into 19 ten-game sections (i.e. 1-10, 6-15, 11-20, etc.), calculated the OPS for each section, and then calculated the standard deviation of that data set. I threw out any of those sections where the player had less than 15 plate appearances, which gave the results in the table at the left.

The most inconsistent performers (Tyner, Redmond, Cirillo) are those that don’t play as often. The next rung on the inconsistency ladder belongs to Minnesota’s “big 4”; Cuddyer, Mauer, Hunter, Morneau. The most consistent are the “little 4”; the regular players with OPS under .800 (Kubel, Bartlett, Punto, Castillo). The biggest surprise to me was Joe Mauer’s numbers. It’s hard to believe that Mauer is less consistent than Torii Hunter. And, in fact, if you eliminate just the 10 game stretch immediately after he came off the disabled list the standard deviation drops significantly. So I dropped the highest and lowest sections from each players data and recalculated the data.

Player OPS St. Dev. Adjusted High Low
Jeff Cirillo .741 .202 .175 1.019 .449
Jason Tyner .649 .221 .164 1.026 .266
Mike Redmond .680 .224 .163 1.173 .343
Torii Hunter .901 .174 .156 1.180 .623
Michael Cuddyer .923 .184 .154 1.156 .508
Justin Morneau .949 .160 .136 1.260 .689
Luis Castillo .702 .156 .128 .901 .372
Nick Punto .594 .137 .122 .816 .370
Jason Bartlett .666 .155 .117 .867 .247
Joe Mauer .849 .182 .113 1.249 .547
Jason Kubel .709 .120 .104 .919 .512

For the most part the trends remain the same. The bench players are the most inconsistent, probably due to the uneven amount of playing time they get during the season. Cuddyer, Hunter, and Morneau remain in the same position. They produce more overall, but their production is less consistent than others in the offense. The biggest movers in this adjustment are Joe Mauer and Jason Bartlett, who both suffered from one below average cross-section (Mauer coming off the DL, Bartlett’s first 10 games of ’07). Mauer is pretty remarkable. Not only is he able to produce at a high level, he’s one of the most consistent hitters in the Twins lineup this season. The other players at the bottom of the table are all of the lighter hitting variety, consistently providing a lower level of offense.

Therein lies the problem with the Twins offense. The top and bottom of the order are consistently producing subpar offensive numbers (OPS of .594 to .709) so they rarely are able to pick up the slack when the middle of the order isn’t at its top production. Unfortunately, it seems that the middle of the order this season has been the most unpredictable part of the offense, whether it’s availability (Mauer spending significant time on the DL) or just production. If one or two (or sometimes three) of the “big 4” aren’t producing, the offense almost completely evaporates. It’s also interesting to note that the right-handed component of the “big 4” is the less consistent half. That might be a factor in the Twins struggles against left-handed pitching.

The inconsistency is not the fault of the middle of the order being more or less consistent than the rest of the lineup. In fact, I would predict that most of the high OPS hitters would see more variance than lighter-hitting players. Rather, it is an offense that is constructed to rely on a few people in the lineup to produce the majority of the runs, and when the remainder of the lineup doesn’t heat up to cover the downswings of the big bats, the offense is doomed to sputter.