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I created a bit of controversy on Twitter a few days ago (imagine that) when I tweeted my top 10 to-date 2018 projections for the total value of position players, including batting, base running, and defense, including positional adjustments. Four of my top 10 were catchers, Posey, Flowers (WTF?), Grandal, and Barnes. How can that be? Framing, my son, framing. All of those catchers in addition to being good hitters, are excellent framers, according to Baseball Prospectus catcher framing numbers. I use their season numbers to craft a framing projection for each catcher, using a basic Marcel methodology – 4 years’ weighted and regressed toward a population mean, zero in this case.

When doing this, the spread of purported framing talent is quite large. Among the 30 catchers going into 2018 with the most playing time (minors and majors), the standard deviation of talent (my projection) is 7.6 runs. That’s a lot. Among the leaders in projected runs per 130 games are Barnes at +18 runs, and Grandal and Flowers at +21. Some of poor framers include such luminaries as Anthony Recker, Ramon Cabrera, and Tomas Telis (who are these guys?) at -18, -15, and -18, respectively. Most of your everyday catchers these days are decent (or a little on the bad side, like Kurt Suzuki) or very good framers. Gone are the days when Ryan Doumit (terrible framer) was a full-timer and Jose Molina (great framer) a backup.

Anyway, the beef on twitter was that surely framing can’t be worth so much that 4 of the top 10 all-around players in baseball are catchers. To be honest, that makes little sense to me either. If that were true, then catchers are underrepresented in baseball. In other words, there must be catchers in the minor leagues who should be in the majors, presumably because they are good framers though not necessarily good hitters or in other arenas like throwing, blocking pitches, and calling games. If this beef is valid, then either my projection methodology for framing is too strong, i.e., not enough regression, or BP’s numbers lack some integrity.

As a good sabermetricians should be wont to do, I set out to find out the truth. Or at least find evidence supporting the truth. Here’s what I did:

I did a WOWY (without and with you – invented by the illustrious Tom Tango) to compare every catcher’s walk and strikeout rate with each pitcher they worked with to that of the the same pitchers working with other catchers – the without. I did not adjust for the framing value of the other catchers. Presumably for a good framing catcher they should be slightly bad, framing-wise, and vice versa for bad-framing catchers, so that there will be a slight double counting. I did this for each projected season 2014-2017, or 4 seasons.

I split the projected catchers into 3 groups, Group I were projected at greater than 10 runs per 150 games (8.67 per 130), Group II at less than -10 runs, and Group III, all the rest. Here is the data for 2014-2017 combined. Remember I am using, for example, 2017 pre-season projections, and then comparing that to a WOWY for that same year.

Total PA Mean Proj per 130 g W/ BB rate WO/ BB rate Diff W/ SO rate WO/SO rate Diff
74,221 -12.6 .082 .077 .005 .197 .206 -..009
107,535 +13.3 .073 .078 -.005 .215 .212 .003
227,842 -.2 .078 .078 0 .213 .212 .001


We can clearly see that we’re on the right track. The catchers projected to be bad framers had more BB and fewer SO than average and the good framers had more SO and fewer BB. That shouldn’t be surprising. The question is how accurate are our projections in terms of runs. To answer that, we need to convert those BB and SO rates into runs. There are around 38 PA per game, so for 130 games, we have 4,940 PA. Let’s turn those rate differences into runs per 130 games by multiplying them by 4,940 and then by .57 runs which is the value of a walk plus an out, which assumes that every other component stays the same, other than outs. My presumption is that an out is turned into a walk or a walk is turned into an out. A walk as compared to a neutral PA is worth around .31 runs and an out around .26 runs.

Total PA Mean Proj per 130 g W/ BB rate WO/ BB rate Diff in runs/130 W/ SO rate WO/SO rate Diff
74,221 -12.6 .082 .077 +14.0 .197 .206 -.009
107,535 +13.3 .073 .078 -14.0 .215 .212 .003
227,842 -.2 .078 .078 0 .213 .212 .001


Let’s make sure that my presumption is correct before we get tool excited with those numbers. Namely that an out really is turning into a walk and vice versa due to framing. Changes in strikeout rate are mostly irrelevant in terms of translating into runs, assuming that the only other changes are in outs and walks (strikeouts are worth about the same as a batted ball out).

Total PA Mean Proj W/ HR WO/HR Diff W/ Hits WO/Hits Diff W/ Outs WO/


74,221 -12.6 .028 .028 0 .204 .203 .001 .675 .681 -.006
107,535 +13.3 .029 .029 0 .200 .198 .002 .689 .685 .004
227,842 -.2 .029 .029 0 .199 .200 -.001 .685 .683 .002


So, HR is not affected at all. Interestingly, both good and bad framers give up slightly more non-HR hits. This is likely just noise. As I presumed, the bad framers are not only allowing more walks and fewer strikeouts, but they’re also allowing fewer outs. The good framers are producing more outs. So this does in fact suggest that the walks are being converted into outs, strikeouts and/or batted ball outs and vice versa.

If we chalk up the difference in hits between the with and the without to noise (if you want to include that, that’s fine – both the good and bad framers lose a little, the good framers losing more), we’re left with outs and walks. Let’s translate each one into runs separately using .31 runs for the walks and .26 runs for the outs. Those are the run values compared to a neutral PA.

Total PA Mean Proj per 130 g W/ BB rate WO/ BB rate Diff in runs/130 W/ Outs WO/


74,221 -12.6 .082 .077 +7.7 .675 .681 +7.7
107,535 +13.3 .073 .078 -7.7 .689 .685 -5.1
227,842 -.2 .078 .078 0 .685 .683 -2.6


So our bad framers are allowing 15.4 runs more per 130 games than the average catcher or than their others at least, in terms of fewer outs and more BB. The good framers are allowing 12.8 fewer runs per 130 games. Compare that to our projections, and I think we’re in the same ballpark.

It appears from this data that we have pretty strong evidence that framing is worth a lot and our four catchers should be in the top 10 players in all of baseball.


When a team wins the World Series (or even a game), the winning manager is typically forgiven of all his ‘sins.’ His mistakes, large and small, are relegated to the scrap heap marked, “Here lies the sins of our manager and all managers before him, dutifully forgotten or forgiven by elated and grateful fans and media pundits and critics alike.”

But should they be forgotten or forgiven simply because his team won the game or series? I’m not going to answer that. I suppose that’s up to those fans and the media. What I can say is this: As with many things in life that require a decision or a strategy, the outcome in sports rarely has anything to do with the efficacy of that decision. In baseball, when a manager has a choice between, say, strategy A or strategy B, how it turns out in terms of the immediate outcome of the play or that of the game, has virtually nothing to do with which strategy increased or decreased each team’s win expectancy (their theoretical chance of winning the game, or how often they would win the game if it were played from that point forward an infinite number of times).

Of course, regardless of how much information we have or how good our analysis is, we can’t know with pinpoint accuracy what those win expectancies are; however, with a decent analysis and reasonably accurate and reliable information, we can usually do a pretty good job.

It’s important to understand that the absolute magnitude of those win percentages is not what’s important, but their relative values. For example, if we’re going to evaluate the difference between, say, issuing an intentional walk to player A versus allowing him to hit, it doesn’t matter much how accurate our pitcher projections are or even those of the rest of the lineup, other than the batter who may be walked and the following batter or two. It won’t greatly affect the result we’re looking for – the difference in win expectancy between issuing the IBB or not.

The other thing to keep in mind – and this is particularly important – is that if we find that the win expectancy of one alternative is close to that of another, we can’t be even remotely certain that the strategy with the higher win expectancy is the “better one.” In fact, it is a custom of mine that when I find a small difference in WE I call it a toss-up.

The flip side of that is this: When we find a large difference in WE, even with incomplete information and an imperfect model, there is a very good chance that the alternative that our model says has the higher win expectancy does in fact yield a higher win percentage if we had perfect information and a perfect model.

How small is “close” and how big is “a large difference?” There is no cut-off point above which we can say with certainty that, “Strategy A is better,” or below which we have to conclude, “It’s a toss-up.” It’s not a binary thing. Basically the larger the difference, the more confident we are in our estimate (that one decision is “better” than the other from the standpoint of win expectancy). In addition, the larger the difference, the more confident we are that choosing the “wrong strategy” is a big mistake.

To answer the question of specifically what constitutes a toss-up and what magnitude of difference suggests a big mistake (if the wrong strategy is chosen), the only thing I can offer is this: I’ve been doing simulations and analyses of managerial decisions for over 20 years. I’ve looked at pinch hitting, base running, bunting, relievers, starters, IBB’s, you name it. As a very rough rule of thumb, any difference below .5% in win expectancy could be considered a toss-up, although it depends on the exact nature of the decisions – some have more uncertainty than others. From .5% to 1%, I would consider it a moderate difference with some degree of uncertainty. 1-2% I consider fairly large and I’m usually quite certain that the alternative with the larger WE is indeed the better strategy. Anything over 2% is pretty much a no-brainer – strategy A is much better than strategy B and we are 95% or more certain that that is true and that the true difference is large.

With all that in mind, I want to revisit Game 6 of the World Series. In the top of the 5th inning, the Astros were up 1-0 with runners on second and third, one out, and Justin Verlander, arguably their best starting pitcher (although Morton, McCullers and Keuchel are probably not too far behind, if at all) , due to bat. I’m pretty sure that the Astros manager, Hinch, or anyone else for that matter, didn’t even think twice about whether Verlander was going to bat or not. The “reasoning” I suppose was that he’s only pitched 4 innings, was pitching well, and the Astros were already up 1-0.

Of course, reasoning in “words” like that rarely gets you anywhere in terms of making the “right” decision. The question, at least as a starting point, is, “What is the Astros’ win expectancy with Verlander batting versus with a pinch hitter?” You can argue all you want about how much removing Verlander, burning a pinch hitter, using your bullpen in the 5th, and perhaps damaging Verlander’s ego or affecting the morale of the team, affects the outcome of the game and the one after that (if there is a 7th game) and perhaps even the following season; however, that argument can only be responsibly made in the context of how much win expectancy is lost by letting Verlander hit. As it turns out, that’s relatively easy to find out with a simple game simulator.  We know approximately how good or bad of a hitter Verlander is, or at least we can estimate it, and we know the same about a pinch hitter like Gattis, Fisher, or Maybin. It doesn’t even matter how good those estimates are. It’s not going to change the numbers much.

Even without using a simulator, we can get a pretty good idea as to the impact of a pinch hitter in that situation: The run expectancy with a typical hitter at the plate is around 1.39 runs. With an automatic out, the run expectancy decreases to .59 runs, a loss of .78 runs or 7.8% in win expectancy. That’s enormous. Now, Verlander is obviously not an automatic out, although he is apparently not a good hitting pitcher, having spent his entire career in the AL prior to a few months ago. If we assume a loss of only .6 runs, we still get a 6% difference in win expectancy between Verlander and a pinch hitter. These are only very rough estimates however, since translating run expectancy to win expectancy depends on the score and inning. The best thing we can do is to run a game simulator.

I did just that, using the approximate offensive line for a poor hitting pitcher, and that of Evan Gattis as pinch hitter. The difference after simulating 100,000 games for each alternative was 6.6%, not too far off from our basic estimate using run expectancies. This is a gigantic difference. I can’t emphasize how large a difference that is. Decisions such as whether to IBB a batter, bunt, replace a batter or pitcher to get a platoon advantage, remove a starter for a reliever, replace a reliever for a better reliever, etc. typically involve differences in win expectancy of 1% or less. As I said earlier, anything greater than 1% is considered significant and anything above 2% is considered large. 6.6% is almost unheard of. About the only time you’ll encounter that kind of difference is exactly in this situation – a pitcher batting versus a pinch hitter, in a close game with runners on base, and especially with 1 or 2 outs, when the consequences of an out are devastating.

To give you an idea of how large a 6.6% win expectancy advantage is, imagine that your manager decided to remove Mike Trout and Joey Votto, perhaps the two best hitters in baseball, from a lineup and replace them with two of the worst hitters in baseball for game 6 of the World Series. How much do you think that would be worth to the opposing team? Well, that’s worth about 6.6%, the same as letting Verlander hit in that spot rather than a pinch hitter. What would you think of a manager who did that?

Now, as I said, there are probably other countervailing reasons for allowing him to hit. At least I hope there were, for Hinch’s and the Astros’ sake. I’m not here to discuss or debate those though. I’m simply here to tell you that I am quite certain that the difference between strategy A and B was enormous – likely on the order of 6-7%. Could those other considerations argue towards giving up that 6.6% at the moment? Again, I won’t discuss that. I’ll leave that up to you to ponder. I will say this, however: If you think that leaving Verlander in the game for another 2-3 innings or so (he ended up pitching another 2 innings) was worth that 6.6%, it’s likely that you’re sadly mistaken.

Let’s say that Verlander is better than any bullpen alternative (or at least the net result, including the extra pressure on the pen for the rest of game 6 and a potential game 7, was that Verlander was the best choice) by ½ run a game. It’s really difficult to argue that it could be much more than that, and if it were up to me, I’d argue that taking him out doesn’t hurt the Astros’ pitching at all. What is the win impact of ½ run a game, for 2.5 innings? Let’s call the average leverage in the 5th-7th innings 1.5 since it was a close game in the 5th. That comes out to 2.1%. So, if letting Verlander pitch through the middle of the 7th inning on the average was better than an alternative reliever by ½ run a game, the impact of removing Verlander for a pinch hitter would be 4.5% rather than 6.6%. 4.5% is still enormous. It’s worth more than the impact of replacing George Springer with Derek Fisher for an entire game because Springer didn’t say, “Good morning” to you – a lot more. Again, I’ll leave it to you to mull the impact of any other countervailing reasons for not removing Verlander.

Before we go, I want to also quickly address Roberts’ decision to walk Springer and pitch to Bregman after Verlander struck out. There were 2 outs, runners in second and third, and the Astros were still up 1-0. Of course Roberts brought in Morrow to pitch to the right-handed Bregman, although Morrow could have pitched to Springer, also a righty. What was the difference in win expectancies between walking and not walking Springer? That is also easy to simulate, although a basic simulator will undervalue the run and win expectancy when the bases are loaded because it’s difficult to pitch in that situation. In any case, the simulator says that not walking Springer is worth around 1.4% in win expectancy. That is considered a pretty large difference, and thus a pretty significant mistake by Roberts, although it was dwarfed by Hinch’s decision to let Verlander bat. It is interesting that one batter earlier Hinch gratuitously handed Roberts 6.6% in win expectancy and then Roberts’ promptly handed him back 1.4%! At least he returned the generosity!

Now, if you asked Hinch what his reasons were for not pinch hitting for Verlander, regardless of his answer – maybe it was a good one and maybe it wasn’t – you would expect that at the very least he must know what the ‘naked’ cost of that decision was. That’s critical to his decision-making process even if he had other good reasons for keeping Verlander in the game. The overall decision cannot be based on those reasons in isolation. It must be made with the knowledge that he has to “make up” the lost 6.6%. If he doesn’t know that, he’s stabbing in the dark. Did he have some idea as to the lost win expectancy in letting his pitcher bat, and how important and significant a number like 6.6% is? I have no idea. The fact that they won game 7 and “all is forgiven” has nothing to do with this discussion though. That I do know.

It’s quite simple actually.

Apropos to the myriad articles and discussions about the run scoring and HR surge starting in late 2015 and continuing through 2017 to date, I want to go over what can cause league run scoring to increase or decrease from one year to the next:

  1. Changes in equipment, such as the ball or bat.
  2. Changes to the strike zone, either the overall size or the shape.
  3. Rule changes.
  4. Changes in batter strength, conditioning, etc.
  5. Changes in batter or pitcher approaches.
  6. Random variation.
  7. Weather and park changes.
  8. Natural variation in player talent.

I’m going to focus on the last one, variation in player talent from year to year. How does the league “replenish” it’s talent from one year to the next? Poorer players get less playing time, including those who get no playing time at all (retired, injured, or switch to another league). Better players get more playing time and new players enter the league. Much of that is because of the aging curve. Younger players generally get better and thus amass more playing time and older players get worse, playing less – eventually retiring or released.  All these moves can lead to each league having a little more or less overall talent and run scoring than in the previous year. How can we measure that change in talent/scoring?

One good method is to look at how a player’s league normalized stats change from year X to year X+1. First we have to establish a base line. To do that, we track the average change in some league normalized stat like Linear Weights, RC+ or wOBA+ over many years. It is best to confine it to players in a narrow age range, like 25 to 29, so that we minimize the problem of average league age being different from one year to the next, and thus the amount of decline with age also being different.

We’ll start with batting. The stat I’m using is linear weights, which is generally zeroed out at the league level. In other words, the average player in each league, NL and AL separately, has a linear weights of exactly zero. If we look at the average change from 2000 to 2017 for all batters from 25 to 29 years old, we get -.12 runs per team per game in the NL and -.10 in the AL. That means that either these players decline with age and/or every year the quality of the league’s batting gets better. We’ll assume that most of that -.12 runs is due to aging (and that peak age is close to 25 or 26, which it probably is in the modern era), but it doesn’t matter for our purposes.

So, for example, if in year X to X+1 in the NL, all batters age 25-29 lost -.2 runs per game per team, what would that tell us? It would tell us that league batting in year X+1 was better than in year X by .1 runs per team per game. Why is that? If players should lose only -.1 runs but they lost -.2 runs, and thus they look worse than they should relative to the league as a whole, that means that the league got better.

Keep in mind that the quality of the pitching has no effect on this method. Whether the overall pitching talent changes from year 1 to year 2 has no bearing on these calculations. Nor do changes in parks, differences in weather, or any other variable that might change from year to year and affect run scoring and raw offensive stats. We’re using linear weights, which is always relative to other batters in the league. The sum of everyone’s offensive linear weights in any given year and league is always zero.

Using this method, here is the change in batting talent from year to year, in the NL and AL, from 2000 to 2017. Plus means the league got better in batting talent. Minus means it got worse. In other words, a plus value means that run scoring should increase, everything else being the same. Notice the decline in offense in both leagues from 2016 to 2017 even though we see increased run scoring. Either pitching got much worse or something else is going on. We’ll see about the pitching.

Table I

Change in batting linear weights, in runs per game per team

Years NL AL
00-01 .09 -.07
01-02 -.12 -.23
02-03 -.15 -.11
03-04 .09 -.11
04-05 -.10 -.14
05-06 .15 .05
06-07 .09 .08
07-08 -.05 .08
08-09 -.13 .08
09-10 .17 -.12
10-11 -.18 .04
11-12 .12 0
12-13 -.03 -.05
13-14 .01 .07
14-15 .06 .09
15-16 .01 .05
16-17 -.03 -.12


Here is the same chart for league pitching. The stat I am using is ERC, or component ERA. Component ERA takes a pitcher’s raw rate stats, singles, doubles, triples, home runs, walks, and outs, per PA, park and defense adjusted, and converts them into a theoretical runs per 9 inning, using a BaseRuns formula. Like linear weights, it is scaled to league average. A plus number means that league pitching got worse, and hence run scoring should go up.

Table II

Change in pitching, in runs per game per team

Years NL AL
00-01 .02 .21
01-02 .03 .00
02-03 -.04 -.23
03-04 .07 .11
04-05 .00 .07
05-06 -.14 -.12
06-07 .10 .06
07-08 -.15 -.10
08-09 -.13 -.17
09-10 .01 .04
10-11 .03 .16
11-12 .03 -.06
12-13 -.02 .26
13-14 -.02 -.04
14-15 .06 -.02
15-16 .03 .04
16-17 .04 -.01


Notice that pitching in the NL got a little worse. Overall, when you combine pitching and batting, the NL has worse talent in 2017 compared to 2016, by .07 runs per team per game. NL teams should score .01 runs per game more than in 2016, again, all other things being equal (they usually are not).

In the AL, while we’ve seen a decrease in batting of -.12 runs per team per game (which is a lot), we’ve also seen a slight increase in pitching talent, .01 runs per game per team. We would expect the AL to score .13 runs per team per game less in 2017 than in 2016, assuming nothing else has changed. The overall talent in the AL, pitching plus batting, decreased by .11 runs.

The gap in talent between the NL and AL, at least with respect to pitching and batting only (not including base running and defense, which can also vary from year to year) has presumably moved in favor of the NL by .04 runs a game per team, despite the AL’s .600 record in inter-league play so far this year compared to .550 last year (one standard deviation of the difference between this year’s and last year’s inter-league w/l record is over .05, so the difference is not even close to being statistically significant – less than one SD).

Let’s complete the analysis by doing the same thing for UZR (defense) and UBR (base running). A plus defensive change means that the defense got worse (thus more runs scored). For base running, plus means better (more runs) and minus means worse.

Table III

Change in defense (UZR), in runs per game per team

Years NL AL
00-01 .01 -.07
01-02 -.01 .05
02-03 .18 -.07
03-04 .10 .03
04-05 .12 .00
05-06 -.08 -.07
06-07 .02 .03
07-08 .04 .01
08-09 -.02 -.02
09-10 -.01 -.02
10-11 .15 -.04
11-12 -.10 -.07
12-13 -.02 .03
13-14 -.10 .03
14-15 -.02 -.02
15-16 -.07 -.05
16-17 -.06 .05


From last year to this year, defense in the NL got better by .06 runs per team per game, signifying a decrease in run scoring. In the AL, the defense appears to have gotten worse, by .05 runs a game. By the way, since 2012, you’ll notice that teams have gotten much better on defense in general, likely due to an increased awareness of the value of defense, and the move away from the slow, defensively-challenged power hitter.

Let’s finish by looking at base running and then we can add everything up.

Table IV

Change in base running (UBR), in runs per game per team

Years NL AL
00-01 -.02 -.01
01-02 -.02 -.01
02-03 -.01 .00
03-04 .00 -.04
04-05 .02 .02
05-06 .00 -.01
06-07 -.01 -.01
07-08 .00 .00
08-09 .02 .02
09-10 -.02 -.02
10-11 .04 -.01
11-12 .00 -.02
12-13 -.01 -.01
13-14 .01 -.01
14-15 .01 .05
15-16 .01 -.03
16-17 .01 .01


Remember that the batting and pitching talent in the AL presumably decreased by .11 runs per team per game and they were expected to score .13 fewer runs per game per team, in 2017, as compared to 2016. Adding in defense and base running, those numbers are a decrease in AL talent by .15 runs and a decrease in run scoring of only .07 runs per team per game.

In the NL, when we add defense and base running to batting and pitching, we get no overall change in talent, from 2016 to 2017, and a decrease in run scoring of -.04.

We also see a slight trend towards better base running since 2011, which should naturally occur with better defense.

Here is everything combined into one table.

Table V

Change in talent and run scoring, in runs per game per team. Plus means gain in talent and score more runs.

Years NL Talent AL Talent NL Runs AL Runs
00-01 .04 -.22 .09 .06
01-02 -.16 -.29 -.12 -.19
02-03 -.30 .19 -.02 -.41
03-04 -.08 -.29 .26 -.01
04-05 -.20 -.19 .04 -.05
05-06 .37 .23 -.07 -.15
06-07 -.02 -.02 .23 .16
07-08 .06 .17 -.16 -.01
08-09 .04 .29 -.26 -.09
09-10 .15 -.16 .05 -.12
10-11 -.31 -.09 .04 .15
11-12 .19 .11 .05 -.15
12-13 0 -.35 -.08 .23
13-14 .14 .07 -.10 .05
14-15 .03 .18 .11 .10
15-16 .06 .03 -.02 .03
16-17 0 -.15 -.04 -.07

Someone asked me to post my 2017 W/L projections for each team. I basically added up the run values of my individual projections, using Fangraphs projected playing time for every player, as of around March 15.

I did use the actual schedule for a “strength of opponent” adjustment. I didn’t add anything additional for injuries, chances of each team making roster adjustments at trade deadline or otherwise, managerial skill, etc. I didn’t try and simulate lineups or anything like that. Plus, these are based on my preliminary projections without incorporating any Statcast or pitch F/X data. Also, these kinds of projections tend to regress toward a mean of .500 for all teams. That’s because bad teams tend to weed out bad players and otherwise improve, and injuries don’t hurt them much – in some cases improving them. And good teams tend to be hurt more by injuries (and I don’t think the depth charts I use account enough for chance of injury). As well, if good teams are not contending at the deadline, they tend to trade their good players.

So take these for what they are worth.

team wins div wc div+wc ds lcs ws


was 89 0.499 0.097 0.597 0.257 0.117 0.048
nyn 88 0.437 0.114 0.55 0.239 0.106 0.044
mia 78 0.046 0.02 0.066 0.024 0.01 0.004
phi 72 0.007 0.002 0.009 0.003 0.001 0
atl 72 0.011 0.004 0.014 0.006 0.002 0.001

NL Central

chn 100 0.934 0.044 0.978 0.56 0.303 0.146
sln 86 0.049 0.273 0.322 0.137 0.059 0.022
pit 82 0.017 0.129 0.146 0.056 0.023 0.008
cin 67 0 0.001 0.001 0 0 0
mil 61 0 0 0 0 0 0


lan 102 0.961 0.025 0.987 0.591 0.327 0.164
sfn 85 0.03 0.214 0.245 0.098 0.041 0.016
col 78 0.005 0.047 0.052 0.018 0.007 0.003
ari 77 0.003 0.03 0.033 0.011 0.004 0.002
sdn 66 0 0 0 0 0 0


tor 87 0.34 0.114 0.455 0.229 0.118 0.061
bos 87 0.359 0.129 0.487 0.238 0.117 0.064
tba 83 0.15 0.077 0.227 0.105 0.051 0.027
bal 81 0.099 0.056 0.155 0.071 0.032 0.014
nya 79 0.053 0.035 0.088 0.038 0.018 0.008


cle 93 0.861 0.027 0.888 0.471 0.254 0.146
det 82 0.097 0.077 0.174 0.076 0.033 0.016
min 76 0.021 0.015 0.036 0.014 0.005 0.002
kca 75 0.02 0.014 0.033 0.014 0.005 0.003
cha 68 0.001 0.001 0.002 0 0 0


hou 91 0.541 0.13 0.671 0.362 0.188 0.11
sea 86 0.228 0.155 0.383 0.192 0.09 0.047
ala 84 0.181 0.12 0.301 0.146 0.071 0.036
tex 80 0.044 0.042 0.086 0.038 0.017 0.008
oak 73 0.006 0.007 0.014 0.006 0.002 0.001




The most important thing, bar none, that your government can do – must do – is to be truthful and transparent regardless of party, policy, or ideology. Your government works for you. It is your servant. As Lincoln famously said, in America we have a government, “of the people, by the people and for the people.” That is the bedrock of our Democracy.

A government that withholds, obfuscates, misrepresents or tells falsehoods should never be tolerated in a democracy. Raw, naked honesty is the first thing you must demand from your government. They. Work. For. You. Regardless of what you think of their promises and policies, if they are not honest with you, they cannot govern effectively because you can never trust that they have your best interests in mind.

Demand that your politicians are honest with you. If not, you must vote them out. It is every American’s responsibility to do so. It doesn’t matter what their party is or what you think they may accomplish. A dishonest government is like a dishonest employee. They will eventually sink your company. Anything but a transparent and forthright government is a cancer in a Democracy. It is self-serving by definition. You should demand honesty first and foremost from your public servants or our Democracy will crumble.

Let me explain game theory wrt sac bunting using tonight’s CLE game as an example. Bottom of the 10th, leadoff batter on first, Gimenez is up. He is a very weak batter with little power or on-base skills, and the announcers say, “You would expect him to be bunting.” He clearly is.

Now, in general, to determine whether to bunt or not, you estimate the win expectancies (WE) based on the frequencies of the various outcomes of the bunt, versus the frequencies of the various outcomes of swinging away. Since, for a position player, those two final numbers are usually close, even in late tied-game situations, the correct decision usually hinges on: On the swing side, whether the batter is a good hitter or not, and his expected GDP rate. On the bunt side, how good of a sac bunter is he and how fast is he (which affect the single and ROE frequencies, which are an important part of the bunt WE)?

Gimenez is a terrible hitter which favors the bunt attempt but he is also not a good bunter and slow which favors hitting away. So the WE’s are probably somewhat close.

One thing that affects the WE for both bunting and swinging, of course, is where the third baseman plays before the pitch is thrown. Now, in this game, it was obvious that Gimenez was bunting all the way and everyone seemed fine with that. I think the announcers and probably everyone would have been shocked if he didn’t (we’ll ignore the count completely for this discussion – the decision to bunt or not clearly can change with it).

The announcers also said, “Sano is playing pretty far back for a bunt.” He was playing just on the dirt I think, which is pretty much “in between when expecting a bunt.” So it did seem like he was not playing up enough.

So what happens if he moves up a little? Maybe now it is correct to NOT bunt because the more he plays in, the lower the WE for a bunt and the higher the WE for hitting away! So maybe he shouldn’t play up more (the assumption is that if he is bunting, then the closer he plays, the better). Maybe then the batter will hit away and correctly so, which is now better for the offense than bunting with the third baseman playing only half way. Or maybe if he plays up more, the bunt is still correct but less so than with him playing back, in which case he SHOULD play up more.

So what is supposed to happen? Where is the third baseman supposed to play and what does the batter do? There is one answer and one answer only. How many managers and coaches do you think know the answer (they should)?

The third baseman is supposed to play all the way back “for starters” in his own mind, such that it is clearly correct for the batter to bunt. Now he knows he should play in a little more. So in his mind again, he plays up just a tad bit.

Now is it still correct for the batter to bunt? IOW, is the bunt WE higher than the swing WE given where the third baseman is playing? If it is, of course he is supposed to move up just a little more (in his head).

When does he stop? He stops of course when the WE from bunting is exactly the same as the WE from swinging. Where that is completely depends on those things I talked about before, like the hitting and bunting prowess of the batter, his speed, and even the pitcher himself.

What if he keeps moving up in his mind and the WE from bunting is always higher than hitting, like with most pitchers at the plate with no outs? Then the 3B simply plays in as far as he can, assuming that the batter is bunting 100%.

So in our example, if Sano is indeed playing at the correct depth which maybe he was and maybe he wasn’t, then the WE from bunting and hitting must be exactly the same, in which case, what does the batter do? It doesn’t matter, obviously! He can do whatever he wants, as long as the 3B is playing correctly.

So in a bunt situation like this, assuming that the 3B (and other fielders if applicable) is playing reasonably correctly, it NEVER matters what the batter does. That should be the case in every single potential sac bunt situation you see in a baseball game. It NEVER matters what the batter does. Either bunting or not are equally “correct.” They result in exactly the same WE.

The only exceptions (which do occur) are when the WE from bunting is always higher than swinging when the 3B is playing all the way up (a poor hitter and/or exceptional bunter) OR the WE from swinging is always higher even when the 3B is playing completely back (a good or great hitter and/or poor bunter).

So unless you see the 3B playing all the way in or all the way back and they are playing reasonably optimally it NEVER matters what the batter does. Bunt or not bunt and the win expectancy is exactly the same! And if the 3rd baseman plays all the way in or all the way back and is playing optimally, then it is always correct for the batter to bunt or not bunt 100% of the time.

I won’t go into this too much because the post assumed that the defense was playing optimally, i.e. it was in a “Nash Equilibrium” (as I explained, it is playing in a position such that the WE for bunting and swinging are exactly equal) or it was correctly playing all the way in (the WE for bunting is still equal to or great than for swinging) or all the way back (the WE for swinging is >= that of bunting), but if the defense is NOT playing optimally, then the batter MUST bunt or swing away 100% of the time.

This is critical and amazingly there is not ONE manager or coach in MLB that understands it and thus correctly utilizes a correct bunt strategy or bunt defense.

* And why I am getting tired of writers and analysts picking and choosing one or more of a bushel of statistics to make their (often weak) point.

Let’s first get something out of the way:

Let’s say that you know of this very good baseball player. He is well-respected and beloved on and off the field,  he played for only one, dynastic, team, he has several World Series rings, double digit All-Star appearances, dozens of awards, including 5 Gold Gloves, 5 Silver Sluggers, and a host of other commendations and accolades. Oh, and he dates super models and doesn’t use PEDs (we think).

Does it matter whether he is a 40, 50, 60, 80, or 120 win (WAR) player in terms of his HOF qualifications? I submit that the answer is an easy, “No, it doesn’t” He is a slam dunk HOF’er whether he is indeed a very good, great, or all-time, inner-circle, great player. If you want to debate his goodness or greatness, fine. But it would be disingenuous to debate that in terms of his HOF qualifications. There are no serious groups of persons, including “stat-nerds,” whose consensus is that this player does not belong in the HOF.

Speaking of strawmen, before I lambaste Mr. Posnanski, which is the crux of this post, let me start by giving him some major props for pointing out that this article, by the “esteemed” and “venerable” writer Allen Barra, is tripe. That is Pos’ word – not mine. Indeed, the article is garbage, and Barra, at least when writing about anything remotely related to sabermetrics, is a hack. Unfortunately, Posnanski’s article is not much further behind in tripeness.

Pos’ thesis, I suppose, can be summarized by this, at the beginning of the article:

[Jeter] was a fantastic baseball player. But you know what? Alan Trammell was just about as good.

Here are Alan Trammell’s and Derek Jeter’s neutralized offensive numbers.

Trammell: .289/.357/.420
Jeter: .307/.375/..439

Jeter was a better hitter. But it was closer than you might think.

He points out several times in the article that, “Trammell was almost as good as Jeter, offensively.”

Let’s examine that proposition.

First though, let me comment on the awful argument, “Closer than you think.” Pos should be ashamed of himself for using that in an assertion or argument. It is a terrible way to couch an argument. First of all, how does he know, “What I think?” And who is he referring to when he says, “You?” The problem with that “argument,” if you want to even call it that, is that it is entirely predicated on what the purveyor decides “You are thinking.” Let’s say a player has a career OPS of .850. I can say, “I will prove that he is better than you think, assuming of course that you think that he is worse than .850, and it is up to me to determine what you think.” Or I can say the opposite. “This player is worse than you think, assuming of course, that you think that he better than an .850 player. And I am telling you that you are thinking that (or at least implying that)!”

Sometimes it is obvious what, “You think.” Often times it is not. And that’s even assuming that we know who, “You” is. In this case, is it obvious what, “You think of Jeter’s offense compared to Trammell?” I certainly don’t think so, and I know a thing or two about baseball. I am pretty sure that most knowledgeable baseball people think that both players were pretty good hitters overall and very good hitters for a SS. So, really, what is the point of, “It was closer than you think.” That is a throwaway comment and serves no purpose other than to make a strawman argument.

But that is only the beginning of what’s wrong with this premise and this article in general. He goes on to state or imply two things. One, that their “neutralized” career OPS’s are closer than their raw ones. I guess that is what he means by “closer than you think,” although he should have simply said, “Their neutralized offensive stats are closer than their non-neutralized ones,” rather than assuming what, “I think.”

Anyway, it is true that in non-neutralized OPS, they were 60 points apart, whereas once “neutralized,” at least according to the article, the gap is only 37 points, but:

Yeah, it is closer once “neutralized” (I don’t know where he gets his neutralized numbers from or how they were computed ), but 37 points is a lot man! I don’t think too many people would say that a 37 point difference, especially over 20-year careers, is “close.”

More importantly, a big part of that “neutralization” is due to the different offensive environments. Trammell played in a lower run scoring environment than did Jeter, presumably, at least partially, because of rampant PED use in the 90’s and aughts. Well, if that’s true, and Jeter did not use PED’s, then why should we adjust his offensive accomplishments downward just because many other players, the ones who were putting up artificially inflated and gaudy numbers, were using? Not to mention the fact that he had to face juiced-up pitchers and Trammell did not! In other words, you could easily make the argument, and probably should, that if (you were pretty sure that) a player was not using during the steroid era, that his offensive stats should not be neutralized to account for the inflated offense during that era, assuming that that inflation was due to rampart PED use of course.

Finally, with regard to this, somewhat outlandish, proposition that Jeter and Trammell were similar in offensive value (of course, it depends on your definition of “similar” and “close” which is why using words like that creates “weaselly” arguments), let’s look at the (supposedly) context-neutral offensive runs or wins above replacement (or above average – it doesn’t matter what the baseline is when comparing players’ offensive value) from Fangraphs.


369 runs batting, 43 runs base running


124 runs batting, 23 runs base running

Whether you want to include base running on “offense” doesn’t matter. Look at the career batting runs. 369 runs to 124. Seriously, what was Posnanski drinking (aha, that’s it – Russian vodka! – he is in Sochi in case you didn’t klnow) when he wrote an entire article mostly about how similar Trammell and Jeter were, offensively, throughout their careers. And remember, these are linear weights batting runs, which are presented as “runs above or below average” compared to a league-average player. In other words, they are neutralized with respect to the run-scoring environment of the league. Again, with respect to PED use during Jeter’s era, we can make an argument that the gap between them is even larger than that.

So, Posnanski tries to make the argument that, “They are not so far apart offensively as some people might think (yeah, the people who look at their stats on Fangraphs!),” by presenting some “neutralized” OPS stats. (And again, he is claiming that a 37-point difference is “close,” which is eminently debatable.)

Before he even finishes, I can make the exact opposite claim – that they are worlds apart offensively, by presenting their career (similar length careers, by the way, although Jeter did play in 300 more games), league and park adjusted batting runs. They are 245 runs, or 24 wins, apart!

That, my friends, is why I am sick and tired of credible writers and even some analysts making their point by cherry picking one (or more than one) of scores of legitimate and semi-legitimate sabermetric and not-so-sabermetric statistics.

But, that’s not all!  I did say that Posnanski’s article was hacktastic, and I didn’t just mean his sketchy use of one (not-so-great) statistic (“neturalized” OPS) to make an even sketchier point.


By Baseball Reference’s defensive WAR Trammell was 22 wins better than a replacement shortstop. Jeter was nine runs worse.

By Fangraphs, Trammell was 76 runs better than a replacement shortstop. Jeter was 139 runs worse.

Is an abomination. First of all, when talking about defense, you should not use the term “replacement” (and you really shouldn’t use it for offense either). Replacement refers to the total package, not to one component of player value. Replacement shortstops, could be average or above-average defenders and awful hitters, decent hitters and terrible defenders, or anything in between. In fact, for various reasons, most replacement players are average or so defenders and poor hitters.

And then he conflates wins and runs (don’t use both in the same paragraph – that  is sure to confuse some readers), although I know that he knows the difference. In fact, I think he means “nine wins” worse in the first sentence, and not, “nine runs worse.” But, that mistake is on him for trying to use both wins and runs when talking about the same thing (Jeter and Trammell’s defense), for no good reason.

Pos then says:

You can buy those numbers or you can partially agree with them or you can throw them out entirely, but there’s no doubt in my mind that Trammell was a better defensive shortstop.

Yeah, yada, yada, yada. Yeah we know. No credible baseball person doesn’t think that Trammell was much the better defender. Unfortunately we are not very certain of how much better he was in terms of career runs/wins. Again, not that it matters in terms of Jeter’s qualifications for, or his eventually being voted into, the HOF. He will obviously be a first-ballot, near-unanimous selection, and rightfully so.

Yes, it is true that Trammell has not gotten his fair due from the HOF voters, for whatever reasons. But, comparing him to Jeter doesn’t help make his case, in my opinion. Jeter is not going into the HOF because he has X number of career WAR. He is going in because he was clearly a very good or great player, and because of the other dozen or more things he has going for him that the voters (and the fans) include, consciously or not, in terms of their consideration. Even if it could be proven that Jeter and Trammell had the exact same context-neutral statistical value over the course of their careers, Jeter could still be reasonably considered a slam dunk HOF’er and Trammell not worthy of induction (I am not saying that he isn’t worthy). It is still the Hall of Fame (which means many different things to many different people) and not the Hall of WAR or the Hall of Your Context-Neutral Statistical Value.

For the record, I love Posnanski’s work in general, but no one is perfect.

Question for tech savvy readers?

Posted: October 17, 2013 in Uncategorized

I would like to change the address of this blog. I actually purchased the domain name, and when someone goes to that site, it automatically goes to

I am using WordPress blog software, obviously. What is the best way to do this? Can I link one site to the other for a while? Can I put up a duplicate blog on another domain and then just tell people for a while on this site that I am transferring domains? Any other suggestions would be helpful. Thanks in advance!

Edit: I was able to complete the new address and redirect. Thanks for all your help!

The big unknown is how it will affect his performance tonight and in the future. Since starters don’t generally pitch on 3 days’ rest, even the great ones, and even the great ones with bodies that can presumably handle a big workload, we have to assume that it will hurt their performance and/or their chance of future injury.

The Dodgers are up 2 games to 1, so Kershaw is guaranteed to pitch tomorrow unless they win and don’t need him. Conventional wisdom says that you only pitch your ace on 3 days’ rest when his team is facing elimination.

Is that right in this situation? Once we estimate Kershaw’s change in talent, we can pretty much figure out which alternative is correct.

According to The Book, modern pitchers that have pitched on 4 days rest pitched 17 points in wOBA against worse than usual. That is the equivalent of .56 runs in RA per 9. That is a lot. In deference to Mattingly and Kershaw (keep in mind that the pitchers who were studied were also deemed well-suited to pitch on 4 days’ rest, or at least not ill-suited), we’ll make that an even .5 runs. For 6 innings (we will also assume that he will be on somewhat of a short leash after throwing 124 pitches in his last outing), that is a difference of .33 runs plus another .11 runs for that extra inning that Kershaw does not pitch, for a total of .44 runs, or a win percentage of .0044, or 4.4%. So the

Dodgers are 4.4% worse off in game 4 with him pitching then in game 5.

Now let’s do the math. In today’s game, Vegas has the Dodgers as a 67.7% favorite. Presumably if he pitched on 4 days rest, they would be be a 72.1% fave, or a 64.1 % favorite in Atlanta). With Nolasco on the mound, the Dodgers were a 60% favorite in LA, which makes them a 52% favorite in Atlanta.

Chances of Dodgers winning:

1) With Kershaw tonight: .677 + .323*.52 = 84.50%
2) With Kershaw in game 5: .6 + .4 * .644 = 85.76%

So, by pitching Kershaw tonight on 3 days’ rest, assuming that he is .5 runs per 9 inning worse than he would be on the normal 4 days’ rest, costs the Dodgers only 1.26% in win expectancy for the series.

What is the break-even point, in terms of how much worse Kershaw has to be for it to be a tossup? About .22 runs per 9 innings.
For what it is worth, on the broadcast commentary, Pedro was asked about Kerhsaw pitching on short rest. He said that as long as he had time to prepare – to adjust his training routine – that is shouldn’t be a problem. He implied that if it was a last minute decision, then it was problematic. We don’t know how long Kershaw has known or suspected this. Hayhurst (as in our friend Dirk) didn’t think it was a good idea. He called it a “panic” by Mattingly. That is probably a bad choice of words as that would clearly apply to the team that was losing the series.

An astute reader on The Book blog pointed out something which I failed to consider. With Kershaw pitching tonight, Greinke can go tomorrow rather than Nolasco, and Greinke is the better pitcher. (Also, the entire rotation for the NLCS changes, right?)

With Greinke, rather than Nolasco, I estimate the the Dodgers gain 3.7% in win expectancy for that one game, which is a lot.

So now, we have:

1) With Kershaw tonight and Greinke in game 5: .677 + .323*.557 = 85.69%
2) With Kershaw in game 5 and Nolasco tonight: .6 + .4 * .644 = 85.76%

So now it is virtually a tossup! Of course if you use Greinke tomorrow, you have to start the NLCS with Nolasco (then Kershaw and Geinke), etc. If you use Nolasco tonight and Kershaw tomorrow, not only would you get to start the NLCS with Greinke if there is a game 5 in NLDS, but if Nolasco were to win tonight, you can start the NLCS with Kershaw. I am too lazy to see how the different pitching scenarios for the NLCS would pan out and, more importantly, affect the Dodgers’ chances of winning that.