## Modeling Walk Rate Between Minor League Levels

After reading through Projecting X by Mike Podhorzer I decided to try and predict some rate statistics between minor league levels. Mike states in his book “Projecting rates makes it dramatically easier to adjust a forecast if necessary.”; therefore if a player is injured or will only have a certain number of plate appearances that year I can still attempt to project performance. The first rate statistic I’m going to attempt project is walk rate between minor league levels. This article will cover the following:

Raw Data

Data Cleaning

Correlation and Graphs

Model and Results

Examples

Raw Data

For my model I used data from Baseball Reference and am using the last seven years of minor league data(2009-2015). Accounting for the Short-Season A (SS-A) to AAA affiliates I ended up with over 28,316 data points for my analysis.

Data Cleaning

I’m using R and the original dataframe I had put all the data from each year in different rows. In order to do the calculations I wanted to do I needed to move each player’s career minor league data to the same row. Also I noticed I needed to filter on plate appearances during a season to make sure I’m getting rid of noise. For example, a player on a rehab assignment in the minor leagues or a player who ended up getting injured for most of the year so they only had 50-100 plate appearances. The minimum plate appearances I ended up settling on was 200 for a player to be factored into the model. Another thing I’m doing to remove noise is only attempting to model player performance between full-season leagues (A, A+, AA, AAA). Once the cleaning of the data was done I had the following data points for each level:

• A to A+ : 1129
• A+ to A: 1023
• AA to AAA: 705

Correlation and Graphs

I was able to get strong correlation numbers for walk rate between minor league levels. You can see the results below:

• A to A+ : .6301594
• A+ to AA: .6141332
• AA to AAA: .620662

Here’s the graphs for each level:

Model and Results

The linear models for each level are:

• A to A+: A+ BB% = .63184*(A BB%) + .02882
• A+ to AA: AA BB% = .6182*(A+ BB%) + .0343
• AA to AAA: AAA BB% = .5682(AA BB%) + .0342

In order to interpret the success or failure of my results I compared how close I was to getting the actual walk rate. FanGraphs has a great rating scale for walk rate at the major league level:

Image from Fangraphs

The image above gives a classification for multiple levels of walk rates. While based on major league data it’s a good starting point for me to decide a margin of error for my model. The mean difference between each level in the FanGraphs table is .0183. I ended up rounding and made my margin for error .02. So if my predicted value for a player’s walk rate was within .02 of being correct I counted the model as correct for the player and if my error was greater than that it was wrong. Here are the models results for each level:

• A to A+
• Incorrect: 450
• Correct: 679
• Percentage Correct: ~.6014
• A+ to A
• Incorrect: 445
• Correct: 578
• Percentage Correct: ~.565
• AA to AAA
• Incorrect: 278
• Correct: 427
• Percentage Correct: ~.6056

When I moved the cutoff up a percentage to .03 the model’s results drastically improve:

• A to A+
• Incorrect: 228
• Correct: 901
• Percentage Correct: ~.798
• A+ to AA
• Incorrect: 246
• Correct: 777
• Percentage Correct: ~.7595
• AA to AAA
• Incorrect: 144
• Correct: 561
• Percentage Correct: ~.7957

Examples

Numbers are cool but where are the actual examples? OK, let’s start off with my worst prediction. The largest error I had between levels was A to A+ and the error was >10% (~.1105). The player in this case was Joey Gallo. A quick glance at the player page will show his A walk rate was only .1076 and his A+ walk rate was .2073 which is a 10% improvement between levels. So why did this happen and why didn’t my model do a better job of predicting this? Currently the model is only accounting for the previous season’s walk rate, but what if the player is getting a lot of hits at one level and stops swinging as much at the next? In Gallo’s case he only had a .245 BA his year at A-ball so that wasn’t the case. More investigation is required to see how the model can get closer on edge cases like this.

Gallo Dataframe Snippet

The lowest I was able to set the error to and still come back with results was ~.00004417. That very close prediction belongs to Erik Gonzalez. I don’t know Erik Gonzalez, so I continued to look for results. Setting the min error to .0002 brought back Stephen Lombardozzi as one of my six results. Lombo’s interesting to hardcore Nats fans (like myself) but I wanted to continue to look for a more notable name. Finally after upping the number to .003 for A to A+ data I was able to see that the model successfully predicted Houston Astros multi-time All-Star 2B Jose Altuve‘s walk rate within a .003 margin of error.

Altuve Dataframe snippet

What’s Next:

• Improve algorithm for generating combined season dataframe
• Improve model to get a lower error rate
• Predict strikeout rate between levels
• Eventually would like to predict more advanced statistics like wOBA/OPS/wRC+

## Paul Goldschmidt Has a Pop-Up Problem

When we were growing up, my dad would sometimes refer to my sister and me as ingrates. I always had a sneaking suspicion that statement was ruthless. I was young and under the assumption that he provided us everything we needed and wanted because that was what he was designed to do. In a sense, that perception of him probably does reflect the “ungratefulness” that young children tend to posses, innocent as it may be, what with a child’s inherently feeble comprehension of interpersonal relationships. I am now the parent of a two-year-old boy and just the other night he saw a commercial for a Power Wheels Jeep Wrangler that elicited the following outburst:

“I want to go in there!”

“I want one!”

Finally he turned to peer into my eyes and, in order to accentuate the severity of his next mandate, he raised his index finger and spoke;

His tone became dramatically more somber than it had been for the first two exclamations, and it made me laugh the hardest. I am certain I was the narrator of many statements similar to this as a kid, but the reality is, when kids are given everything they want, it’s up to the parent to understand that if there is a perceived lack of gratitude, it is a direct byproduct of the parent’s efforts to make them happy or even to keep them alive.

Lately I’ve been thinking of how I can be really ungrateful for even truly fine baseball seasons. Even some All-Star seasons disappoint me, and I know I’m not alone. If Mike Trout was in the middle of putting up a 5-win season, we’d all be talking about what could be wrong with Mike Trout. When players set the bar so ridiculously high we tend to hold them to that standard for better or worse. As an actual example, it’s completely understandable to be disappointed by Bryce Harper’s 2016 season after last year’s masterpiece. The reality is, however, that he’s 23 and has currently produced 3.4 WAR. His baserunning and defense have been positives and he’s compiled over 20 home runs and 20 stolen bases while hitting 14 percent better than league average; that’s damn fine and yet it’s still a damn shame.

Paul Goldschmidt, meanwhile, is hitting .301/.414/.494 and has accrued 4.7 WAR and might surpass 30 SB this year. His 136 wRC+ is still great even if it’s not quite the 158 he’s put up over the last three seasons. So why do I feel the loathsome inklings of disappointment bubbling inside of me? Firstly, and admittedly shallow of me, I like my Goldschmidt with more extra-base hits. For the first time in his professional career, at any level, Goldschmidt’s ISO starts with a number under 2. It’s possible he has a nice final week and brings that number up into the .200 range, but there are still some potentially concerning blips in his batted-ball profile that could portend of further decline in production. What I’m referring to most specifically, as the title suggests, is that Paul Goldschmidt has developed a pop-up problem.

From 2011 through 2015, Goldschmidt’s cumulative IFFB% was 4.8%. This year it sits at 14%. He has 17 IFFB this year, which is the same amount he had in the three previous seasons combined. Pop-ups aren’t good as they’re essentially as productive as a strikeout. Here are the 10 players with the biggest increases in IFFB% in 2016 compared to 2015 among qualified hitters in both years.

I’m not suggesting there’s a positive correlation between popping up and performance, but it’s easy to make sense of some of the names that appear on this list. If you watched Josh Donaldson break down his swing on the MLB Network, you know that a lot of players are thinking about not hitting the ball on the ground because damage is done in the air. Did you know that DJ LeMahieu, at the time of this writing, has a higher slugging percentage than Goldschmidt? That’s bonkers. The league’s slugging percentage last year was .405, and this year it’s .418, but this group of players, minus Goldschmidt, have added, on average, 21 points to their slugging percentage, and part of that, for this group, has to be chalked up to putting more balls in the air.

What I’m hoping to highlight is that what is even more troublesome for Goldschmidt is that he is the only player in this top 10 who had an increase in their IFFB% while also seeing his fly-ball rate and hard-hit rate drop.

So I have what could be an insultingly obvious hypothesis: since Goldschmidt has long been a quality opposite-field hitter, I am theorizing that pitchers are exploiting him with more fastballs up and in where he can’t quite get his hands extended. A cursory glance at his heat map vs. fastballs in 2015 and 2016 reveals a minor shift in approach by the league.

Besides the obvious, which is that pitchers are avoiding the zone even more than they had before, we can see just a bit more red in the specific zone I was referring to. It’s not so glaring or even enough information to make any conclusions, so let’s see if that area is where pitchers are getting Goldy to pop up. On the year, per Brooks Baseball, he has 22 pop-ups, 19 from fastballs and three from offspeed pitches. The 17 that are classified as IFFB by FanGraphs are plotted in the graph below.

*the two pitches towards the outside corner (for Goldschmidt) are sliders.

However, it’s not as if pitchers have previously avoided throwing Goldschmidt up and in; it just appears, despite his overall swing rate being at a career-low 39%, he’s upped his swing rate against fastballs by over five percentage points in that specific area just above 3.5 ft. And that area has the largest concentration of his pop-ups.  Looking at the entire area middle/up/and in to Goldschmidt, he has increased his swing rate from 57.2% in 2015 to 60.7% in 2016 while staying away from lower pitches in general. It’s a philosophy that is being echoed throughout baseball right now, and it is not at all a bad plan, but it has caused him, either deliberately or due the effect of swinging at these pitches more often, to go to the opposite field this season less than he ever has. This also is not necessarily a negative shift in regards to a batted-ball profile, but from 2013 – 2015 Goldschmidt was the fifth-most productive hitter in baseball going the other way, and in 2016 he’s 33rd. That represents a drop in wRC+ from 204 to 158, and from a .729 SLG (.329 ISO) to a .647 SLG (.255 ISO). I’ve long since regarded Goldschmidt to be in the same tier of hitters as Trout, Votto, Cabrera, and pre-2016 McCutchen, and it would be a shame for him to move away from a facet of his game that enables him to produce at that elite level.

At the end of this season I don’t think I’ll actually be all that worried about Goldschmidt; I can reconcile a 136 wRC+, even if it would feel a little disappointing. I wrote about Paul Goldschmidt last year and I wasn’t worried then, either. But I do think if I’m going to take a 136 wRC+ for granted I should place that appreciation toward the catalyst for this change in Goldschmidt’s performance, and a lot of that credit has to go to the pitchers who have induced 17 IFFB from a player who only averaged 5.7 over the last three seasons.

Now I know that setting up a pitch has so much more to do with an entire at-bat, game, or even season than the pitch that was thrown immediately before it, but for this exercise I want to look at the pitch that caused Goldschmidt to pop up and how it relates to the pitch thrown immediately before it. It’s crude and does not tell the whole story, but it still shows a definite approach — and, for all intents and purposes, it’s probably a decent representation of a general tactic used across the league for inducing pop-ups. I found all the data I needed using PITCHf/x at Brooks Baseball and I recorded the velocity, horizontal movement, vertical movement, horizontal location, and vertical location of each pitch Goldschmidt popped out on as well as the same data set for each set-up pitch if there was one (which would be in any situation where Goldschmidt did not pop up on the first pitch of an-bat). Below you’ll find a plot that shows the average location and characteristics of each pitch.

And here is that data in a table represented as the average difference between the two plot points.

Doesn’t it make you feel warm when something fits into the shape you had pegged it to be? That’s just really simple and makes a hell of a lot of sense. Or maybe I feel warm for taking something that was disappointing and turning it into something I can really appreciate.  Now if you’ll excuse me, I have a Power Wheels Jeep Wrangler to buy.

## On Jose Fernandez and The 2016 Cy Young

You all know what happened this past Sunday. One of my — and the game’s — favorite players died. Selfishly, I was upset. I never knew the guy, and my team, the Atlanta Braves, had recently thrown at his head. But now he’s gone. He leaves behind a legacy that won’t soon be forgotten — Dave Cameron can’t wait to tell his children about Jose Fernandez, and the Marlins are going to retire his number so that little kids who attend games will forever ask their parents about Number 16 — but I think Max Frankel summed it up perfectly on Sunday: this sucks.

A few days have passed and baseball had its Jose Fernandez wake on Monday, and Dee Gordon hit the most important and magical home run of his life. But I still feel like Jose Fernandez is around. I am not going to try to do any sort of tribute article; I simply would not do a good job if I tried. Someone a year younger than me dying leaves me a bit scatterbrained.

Instead, I’ll try and write a baseball article, which I think will be difficult and awkward, but necessary. The realization crept into my head today that Jose Fernandez is going to appear on Cy Young ballots. The voters list their top three pitchers and Fernandez leads the Majors in fWAR, is second in strikeouts, and while he sits eighth in ERA, he actually leads the league in the two primary ERA predictors, FIP and xFIP. He was among the top handful of starting pitchers in the game in his first real season back from Tommy John.

Now, human voters, likely struggling through the same feelings I am, are going to be forced to wrestle with Fernandez’s place on the ballot.

If he were markedly better than his comrades, it would be an easy decision to honor him posthumously as the National League’s best pitcher. But it’s a great class again, and tragedy aside, it would still be a nail-biter of a vote.

So the voters are faced with an uncomfortable question: does symbolism have a place in Cy Young voting? If a writer felt that Fernandez had the second-best 2016, does he get a nod to the top in deference to the legacy?

I don’t know. I don’t know what I think. I think I know how I feel.

I feel that Fernandez deserves all the recognition. I’m not sure if it’s possible to separate Fernandez’s story from the Cy Young vote. Everyone’s story plays a role in awards voting — it’s just usually a subtext rather than a centerpiece. Fernandez’s story was already part of what made him so special. Were he alive, that story — his recovery from TJ surgery, and of course the accompanying charisma — would have played a meaningful role in the voting.

But now the “story” is a tragic centerpiece, not the subtext. Writers will have to grapple with that as they consider Fernandez’s candidacy alongside Max Scherzer’s dominance, and Clayton Kershaw’s perseverance.

In some way, Fernandez’s 2016 season deserves to be celebrated. It’s a situation that is sure to make for a somber and uncomfortable awards dinner. There will be someone missing no matter how the voting plays out.

One idea that keeps floating to the fore is a “Jose Fernandez Award.” It seems to make all the sense in the world and could help voters decouple their desire to honor the man from the sanctity of the Cy Young. But maybe the Cy Young isn’t some sacred award to be bestowed only upon the very best pitcher by given concrete metrics. No. The nuances and reasoning behind voting for a given player are subjective. Baseball, and particularly baseball voting, have a way of reflecting the circumstances of a particular year. Jose Fernandez’s story is now linked to this particular year.

So let’s give him the Cy Young. Let’s make an award in his honor, too. Who is really going to complain about that?

## WAR by Position: Why Do Catchers Lag?

(Author’s note: This analysis was originally published on the Baseball-Fever forum a year ago. I thought it might be of interest to the FG community.)

As I have perused the all-time list of career WAR, one feature has always struck me as odd and in need of some explanation: catchers are much lower than other position players. The highest career WAR (I’m using FG WAR or fWAR here, but BBRef’s rWAR makes the same point) of any catcher is Johnny Bench, who comes in at 42d among all position players, at about 75 WAR. That is not just lower than the highest career WAR at any other position. It’s less than two-thirds the next lowest WAR; moreover, every other position has multiple players with higher career WAR, the fewest being SS with four (and that doesn’t include Alex Rodriguez, who I count here as a third baseman).

The following table, which compares the top ten players in career WAR at each position, provides further perspective (If a player is listed at more than one position, I included him only in the position in which he played more. Since the corner outfielders comprise two positions, I took the average of the top two as highest, and the average of the top 20 as equivalent to the average of the top 10.):

Table 1. Top 10 Players in Career WAR by Position

Pos       Ave. PA     Highest WAR     Ave. top 10    Ave./700 PA

C               8677              74.8                     63.4               5.11

1B           10,137             116.3                     85.9              5.93

2B          10,458             130.3                    88.1              5.90

SS           10,440             138.1                    81.1               5.44

3B           10,534             114.0                    86.9               5.77

CF            10,210            149.9                    97.5               6.68

L/RF        11,267            166.4                    98.5               6.09

Except for catcher, the highest career WAR at every position is well over 100. Moreover, if we take the average WAR of the top 10 at each position, catcher again is well below the others. In fact, on that basis, there seem to be three general groups. The highest values are clearly associated with the OF. The highest WAR values of all time were achieved by outfielders, and the average WAR of the top ten center fielders as well as of the corner fielders is nearly 100.

A second group is comprised of all the infield positions. The highest WAR values of players in any position in this group are somewhat lower than the highest values of outfielders, but are roughly equal to the highest values at any other position in this group. Thus the highest WAR values range from about 115-140, and the average WAR of the top ten at each position ranges from 81-88, with an overall average of 85.5. This is 87.3% as high as the average value of the OF.

Finally, catchers are clearly in a class by themselves — and not in a good sense. As noted earlier, the highest career WAR attained by any catcher is only about 75, and the average of the top ten is about 63. This is less than two-thirds as high as the average value of the outfielders (64.8%), and about three-quarters as high as the average value of the infielders (74.2%).

At first glance, this ranking might not seem surprising. For most players, hitting is by far the most important component of WAR, and outfielders are on average better hitters than most infielders, who in turn are on average better hitters than catchers. But these differences are supposed to be compensated for by positional adjustments. For example, catchers are given more positional runs than players at other positions, and corner outfielders are given fewer positional runs than players at all other positions except first base. Specifically, the positional run benefit has the following general ranking: C > SS > 2B/3B/CF > LF/RF > 1B.

This raises the question, if these positional adjustments are approximately correct, why don’t the best catchers have about as much career WAR as the best outfielders? In fact, why are there significant differences also between outfielders and infielders? I will explore these discrepancies here.

This is not just an academic question. The relatively low WAR values for catchers have implications for their HOF chances. Assuming that WAR has some meaning for HOF voters — and even if some of them aren’t fans of this approach, they may still evaluate players using stats that are ultimately reflected in or correlated with WAR — they must either select fewer catchers than players at other positions, or set the bar somewhat lower for catchers. Based on the current HOF composition, one could argue that a little of both are occurring. Thirteen catchers are in the HOF, which is the lowest of any position except third base, which has 11. On the other hand, the mean WAR value for these catchers is about 50, well below the overall mean for the HOF of about 60. Moreover, 70% of the catchers in the HOF have a WAR of less than 60. No other position has more than 50% of its members below this value.

So it may be that, consciously or not, HOF voters think the best catchers are not quite as good as the best players at other positions, yet at the same time, go a little easier on them than they do on other players. I hope the following discussion will shed some light on how we are to understand the value of players at this position, which everyone recognizes as the most important one on the diamond for everyday players.

I posed the issue earlier by pointing out that if one takes the positional adjustments seriously, one would think that the best players at every position would have about the same WAR. Though players at some positions don’t hit as well as players at other positions, they get extra value for playing what is considered a more difficult position. The positional adjustments are supposed to correct for the differences in hitting.

One might therefore first wonder if the positional adjustments are simply wrong, that catchers need to be given more runs. While this is a possibility — there has been some interesting work recently re-evaluating these adjustments — the amount of correction necessary appears far too large. For example, the difference between the average WAR of the top ten catchers and the average WAR of the top ten shortstops is about 18. The average length of the catchers’ careers is about 2200 games, or 13-14 full seasons, so to bring the catchers’ WAR up to that of the shortstops, one would have to give them an additional positional adjustment of about 1.3 WAR, or 13 runs, per year. That is two and a half times the current difference in positional runs between the two positions of 5 runs. An even larger adjustment in absolute though not relative terms would be necessary to bring the catchers’ WAR up to that of the outfielders.

Now the recent appreciation of pitch-framing — the ability of some catchers to receive the ball in such a way that a borderline call is more likely to be called a strike than it would if it were not for the catcher’s manipulations — could in fact add that much WAR, if not more, to the totals of some catchers. But that is not really relevant here, because when the positional adjustments were first developed, they did not (and still don’t) take into account pitch-framing. That is, it was assumed that even without pitch-framing, the positional runs actually given the catchers were adequate, and if pitch-framing does become adopted by the major sabermetric sites, it won’t be to compensate for some perceived shortage in positional runs.

That said, even before efforts to quantify pitch-framing were developed, it was recognized as a valuable skill by many observers familiar with the game. And it’s conceivable that when HOF voters decide on their choices, one reason that they’re fine with selecting catchers who, by WAR or by more traditional stats, may be inferior to some position players who are not chosen is that they feel that there is some hidden value in catching that WAR or traditional stats are not capturing. And pitch-framing could be a large part of that value. I won’t discuss pitch-framing further, but I think this is an important point to keep in mind.

Do Catchers Decline Faster than Other Players?
A second reason why the best catchers have lower WAR values might be that because of the demands of their position, they decline with age sooner and/or faster than other players, and thus don’t accumulate enough counting stats to finish their careers with really high WAR levels. Table 1 provides some support for this. The average number of PA by the top ten catchers, 8677, is significantly less (15-20%) than the average number of PA by the top ten at any other position, which ranges from about 10,000 to 11,000. If we normalize the WAR values per 700 PA, the differences between catchers and other position players are therefore reduced. However, catchers are still lowest, and they are quite a bit lower than all the other position players except for SS.

Of course, if catchers do decline sooner and/or faster than players at other positions, this might affect not only their counting stats, but their rate stats as well. How would we assess this possibility? If that were the case, one might expect that the WAR differences that do exist between them and other position players would be reduced if not eliminated earlier in their careers. It’s widely accepted that age-related decline in production begins in the late 20s. Traditionally, it has been thought that players improve steadily in their early 20s up until that age; more recent evidence suggests that players may actually peak at a younger age, then stay more or less at a plateau until their late 20s. But in any case, there is no evidence of a decline much before the late 20s, barring, of course, injuries or other health problems.

Accordingly, I next examined career WAR values at each position through age 27. As before, all OF comprise one group, and the highest WAR and average of top ten were modified for this group accordingly. I also remind the reader that the ten players in each group are not all the same players as the ten in the career cohorts shown in Table 1, though there is substantial overlap. That is, the leaders in WAR through age 27 are not necessarily the ultimate winners, as determined by career totals.

Table 2. Top 10 Players in WAR by Position Through Age 27

Pos   Average PA      Highest WAR     Ave. top 10     Ave./700 PA

C            4029                     50.4                   31.7                    5.51

1B           4115                      64.6                   39.8                   6.77

2B          3976                     64.6                    37.9                   6.67

SS          4937                     62.0                    38.0                   5.39

3B          4326                     53.5                    39.8                   6.44

CF          4921                     68.8                    51.3                    7.30

L/RF     4365                     68.3                    41.9                    6.72

Compared to the WAR values for full careers (Table 1), a number of differences are apparent in this table. First, the average PA for catchers is now about the same as that for several other positions, including 1B and 2B, and not much different (< 10%) from that for 3B or corner outfielders. This is consistent with the possibility that their lower average career PA results largely from earlier or faster decline, since if that were the case, we would expect to see less, if any, of this decline through age 27.

Interestingly however, the best SS and CF have a much larger number of average PA than players at all other positions. This might be because players at these two premier positions develop sooner, but I won’t pursue this further except to point out that this relationship is reversed later for CF. That is, if we compare Table 2 with Table 1, we see that while the top ten CF through age 27 had more PA than the corner outfielders, the latter had more at the end of their careers. When we normalize for PA, the CF are clearly higher than the corner outfielders, as well as any other position, but because their PA drops relative to corner outfielders as they age, their total career WAR values are comparable. One could speculate that CF decline a little faster because of the greater amount of outfield territory they’re expected to cover.

Second, the WAR differences observed over the full careers of these top position players are quite evident even at this earlier age. The average WAR of the top infielders through age 27, 38.8, is 86.2% the average WAR of OF, very similar to the 87.3% observed when comparing over a full career. Actually, the average WAR of IF is fairly close to the average WAR of the top corner outfielders, but as I noted above, the average WAR of the best CF at this age is much higher. Thus the ratio of the IF WAR to CF is only about 75%.

Similarly, the average WAR of catchers, 31.7, is 70.2% of the average WAR of OF, a little but not too much higher than the 64.8% observed over a full career, and much lower relative to CF, about 60%. The C WAR is also 81.7% of the average WAR of all the top IF taken together, compared to 74.2% for the career comparisons. While the highest WAR for a catcher through age 27 is much closer to the highest WAR at other positions at this age, indeed, is almost as high as the highest value for 3B, this value appears to be an outlier, as it is much higher than the next-highest value for a catcher at this age.

This finding of large WAR gaps between catchers and other players even at a young age is somewhat surprising, because it suggests, contrary to the evidence discussed earlier, that the lower WAR values for catchers are not in fact the result of an accelerated decline — not unless this decline begins much sooner than age 27. In fact, based on this evidence, it appears that most catchers produce lower WAR from the get-go.

A third conclusion we can draw from Table 2 is that the general order of OF > IF > C is for the most part preserved when WAR values are normalized for PA, though some of the differences are reduced. However, the normalized WAR value for SS at this age is much reduced, in fact, is a little lower than that for C. So the general order is now OF > 1B/2B/3B >> C/SS.

Offensive and Defensive Components of WAR Differences
To summarize the discussion so far, career WAR values generally trend as OF > IF > C. If the values are normalized for playing time, the differences are reduced somewhat in some cases, but the general order remains the same. If we consider WAR just through age 27, the order is still the same, OF > IF > C. If we normalize these values for playing time, the order is still generally preserved, except that now SS join catchers as the lowest group.

The fact that the order is generally preserved at age 27 suggests that while decline might be an issue for catchers — because of the lower average PA for their careers — some other factors must play a major role in accounting for the WAR gap. At this point, we need to look more closely at how WAR is determined. WAR at FG has four main components: offensive runs, defensive runs, league runs and replacement runs. Replacement runs are proportional to PA, and thus won’t account for any differences between players and groups when WAR is normalized for the same amount of PA, though they will add to differences when total PA are different. The same is true for league runs, which are a very minor component, anyway.

So let’s look at offensive and defensive runs. Offensive runs include batting runs and baserunning runs, and defensive runs include fielding runs and the positional adjustment. In the table below, I have listed the top 10 in career WAR at each position, the same groups that appeared in Table 1. For each group is shown offensive runs, defensive runs, fielding runs and positional runs. I have also listed the average wRC+ for each group of ten. This is a rate stat that measures hitting, so is useful to compare among the best players at each position.

Table 3. Offensive and Defensive Performance of Top 10 Position Players

Pos      wRC+    Off Runs     Fielding Runs     Pos Runs     Def Runs  Pos Runs/700PA

C           122          200.6               32.7                     79.2              111.9             6.39

1B          147          579.4               46.8                 -106.1               -59.3           -7.32

2B         132          427.6                40.5                   40.8                 81.3            2.73

SS          121          264.7                78.6                   112.1               190.7            7.52

3B         128          388.5               93.8                    22.8               116.6             1.52

CF         143           591.9                65.4                  -39.3                26.1            -2.69

L/RF     147           653.1                51.9                  -111.9             -60.0            -6.95

Consider wRC+ first. Notice that as expected, the best hitters are first basemen and corner outfielders, who have the highest positional adjustment. The center fielders are close behind, followed by the second and third basemen, while shortstops and catchers are the lowest, and also very close to each other. So the ranking is 1B/L-RF > CF > 2B > 3B > SS/C, which compares fairly well with the positional adjustments of 1B > L-RF > 2B/3B/CF > SS > C. The most significant discrepancies are that CF are somewhat better hitters than indicated by their positional adjustment, while SS are somewhat worse.

Now let’s turn to offensive runs. This includes baserunning as well as hitting, and since it’s a counting stat, it also reflects PA. The order is similar to that with wRC+, except corner fielders now are ahead of first basemen, who even trail center fielders slightly, and shortstops are ahead of catchers: L-R/F > CF/1B > 2B/3B > SS > C. This makes sense if we assume that outfielders, particularly center fielders, tend to be better baserunners than first basemen, and shortstops tend to be better baserunners than catchers; this can be confirmed by comparing the baserunning values of these groups (not shown). In addition, the top ten SS, as we have seen earlier, have a significantly larger average PA than catchers, so even if they are no better as hitters, they will accumulate more total value through hitting.

So some of the WAR difference between catchers and infielders, particularly SS, results from greater offensive runs, a reflection mainly of more playing time and, to a lesser extent, of better baserunning. In fact, the difference of about 60 runs corresponds to about 6 WAR. Recall that I showed earlier that the top ten catchers average about 18 WAR less than the top ten SS. So about one-third of this difference comes from offense, and mostly simply because of more playing time (because most offense is hitting, and by wRC+, the two groups are the same).

Now consider defense, where things get interesting. Defensive runs at FG are the sum of fielding runs, which evaluate a player’s actual defense, and positional runs, which vary according to the position. Catchers have a large total here (average about 112), which is to be expected, given that they have a large number of positional runs, about 80 on average. Note, though, that the top ten 3B have a slightly larger average number of defensive runs than catchers (about 116), and the SS have a much larger number (about 190). Why is this?

From the positional runs total, we can see that catchers have a much larger total than 3B, as would be expected, since their positional adjustment is much greater. The third basemen, though, have a much higher total of fielding runs, nearly 100 on average, vs. a little over 30 on average for the catchers. In other words, the top ten 3B were on average much better defensively at their position than the top ten catchers were at theirs, and this more than compensates for their lower positional adjustment. I will return to this point later.

SS, on the other hand, have a higher total of positional runs than catchers (about 112 on average), as well as of fielding runs (nearly 80). So on average they, like the third basemen, are also better defensively than the catchers. But how can shortstops have a higher total of positional runs than catchers, given that the latter have a higher positional adjustment? Clearly, because catchers don’t play every game at that position. They are sometimes rested by moving them to another defensive position, and that position is usually the one with the worst positional adjustment: first base. Thus it doesn’t take a lot of time at that position to have a significant impact on a catcher’s net positional adjustment.

How much impact? From the last column in the table, we can see that the top ten catchers averaged about 6.4 positional runs per full season over their career. This compares to the current positional adjustment of 12.5 runs that would be given them if they played exclusively at catcher. Since first base has a positional adjustment of -12.5 runs, we can estimate that the top ten catchers played an average of about 25% of their time at first base. The SS, in contrast, had a career positional adjustment of 7.5 runs, which is just about what they should have playing full time at that position.

The net result is that SS average about 80 defensive runs more than catchers (from the table, 190.7 – 111.9). This accounts for another 8 WAR or so in their differences, bringing the total up to 14 (6 for offense plus 8 for defense). We saw earlier that the top SS on average accumulate about 18 WAR more than the best catchers. Where do the other 4 WAR come from? Replacement. As was shown in Table 1, the top ten SS on average had about 1800 more PA than the top ten catchers. This corresponds to roughly 50 more replacement runs, or about 5 WAR, close enough for this rough estimate. Since all of the difference in replacement runs (50), and most of the difference in offensive runs (60, from Table 3) is due to the greater playing time of the SS, we can say that roughly 60% of the WAR difference is due to this greater longevity, and the other 40% (80 runs, from Table 3) to better defensive value. Of the latter, a little more than half results from better defense (45 more fielding runs, from Table 3), and the remainder from a net positional advantage (33 more runs, Table 3).

The other positional run averages shown in the table are fairly easy to account for. For second basemen, it’s about 2.7 runs, slightly higher than the 2.5 value for this position. This could reflect some time playing SS for some of these players, or higher positional adjustments in the past. I haven’t looked into the historical trends in positional runs, and am just going on what are generally considered the current values. For third base, it’s 1.5 runs, slightly lower than the 2.5-run adjustment, and may reflect a little action at 1B or in the OF. The negative value for CF, which have a positive positional adjustment of 2.5 runs, is not unexpected, because most CF play part of their careers, particularly as they get older, at the corners, where the adjustment is negative. The higher negative positional runs of the corner outfielders is of course expected. It’s actually slightly higher (less negative) than the positional adjustment of -7.5 runs, which probably reflects that most corner fielders have played a little at CF. Since the difference in adjustment between these two positions is 10 runs, the corner outfielders would only have to play CF about 5% of the time to bring their positional run average up to -7.0.

Summary
We’re now in a position to understand why the greatest catchers finished their careers with lower WAR than the best players at any other position, despite having the advantage of a greater positional adjustment. One factor, which I discussed earlier, is that on average they had fewer PA than other players, by about 15-20%. When we normalize WAR to PA, catchers are still the lowest, but the differences are reduced somewhat. We came to the same conclusion by showing that about 60% of the WAR difference between catchers and shortstops is due to offensive runs and replacement runs, which are mostly a reflection of more PA for the SS.

In addition, though, catchers rarely get full advantage of their positional benefit, because they play some of the time at another position, generally first base. Many catchers may move permanently to this position later in their career, but even when they are younger, they are likely to put in some time at 1B. This, I suggest, is a major reason why we find that even at age 27, when they should be at their peak and when they have played a comparable amount of time to players at several other positions, catchers still lag behind all other position players in WAR. Statistically speaking, they aren’t “pure” catchers; they’re in effect competing with other players who are supposed to be better hitters.

In fact, Johnny Bench, whose 50 WAR through age 27 I earlier described as an outlier among catchers, averaged 8.2 positional runs/700 PA at this point in his career. This relatively high net positional adjustment, together with a high amount of PA, account for his unusually high WAR.

There is a third factor evident from the analysis, though. As I noted above, the top ten catchers have a lower average total of fielding runs — meaning they are poorer defensively at their position — than players at other positions. This difference is especially great in comparing them to shortstops and third basemen, but in fact, catchers have the lowest average total of fielding runs of any group analyzed.

It’s not hard to understand why this might be the case. Since catchers as a group are relatively poor hitters, and since the largest component of WAR is usually hitting, a catcher who hits well but doesn’t play the position well is likely to rack up more WAR than a poor-hitting catcher who plays excellent defense. In fact, three of the top ten catchers — Joe Torre, Ted Simmons and Mike Piazza — finished their careers with negative fielding runs. Only one top-ten SS — Derek Jeter — and one top-ten 3B — Chipper Jones — finished their career with negative fielding runs.

That’s not to say that good-hitting, poor defensive players can’t make it at other positions, but there the difference in hitting between best and worst is not so great. The hitting standard is higher at these positions, which means that even an excellent hitter can’t exceed it as much as he might catching. That being the case, the bar for defense is in effect set a little higher.

What implications does this have for evaluating catchers? I think it justifies lowering the WAR bar a little for them. From Table 3, we can estimate that if catchers played full-time at that position throughout their career, they would add about 6 runs per season to their defensive total. Over an average career of 13-14 full seasons, that amounts to about 8 WAR. As we also saw, catchers lose 4-5 WAR in replacement value relative to other position players because of shorter careers. So if they played a career of normal length, and exclusively at catcher, they could add about a dozen WAR to their total, even assuming that they were little better than replacement at the end. That would raise the 50 WAR average for current members of the HOF to a little over 60, right in keeping with the average for other position players.

I think the nub of the problem is that when positional runs are adjusted, they assume that a player can and will play the entire season at a particular position, and that doing so will have no adverse effect on his career, above and beyond the normal aging process that all players undergo. In other words, positional runs do not look at the long-term picture. They consider the demands of the position in the present. It’s rather like comparing two cars, one that is expected to drive in snow, mud, extremes of heat and other challenging weather conditions on bad roads, while the other is used in mostly temperate weather on good roads. Just because the two cars have a certain relative performance at the outset does not mean that we should expect this relative performance to be maintained over their lifetime.

I’ll close by pointing out that other questions remain, in particular the WAR differences between OF and IF. Returning to Table 1, the top OF have an average WAR about 15% higher than the average IF. If WAR is normalized for PA, the difference between corner outfielders and infielders drops somewhat, but the difference between center fielders and infielders remains. Center fielders clearly have the highest WAR/PA of any of the positions.

The other factors that underlie the differences between C and the other players do not appear to contribute to the difference between OF and IF. The fielding-run average of OF, both CF and corner outfielders, is about in the middle, higher than that for C, 1B and 2B, but lower than for SS and 3B. While CF have a positive positional run adjustment, like catchers, their net adjustment is reduced by significant playing time at another position with a negative adjustment. Corner OF get a slight boost in their net positional runs when they play at CF, or in some cases perhaps at 3B, but this is a minor effect. So on the face of it, it seems that OF, and particularly CF, hit better than their positional adjustment would imply. This is also reflected in their average wRC+ values (Table 3), which are about on par with those of first basemen, which of course have a much larger positional adjustment.

## The Flame-Throwing Myth

Is pitch velocity an indicator of a good pitcher?

Over this past summer, the Twins struck a deal with the Boston Red Sox to send specialist Fernando Abad to Boston for prospect Pat Light. Light, 25, first pitched in the majors in 2016, where in two innings with the Red Sox, he had allowed 8 runs (7 earned). After the deal, he has spent the rest of the season with the Twinkies. His numbers do not look much better, with an ERA of 10.22 in 12.1 innings pitched. Over his minor-league career, he has posted a 4.35 ERA in five seasons. Why did the Twins want this guy? He was 25, fully established as a reliever, and has only dominated the minors in 2016.

One of my theories is that the Twins saw that Light is a flame-thrower. Recently, he hit 101 miles per hour on a pitch. Are the Twins fixated on his high velocity? Looking at the Twins’ bullpen, another below-average pitcher, Ryan Pressly, is also touted for his high velocity.

I am not saying definitively that the Twins are focusing on pitchers’ velocities to value prospects and players; previously I wrote about how teams have focused on batters’ exit velocities, so perhaps the Twins have tried to apply this mentality toward pitchers.

Either way, I decided to delve into this topic, seeing if a pitcher’s velocity indicates a lower ERA, FIP, and BABIP, or a higher strikeout rate and walk rate. Using MLB’s Statcast, I was able to parse their data to record a pitcher’s average velocity. Using these data, I tried to establish the skill set of a flame-thrower.

To do this, I performed linear regressions between these different factors, seeing if any of these values are highly related to or influenced by faster pitching.

First, I looked at FIP and velocity. Below are the results:

Not a strong relationship, yielding an R-squared of 0.09. This relationship does show that as velocity increases, FIP tends to decrease, but again, not a very convincing relationship.

Next, I looked at ERA and velocity:

It yielded a similar result, a weak negative relationship, if any.

While the results for ERA and FIP were disappointing, I figured BABIP might look better. If a pitcher can throw faster, it would make sense that the batter would have a tougher time making contact, leading to weaker contact and a lower BABIP. Did the results agree? Have a look:

Disappointing. No relationship at all.

On to strikeout rate and walk rate.

I immediately thought of Aroldis Chapman. He has the fastest heater in the league, and his strikeout rate is above 40%, nearing the top of the league. I was much more optimistic for these metrics.

Here is velocity to strikeout rate:

Not a great relationship, yielding an r-squared of .13. It is a little stronger than anything else we have seen, but that is not saying much at all.

Finally, here is velocity and walk rate:

Not much going on here as well.

What does this all mean? Well, for starters, it shows that there are other factors that determine how effective a pitcher is. These data show that these metrics are not the end-all-be-all of a pitcher’s skill. Velocity is not a key indicator of an effective pitcher. Sure, the fastball probably needs to be upward of 85 miles an hour, but speed is not the most important factor. Rather, other skills, such as control, deception, and quality of breaking pitches might be just as important, if not more important, than velocity.

I don’t know if the Twins specifically targeted Light because of his velocity, but in his stint with the Twins, he’s averaged 10.9 walks per 9 innings. What good does his speedy fastball do if he cannot get it over the plate?

After my analysis, I’ll admit I’m a little surprised. I would think a higher velocity would mean a higher strikeout rate. But I am wrong. I guess for every flame-throwing Aroldis Chapman, there is an equally effective Andrew Miller, who does not posses the 105 mile-an-hour heater, but has a higher strikeout rate.

## Why Is There No Version of wRC+ Including Baserunning?

Would someone be so good as to please implement this for me? This statistic can and should definitely exist and would be easily derivable from the stats FanGraphs already has. Yes, there is the offensive runs above average figure, but this is not a rate stat, and is not scaled to 100. What the people want is a wRC+ rate stat for offense that includes baserunning — for why should baserunning be excluded if what we want to know is who are the best offensive players on our team, which certainly includes their contributions on the bases or the lack thereof? (I’m looking at you Wilmer Flores; I saw you thrown out three times on the bases in a single game last week.)

I think the argument is clear for adding a baserunning-included linear weights rate stat, and one which is park- and league-adjusted, and one on the intuitive, familiar, and non arbitrary 100 point scale. (I prefer wRC+ because the scale of wOBA seems entirely arbitrary and unintuitive in meaning.) I don’t think the user should need to try to cobble together figures on their own and perform division to determine various players’ offensive contribution per plate appearance, if they have a simple question, such as “Who are the best 8 or 10 or 12 or 14 offensive players on our team, in their careers, or this season?” Despite all the stats given to us, I don’t really see one that answers this without my performing calculations.

That’s right; I have figured out how to calculate this myself, but it would be nice to have it automatically calculated. Here’s how you do it. (There is probably an easier way than this, but this is the way I figured from the materials available.)

Take the Players (Park/League Adjusted) Batting Runs above Average (listed at the bottom of the page under Value, or subtract baserunning runs from offense runs) and divide by wRC+ the percentage above average, converting wRC+ to a percentage and subtracting 100 (i.e. for a wRC+ of 104, divide by .04). This will give you the League Average Batting Runs in the player’s plate appearances. Now that you have this figure, simply add back in the Offensive runs above average (or the Batting Runs above average + Baserunning runs above average) to get the total park- and league-adjusted linear weights runs for the player including baserunning and hitting. And of course, to get our wRC+ baserunning-included statistic, we simply now divide by the league-average runs (which we already calculated, and which has the park and league adjustments built into it because of how it was calculated) and we arrive at the desired baserunning adjusted wRC+. Voila.

As an illustration, take Curtis Granderson, who I have down, as of September 15 having a career 117 wRC+, and a sum total of 192.9 Offense Runs, and 50.9 Baserunning Runs. By the way, we should immediately see that his career wRC+ is going to be seriously under-rating his overall offensive contribution since roughly 1/4 of his career offensive runs above average derive from his good works on the bases.

1) We start by subtracting the Baserunning runs from the Offense runs to get the Batting Runs above average (You can skip this step if you look down under value, where this stat is listed, though you can probably calculate it faster than you can scroll if you’re like me):

192.9 – 50.9 = 142

2) Next, since a wRC+ simply means his batting was 17 percent above league average for his career (park-adjusted), we divide the batting runs only by .17 to get the league-average batting runs, park-adjusted:

142 / .17 = 835.29

3)  Next, we add back the player’s total offensive runs above average, to the league-average figure over that span, park-adjusted for where the player played already, to get the player’s total park- and league-adjusted runs, including baserunning.

835.29 + 192.9 = 1028.19

4) Last, but not least, simply divide by the figure we arrived at in step 2 (the park-adjusted league-average runs a player would have produced in however many plate appearances) to arrive at your magnificently complete new baserunning-included wRC+:

1028.19 / 835.29 = 1.2307, or 123 on the 100 point wRC+ scale.

Thus, in the case of Granderson, his ostensible wRC+ of 117 is significantly under-playing how much better than average he’s been over his career on offense, relative to his opportunities, since his “true” wRC+, including baserunning, is actually 123, not 117.

I can’t see what the argument for baserunning not being included would even be; I understand why one would also want the batting-only figure, but the batting + baserunning figure is surely also important to know, and if I had to only have one, to my mind, I’d unequivocally take the figure that gives total offensive contribution relative to opportunities and adjusted by context, rather than a partial figure that tells me only about batting. Luckily, there’s no real reason to choose; we can and should have both.

You might now be thinking, wait, what about below-average players? (I momentarily had this trivial thought, but the negative runs above average, and the percentage wRC+ below 100 will cancel out, of course.)

A demonstration, using the aforementioned lead-footed Wilmer Flores as our exemplar. Flores, has -7.0 batting runs above average for career, -2.8 Baserunning above average, -9.8 offense above average, and a 95 career wRC+. Here I’ve skip step 1 by just finding Batting Runs on the bottom of the page.

1. -7/.05=140 (which represents league average runs in Flores’s career plate appearances, including adjustments)
2. 140-9.8=130.2 (the number of offensive runs Flores actually contributed, including his base-running miscues.)
3. 130.2/140=.93 or a wRC+ of 93, once we appropriately dock Flores for his base-running.

Now, while this isn’t that complicated for me to calculate, I propose this, or something like it be implemented for a total wRC+ that includes baserunning. Obviously it could be calculated for season stats too just as easily. If you have baserunning runs, Offensive total runs, and wRC+, the figure I’m looking for can easily be implemented. Thanks for reading.

## Hardball Retrospective – What Might Have Been – The “Original” 1992 Padres

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

# Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

# Assessment

OWAR: 52.6     OWS: 324     OPW%: .595     (96-66)

AWAR: 37.3      AWS: 246     APW%: .506     (82-80)

WARdiff: 15.3                        WSdiff: 78

The ’92 Friars fiercely engaged the Braves but when the dust settled, the San Diego crew emerged two games behind Atlanta. The Padres led National League in OWAR and OWS. Roberto Alomar (.310/8/76) nabbed 49 bags in 58 attempts and registered 105 tallies. Carlos Baerga (.312/20/105) collected 205 base knocks, rapped 32 doubles and merited his first All-Star selection. Shane Mack supplied a .315 BA and scored 101 runs. Dave Winfield drilled 33 two-baggers, walloped 26 big-flies and plated 108 baserunners. Dave “Head” Hollins manned the hot corner and responded to full-time status with personal-bests in home runs (27), RBI (93) and runs scored (104). John Kruk laced 30 two-base hits and posted a .323 BA. In the final season of a 13-year consecutive Gold Glove Award streak, Ozzie Smith aka “The Wizard of Oz” delivered a .295 BA and succeeded on 43 of 52 stolen base tries. “Mr. Padre” Tony Gwynn contributed a .317 BA with 27 doubles.

Gary Sheffield (.330/33/100) and Fred “Crime Dog” McGriff secured their first invitations to the Mid-Summer Classic and accounted for a substantial chunk of the “Actuals” offensive production. “Sheff” claimed the batting title and placed third in the 1992 NL MVP balloting. McGriff topped the Senior Circuit with 35 bombs while driving in 104 runs.

Tony Gwynn rated sixth among right fielders in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” San Diego teammates enumerated in the “NBJHBA” top 100 lists include Ozzie Smith (7th-SS), Roberto Alomar (10th-2B), Dave Winfield (13th-RF), Kevin McReynolds (45th-LF), John Kruk (72nd-1B), Ozzie Guillen (74th-SS) and Carlos Baerga (93rd-2B). Fred McGriff (21st-1B), Tony Fernandez (24th-SS) and Gary Sheffield (54th-RF) attained top-100 status among those who played exclusively for the “Actual” 1992 Padres.

 STARTING LINEUP POS OWAR OWS STARTING LINEUP POS OWAR OWS Shane Mack LF 6.17 27.47 Jerald Clark LF -0.67 9.94 Thomas Howard CF/LF 0.05 6.44 Darrin Jackson CF 0.46 13.54 Tony Gwynn RF 1.69 17.86 Tony Gwynn RF 1.69 17.86 John Kruk 1B 4.35 25.38 Fred McGriff 1B 3.6 27.38 Roberto Alomar 2B 5.37 31.53 Tim Teufel 2B -0.48 5.17 Ozzie Smith SS 3.24 22.13 Tony Fernandez SS 1.41 18.31 Dave Hollins 3B 3.61 25.6 Gary Sheffield 3B 5.92 32.28 Sandy Alomar, Jr. C 0.09 8.2 Benito Santiago C 0.81 8.17 BENCH POS OWAR OWS BENCH POS AWAR AWS Carlos Baerga 2B 4.83 28.54 Dan Walters C 0.36 5.43 Dave Winfield DH 3.53 25.75 Kurt Stillwell 2B -1.98 4.93 Kevin McReynolds LF 1.27 12.89 Craig Shipley SS -0.37 1.61 Jerald Clark LF -0.67 9.94 Tom Lampkin C 0.21 1.03 Benito Santiago C 0.81 8.17 Paul Faries 2B 0.19 0.82 Warren Newson RF 0.25 4.04 Guillermo Velasquez 1B 0.08 0.7 Joey Cora 2B 0.66 3.98 Dann Bilardello C -0.3 0.59 Ron Tingley C 0.13 3.36 Jim Vatcher RF 0.02 0.54 Mark Parent C 0.25 1.42 Kevin Ward LF -0.8 0.52 Paul Faries 2B 0.19 0.82 Oscar Azocar LF -1.14 0.44 Guillermo Velasquez 1B 0.08 0.7 Jeff Gardner 2B -0.22 0.27 Gary Green SS 0.08 0.46 Gary Pettis CF -0.08 0.24 Rodney McCray RF 0.09 0.45 Phil Stephenson LF -0.5 0.19 Ozzie Guillen SS -0.01 0.41 Thomas Howard – 0 0.05 Mike Humphreys LF -0.15 0.12 Jim Tatum 3B -0.1 0.08 Luis Quinones DH -0.04 0.02 Jose Valentin 2B -0.03 0

Andy Benes fortified the “Original” and “Actual” Padres rotations with 13 victories and a 3.35 ERA. Rich Rodriguez and Mike Maddux enhanced the “Actuals” bullpen with identical 2.37 ERA’s while southpaw Bruce Hurst contributed to the starting rotation with a 14-9 record. Omar Olivares registered 9 wins with a 3.84 ERA and Bob Patterson posted a career-best 2.92 ERA for the “Originals”.

 ROTATION POS OWAR OWS ROTATION POS AWAR AWS Andy Benes SP 4.22 15.68 Andy Benes SP 4.22 15.68 Omar Olivares SP 1.89 8.33 Bruce Hurst SP 2.56 12.47 Jimmy Jones SP 0.41 4.89 Craig Lefferts SP 1.27 9.7 Ricky Bones SP -0.35 4.22 Frank Seminara SP 0.93 6.47 Greg W. Harris SP 0.4 3.81 Jim Deshaies SP 1.39 5.78 BULLPEN POS OWAR OWS BULLPEN POS AWAR AWS Bob Patterson RP 0.95 7.52 Rich Rodriguez RP 1.6 9.21 Jim Austin RP 1.21 6.79 Mike Maddux RP 1.56 8.9 Mitch Williams RP -0.27 4.99 Jose Melendez RP 1.28 7.3 Mark Williamson RP 0.4 2.48 Randy Myers RP -0.04 7.16 Steve Fireovid RP -0.18 0.3 Larry Andersen RP 0.31 3.6 Matt Maysey RP -0.01 0.08 Greg W. Harris SP 0.4 3.81 Doug Brocail SP -0.23 0 Pat Clements RP 0.22 2.12 Jeremy Hernandez RP 0.05 1.49 Gene Harris RP 0.31 1.37 Tim Scott RP -0.65 0.91 Doug Brocail SP -0.23 0 Dave Eiland SP -0.51 0

Notable Transactions

Roberto Alomar

December 5, 1990: Traded by the San Diego Padres with Joe Carter to the Toronto Blue Jays for Tony Fernandez and Fred McGriff.

Carlos Baerga

December 6, 1989: Traded by the San Diego Padres with Sandy Alomar and Chris James to the Cleveland Indians for Joe Carter.

Shane Mack

December 4, 1989: Drafted by the Minnesota Twins from the San Diego Padres in the 1989 rule 5 draft.

Dave Winfield

October 22, 1980: Granted Free Agency.

December 15, 1980: Signed as a Free Agent with the New York Yankees.

May 11, 1990: Traded by the New York Yankees to the California Angels for Mike Witt.

October 30, 1991: Granted Free Agency.

December 19, 1991: Signed as a Free Agent with the Toronto Blue Jays.

Dave Hollins

December 4, 1989: Drafted by the Philadelphia Phillies from the San Diego Padres in the 1989 rule 5 draft.

Ozzie Smith

Traded by the San Diego Padres with a player to be named later and Steve Mura to the St. Louis Cardinals for a player to be named later, Sixto Lezcano and Garry Templeton. The San Diego Padres sent Al Olmsted (February 19, 1982) to the St. Louis Cardinals to complete the trade. The St. Louis Cardinals sent Luis DeLeon (February 19, 1982) to the San Diego Padres to complete the trade.

# Honorable Mention

OWAR: 47.6     OWS: 298     OPW%: .518     (84-78)

AWAR: 29.2       AWS: 222      APW%: .457    (74-88)

WARdiff: 18.4                        WSdiff: 76

The ’86 Padres ended the season in a virtual tie with the Dodgers. Tony Gwynn (.329/14/51) paced the Senior Circuit with 211 base hits and 107 runs scored. He swiped 37 bases in 46 attempts and collected his first Gold Glove Award. Kevin McReynolds (.288/26/96) began a streak of five successive seasons with at least 20 round-trippers. Ozzie Smith succeeded on 31 of 38 stolen base attempts. Dave Winfield crushed 24 moon-shots and plated 104 baserunners. Johnny Grubb contributed a .333 BA with 13 jacks in a part-time role and John Kruk delivered a .309 BA in his inaugural campaign. Eric Show fashioned a 2.97 ERA and tallied 9 victories for the San Diego starting staff.

# On Deck

What Might Have Been – The “Original” 2002 Blue Jays

# References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Sean Lahman Baseball Archive

## Someone Give Juan Uribe a Job

Todd Frazier has 38 home runs this year. That’s probably a strange way to start off a post about Juan Uribe but hang with me.

Todd Frazier has 38 home runs this year. Todd Frazier also has a wRC+ of 100 this year. That is a pretty remarkable combination. According to wRC+ Frazier has been exactly an average hitter this year despite the fact that he is currently 8th in all of baseball in home runs. This interesting and seemingly unlikely union piqued my curiosity and sent me down a statistical rabbit hole in search of home runs and terrible wRC+’s. At the bottom of that rabbit hole is where I ran into Juan Uribe.

Juan Uribe has not played an MLB game since July 30th. In that game he went 0-for-3 and he was released by Cleveland a few days later on August 6th. This probably wasn’t a surprise to most people as A) Most people probably would be more surprised to learn he was still in the league to begin with, and B) he was running a 54 wRC+ over 259 PA with Cleveland this year.

But I’m not here to argue that someone should give Uribe a job because his current talent level deserves one (although you probably could; he was nearly a 2-WAR player as recently as last year). I’m here to argue for someone to give him a job because Juan Uribe is on the cusp of history. Juan Uribe has 199 career home runs.

You might think that 200 career home runs isn’t that much of a milestone and it’s only because humans love round numbers that we even recognize it as a milestone. And you would be absolutely correct in saying that. But much like Todd Frazier’s 38 home runs this year, Juan Uribe’s 200 career home runs would be fairly unique. In fact they would be entirely unlike anyone before him because Juan Uribe would be the worst hitter to ever hit 200 home runs.

That is the board of directors of the Terrible 200 Club (patent pending) and as you can see Juan Uribe is poised to unseat Tony “why the hell am I standing sideways at the plate” Batista as CEO with one more measly home run, and by a pretty decent margin. Obviously though his bid is now under threat because he is 37 years old, has been without a team for over a month now and was absolutely awful when he did have a team. It is entirely possible, maybe even likely, that he never hits another MLB home run. And it’s not like there is another current player who is a slam dunk to make a run at Batista if Uribe never steps into the batter’s box again:

Brandon Phillips will get to 200 but he is sneaky old. He turned 35 in June, so while he is nowhere near what he was earlier in his career it seems unlikely that he plays long enough to see his career wRC+ fall below 90.

AJ Pierzynksi is all but done at this point. At 39 years old and nearly a win below replacement level this year it’s probably more likely that the ghost of Clete Boyer gets signed and hits 38 home runs to get to 200 as it is Pierzynski hits 12 more in his career.

-Which bring us to James Jerry Hardy. Hardy seemed to be doing his best to crater his wRC+, posting a dreadful 50 last year, but he has rebounded (relatively speaking) to post a 93 so far this year. One has to wonder if he can even get to 200 home runs (he still needs 16 more to get there and he has hit only 26 over his past 1404 PAs), and secondly, if he does, will he post a wRC+ low enough to “best” Batista? You could probably argue that any version of Hardy that is good enough to get to 200 homers is probably also good enough to not decimate his career wRC+.

The easiest solution is for some intrepid and/or awful team to just give Uribe a spot so that he can chase history with each swing. Atlanta, Arizona, Minnesota, what have you guys got to lose? Would a Kickstarter or GoFundMe to pay some of his salary help? It would just be such a shame for the baseball public to be denied a potentially marvelous thing when it’s so close to realization. Like teasing a dog by pretending to throw a ball or every season after the first one of Homeland.

Somewhere Tony Batista is sitting in a recliner, probably in some crazy way that no one else sits in recliners because he is Tony Batista, just waiting for the news that Uribe has been picked up by someone so he can hand the crown to the new king of the Terrible 200 (patent pending). He just needs a little help. Let’s make this happen, MLB.

## Bases Produced and a Consideration of the 2016 AL/NL MVPs

Bases Produced is the keystone stat in a paradigm for baseball statistics that I have been developing, off and on, for the past 18 years.* Bases Produced measures a player’s overall offensive productivity by counting, quite simply, the number of times that player enables either himself or a teammate to advance to the next base. Each time this happens, a player is considered to have “produced a base.” Counting these events is important because producing bases is quite literally the only way that a baseball player can contribute to the scoring of runs by his team. When a player scores a run, after all, he has done nothing more than advance to all four bases in succession.

The Bases Produced system assigns credit for the production of these bases in a way that is based on traditional baseball statistics, but is also an expansion thereof. This expansion enables most traditional numbers to be tied together into a unified whole, evaluated in terms of Bases Produced, rather than remaining the haphazard collection of unrelated counts that they have always seemed to be.

How does it work? To calculate Bases Produced (BP), I first unify all of a player’s productive batting stats into one sub-total called “Batting Bases Produced” (BBP). This counts each base the player reaches on his own base hits, walks, or times hit by pitch:

BBP = 1 * 1B + 2 * 2B + 3 * 3B + 4 * HR + BB + HBP

A player’s success at producing BBP may be contextualized by dividing his BBP by his total number of “Batting Base Production Chances” (BBPC). This total includes all of a player’s plate appearances (PA), except for those times when a player has attempted to lay down a sacrifice bunt (SHA) — where his primary goal is ostensibly to produce bases for his teammates, rather than himself — and also his catcher’s interferences (CI), where the defense literally takes away his ability to put the ball in play.

BBPC = PA – SHA – CI

The ratio of BBP to BBPC then becomes a player’s “Batting Base Production Average” (BBPAVG):

BBPAVG = BBP / BBPC

Secondly, a player may produce bases for himself as a runner, by either stealing bases (SB), advancing on fielder’s indifference (FI), or “gaining” bases (BG). “Gaining Bases” is the term I use for a player who advances a base when the defense attempts to make a play on a runner somewhere else on the basepaths. For example, if a runner tries to score from second on a single, the batter may advance to second when the defense tries to throw out the runner at the plate. In this case, the batter/runner “gains” second base.

Taken altogether, the bases a player produces for himself as a runner are then called “Running Bases Produced” (RBP):

RBP = SB + FI + BG

Lastly, an offensive player can produce bases for teammates who are already on base by either drawing walks, getting hit by a pitch, or by putting the ball in play. Collectively, these bases are known as “Team Bases Produced” (TBP). The number of times a batter enables a teammate to reach home (TBP4) can be intuitively understood as the number of RBIs he has produced for his teammates, without including any that he has produced for himself. Overall, Team Bases Produced expands this concept by including the number of times a player enables his teammates to advance to second (TBP2) or third (TBP3), as well:

TBP = TBP2 + TBP3 + TBP4

While of course the batter depends on the presence — and subsequent baserunning actions — of a teammate on base to produce these bases, I assign the credit for producing them solely to the batter, without whose actions the runner(s) would not be able to advance on the play. The presence of the runners on base, however, is important to recognize when trying to evaluate how successful a batter is at producing team bases; each runner on base therefore counts as one “Team Base Production Chance” (TBPC) for a batter. (Note: When a batter draws an intentional walk, I do not count TBPC for runners whom the batter cannot force ahead to the next base.)

A batter’s Team Base Production Average (TBPAVG) then becomes, generally (and simply):

TBPAVG = TBP/TBPC

Overall, a player’s total Bases Produced (BP) is simply the sum of his Batting Bases Produced, Running Bases Produced and Team Bases Produced:

BP = BBP + RBP + TBP

This number may also be evaluated in terms of the player’s total number of chances to produce bases (BPC), including his Plate Appearances, Team Base Production Chances, and the number of times he enters the game as a pinch runner (PRS):

BPC = PA + PRS + TBPC

Rounding out this approach, I calculate a general measure of “Base Production Average” as the ratio of Bases Produced to Base Production Chances:

BPAVG = BP / BPC

On my website, www.basesproduced.com, I fill in the blanks of this general paradigm with similar breakdowns for “Outs Produced” and “Bases Run” (= bases a player reaches, but does not necessarily produce); interested readers may follow the link to learn all of the gruesome details for themselves. On the same website, I also calculate and update the BP stats for the current MLB season on a daily basis. You are welcome to check it out to follow along and see how they play out in real life.

While the Bases Produced paradigm may not enjoy all of the mathematical sophistication that goes into many modern sabermetric measures of offensive performance, it does have the advantage of reflecting straightforward facts and events that take place in every baseball game that any fan can quickly recognize and easily count for themselves (with or without a smartphone!). A grand slam home run, for instance, counts as 10 BP: 4 for the batter, 3 for the runner at first, 2 for the runner at second, and 1 for the runner at third. 10 Bases Produced is also a pretty good standard for an excellent game of baseball: I’ll mention in passing that there were just 7 performances of 10 BP or greater in last night’s (9/16) slate of 15 MLB games, with 14 BP topping the list (by three different players).

On basesproduced.com, I have also tabulated the same stats, using data from retrosheet.org, going back to the 1922 season. For those who are curious, the highest single-season BP total in history is 1005, by Lou Gehrig in 1927, while the highest BPAVG of all time is Barry Bonds’ .885, in 2004. There are still many bases produced statistics left to be calculated from the very olden days of baseball, however, before any of these numbers might be considered “records.”

Although Bases Produced is not, strictly speaking, a system that was designed to determine who ought to be the “Most Valuable Player” in any given season (whatever you might interpret that to mean), it is fun to use as another data point in the never-ending discussions about who most deserves the MVP award each year. So let’s consider what the system can show us about the best players in the American and National Leagues in 2016.

The AL MVP race has generally been described this season as a five-man horse race between David Ortiz, Mike Trout, Jose Altuve, Josh Donaldson and Mookie Betts. The Base Production Average numbers back that perception up, as all five of those players sit on top of the current AL BPAVG leaderboard, as of September 16th:

Player                             BPAVG      BBPAVG     TBPAVG

1. David Ortiz               .709            .673              .760

2. Mike Trout               .649            .628              .613

3. Jose Altuve              .645             .590             .652

4. Josh Donaldson      .644             .630             .651

5. Mookie Betts            .605             .564             .607

Although these numbers should ideally be normalized to account for the influence of hitter-friendly venues like Fenway Park, Ortiz is still enjoying his best season there ever (his previous season high BPAVG was .697, in 2007), and he’s well ahead of his career BPAVG of .620, too. As far as base-production statistics are concerned, David Ortiz is unambiguously the 2016 AL MVP.

Over in the National League, I have heard many people talk about the great year that Kris Bryant is having, but his performance fails to even register in the NL’s top five base producers, by average:

Player                             BPAVG      BBPAVG     TBPAVG

1. Daniel Murphy         .665            .619              .718

2. Anthony Rizzo         .634            .607              .659

3. Joey Votto                .619             .602             .617

4. Nolan Arenado        .617             .607             .624

5. Freddie Freeman    .612             .612              .597

(9. Kris Bryant             .601             .618             .541)

Daniel Murphy of the Nationals has clearly had the standout year, instead. And it is worth noting that Bryant’s teammate, Anthony Rizzo, is actually doing considerably better than Bryant in overall BPAVG. The big difference amongst these three players can largely be attributed to Bryant’s mediocre TBPAVG, which is near the National League median of .529 (Aledmys Diaz). That difference can, in turn, be attributed to a combination of Bryant’s high strikeout percentage (.219) and very low ground-out percentage (.113). The one outcome of a plate appearance that never produces bases for teammates is a strikeout, and ground outs tend to be about three times as team-productive as fly outs, in those situations where a batter hasn’t succeeded in producing a base for himself. Bryant’s current numbers place him squarely on the wrong side of both of these team-base-production tendencies.

While Kris Bryant has had a great baserunning season this year…these numbers give reason to question any suggestion that he might have been the best player in the league this season — or even, for that matter, the best player on his own team. But at least it is manifestly clear that Joe Maddon has Bryant and Rizzo in the correct order in the Cubs’ lineup. :-)

*While I am not as up on the current literature in baseball statistical analysis as I should be, I do know that others have developed similar statistical measures independently of me, including at least Gary Hardegree, Alfredo Nasiff Fors, and someone named EvanJ on this forum. If there are other similar thinkers out there, then I apologize for my ignorance of their work.

## Three Fringe NL Central Prospects Assigned to the AFL

*College stats taken from thebaseballcube.com, minor-league stats taken from fangraphs.com and MLBfarm.com

Last week, Baseball America released their Arizona Fall League (AFL) rosters. For those not familiar with the AFL, read more here. In short: each August, all 30 MLB clubs select six players from their minor-league rosters to participate in the fall league. While the minor-league playoffs wrap up toward the end of September, the AFL serves as a domestic developmental league starting in October.

The AFL is prestigious, bringing together some of the top minor-league talent each year. Aside from well-known names, organizations tend to also invite rising prospects who have flown under the radar. Although these NL Central prospects have gotten little public hype, their recent numbers have impressed enough to earn an invite to the AFL, making them intriguing names to watch in the coming months.

Barrett Astin – RHP 6’1” 200, Blue Wahoos (Reds AA), Age: 24 (Video)

Astin had a strong 2012 season as a closer during his sophomore year at the University of Arkansas, helping a well-staffed Razorback team to the College World Series. However, he started all five of his appearances in the Cape Cod league that off-season, where he posted an underwhelming 6.23 K/9 and 2.91 BB/9 through 21.2 IP. He went back to college to find himself in the rotation for the majority of the year, though scouts questioned his durability as a starter as he continued to struggle to go deep into games, going more than 6 IP in only one start. He was signed in the 3rd round in the 2013 draft at slot value by the Brewers, soon being dealt to the Reds for Jonathan Broxton a year later.

Despite being omitted from MLB.com’s top 30 Reds prospects this season, the Reds chose to send Astin to the AFL after having an impressive season in AA alternating between the bullpen and the rotation. In 103.1 IP, he posted an 8.39 K/9 (his career high) with a 2.18 BB/9 and a strong 65.02 GB%, numbers that would play well at hitter-friendly Great American Ball Park.  His ERA sits at 2.26, which is best in the Southern League and roughly 40% better than the league’s average ERA. His low BABIP (.246) and high LOB% (78.9%) may lead to some regression when it comes to run prevention, but FIP still has him pegged at an above average 3.37. His 11 starts have yielded similar peripherals to his numbers from out of the bullpen. However he still showed durability issues, only averaging 5.1 IP/GS this year.

The question is the same now as it was the day he was drafted: can he stay a starter? Considering the Reds have Homer Bailey, Anthony DeSclafani, and possibly Cody Reed solidified in the rotation with prospects Amir Garrett and Robert Stephenson expecting to be in the rotation as well, my guess is that Astin’s ticket to the big leagues will be as part of the relief corps for the Reds. His inability to show consistent stamina and his better numbers against righties than lefties (all 8 HR allowed this year have been off of lefties) all indicate he is better suited as a bullpen option. Considering the Reds’ well documented bullpen problems this year, Astin could have his MLB debut with a rebuilding Reds team sometime next year if all goes well. His AFL stint should give a good indication on which direction he is trending heading into his 25th birthday.

James Farris – RHP 6’2” 210, Smokies (Cubs AA), Age: 24 (Video)

Another participant in the 2012 College World Series, Farris started and pitched seven innings in Arizona’s World Series-clinching win. He was drafted in the 15th round by the Astros after a below-average junior campaign, only to return to Arizona for his senior year. He was drafted in the 9th round by the Cubs at the end of the his last and best year playing in the Pac-12.

Baseball America’s draft-day scouting report notes that Farris does not have overpowering stuff and transformed into a smart, command-oriented pitcher over the course of his four seasons with the Wildcats (subscription required). His best pitch is his changeup, with a 85-89 mph fastball, which he mixes speeds to add cut to, and a below-average curveball to round out his arsenal. His lack of an average third pitch gave the Cubs reason to put him in the bullpen, where he has spent all 127 innings in the minors thus far, and is part of the reason he is not a top-30 prospect in a highly talented Cubs farm system according to MLB.com.

The Cubs’ decision to put Farris in late-inning situations out of the bullpen has paid dividends thus far. In his minor-league career, he holds a 2.91 ERA with a 10.70 K/9 to a 2.69 BB/9, despite only holding a 6.95 K/9 throughout his four years starting at Arizona. He has an average ground-ball rate and the ability to suppress power (as he also did in college), only yielding 2 HR in his professional career thus far. Because of his high strikeout rate and low HR/FB%, ERA estimators have been lower than his ERA.

Farris’ performance thus far has been a pleasant surprise considering the bargain the senior signed for only \$3,000. The question surrounding Farris is whether or not he can sustain the numbers he has put up to this point in his career. His sample size has been relatively small, so tracking Farris’ outings in the AFL should shed more light onto the legitimacy what he has done the past couple years. With key pieces Aroldis Chapman, Pedro Strop, Trevor Cahill, and Travis Wood all free agents to be, there could be some room for Farris sometime next year depending on how the Cubs’ off-season and spring training play out.

Corey Littrell – LHP 6’3” 185, Redbirds (Cardinals AA), Age: 24 (Video)

Littrell was drafted out of high school in the 43rd round by the Nationals, but was too committed to the University of Kentucky to sign. After starting for the Cats for three years, he was drafted in the 5th round by the Red Sox for near slot value in 2013. He was traded the next year to the Cardinals in the deal that brought Joe Kelly and Allen Craig to Boston in exchange for John Lackey, Littrell and \$1.75MM in cash.

A lanky pitcher who lost 10 pounds since draft day according to the Memphis Redbirds official roster, Corey has a similar frame to his father and grandfather, who both played professional baseball as well. According to MLB.com, Littrell is the 29th-best prospect in the Cardinals organization. He throws a fastball that sits 88-90 that plays as average because of above-average command down in the zone. He also has three other average offerings: a changeup, a curveball and a cutter with slider-like action. He was a starter until this year, where he has come out of the bullpen in 52/53 appearances between AA Springfield and AAA Memphis.

After a quick and effective stint in AA to start off his 2016 campaign, Littrell struggled with control in AAA with a hefty 5.08 BB/9 paired with a slightly above-average 8.59 K/9 in 51.1 IP. One positive note for Littrell is that he has done well controlling balls in play since his switch to the bullpen. His 2016 ground-ball rate is up to an above-average 51.5%, which is a career high. His run prevention, however, has been subpar due to his high walk rate, yielding a 4.56 ERA and 5.01 FIP in Springfield.

Since the Cardinals bullpen has been average to date according to WAR and the majority of the relievers are controlled through next year, there may not be a spot for Littrell to begin the Cardinals’ 2017 season unless he impresses from here on out. However, if he can regain the control in the AFL that he had before his promotion to AAA and keep it through the beginning of next year, he could become an option for the Cardinals sometime next season.