We’re nearly done electing pioneers/executives to the Hall of Miller and Eric. We’ve previously talked about scouts, noting that they don’t seem to quite rise to the level of contribution we’re looking for. Today, I’ll tell you where we stand on coaches.
Rather than tediously go through coach after coach, we decided to look at four coaches whose reputations represent excellence in the minds of nearly every baseball observer. We examined Johnny Sain, Leo Mazzone, Dave Duncan, and Charley Lau. If any coaches make a big difference to their teams, it’s going to be these guys. Here’s the elevator pitch for each:
With our subjects in hand, we simply tracked the performance of any pitcher 27 or older with 400 or more innings prior to meeting one of these guys in their first three years under the coach, and for the rest of their career. Same for batters, only we used 1,000 plate appearances. Our goal was to see whether these gurus were so effective that they had a dramatic effect on their pupils and how lasting the effect was. If so, we would keep studying coaches. If not, not so much. To determine this, we also tracked 20 of the closest comps we could find to test subjects and compared the subject to the controls.
In plain English, if Joe Schmuckface was a 28 year old lefty pitcher with 800 career innings, we found 20 pitchers who at age 28 had similar career totals, and whose ERA+ was similar to control for talent. We freely admit that this isn’t a perfectly scientific study, and also that this kind of work had been done previously by others (especially J.C. Bradbury). We were looking for reasons to keep coaches in the mix for a HoME plaque.
The Scoop on the Gurus
It’s pretty clear to us that Leo Mazzone stands out here. He’s the only one of the four where the differences are dramatic and where they persist to the greatest degree. All of these guys improved their players, especially in terms of innings or PAs, which is, of course, correlated to improving overall performance. But except for Mazzone, the improvement among the other coaches was close enough to the controls’ averages that we felt we couldn’t build a strong case that the effects we saw could be isolated only to the coach’s teaching. We ended this experiment at this point and pushed Leo into our group of final candidates.
Back to the Bench
Well, we’re going to spoil things for you. We had a quick hook on Leo. The evidence in the literature on him is mixed. According to Chris Jaffe’s wonderful Evaluating Baseball Managers, Bobby Cox had a relatively quick hook for much of his career, including his time in Atlanta. Is that Cox pushing the bullpen button or Mazzone? If it’s Cox, that may bias the numbers slightly in Mazzone’s favor by saving his tired pitchers some runs. And we just can’t know. But also, we can’t know precisely how much input Mazzone had on veteran pitchers the Braves acquired. If he had a lot of input, that’s probably helpful to his cause, but Cox and John Schuerholz weren’t exactly slouches either. Those early Braves teams were built around great defense. Pendleton, Lemke, Bream, Nixon, Belliard, all good defenders. Giving pitchers the confidence to use their defense is vital, but much of that confidence comes from Schuerholz and Cox assembling a good defense. And around and around we can go.
Look, Bobby Cox won before Leo Mazzone, with Leo Mazzone, and after Leo Mazzone. John Schuerholz won before, with, and after Leo Mazzone. Leo Mazzone did very little with the Orioles for a couple years after leaving the Braves. Which leaves us with the decision to send Leo to the showers. The state of research on the effect of coaching is not nearly so advanced as the many player-based information streaming out of MLB.com’s statcast and websites devoted to analytics. If someone finally cracks the code on coaching, we’re absolutely willing to pull the trigger on Mazzone or anyone else who is proven excellent. But we’re not there yet.
Beer and tacos. Chocolate and peanut butter. Scouts and stats. Better together! Well that’s where I’m at today.
I cribbed the original idea for CHEWS (CHalek’s Equivalent WAR System) from Jay Jaffe’s JAWS. With apologies to the mustachioed man, I tweaked it a little and gave it a nice punnish name to call my own. As time has gone on, however, I’m more and more drawn to Adam Darowski’s Hall Rating. It indexes Adam’s inputs against a positional average and anyone at 100 is in the Hall of Stats.
So today, I want to introduce my toy sifting score. I call it CHEWS+. Here’s why I went to the bother of all this.
With this in mind, I can tell you nine pieces of information about any given player’s case for a given Hall and how he stacks up relative to other cases. For example, here’s what I can tell you about Dick Allen:
I like to be able to express all of this if I want to. With JAWS, I can compare to the positional average but why compute 42.5/38.6 if I don’t have to? Indexing to 100 is so much more intuitive. On the other hand, with Hall Rating, I don’t get much of a sense about how the rating works or why. Now I can display that information more simply. When I tell you someone has a peak-oriented case, I can now show you more readily what that really looks like.
So let me tell you how I’m doing this, and then I’ll show you what it looks like. To be honest, not many players’ ranking changed considerably, but a few moves were notable and worth looking at. More will be revealed.
Making the right CHEWS
An important idea about various Halls of Fame that many folks don’t think about is how the positions balance out. Since you have to have nine guys in your batting order, any of them can be an asset or a liability. Seeking some balance is reasonable. So I’ve set the system up to reflect that belief.
Feel free to substitute your own measures into this framework. You want to use a 5 year peak or a 10 year prime or not count negative seasons, go for it. The important thing in this approach is to carefully select your top 38 candidates per position and top 66 pitchers (or larger numbers as time moves along) and use the median as the basis of comparison because that’s the simplest way to ensure some degree of balance. You can eliminate a category or weight it differently than I do. No need to do it just like I do.
Interpreting CHEWS+ is like anything else. It is not intended to populate the HoME like Hall Rating does. It serves as a benchmark. Context is always important, and we should always make mental allowances for potential imbalances no matter what measurement we use.
CHEWSing the fat
Now that you see how it works, here’s some taste of how it works.
At the positional level, the positional score for hitters indicates 152 players at 99.5 or greater out of the 154 I was shooting for. Turning to the overall score, it shows 157 such players. And when combined into CHEWS+’, we hit 154 exactly. Among pitchers, the figure is 65 (we’re looking for 66) with one other greater than 99.0 but lest than 99.5. I feel good about these results.
Let’s break it down by position.
Pos. Overall POS Score Score CHEWS+ ======================== C 19 14 17 1B 19 23 22 2B 20 18 19 3B 20 18 19 SS 19 21 19 LF 17 23 20 CF 19 17 18 RF 19 23 20 P 65 65 65 ------------------------ 217 222 219
This distribution passes the sniff test. The positions that are generally underrepresented are, and those generally overrepresented are. The one weirdo is the position score for leftfield, but this is rectified by the time we reach CHEWS+.
Here’s the players that CHEWS+ indicates as HoME worthy who aren’t in yet and who they would replace:
POS IN CHEWS+ OUT CHEWS+ =========================================== CATCHER Gene Tenace 102 Ted Simmons 95 Roger Bresnahan* 102 *Technically, Bresnahan is in as a pioneer/player combo FIRST BASE Will Clark 101 Frank Chance* 101 Harry Stovey 100 *Chance is in as a manager/player combo SECOND BASE Cupid Childs 106 Bobby Doerr 99 Jeff Kent 96 THIRD BASE John McGraw* 108 Sal Bando 99 Ned Williamson 104 Heinie Groh 101 *McGraw is in as a manager/player combo SHORTSTOP Hughie Jennings 102 Dave Bancroft 99 Joe Sewell 99 Monte Ward 97 George Wright* 96 *Includes no credit for pre-1871 play LEFT FIELD Charlie Keller 101 Zack Wheat 99 Jose Cruz 97 Jim O’Rourke 97 CENTER FIELD Pete Browning 105 George Gore 102 Mike Griffin 100 RIGHT FIELD Vlad Guerrero 101 Sammy Sosa 99 Willie Keeler 98 Dave Winfield 98 Harry Hooper* 97 Sam Rice* 94 *Neither Hooper nor Rice is credited for running, double-play avoidance, or throwing-arm value that we’ve written about extensively. PITCHER Bob Caruthers 116 Whitey Ford 98 Charlie Buffinton 107 Bucky Walters 97 Dizzy Dean 106 Pud Galvin 95 Eddie Rommell 106 Early Wynn 94 Jim McCormick 103 Nap Rucker 102 Clark Griffith* 101 *Griffith is in as a manager/player/exec combo
This is a pretty good record. Most of the players in either column fall into one of three categories:
a) late cuts or selections that we deliberated over for months or years
b) Nineteenth-century players, of whom we already have too many and went against for chronological balance
c) Charlie Keller.
Keller’s situation is really simple. He did a lot of damage in very little time. His peak is better than the average, his career well below, but his WAR rate is outstanding. Then again, the guy only accumulated 4600 PA in his real career, and I only adjust it up to 4840 or so. He missed time to World War II, and his body betrayed him, ending his career prematurely. But even so, he barely sneaks over the line by CHEWS+, and anyone below 105 is probably interchangeable with anyone over 95. Especially if they come from an overstuffed position or an overstuffed era (like, say, the 1890s).
Also, a few important caveats apply. Many of these borderline players don’t yet have official PBP data attached to them. Some, like Keeler, may never or won’t for years. Others like Rice or Hooper might have that information soon for some or all of their campaigns. Our guesstimates for those guys probably lift several of them up over the line. But officially, this is what they look like now.
Finally, let’s zero in on a few players from the list above to see what’s driving their ratings.
POSITION | OVERALL | NAME Peak Career Rate Score| Peak Career Rate Score| CHEWS+ ===================================================================== Hughie Jennings 111 83 118 101 | 114 87 117 103 | 102 Charlie Keller 102 74 143 99 | 107 80 146 104 | 101 Jim O’Rourke 81 119 70 94 | 85 128 72 100 | 97 Ted Simmons 101 109 79 100 | 93 111 65 90 | 95 Ned Williamson 110 101 106 105 | 109 94 103 102 | 104 Dizzy Dean | 107 90 138 | 106 Whitey Ford | 83 111 101 | 98
If you detect an orientation toward peak performance, it’s because I have one. In the past I’ve been more cautious about it. But after reading this article and seeing the inclusion of rate-based performance, I felt it was important to include it as well. I used to weight peak at 22% higher than career. Now I rate them equally but also include rate at 50%.
We see how this influences CHEWS+ above with peak-first candidates such as Jennings, Keller, Williamson, and Dean getting better scores than O’Rourke, Simmons, and Ford. But we can see buried in all of this how the increased transparency of this system can support good decision making. Ted Simmons is at an even 100 among catchers. If I felt it was specifically important to add another catcher, I would have good justification to do so based on his score among those at his primary position. For someone like Dizzy Dean, I might find persuasive the idea that while his peak is above average, his ability to create value may actually be understated by his peak.
Let’s linger for a moment on Simmons and Dean. I differ with many HOM voters and other writers who toss out seasons below replacement. One argument for doing so goes like this: The team should have known better and not continued to run him out there. I agree to this point, it’s the predicate of this that’s problematic: So why should the player be penalized? In my opinion, the player is not penalized by counting everything he did on the field. Instead, we are trying to get an accurate picture of the player’s entire career. Everything counts. The player is accurately measured by including his entire body of MLB work. The classic case where this comes into play is Pete Rose. From age 39 on, he racked up -1.4 WAR over 3694 PAs. But not every player earns negative numbers strictly during their baseball senescence. Ted Simmons, for example. The 1981 version of Ted Simmons, slugging catcher, hit 216/262/376 for a whopping 87 OPS+ and 0.3 WAR (BBREF style). He rebounded for 7.3 WAR over the next two years. The wheels came off again in 1984, when he “earned” -2.6 WAR, and from there the end came quickly. In 2016, we saw a younger player in mid-career do exactly what Simmons did. Coming off 38 WAR over 7 years, Andrew McCutchen served up -0.7 WAR. Want some more? Early Wynn had a full season at age 28 where he posted -1.0 WAR. Burleigh Grimes pooped out a -0.5 season at age 25. Jimmy Wynn coughed up a -0.6 hairball at age 29. Lefty Grove would rather have forgotten 1934 (-0.3). Anyway, these seasons exist. They are rare among Hall-level players, of course, but they are there, and they cost their teams wins. To me, not counting those bad seasons is akin to ignoring the F on Johnny’s report card because he otherwise got As and Bs.
A reasonable argument against my position might be that someone like Dizzy Dean or Charlie Keller or Sandy Koufax benefits on a rate basis due to a sudden injury-forced departure rather than a parade of crappy decline seasons. I hear ya. If you think the deck is stacked against long-career players, well, maybe it is. But that brings me back to the important point that JAWS, Hall Rating, CHEWS+, Hall of Fame Monitor, what have you are not gospel. They are sifting mechanisms. Draw up your long list with them, then look closely to see what they fail to capture. Because there’s no bulletproof stat and there’s no silver-bullet number to end all arguments.
Instead, what we have is thoughtful people creating thoughtful tools to get us near to an answer quickly so that we can spend more time on the borderline where the tough decisions are. And that’s what I like about this improvement over CHEWS. It gives me a simpler number as well as more and understandable details to form a decision on. I’ll soon start adding it to the HoME Stats you can find on our Honorees page.
Joe Sheehan penned a piece over at Fangraphs that caught my eye. He talks about how the new-school kids of the 1990s have become the old-school adults of the 2010s. Tell you what, it’s true for me. But then again, I spend my time looking backwards at baseball, not forwards.
I am fortunate to work for a company that publishes professional literature for teachers. I’ve learned an awful lot about what comprises best-practice instruction. Good teaching hinges on effective assessment. Assessment is figuring out what students know and can do. Don’t worry, I’m actually headed toward a point here.
There are three basic forms of assessment that go by many names but boil down to these:
In baseball analysis today these three assessments are mirrored this way:
Take a player’s offensive value. At the primary level, offensive wins are what we’re after, and those derive from batting performance against average in most value-stat systems. Secondarily, part of that value resides in, for example, how many singles the player collects. Ground ball/flyball data exists for much of the game’s history. This tertiary level of information doesn’t carry enough meaning to matter when measuring value, no matter how our player got his singles. At a quaternary level, Miller and I have no need for launch angle even though it tells us a great deal about the nature of a player.
This isn’t a fault or bug with radar-based numbers. Not in the least!!! What they do is provide a detailed cross-section of the machinery of baseball’s numerous offense/defense relationships so that we can see minutely how each of its gears operates. But for me and Miller that granularity is superfluous. That and the lack of this information for the other 99% of big-league history makes these stats nearly useless to the mission of the Hall of Miller and Eric. We cannot compare Babe Ruth’s exit velocity to Ted Williams’ to Willie Mays’ to Mike Schmidt’s to Barry Bonds’ to Mike Trout’s. We can only currently compare Trout to his exact contemporaries. Nor are we yet convinced that doing so would be helpful.
So we do what we can. We use summative numbers to compare apples to apples to the best of our abilities. WAR to WAR. Of course, we make lots of adjustments to WAR because of certain beliefs we have about baseball history and about the statistical record. The skill-based big-data numbers can’t really help. Does that mean we ignore them? Well, actually, yes. Not maliciously; they just aren’t terribly meaningful yet for our work. Could they somehow prove helpful to us? Never say never, but it seems unlikely, at least for a couple more decades, by which time the field may have moved onto bio- or psychometrics.
Come to think of it, there’s one place I can see us potentially drawing on Statcast, and that’s for range-based fielding stats. Those amazing plots that show the easy to super difficult plays for an outfielder and how many he converted to outs are pretty snazz. That might be a spot for us, but even then, there’s work to be done about how those charts translate to value.
So we apologize if you come to us hoping for utterly state-of-the-art thinking. It’s not our thing—not that we’re big sticks in the mud. We hope, however, that you’ll stick around because you like the retrospective nature of our quest for a better Hall of Fame. And maybe some of our dumb jokes. Well, my dumb jokes. Miller’s are never dumb. Thanks, as always, for reading.
Miller’s 17 for 17 article for Opening Day covered a lot of ground. Even though the season is ten days old, here’s a better-late-than-never guide to what I’m watching for in the 2017 season. I’ve mostly left the players to Miller and called out some events, and some things that might be best described as coming out of left field. But isn’t that what you’ve come to expect?
The BBWAA electors: How many plaques will they award?
Since 1970, 34 players have reached 65.0%–74.99% on a given ballot during their candidacy. Of them, 30 of them were subsequently elected by the BBWAA (88% if you’re scoring at home). No one who’s reached 65% in their first ten years on the ballot has failed to win election. Among the 30 who became Hall members, they BBWAA waited an average of 1.4 years to bestow a plaque. In 2017, the writers gave Trevor Hoffman 74.0% (up from 67.3% in 2016) of the necessary votes and Vlad Guerrero 71.0% of the vote in his first campaign. Hoffman will go in for 2018, and Vlad is pretty much a lock. No player who has picked up 70%+ very early in their Hall trajectory has failed to win election the next year. So that’s two. Chipper Jones will get his due as well to make three. Could Jim Thome make four? He’s never had a whisper of steroid use, was considered a great player, and hit more than 600 homers. If he doesn’t make it this year, it’s because he finished at 65%+, and he’ll get bronzed in 2019.
The Veterans Committee (Modern Baseball) electors: Will they give out any plaques?
The odds are with no, of course. The VC has failed to elect highly qualified players their due for a decade or so. Why should this one be any different? Danger looms in the person of Jack Morris whose final-year 66.7% with the BBWAA may portend his eventual selection. Also under potentially under consideration are actually great players such as Jack’s teammates Alan Trammell, Lou Whitaker, Darrell Evans, Keith Hernandez, Bobby Grich, Rick Reuschel, Dave Stieb, Thurman Munson, Ted Simmons, Dick Allen, Willie Randolph, Buddy Bell, Graig Nettles, Sal Bando, Jimmy Wynn, Dwight Evans, Bobby Bonds, Roy White, Jose Cruz, and Reggie Smith. (A few of these guys straddle the Golden Days era; it’s not yet known where they’ll be placed.) I suppose the smart money is on a big fat goose egg because a ten-man ballot with this much potential quality seems unlikely to yield even one winner. Though it would be cool if the Detroit DP duo were inducted together.
Baseball-Reference.com: Will they update WAR into the 1930s?
Retrosheet recently released data that adds a great deal of missing play-by-play information to the World War II era. In correspondence with me, the world’s greatest ever website said they were awaiting that data before updating historical WAR calculations. Because there’s quite a lot of PBP data for several years prior to the war already available, it’s possible that we will have five to ten years’ worth of updated WAR info sometime this year. In the meantime, we’ve rummaged through the existing data to provide a guesstimate of what we think this new WAR might look like.
MLB ownership: Will they start talking expansion?
We’re about due for an expansion. MLB has typically gone 10–20 years between each round. In the last year to 18 months, I’ve noticed an awful lot of articles about expansion and particularly about Montreal as a focal point for it. Makes sense. At 4.09 million people, Montreal would rank about 15th among the marketing statistical areas in the US and Canada, highest among markets not currently hosting an MLB team. Here’s the other teamless towns with at least 2.00 million people in their metro area now and who would be among the top 32 US markets by population according to projected growth through the year 2030 and the same info for Montreal and Vancouver :
Some of these places may already be served by a big-league team and would be hard to get territorial rights for (especially Riverside, Orlando, Columbus, Sacramento, and San Jose). One of these towns is a place that would be incredibly unwise to put a team (Vegas, natch). Montreal is an obvious choice. After it probably comes Charlotte, San Antonio, or Austin. Mayors, line up today!
The City of Oakland: Will the A’s stadium situation get resolved?
With the Raiders officially gone, Oakland is a one-horse town sports-wise. That might mean that the city has more flexibility to work with the A’s to develop a new home park. Or at the worst redesign the Coliseum. Oakland is a plenty large market by population, so it makes sense for the team to stay put unless they are heading for the great, French-speaking North.
Stu Sternberg: Will they turn up the heat on a new stadium?
Stu Sternberg has made increasing noise over the last year about finding a new stadium site, so all the cities listed above may well have another chance in a few years to land a squad. With Tampa being a recipient cities of revenue sharing, the longer this situation goes on, the more big-team owners will want to resolve it. Especially because Tampa is rated somewhere between the 12th to 20th biggest media market in the US and could be a donor team instead of a lagging recipient. Getting out of the Trop, which is very difficult to drive to and has little public transportation flowing into it, and into a baseball-only fan-friendly park will go a long way. The team’s lease is up in 2027. That seems like a long time from now, but getting a new park negotiated and completed can take a very long time. Sternberg’s getting started now so that he can hold the city hostage later, if necessary.
Jeffrey Loria: Will he get the jillion-dollar price tag he wants or take his team and go home?
The worst owner in MLB and maybe all sports has the team on the market. Godspeed to him selling it. But this guy always ends up doing something I hate, so count on his not selling it and staying here to haunt us. Then again Cap’t Jeter is supposedly interested in buying the Fish, which might in some ways be worse yet.
Buck Showalter: Can he finally win a World Series?
Buck’s big shot at a ring was 1994 when his Yankees were cruising toward their first divisional championship since 1981. He’s a very good manager, but without even one World Series appearance, he’s got a massive hole in his resume. Fixing that hole is step one toward possible Hall of Fame enshrinement.
Terry Francona: Can he punch his ticket to Cooperstown?
Tito has likely done enough to merit a plaque at the Coop, but winning a World Series with another bad-luck team would make his case bulletproof. No eligible manager with three World Series wins has failed to reach the Hall. (Bruce Bochy has three but is still plying his trade.)
The Astros, Nationals, and Indians: Can one of these teams break their fugue of futility?
These three teams are preseason favorites to take their divisions. They are also three of the more cursed teams in the league. Tito Francona’s Indians haven’t won the World Series since 1948, the longest such drought in MLB. The Astros haven’t won the big one since their inception in 1962. The Nationals nee Expos have never even been to the Series. Can any of them work some of that old Cubs/White Sox/Red Sox magic this year?
Let’s round things up with a few quick-hits for players:
Team nicknames are kinda weird. Why is the New York team, not the Boston team, called the Yankees? The LA team called the Dodgers but the Anaheim team called the Angels? Do the Giants only sign seven footers? Aren’t Cardinals from Rome? Or the Kremlin?
Well, this article by John Paschal, which came to my attention via the Joe Sheehan newsletter (subscribe today!) got me thinking about team nicknames. What would I call each team if in some parallel dimension MLB suddenly started up today with 30 teams in all the same cities? In this case, I’m pretending that professional baseball never existed. Any name from any baseball team is, therefore, up for grabs. But we can’t use the nickname of a current or defunct team from the major football, basketball, and hockey leauges or their college analogs. I’m sorry to the smaller sports (soccer, lacrosse, arena football, etc…), but I’m going to pillage a couple names from you.
Ideally, a nickname should have a strongly local flavor. It shouldn’t be generic, and it shouldn’t be something not related at all to the town or region (looking at you, Tigers, Cubs, and Braves!). It also needs some level of sticking power. You don’t use a faddish name that’ll be off the map in ten years. It’s got to sound good too. The more it rolls off the tongue, the better. It also shouldn’t be too, well, froofy. This is baseball, not football or hockey, so violent-sounding names aren’t required. But we nonetheless need to remember that men are putting on these uniforms. I would rate their chances of signing with a team called the Barbies as very poor—unless the money was amazing….
So, given those parameters and my slightly twisted sense of humor, I created the following kinds of nicknames for each city (or state):
In a few cases, I’ve made some notes for those who aren’t from the region or city in question. Also, I didn’t actually read Paschal’s article or Joe’s response to it because I didn’t want to be influenced by them. Any similarities are purely coincidental. Anyway, I’ve got the names, you can dream up the mascots….
New York A
New York B
Your turn. Tell us if you love, hate, or have better ideas than what’s above!
My Pop-Pop used to say to me all the time, “You know something. I just don’t know.” That’s deep on a lot of spiritual and philosophical levels, but today I’m reminded of it from a baseball history perspective. Miller and I have had our head stuck in the 1930s and 1940s these past few weeks. Like a machete-wielding Indiana Jones in the jungles of South America, we’ve hacked through some play-by-play (PBP) stats on BBREF to see whether there’s some hidden treasures in pre-war MLB.
Turns out there probably are, and it made me realize just how much we don’t know about the 1910s and 1920s. However, we’ve learned enough from our little journey through Depression-era baseball that we can point to a few places where we don’t know much yet but see a glimmer of what we might could know someday. So let’s see if we can’t identify the traits to look for and those who might possess them.
In our previous articles, we identified three areas of hidden value: baserunning, double-play avoidance, and outfield throwing. So let’s make a little rubric out of these traits. In classic Bill James fashion, we’ll assign each area of value on a five-point scale, so a perfect 15 is the most likely to have hidden value.
Baserunning: We have steals and for some seasons we have caught stealing, and we know that league-wide steals rates were about 55%.
Provided a player is under 5 points,
Double-play avoidance: We have no info here, but we know that three things affect a batter’s likelihood to ground into a double play: Handedness, speed, and groundball/flyball tendencies. The groundball/flyball tendencies are tough to spot in the data, so we’ll have to set them aside now.
Strong outfield arm: Our lone means to examine this for early players is how often a player led his league in assists or finished in the top five. But the correlation in left field and centerfield between assists and a valuable arm is pretty weak.
Led right fielders in assists three times or more and regularly finished among top five
Led centerfielders in assists five or more times, frequently finished among top five
Led left fielders in assists 10 times
Led right fielders in assists one or two times and regularly finished among top five
Led centerfielders in assists three or more times, frequently finished among top five
Led left fielders in assists 7+ times, regularly finished among top five
Frequently finished among top five in right field assists
Led centerfielders in assists 1-2 times or more, regularly finished among top five
Led left fielders in assists five or more times, frequently finished among top five
Sometimes finished among top five in right field assists
Frequently finished among top five in centerfield assists
Led left fielders in assists three times or more and regularly finished among top five
Now, with our scoring rubric in hand, let’s look at the players with most potential hidden value.
Max Carey (centerfielder, 14 points)
Scoops leads the pack, and I’m going to go into greater detail on him than the others because he’s probably got the most dynamic upward jump. Not only did he steal 40+ bags a year, but he did so at a 79% clip in the nine years we have data for. Remember, that’s against a 55% league average. As a speedy switch hitter, he’s a prime candidate to have strong DP avoidance. He led NL centerfielders in assists six times and finished three other times in the top five. He also led left fielders twice, and finished second another year. I did a little comping to see what kind of value Carey might gain once his full PBP story is known.
In so far as baserunning is concerned, for the nine seasons in which we have Carey’s steals and caught stealings, he would earn him about 60 runs above average. Even if Carey stole at a rate ten points worse in the seasons we don’t know about than those we do, he’d gain another 30 or so runs. I looked at all players since 1947 who stole 500 bases. The portion of their baserunning earned by stealing was 62%. Applying that same ratio to Carey with 90 steals runs, he would earn an additional 56 runs. Call it 150 runs, or about 60 more than the 88 rBaser that BBREF has him down for.
I also comped him against speedy switch hitters with at least 5000 PA and 30 or more rBaser. Using their per-PA average, I get about +20 runs against the league. BBREF has no rDP values prior to 1948.
Finally, in the outfield, I looked at centerfielders since 1952 with as similar a record of assists leaderboard placements to Carey’s as I could. I figured their rOF/game and applied it Carey’s games played. The result is another 20 or so runs. Here’s that group in case you were interested. It turns out that no one in the PBP era has a throwing record like Carey’s, so we do our best:
DEF G CF LED TOP 5 NAME AS CF rOF IN A IN A ========================================== Carlos Beltran 1572 0 4 3 Willie Davis 2239 + 7 2 9 Jim Edmonds 1768 +42 4 5 Steve Finley 2314 +13 3 6 Curt Flood 1692 -15 3 8 Ken Griffey, Jr. 2145 +57 2 9 Andruw Jones 1724 +48 3 4 Kenny Lofton 1984 +30 4 5 Mickey Mantle 1742 +16 2 8 Willie Mays 2829 +49 3 12 Amos Otis 1825 - 3 2 5 Vada Pinson 1681 +25 3 7 Kirby Puckett 1432 +20 3 5 Bill Tuttle 1146 +22 5 1 Del Unser 1117 + 8 4 2
So in total, Carey could pick up as many as 80 runs or 8 WAR on offense and whatever the difference is between the rOF reckoned here and the credit his arm gets in whatever defensive system you use. That’s a boatload of value that could push him into the top 10 centerfielders of all time.
In the rest of this post, I’m going to use the same comping methods to assess potential value.
Tris Speaker (centerfielder, 13 points)
Doing the same sorts of comps-based analysis with Speaker, I get 5 rBaser (vs BBREF’s 1) for the seasons we have SB/CS data for. They account for 60% of his career, so call it 7 runs total. He also gets 15 runs of rDP, and at least the same number of throwing runs that Carey gets. En toto, that’s a net gain of about 40 runs. Probably not enough to catch up to Willie Mays and the next guy….
Ty Cobb (centerfielder, 13 points)
The Georgia Peach’s 64% known stolen base percentage is almost 10 points above the league average. But mostly those are his elder baseball years, and they are only about 55% of his seasons. His comps would be the same as Carey’s, and I would estimate Cobb could be as many as 20–25 runs higher than the 52 BBREF gives him. As a fast, hustling, lefty swinger, he’s got a chance at a lot of value on the DP front. Comping nets him about 35 runs of DP avoidance. In the outfield, his assists record isn’t very strong at all. Best to reckon him at 0 runs for now. So overall, I suspect Cobb gains 50 to 60 runs to keep ahead of The Say Hey Kid.
Harry Hooper (right fielder, 13 points)
Hoop gets dinged for his baserunning by BBREF’s current estimator, to the tune of -7 runs. His comps suggest that figure might be the inverse, +10 runs. He’ll probably add about the same in DP avoidance runs as well. We’ve previously estimated that Hooper, one of the reputed best arms prior to Clemente, could add dozens of runs of throwing. He led AL right fielders in assists three times and was in the top 5 nine other times. He is second all-time in right field assists to you-know-who. In addition, BBREF doesn’t have pre-1913 outfield breakouts, but in 1910, he led the AL in outfield assists. His 344 assists are 6th all-time among all outfielders. Seems likely the guy had a gun and wasn’t afraid to use it. I pulled comps, but few guys have a record like Hooper’s, and I’m a little shy of putting Clemente and Barfield into anyone’s comps because they break the scale. Without them, Hooper’s looking at about 50 runs per the comps. With them, he’d be pushing 60 runs. All told, it’s about 70 to 80 runs, which ain’t half bad, and would give him a surge to 70+ WAR. It looks like we probably got this one right. We get a f__ing medal for that, right?
Ross Youngs (right fielder, 13 points)
Youngs likely sees little change in his baserunning. A couple runs here or there, though at least a couple/few better than the -4 BBREF calculates. Same for double-play avoidance. He might be up to 5 or 10 additional runs so far. However, in his 10 years, he led NL right fielders in assists five times and finished in the top 5 six times. Obviously, that’s a great record. Using the same comps as Hooper, I’d peg him around 25 rOF. En toto, he probably gains 30 or 40 runs, enough to put him in a league with guys like Paul O’Neill or Gavy Cravath at a career level. He was a better player than many Hall bashers give him credit for, but not nearly the player Frankie Frisch claimed.
Clyde Milan (centerfielder, 12.5 points)
This forgotten deadball centerfielder already gets +24 baserunning runs from BBREF. However, in the seasons we have full SB/CS records, his comps suggest there’s perhaps another 10 runs out there for him. However, that’s only about half his career. Milan could, therefore, have another 35 runs in the hopper, totaling out around 70 rBaser. That’s about 50 more runs that BBREF pegs him at. As a fast lefty, comps indicate he could also be missing 20 or more runs DP avoidance runs. Unfortunately for Milan, his record of 1 assists title and 4 other trips into the top 5 probably means he was an average thrower, maybe a little better. But tacking on another 70 runs, or roughly 7 WAR, pushes him up over 50 wins (as I calculate it), making him a low borderliner.
Edd Roush (centerfielder, 11 points)
The Hall of Famer and Hall of Merit member gets hit by BBREF WAR with -8 rBaser and iffy fielding numbers. The SB/CS data suggests he wasn’t a great base thief, which drags down the likelihood of his gaining much by rBaser. Currently, I’m guessing he was an average runner, but he might have been a Brett Butler type: good at everything but stealing. Roush probably picks up 5–10 runs of DP avoidance thanks to being a lefty without a limp. In the outfield, there are few centerfielders with his assists record. He’s fourth all-time in centerfield assists and finished in the top 5 eleven times—he only played 100 or more games in 14 seasons. But he only led the NL once during all that time. Not many modern players fit that kind of profile. The very small handful I found suggest he could have added 10–20 runs with his arm. En toto, he could pick up one to three WAR, which is enough to rise two or three spots in the centerfield rankings, but not enough to push him onto the borderline. Still, he’s one to watch because if his PBP data is really strong, and the comps are underselling him, he could climb fast.
Eddie Collins (second baseman, 10 points)
Collins has the same comps for baserunning as Carey and Cobb, and his known stolen base percentage is better than Tyrus Raymond’s. But Cobb’s volume of steals allows him to finish ahead of Collins in total running runs. Collins appears to be a +60–65 runner. BBREF has him at +40, so he gains 20–25 runs on the bases. He’s got the same comps as Cobb for DP avoidance, which puts him at about +30 in that department. Call it 50 additional runs. The contest among Collins, Nap Lajoie, and Rogers Hornsby for best prewar second baseman, just got a little tighter…particularly because as a relatively slow righty, Hornsby will likely lose runs when PBP data comes along.
George Sisler (first baseman, 9.5 points)
It really is a terrible shame that Sisler’s career never recovered from a terrible bout with sinusitis in 1923 that left him with double vision. This condition affected him on the bases as well as at the plate. He immediately dropped from 35–40 stolen bases a year to 15–20. His last hurrah on the bases was 1927, when his 27 thefts led the league. It was his only season post-sinusitis where he reached 20 steals. Anyway, we are missing CS info for Sisler’s age-24 through age-26 seasons, when he averaged around 35 swipes a year. Still, his comps imply a +40 runner. BBREF figures him at +13. In terms of staying out of the deuce, Sisler looks like roughly a +20 hitter. Together, that’s about 45 additional runs on his resume. Depending on what position you assign Rose, Musial, Banks, Thomas, and Carew to, Sisler could wind up as a top-tenner at first base.
George J. Burns (left fielder, 8 points)
This is the NL Burns, the one whose nickname wasn’t “Tioga.” Nor the cigar-chomping comedian. He was a better player than either of those other Burnses, too. He doesn’t look great on the bases. He stole 383, but among the seasons we know about, his percentage is worse than the league. BBREF shows him at +1 rBaser, and I’ve got him at -10. He was a speedy guy, could be he’ll get a lot of value back from advancement. Burns comps out at about -15 rDP thanks to being right handed. In the outfield, Burns doesn’t do much better. He led the NL in left field assists once, placed in the top 5 five times. Call it -10 runs. So George J. Burns could be looking at a drop of 30 runs. Unless the real stats come in significantly different, he’s got little hope of improving his low-borderliner status in left field. I admit to a little skepticism until we see his actual advancement data. Could be he loses nothing and ends up as a 0 rDP guy.
That’s the highlights reel. There’s also a lot of players likely to lose value. The aforementioned Hornsby, Miller and Eric favorites Dave Bancroft and Art Fletcher. Hall of Merit honoree, Heinie Groh is another. Somewhere in the middle are our pal Wally Schang, and the famed Harry Heilmann and Zack Wheat and the less famed Bobby Veach. Sadly, though, we have to wrap up by looping back to what my Pop-Pop said: “I just don’t know.” And we won’t until Retrosheet is able to deliver the data they need. Since that data is in some cases more than 100 years old, it may never happen.
So you have a pile of missing value for a bunch of 1930s and 1940s ballplayers. Now what?
Let’s have a look at some key guys from the 1920s, 1930s, and 1940s whose new PBP data gives us a better glimpse at any hidden value they may have accrued. We looked specifically at players who were either:
We’ve broken them down by position below.
In general, doing this work suggests that BBREF’s regression scores for baserunning may, if our math is reasonable, suppress a good deal of baserunning value—or stinkiness. It appears that BBREF bases its formula for pre-PBP running on steals, steal attempts, and/or success rates. That’s how Ernie Lombardi, a strong candidate for the slowest man to ever don cleats, is listed with positive running value. The reality, as we’ll soon discover, is likely far worse for Lom. Generally, we found a lot of very positive baserunning value. This may stand to reason since we examined the best of the best from this timespan, and good players are often good athletes. Or we need to review our mathematics…. Oh, and here’s this other item. The thing that drives baserunning value isn’t what you probably think it is. Just ask Joe Sewell.
In the tables below, “NOW” refers to a player’s value as calculated by me with my adjustments but without the “missing” value. Which means that “EST” includes the value we’ve calculated for running, GIDP avoidance, and throwing.
NOW EST NOW EST NOW EST NAME WAR WAR CHEWS CHEWS RANK RANK =============================================== CATCHER Berra 77 77 62 62 7 7 Hartnett 73 69 56 53 8 10 Dickey 71 69 56 54 9 9 Cochrane 65 67 55 56 10 8 Lombardi 59 50 46 40 16 24 Lollar 41 41 35 35 34 34 Cooper 40 38 34 33 37 37 Ferrell 41 40 32 32 39 40
Two of these catchers are polar opposites. At least among backstops. On one hand, Mickey Cochrane appears to have positive baserunning value, unlike pretty much every other catcher here. He’s also got positive rDP value, which even fellow lefty swinger Bill Dickey doesn’t. Black Mike is the only catcher to gain value in this group.
Then there’s Ernie Lombardi. We’ve run through his story before, but believe it or not, I underestimated how bad a baserunner he was. Here’s how the sad story of Schnozz’s plummeting value goes. Lombardi appears to have surprisingly un-bad stolen base value. Something like -1 against the league in his number of steal attempts. He was only picked off four times in his career, while I figure a league average runner to have been picked off 10 times. That makes Lom about +2.5 runs. Lombardi was a very cautious baserunner, which, despite his incredible slowness meant he didn’t get thrown out very often. He was +6 runs against the league on that account. Despite his lack of foot speed, Lombardi did manage to take 44 bases in non-batted ball situations. That accounts for about 8 runs, where the league would have notched 9. So -1 runs here. On the whole, he’s sitting pretty close to level par with the league. That is, until we account for his taking extra bases on batted balls. Lom took the extra base ahead of the batter on singles and doubles about 32% of the time. The league took the extra base 47% of the time. In our figuring, that means that Lombardi’s legs “earned” -31 runs against the league. So on the whole, His Schnozziness nets out at -25 runs against average.
And then come the twinkillings. No one, not even Jim Ed Rice, banged into so many deuces as this guy on a per-plate appearance basis. He was the lifetime leader in the category for at least a couple decades, but the guy who passed him (someone named Aaron) had about twice the plate appearances. Which means that our estimate for Lombardi is a little more than -60 runs. Add it all up and he dumps about 9 wins of value and falls out of the running for the HoME.
NOW EST NOW EST NOW EST NAME WAR WAR CHEWS CHEWS RANK RANK ========================================= FIRST BASE Musial 135 137 98 100 1 1 Gehrig 113 113 88 88 3 3 Foxx 103 101 81 79 4 5 Mize 74 75 61 63 9 9 Greenberg 64 61 57 56 11 12 Terry 62 65 53 56 16 14 Camilli 44 48 43 46 27 25 Hodges 49 49 43 43 28 28 Bottomley 35 34 31 31 51 53
Bill Terry and Dolph Camilli are the stories here. Terry’s surge in value is primarily driven by excellence on the bases. For the seasons we know about, he was picked off only once, made about two-thirds the outs on base that an average player did, had more bases taken than average, and most important, he took the extra base on a hit 56% of the time, versus leagues around 50%.
Camilli, meantime, is an overlooked star. He appears to have been an above average baserunner, not just a meandering slugger, and he was excellent at avoiding the twinkilling (+16 runs career).
NOW EST NOW EST NOW EST NAME WAR WAR CHEWS CHEWS RANK RANK ========================================== SECOND BASE Gehringer 82 86 66 68 5 5 Frisch 83 83 65 64 6 6 J Robinson 65 66 59 60 8 8 Gordon 62 62 55 55 13 13 Herman 60 58 49 48 17 18 Doerr 56 57 47 47 19 19 Lazzeri 50 47 43 40 24 27 Frey 43 48 38 42 27 26 Stanky 40 42 38 39 28 28 Schoendienst 42 44 37 38 30 29 Bishop 42 42 36 37 33 32
There’s a few items of note here. Charlie Gehringer turns out to be an outstanding baserunner, not merely above average, pushing him upward. On the other hand, Tony Lazzeri turns out to be a poor baserunner and below average at DP-avoidance, driving him downward. Billy Herman’s pretty bad on the deuce too. But let’s pause for a moment and look at Lonny Frey.
Has anyone ever said to you, Hey, Lonny Frey was a damn good ballplayer? Well here’s the first time. Frey is little remembered these days, but as a shortstop and second baseman, he combined a fine glove, an above-average bat, strong baserunning skills, and a penchant for avoiding rally-snuffing double plays. Exactly the kind of player who play-by-play data reveals as a source of subtle value. We show him picking up about five WAR, which is 50 runs of value.
NOW EST NOW EST NOW EST NAME WAR WAR CHEWS CHEWS RANK RANK ========================================== THIRD BASE Elliott 52 53 42 43 19 20 Hack 51 56 41 45 23 19 Traynor 47 48 39 39 31 27 Clift 43 46 39 41 30 24 Kell 34 33 30 29 51 51 Lindstrom 27 28 27 27 65 64 Rolfe 25 29 24 28 73 61 P Martin 20 24 19 22 89 79
Because third base is a very clumpy position, small credits and debits can lead to significant movement on the totem pole. Harlond Clift, for example, surges up six slots with only three additional WAR in his pocket. He could run a little and was that rare bird, a righty hitter good at avoiding the double play.
Stan Hack parlayed an even bigger increase into a climb that leaves him this far from the HoME borderline. We reckoned him with 3 rBaser (versus -9 for BBREF) as well as 32 runs for DP avoidance. I suspect, however, that while the former of those could even inch up a little, the latter is not terribly accurate. That’s because Hack was a leadoff man for nearly all his career, and had a minimum of 1350 fewer opportunities than an average hitter would.
But most interesting of all are Red Rolfe and Pepper Martin. These guys were terrors on the bases. Rolfe, who had about half a career, was worth twenty-odd runs on the bases and another passel in DP avoidance. Red was merely above average in stealing, outs on base, and bases taken. But like Bill Terry, he took extra bases like candy: 57% extra-base-taken average versus a 48% league average, worth 15 runs. Then there’s Pepper Martin, who was hung with the famous sobriquet, “The Wild Horse of the Osage.” Like Rolfe, he had about a half a career, and like Rolfe, he ran wild. He was nearly +15 runs stealing bases, +10 on extra bases taken, and another +2.5 on bases taken for good measure. He took the extra base 63% of the time in a league with a 48% extra-base-taken rate.
NOW EST NOW EST NOW EST NAME WAR WAR CHEWS CHEWS RANK RANK ========================================== THIRD BASE Vaughan 80 84 67 71 4 2 Appling 82 87 64 66 7 5 Cronin 73 72 60 59 11 11 Boudreaux 67 68 59 60 12 12 Reese 67 69 53 55 16 16 Sewell 58 62 48 52 21 19 Stephens 49 48 42 42 27 27 Maranville 45 44 40 39 30 33 Bartell 47 47 39 39 31 31 T Jackson 41 44 38 40 37 30 Rizzuto 41 41 37 38 38 37
Arky Vaughan slides into the #2 spot at shortstop. He was in a big bunch with Cal Ripken, well behind Honus Wagner. Vaughan isn’t as bad a baserunner as his poor stolen base rates suggest, nor as bad as his BBREF estimate. As a lefty with at least some speed, he turns out to be very good at avoiding double plays. Meanwhile, Joe Sewell, whom I elected with a lot of trepidation, improves his lot and gives me a little piece of mind. Sewell’s an interesting one. As a lefty he gets some double-play avoidance credit, but it’s really his baserunning that pushes him upward. You might be surprised by that since his SB% career-wise isn’t quite 51%, but the league back then ran at around a 55% clip, so it’s not nearly the eyesore it appears. Even so, it’s everything else he does on the bases that helps him. We have just four of Sewell’s seasons, but they account for more than 2,000 plate appearances, enough of a sample to get a good sense of his exploits. Sewell was never picked off in those four years. He’s a run better than the league in both outs on base and bases taken. Given his below average steals value, he’s just above par with the league before we get to extra bases taken. Joe took the extra base about 60% of the time, while the league managed just 50% of the time, good for about +6 runs. So he ends up with about 8 runs of running value for 1930–1933. BBREF gives him -2 runs. When we use the comps method to retrocast him, we end up with a little more than 30 runs total for his career.
I would sound this cautionary note about Rabbit Maranville. I feel very tentative about him. While we have several years of data on him, they come from his age 38–43 seasons. Rabbit missed one of those seasons entirely due to a broken leg, and came back for just 23 games after it. But a deeper look into his stolen base numbers shows a different story. As a young player, Maranville stole with some frequency, gaining double digits in steals every year through 1924 (except for a year lost to World War I). His success rates during those seasons for which we have his caught-stealing information (61%) are probably a little better than average for the time. Then Rabbit started to get old. He lost some time due to injury and ineffectiveness in the mid-1920s, appearing to lose a step in the process. So it’s difficult to say with certainty that the data we have is strongly representative. But for now, it works.
NOW EST NOW EST NOW EST NAME WAR WAR CHEWS CHEWS RANK RANK ========================================== LEFT FIELD T Williams 129 129 98 98 2 2 Goslin 70 73 57 59 10 9 A Simmons 71 75 57 61 11 8 B Johnson 62 61 50 49 20 20 Medwick 55 52 47 45 23 25 Kiner 49 48 46 45 25 26 Minoso 51 52 46 46 26 23 Keller 47 46 43 44 27 27 Galan 43 47 37 41 40 38 Manush 39 42 34 37 41 41 Hafey 28 32 27 30 57 53
I didn’t know that Al Simmons was an excellent base runner, but that’s what our PBP data suggests. He was good at every facet of running, whether avoiding outs or taking bases.
This exercise appears to have vindicated certain decisions we made late in our electoral process. We knew that Joe Medwick had issues with double plays, and so we placed him behind Jose Cruz and Roy White in our pecking order. We felt unsure about Ralph Kiner as a fairly extreme peak case. Finally, because we’ve elected solely on Major League play, we didn’t extend any special dispensation to Minnie Minoso. Well the jury is in. Medwick’s double-play addiction cost him about 18 runs versus his leagues. Also, his arm appears less effective than DRA suggests. It’s all enough to push his value low enough that he sinks below Joe Kelley and Minnie Minoso in the rankings and essentially out of sight. Minoso only cashes in outfield arm credit here because his career started after the advent of PBP-based rDP and rBaser. He’s not a good thrower, but he picks up a couple-three runs against DRA, which helps. The fact that he didn’t lose value really helps because if we ever choose to pursue the Negro League angle, he’s so close to the finish line now that even just a couple seasons of above-average play could put him over.
NOW EST NOW EST NOW EST NAME WAR WAR CHEWS CHEWS RANK RANK ========================================== CENTER FIELD Mays 162 161 114 113 2 2 J DiMaggio 81 84 66 69 6 5 Ashburn 74 74 60 60 8 8 Snider 59 59 51 51 12 12 Berger 47 49 43 45 26 23 Doby 49 49 43 44 29 26 Averill 45 46 40 41 36 33 Combs 43 47 38 41 41 31 D DiMaggio 40 42 37 38 46 41 B Chapman 40 38 34 32 52 56 H Wilson 35 33 33 32 53 58 L Waner 23 25 22 23 73 73
Tommy McCarthy is the worst player elected to the Hall of Fame. I’m far less sure now about the second worst. Is it Lloyd Waner or Highpockets Kelly? We can’t say yet with as much certainty as we’d like because we don’t have PBP info for enough of Kelly’s career to say. But right now, I’m leaning toward Little Poison. I would be a little skeptical that Combs’ is gaining that much ground. He’s definitely gaining because his baserunning is much better than BBREF estimates it, probably by 20 runs. But like Stan Hack, Combs is a lefty lead-off man, and so his estimated rDP of +13 is probably too high. It’s a shame that Averill didn’t reach the majors until his age-27 season and that Dom DiMaggio had the heart of his carved out by the war. Both are coulda-been HoMErs. Which brings us to Larry Doby. Like Minoso above, Doby has inched just a little closer toward the borderline, and he may have enough in reserve during his Negro League seasons to creep over the line, should we choose to go down that path.
NOW EST NOW EST NOW EST NAME WAR WAR CHEWS CHEWS RANK RANK ========================================== RIGHT FIELD Ruth 181 181 128 128 1 1 Ott 111 117 80 84 3 3 P Waner 80 82 63 64 7 7 Slaughter 60 62 48 50 26 26 Nicholson 49 51 44 46 27 27 Cuyler 50 51 43 44 30 28 Klein 43 45 41 42 35 32 Holmes 42 43 40 41 37 34 D Walker 44 46 37 40 43 37 Furillo 42 42 36 36 49 49
Mel Ott’s a pretty great player. I never really stopped to think about him much. But now I suspect he’s kind of the Frank Robinson of his time. Mantle, Mays, and Aaron dominated the 1950s and 1960s. Robinson was just a notch below. He sometimes outperformed them, but on the whole, the other guys were just better enough that over time a gap in value developed, as well as one of perception. Similarly, Ruth, Gehrig, and Foxx dominated the baseball scene of 1920s and 1930s. Mel Ott, like Robinson would later, did his thing year in and out and wasn’t quite as exciting or sometimes as valuable as his competition. Like Robinson, he also had a diverse set of skills with sneaky speed and great power plus durability and longevity. Certainly Ott was not overlooked, just as Robinson wasn’t, but he never quite equaled those other guys. Through this process, I discovered that Ott was probably a lot better baserunner than you’d think and that his attempts to pull balls down the rightfield line into the Polo Grounds short porch probably kept him out of the double play so much that he excels in that category of our analysis. Meantime, Slaughter is now neck and neck with Vlad Guerrero, and if the actual BBREF data comes through and looks better than these estimates, Country might pass the Impaler. They are both right on the line in right field, and Slaughter’s advantage may be his era. The post-war era is light on honorees. Finally, KiKi Cuyler. After Sam Rice, he’s a big reason why we needed to do this project. We noticed that he was one of his era’s speed merchants, and we knew that he was reputed to have a good arm. All of which turned out to be true, but unless his real numbers are a lot better than what we’ve seen, he’s not going to creep upward.
Overall, the differences we’ve noted are not earth-shattering. Mostly they don’t suggest that we’ve missed players or elected fellows we shouldn’t have. But it does give us a greater sense of the likely value still out there to discover. Of course, once BBREF calculates these figures and creates formal estimates, our numbers will be wiped away—as they should be. Those guys know more than we do, and we trust them. For now we have these estimates to guide further decision making.
We reckoned “missing” value that put Sam Rice and Bobby Doerr into the Hall of Miller and Eric at Vlad Guerrero’s expense. You probably would like to know how we did that. You’d probably also like to know how other important players might have been effected. You’re in luck, so read on.
This is the first of a three-part series on missing value from the 1930s and 1940s. This time, we’ll share how we are estimating that value. In part two, we’ll show you how we think it will affect our ratings and perceptions of the players we’ve figured so far. Finally, in part three, we’ll look at one player from the era before whose status might be raised when play-by-play stats from his period become available and identify a couple others like him.
Thanks to a major Retrosheet data release last year, Baseball-Reference, the greatest site ever on the internets, displays play-by-play (PBP) information going back to 1930. Not all of it, and more on that later, but an extensive enough amount. Despite the availability of this data, BBREF hasn’t yet recalculated its WAR values for the 1930s and early 1940s. But since the data is there, we can start making some estimations of our own. And that’s what this process we’re going to describe is about. You might say that our results represent rough predictions of what we believe will occur when BBREF is able to calculate WAR for all players back to 1930.
Three components of BBREF’s WAR values rely especially on play-by-play data:
We don’t have access to BBREF’s precise formulae, and neither Miller nor I knows SQL, so we probably can’t replicate BBREF’s equations precisely. But we can get defensibly close, and here’s how.
As best we understand BBREF’s explanations, they sum several sub-component values up to a final rBaser total. These runs seem to be based solely on five kinds of plays occurring after a batter has reached base and the next plate appearance begins:
These are the data that BBREF shows, they may use other data they do not present, and I may have some of these things wrong. Anyway, each player’s rBaser is expressed as a value above the league average in each department. With just a couple rough-edged hacks, we can make a pretty good estimate of each sub-component. By the way, I’m going to use two important abbreviations below:
I try to keep things simple, and this is very straightforward. Because these are all expressed as an average, the simplest form for stolen bases goes like this:
rSB = (p steals runs – lg steals runs) – (p caught stealing runs – lg caught steal runs)
Of course, we simply use the player’s raw steals and caught stealing, but we need to fill in three pieces of information:
A) the league’s steals and caught stealing
B) the run value of a steal
C) the run-value of a caught stealing
It’s relatively simple to determine A, we merely apply the league’s stolen base percentage to the number of steals attempted by the player. For caught stealings, we use 1 minus the stolen base percentage times the number of attempts.
Letters B and C are well beyond my pay grade. So, to keep it simple, I borrowed values from Jim Furtado’s Extrapolated Runs. This regression-based formula uses weights of 0.18 runs per steal and -0.32 runs per caught stealing. Many of you are getting ready to yell at me that Extrapolated Runs is only good for seasons 1955–1997. Yeah, I know, and it’s good enough for gub’ment work. We aren’t building Fort Knox here. So our final stolen base formula looks like this:
rSB = ((pSB * 0.18) – (lgSB% * pSBATT * 0.18)) – ((pCS * -0.32) – ((1-lgSB%) * pSBATT * -0.32))
Excel makes quick work of it. One important note. We don’t have caught stealing data for the NL until the late 1940s. So, for a given player, I took the years he played that have PBP for and averaged the AL’s stolen base/caught stealing percentage for the entire span. If someone played in the NL fom 1930–1936, I used the AL’s averages from that specific time.
For pickoffs, we adapt the same formula. Pick offs are essentially caught stealings, and, of course there’s no positive value to them. It’s all about avoiding pickoffs.
rPO = (pPO * -0.32) – ((lgPO / lgSBOPP) * pSBOPP * -0.32)
SBOPP = stolen base opportunities
These include advancing on past balls, wild pitches, defensive indifference, and similar events. Take the pick off formula and treat it like a stolen base.
rBT = (pBT * 0.18) – ((lgBT / lgSBOPP) * pSBOPP * 0.18)
Like pick offs and bases taken, this sub-component and the next (Outs on Base) are flipsides of one another. But we use a different denominator, and we have to figure out our own run values. BBREF’s baserunning stats include the number of opportunities a player had to go first to third, second to home, and first to home. They also include how many times he did so successfully. So we are going to sum the values of all three events. They each have a different run value, but they all have the same basic formula:
rXBT = r1to3 + r2toH + r1toH
r1to3 = (p1to3 * run value) – ((lg1to3 / lg1to3opp) * p1to3opp * run value)
r2toH = (p2toH * run value) – ((lg2toH / lg2toHopp) * p2toHopp * run value)
r1toH = (p1toH * run value) – ((lg1toH / lg1toHopp) * p1toHopp * run value)
Easy enough. The hard part is the run values. To figure these, we turned to TangoTiger’s run expectancy chart. (Yeah, I know, it’s for 1955–2015, but close enough.) We averaged the change in run expectancy for a successful extra base taken in each base-out state matching the formulae above. Here’s the values:
As usual, we were trying to keep things simple, so we didn’t get real deep into the weeds and adjust them for things like the frequency of each base-out state. We plug the values back in and get this:
(p1to3 * 0.19) – ((lg1to3 / lg1to3opp) * p1to3opp * 0.19)
+ (p2toH * 0.43) – ((lg2toH / lg2toHopp) * p2toHopp * 0.43)
+ (r1toH = (p1toH * 0.38) – ((lg1toH / lg1toHopp) * p1toHopp * 0.38)
Man, am I grateful for Excel.
Like I said, this is essentially the inverse of rXBT, except that we got a bit lazy by this point:
(pOOB * -0.32) – ((lgOOB / (lg1to3opp + lg2toHopp + lg1toH opp)) * (p1to3opp + p2toHopp + p1toH opp) * -0.32)
We used the sum of all three advancement opportunities as our denominator because we figured that most outs on base probably occur in situations where a player is trying to snag an extra base. We also used the XR caught stealing run value. If anything it’s too low, since baserunner kills are very expensive for the batting team.
We tested this setup against players from the post-PBP era. We took two excellent, two average, and two rotten baserunners from each decade 1950–2010, and we calculated their career rBaser by our method then compared the result to BBREF’s calculation. BBREF’s calculation produced 778 rBaser. Our method produced 781. The standard deviation of the difference between these 32 players’ BBREF and Miller and Eric rBaser was 7.9 runs.
The biggest outliers were Joe Morgan (23 runs better by our method), Mickey Mantle (+19), Ichiro (-15), and Willie Mays (+15). No player in the group of excellent runners was misidentified as being average or below. No player in the average group zoomed or plummeted into the excellent or rotten categories. No player among the rotten runners was anything but awful. We feel like our approach at least appears like a relatively quick, easy, and reasonable approximation that will be within roughly 20% of BBREF’s eventual PBP-based figures, and that we will usually be within a win’s worth of runs or fewer.
It’s well worth noting, however, that our method yields very different results than BBREF’s current regression-driven pre-PBP estimates. On the 80 players we tried it on from the 1930s and 1940s, we reckoned 519 runs versus BBREF’s 10. Yes, one-oh. Is this difference explainable? We believe it is. First, because BBREF uses a regression formula, it naturally draws all players toward the mean, which, it appears to us, creates a drastic overfit. Second since we didn’t have this much PBP data when they came up with the regression formula and all that was available were stolen base figures of varying completeness for most of history, the BBREF estimator will naturally suffer from a lack of inputs that would vary the results much more. To see why this is important, just look at someone like Brett Butler whose stolen base percentage was well below the league average, but whose rBaser is very good because he did the other PBP-based baserunning items very well. Finally, the league stolen base percentages in the times we are talking about were terrible. The league seldom stole at even a rate higher than 60 percent. Today’s stolen base rates are nearer 75 percent than 60 percent. This meant that players with an iffy SB% could still make positive (or less-negative) contributions than now, which the BBREF estimator might not pick up.
We have also estimated pre-PBP seasons for players with substantial careers (1000 PA) after 1930. BBREF’s amazing play index makes this possible and easy. Here’s a run-down of the process:
For non-catchers, we don’t include catchers among the comps, nor anyone who played a lot at catcher such as Brian Downing or B.J. Surhoff. Catchers are just a whole different ball of wax. For catchers, we tried to use only catchers.
The upshot is that most players lose speed and baserunning value across time, and this method uses comps to figure out how much similar players lost, which allows us to add back that value to the player in question.
After all that baserunning stuff, this one is easy. We once again turn to XR which has a run value for GIDPs of -0.37.
rDP = (pGIDP * -0.37) – ((lgGIDP / lgPA) * pPA * -0.37)
Now here’s another little workaround that some folks might not lack the imprecision of. BBREF calculates rDP as a function of GIDPs per GIDP opportunities. But we don’t have the luxury of that data in all cases. Some GIDPopp data is not complete. Even more importantly, we don’t have full GIDP data for all teams or players over the entire 1930–1947 period that we’re trying to account for. Plate appearances are known across history, and using them as a denominator is close enough for now. We have good DP data for 1939 onward so we can figure the lgGIDP/PA accurately. Before that, however, the data looks a little funky. But for the few years before 1939, we can use the GIDP rate of 1939–1941 or something like that. However, sometimes we need to dig deeper.
When we don’t have complete DP data or any, we can estimate once again by using the comps method. It’s a little simpler than with rBaser. Here’s the rundown:
Like rXBT and rOOB above, outfield throwing is reliant on runner-advancement information that BBREF provides for each outfield position. So the big idea here is
pRF throwing value – lgRF throwing value
+ pCF throwing value – lgCF throwing value
+ pLF throwing value – lgLF throwing value
But there’s a lot of granularity because in many 16 scenarios are in play instead of just three for running:
Each of those opportunities has three possible outcomes that BBREF provides data for:
And each of these three outcomes carries a different run value.
Finally, BBREF has a catch-all category called Other Assists for all outfield assists that don’t fall within the five scenarios above. Let’s make a quick chart, otherwise, we’re in for a long night:
ADVANCES HOLDS KILLS OTHER =================================== 1to3 -0.18 0.19 0.24 ---- 2toH -0.51 0.48 0.87 ---- 1toH -0.42 0.38 0.99 ---- FO2 -0.11 0.11 0.27 ---- FO3 -0.59 0.59 0.64 ---- OTHER ---- ---- ---- 0.50
These run values are based on the run expectancy chart I talked about previously this time augmented by Tango’s events-frequency chart (aka: the average run-value of each of the five scenarios and all three out situations, adjusted for frequency). The Other Assists figure is, to be honest, a guess. I took the average of the run values for kills in the five scenarios above (0.70) and adjusted downward a bit to keep it a little conservative. That last part isn’t awesome, but it’s about the best I can do.
The cluster of equations is pretty ugly, so here’s the one for the first of the five base-out driven scenarios:
r1to3= ((p1to3adv * -0.18) + (r1to3holds * 0.19) + (r1to3kills * 0.7)) – (((lg1to3adv / lg1to3opp) * p1to3opps * -0.18) – ((lg1to3holds / lg1to3opps) * p1to3opps * 0.19) – ((lg1to3kills / lg1to3opps) * p1to3opps * 0.7))
Same form for the other scenarios, just swap in the right data and the right run values. Then the Other Assists equation:
rOA = (pOA * 0.5) – ((lgOA / lgOPP) * pOPP * 0.5)
The OPP mentioned here is a column in BBREF’s outfield throwing table that represents the total number of opportunities the player had to hold or kill a baserunner. For seasons prior to 1930, we simply used the player’s known career rOF per game at position (RF/CF/LF) and multiplied it by the games at the position during the season in question.
Unfortunately, there’s one last step to take, and that’s to divide the rOF by two. After testing these formulas on about 50 players in approximately equal measure among the three outfield spots, the numbers came back consistent at all three positions. Consistently about double what BBREF got. I won’t tell you I’m psyched by this at all. Obviously, I’m doing something wrong, but I’m doing something right because the guys who are good throwers according to BBREF are good throwers for us too. And the bad ones are bad. And the ones nearest average are again nearest average. Not as perfectly as with baserunning but reasonably close. So that’s my solution. Divide by two. Inelegant, yes, defensible, probably not, but not entirely without merit, and for a ballpark estimate, it’ll do.
Let me run through one example to show you how it all fits together. Here’s Mel Ott’s 1939 season.
Ott stole 2 and was caught 3 times, which yields about -0.6 runs. Using the AL’s SB% during Ott’s career (1930–1947, 59%), we figure the league would have netted -0.1 runs. So there’s -0.4 runs for stealing.
Ott was never picked off in 1939, and we don’t dock him any runs. The league would have been picked off about once, for -0.32 runs. Ott claims +0.3 runs for pickoffs.
Ott took 5 bases in 1939, accounting for about 0.9 runs. The league would have earned about 1.4 runs in his opportunities. Chalk up -0.5 runs for Bases Taken.
Ott went first-to-third 8 times in 29 opportunities; first-to-home 2 times in 4 opportunities; and second-to-home 13 times in 16 opportunities. That all results in about 7.9 runs. In the same opportunities, the league would have gained 7.4 runs. So Ott is +0.2 runs for Extra Bases Taken.
Impressively Ott made no outs on base and loses no runs. In the same opportunities, the league would have been debited -1.6 runs. Ott picks up +1.6 runs for Outs on Base.
So we estimate total baserunning value for Ott in 1939 at +1.2 runs versus the 0 that BBREF shows.
In 1939, Ott hit into 5 deuces. We would expect the league to rap into about 10. After we apply the run value and subtract, Ott comes out +2.0 runs ahead. BBREF does not calculate DP avoidance prior to 1948
To save all the messy calculating, Ott earned about +1.2 throwing runs in rightfield. I’m not sure how BBREF gets assists information into its calculations. I use DRA and weight it 2:1 against BBREF’s rField, but I swap in BBREF’s rOF because DRA’s arm ratings aren’t good enough. DRA shows -0.9 runs.
Master Melvin picks up 4.4 runs overall. Against BBREF, he adds 3.2 runs and an unknown amount for throwing. Against DRA, he tacks on another 2.1 runs in throwing value. Rinse and repeat for all of Ott’s seasons, and he gains:
Now, I do a lot of mumbo-jumbo with all of this stuff, and it gives him an extra five or six wins in book. That’s not chump change.
What we do might be different than what you do. Basically, we substitute them into our various formulae for adjusting WAR. Someday when BBREF updates its WAR for 1930–1952, we’ll simply update our background data with its new calculations. Until then, we can work with these estimations. What will you do with them? We hope you’ll be able to use them somehow, as primitive and rough as they are. But they’re pretty good overall. Enough to tie you over until we get the real thing. Just be sure to remember that they aren’t supposed to be gospel.
Why didn’t we elect Vlad Guerrero? Why did we choose Manny Ramirez, Sam Rice, and Bobby Doerr to back up Ivan Rodriguez instead of Vlad the Impaler?
The answer to this question is part structure, part logic, and part evaluation. Our rules tell us three important things that are key to understanding how we made our decision.
Because of that first point, we have no qualms at all about honoring Manny Ramirez, who was, in our estimation, an obviously better player than Guerrero. As we wrote a couple weeks ago, Sam Rice’s election came as something of a good surprise and was enabled by new Retrosheet data that isn’t yet incorporated into BBREF’s WAR figures. Once we made some accounting for it, even if we used our estimates at partial strength, Rice still looked like a better overall candidate at the same position as Guerrero.
Which brings us to the central question. Why Doerr instead of Vladimir Guerrero? This is all about position. In fact, our most populous position: 21.7 of HoME careers have been spent in right field, and a HoME-leading 11% of players could call it their primary position. We have more right fielders as a percentage of HoMErs than the Halls of Fame, Merit, or Stats do, but two of those other three (Fame and Stats) can claim right field as their most populous position, and the other (Merit) has it tied for second.
Big picture, right field generally has more high-quality candidates than most other positions.
So when we determined that Sam Rice would be third man in our 2017 trio (mandated by the size of the Hall’s incoming player class), we had a choice to make. Because of some housekeeping we had to do, we needed to elect one more backlogger who was eligible by 2015. That could have been Rice, instead of Doerr had we evaluated Vlad as superior to all backloggers. He might yet be, actually, but as we looked at this choice, we saw a couple patterns that made us look elsewhere around the diamond.
Our three least populous positions are catcher, third base, and centerfield. The top backlog candidates at each are Jim Sundberg, Heinie Groh, and a whole bunch of centerfielders with roughly equal qualifications.
We’re still having trouble unknotting Bernie Williams, Chet Lemon, Cesar Cedeno, George Gore, and others. Maybe someday, but nothing’s changed since last year. They are still too close to call. Scratch, scratch, scratch, scratch.
At third base, two events conspired against Heinie Groh. First, we lacked any information about Groh similar to what we dug up on Rice because Retrosheet hasn’t quite gotten back to the 1920s and 1910s yet. Soon, we hope! Without that, and with the razor-thin margins we’re looking at, the possibility that Groh’s baserunning or double-play avoidance could drag him down loomed in our minds. At the same time, and this is really important, the cavalry is on the way. Next year, we will encounter two third basemen <SPOILER ALERT!> we are both likely to support in Chipper Jones and Scott Rolen. </SPOILER ALERT> Should we elect them, third base would achieve some level of parity with the other positions. On top of that, we’re in something of a golden age of third basemen. With an aging Adrian Beltre leading the way and David Wright possible facing a quick exit if his neck problems don’t heal, we won’t wait terribly long to see two more high-profile third sackers hit the ballot. On top of that, Josh Donaldson isn’t all that far away from building a great peak-based case HoME case, and he’s going to be 32 next year. Right behind him is Evan Longoria whose comeback year in 2017 quietly reignited a candidacy that’s already very close to the HoME borderline. Kyle Seager has established some serious bona fidas and could be on the path as well. Then there’s Manny Machado and Nolan Arenado establishing themselves as bright stars. In other words, third base isn’t hurting. If we don’t elect Heinie Groh, we don’t threaten to unbalance the position in the long term. So despite his helping an era we’d like to add to for balance, we crossed him off the list.
Which brings us to catchers, who are always hurting. Jim Sundberg’s case relies heavily on Max Marchi’s work on pitcher handling. Sundberg was really good in this regard, boosting him from also-ran to a possible positional tail-end candidate. He also represents an era where we have some room to add players. Looking forward, however, while Joe Mauer is a surefire HoME catcher, there’s relatively little certainty beyond that. Russell Martin is pretty close to the border, but he is an older player coming off a poor year. Buster Posey and Yadier Molina are positioned for a run at the in/out line, but by dint of their position, catchers can’t be relied on to continue performing at a high level for years and years. And after those guys? You’ll just have to wait. This combination of factors made Sundberg a very attractive candidate.
Then we stumbled into Bobby Doerr. We dismissed Doerr dozens of elections ago for lots of reasons, and one was that his GIDP totals were very, very high. We had less data on them back then, and we didn’t have a good way to estimate the effect on his value. But our analysis of Sam Rice gave us a path forward, and with more data arriving since we wrote his obit, we are more certain about the floor of his value. It’s basically the same as Jeff Kent’s. Plus the World War II era is really hurting for players. Like Sundberg, Doerr also benefits from the long view. There are no high-quality second basemen eligible between now and at the earliest 2023, assuming Chase Utley appears this year on an MLB diamond. He’s not a shoe-in either, very much a borderliner at the moment. Robinson Cano is already a HoMEr, but we’re a minimum of 10 years from his eligbility. Ian Kinsler is just one or two good seasons (or one excellent season) away from joining Utley in the bottom reaches at second base, and he’s probably another 10 years in the offing. Ben Zobrist and Dustin Pedroia are the other two excellent second basemen in the game. Zobrist is older than all these fellows, making him the least likely to reach the level of performance needed to get into the HoME. Pedie has had injury problems in several of his previous seasons. Everyone else is still too young to project well. This means that second base is a bit better off than catcher in the future, but not by much. And the keystone sack is currently about average in its representation in the HoME, so adding someone now makes good sense to keep it in balance.
Which brings us back, finally, to Vlad Guerrero. Right field is two or three guys overrepresented—there’s no pressure on us to elect another. When we decided Sam Rice was a better candidate than Vlad, it necessitated us choosing a backlogger in addition to Rice. However, that hardly means Vladi is done. Looking forward, Ichiro is the only guaranteed honoree. Bobby Abreu is extremely borderline, more so than Guerrero. Enos Slaughter is still out there and a pretty good choice if we want him. Among active players, Jason Heyward looked lost in 2016, Giancarlo Stanton seems unable to stay healthy, Justin Upton has turned into a pumpkin. Of course Bryce Harper is the Great Right Field Hope, but after his incredible 2015, he turned in a pedestrian 2016 and has also been dogged by owies over time. Vlad Guerrero will get his turn, but he has to wait in line. We can elect him any old time we want to because he’s too borderline to command a vote, and his position is a little too packed right now to demand we check his box. But that’s OK. Whenever we get around to electing him, he’ll be tying his position over until it future starts to look a little brighter.
You might think of it as our version of strategic voting.
In the meantime, the exercise of electing a backlogger from among our borderline candidates has given us new insight into an old tyme hidden star, given us confidence in someone we didn’t dare take before, and given us a reason to vote for someone whose subtle contributions have gone unevaluated until somewhat recently. All while helping us look to the future to make the best decisions we can. So, just hang in there Vlad, we’ll call your name soon.
Like we said a little while back, thanks to our own petard-hoisting, we needed to shuffle some deckchairs at the HoME. Which fortunately is not the Titanic. As part of that reshuffling, we needed to elect on backlogger who was eligible before our 2016 election.
We decided in Bobby Doerr’s favor, which I’m sure his surviving family members are right now celebrating with kegs, multiple sheetcakes, and noisemakers by the fistful.
[Editor’s note: Thanks, Michael Mengel, who reminded me that Doerr is alive and our oldest living Hall of Famer. I thought he had passed on this past year, and, of course, I apologize for having my facts wrong. I wasn’t making a joke in bad taste, just a bad joke. Anyway, I hope Doerr and his family remain happy and healthy for many years, and that they are, in fact, tossing a huge blowout shindig over his HoME election. And, thanks, Michael for a slice of my favorite kind of dessert…humble pie. 😉 ]
Now you might ask, how did we arrive at Bobby Doerr’s name when we had written him off previously? The answer in a word is Retrosheet. Specifically, their update last winter update that provided play-by-play data dating back to 1930. When we made our decision to send Doerr to HoME limbo many elections ago, we did so because the little we knew about his propensity to hit into rally-killing double plays boded very badly for him. We only had his seasonal GIDP totals for a few latter-day seasons. And they were bad. Really bad. As in thrice hitting into 20 our more deuces and twice leading the league in that category. Now we have information about him from before the war that suggests Doerr hit into many fewer in his early 20s than he did after the war.
Back when we wrote his obit, Doerr had -6 runs versus the league in DP avoidance for the four seasons BBREF had calculated. They’ve still only calculated those four seasons, but with the data we now have for him and for league-wide GIDP rates, we can do a little estimating ourselves. Cribbing from Extrapolated Runs, we figured out how many runs Doerr cost his team in DPs by using xR’s -0.37 runs per DP. Then we figured the same for MLB based on its DP rates, and applying that rate to Doerr’s PAs. Subtract the latter from the former, and we have a pretty decent guesstimate. Yeah, there’s issues with using xR’s weights (calculated for 1955 to contemporary days), but this level of information was easily enough for us to make an informed decision.
When we didn’t have his DP value, we’d have said this:
So our guesstimate, which put him at “only” another -7 runs felt contained. In addition, however, we’ve been doing some similar calculations about baserunning. BBREF shows Doerr at -8 for his career, but +2 for his final four seasons. Running some guesstimates based on newly available PBP data, we reckoned that where BBREF had used a regression estimate to assign Doerr -10 runs for his first 10 seasons, the real data suggest more like a -1.5 runner. That meant that Doerr picked up about 8.5 runs against average while losing those 7 for DPs.
Overall, Doerr’s value appeared to stabilize right on the borderline, and very close to fellow keystone man Jeff Kent’s. With our estimates now suggesting that Doerr wasn’t going to look like a mistake in hindsight, we also could feel really, really good about electing someone from the World War II era. We’ve got precious few of those fellows for obvious reasons, and we’re glad to bring a little balance to an era that will always have an imbalance. Positionally speaking, we also felt Doerr’s election would maintain second base’s place in the positional-balance spectrum. The position has no upcoming future eligibles with any shot, and most active players with a good case are at least 10 years away from seeing the ballot (exception Chase Utley, but it’s not yet clear how long he wants to play). So Doerr gives us a way maintain balance in our fielding forces.
In other words, everything broke right for Bobby. It could have been Heinie Groh if we’d not had similar doubts about him thanks to a dearth of data on his double plays and baserunning. It could have been Jim Sundberg if we were a little clearer on whether we think pitcher handling and game calling value is well evaluated. It could have been Sam Rice if we’d thought Vlad Guerrero was a superior candidate to him. In the end, though, we’re glad to throw open the door to Bobby Doerr.