The 1977 Boston Red Sox, with a colorful starting rotation including Bill Lee, Luis Tiant, Bob Stanley and Ferguson Jenkins, threw only 15 Wild Pitches all season.  15 Wild Pitches is not a record low; the 1921 Boston Braves threw only 8, but for reasons beyond the need of immediate explanation the 1921 Braves calculate as 1.9 Standard Deviations better than the period norm, or 119, whereas the 1977 Red Sox are 2.7 standard deviations better than the norm, or 127.   It’s a record, but you probably guessed that.  These are the top 10 of all time:

And these poor sufferers were the most Wild Pitch-afflicted, relative to their era:

The 1958 Dodgers threw 83 Wild Pitches, while several teams since then have managed to clear the century mark.   These are the decade norms and standard deviations for Wild Pitches divided by batters faced:

As you can see, Wild Pitch rates have gone steadily upward since 1960.  They were more than twice as high in the 1970s as in the 1920s, and have continued to go up since then.  This is, I would assume, because of the increased velocity of the fastball.  More max-effort pitches.  Also, let me confess to a minor error in the previous report.  In the chart published yesterday, it said that the average of Hit Batsmen in the 1910-1919 era was .00536.  That was actually the average for Wild Pitches, today’s number.  The average for Hit batsmen was .00737.   

As has been true in other categories, the teams which threw fewer wild pitches did better in wins and losses, with the top 510 teams having an average won-lost record of 82-74:

Also, Wild Pitches are fellow-travelers of both Walks and Hit Batsmen.  Teams which had more Wild Pitches also had more Walks, in about the same proportion as teams which had more Hit Batsmen also had more Walks.   And teams which had more Wild Pitches also had more Hit Batsmen, in a slightly smaller proportion.  

Let me explain now what I am actually trying to do, why I am doing these studies.  For at least 35 years it has been my opinion that the idea of evaluating fielders compared to an imaginary center line, what an average fielder would do, is a very poor idea.  I would explain it this way.   In 1876 Cap Anson hit .356—110 for 309—while the National League Batting Average was .265.  A two-game outfielder named Live Oak Taylor hit .375 (3 for 8), while Oscar Bieleski hit .209 (29 for 139), and Bill Craver hit .224 (55 for 246).  

It can be said, then, that Cap Anson was 28 hits better than an average hitter (since the distance between .356 and .265, in 309 at bats, is 28 hits.)  Live Oak Taylor was 1 hit better than average (+1), Oscar Bieleski was 8 hits worse than average (-8), and Bill Craver was 10 hits worse than average, or -10. 

Suppose, then, that the league statistician in 1876, rather than reporting that Cap Anson had hit .356 in 309 at bats, had merely reported what he regarded as the essential fact:  that Cap Anson was 28 hits better than the league.   Cap Anson was +28, Live Oak Taylor was +1, Oscar Bieleski was -8, and Bill Craver was -10; I’m sorry folks, that’s all the information I have to give you. 

Can you begin to see the problems that this would have created for future generations of baseball fans?   Can you understand why that really would not have been the best way to go about it? 

The essential problem is that the evaluation--+28 for Anson, -10 for Craver—the evaluation creates no real basis for understanding.  It lays no foundation for the growth and development of better methods.  It’s an end point.

Pitchers?  Well, the basic thing is Wins and Losses, and Al Spalding was 47-12, so we will record that as +35; that gets the essence of that, don’t you think?  That’s all you need to know.     Jim Devlin was 30-35, which is -5, and Dick McBride was 0-4, so we’ll record that for posterity as -4. 

It is not the basic job of statistics to evaluate players.   It is the basic job of statistics to describe the player’s performance.  If you describe the player’s performance with accuracy and detail, then it becomes possible to evaluate his performance, to find the value of it.  But if you start the process with the evaluation, then there’s no pathway to walk on toward greater understanding.  

As you probably know, John Dewan has created many or most of the modern, sophisticated Defensive Statistics that we now have.  Runs Saved, Runs Above Average, Outs Above Average. . . that’s all John’s work, or most of it is.   John Dewan is my close friend and longtime colleague.  We have been business partners for almost 40 years, have worked together on countless projects, and John is the co-owner of this site, Bill James Online.  I have the highest possible regard for John, and for the work that he does. 

But we have also had this argument for three-plus decades.   I don’t think that the idea of rating players by how they compare to the average is bad; I think it is horribly, absolutely, fantastically bad.   While there is no doubt that we have much better fielding numbers now than we had a couple of decades ago, I believe that John’s initial mistake—rating players first, rather than describing them first—has enormously limited the value of what he has done in this area.   It has hobbled him.   It has prevented his work from leading to the understanding of fielding performance that we SHOULD now have, given the tremendous amount of work that he has done in this area.  

John and I have argued about this issue since the mid-1980s, and John has made dozens of efforts to address what he sees as my concerns.  He has methods now that will tell us that Shortstop X is +7 in going to his left and +1 on balls hit toward him, but is -28 in going to his right, thus -20 overall.   In John’s view, this solves the problem; it fills out the chart, describing the player’s performance.  In my view, it doesn’t address the problem at all; it merely extends the bad methodology into new areas.    It’s like saying that Cap Anson, in addition to being +28 hits, was +7 doubles and -2 triples.   It doesn’t solve the problem. 

There are facts, and there are opinions.   If you say that there were 184 balls hit between the shortstop’s normal position and the second base bag, and Shortstop X made outs on 116 of them, that’s a fact.   If you say that he reached 147 of them, made outs on 116, made errors on 6 of them, made no throw to first on 11 of them and threw too late to first on 14 of them, those would all be facts, assuming that that is what happened.  If you say that he made 97 outs to first base on those and 19 outs at second, those would be additional facts.  If you say that he started 47 double plays on those balls, that would be an additional fact; if you say that he started 9 of those double plays by touching second base (6-3) and started the other 38 by throwing to the second baseman (6-4-3), that would be another fact.  If you can tell us how many double play situations that there were there and how many double plays were NOT turned, and how many double plays were not turned because the runner from first moved up and how many were not turned because the throw to first base was too late or was not made at all, those would be additional facts.  

But if you say that he is +7 runs going to the second base side on ground balls, that is not a fact; that’s just an opinion, stated as if it were a fact.   I’m glad to know your opinion; I value your opinion and I respect your opinion.   But what I really WANT is a better set of facts.  I would like to know what each of those things is at home and on the road, and I would like to know what it is with each pitcher on the mound, and I would like to know what it is against left-handed batters and right-handed batters.  We could have and should have as many facts at our disposal to create an understanding of Nolan Arenado’s defense as we do to create an understanding of his batting.  But what we have is, he’s +8 runs.   

It may seem that I am asking for a vast amount of information here, but really, I am not.   Suppose that we designate the area between second base and a halfway point between second and third as "Zone 4", and we publish a Zone 4 Ground Ball report, like this:

The category totals do not add up to the Zone 4 Ground Ball total because 6-3 Advance (a runner goes first to second on a ground ball to short) is a subset of 6-3 Ground Out.  Also, now that I look at it, I see that I left "errors" out of there; there should have been a column for errors.  But it’s not complicated, is it?   You can detail the performance of all shortstops, on ground balls hit in that zone, in one chart. 

I have been frustrated on this subject for nigh-on 40 years now.  I still hope that, if baseball survives, that we may eventually get defensive records that describe the outcomes and that make sense, so that we can decide for ourselves how we would evaluate the fielders.   I do not have the coding skills to create the records that I would like to see, and, more particularly, being an older person, I am actually more interested in the defensive performance of Yogi Berra and Mark Belanger than I am in the defensive performance of Watch It Buster Posey and Angular Andrelton Simmons.  

What I am trying to do here, with this series of studies, is to fill in a gap in our defensive records, not with John’s approach but with my own.   This is the cornerstone assumption of this series of studies, what I am about to explain in the next few sentences.   The Washington Nationals scored 873 runs last season, 2019.   Based on the batting statistics we have and the methods which have been developed over the years, we can say with a  fair degree of accuracy who created how many of those runs.   Anthony Rendon  created 130 of them,  Juan Soto created 117, Matt Adams created 42, Adam Eaton created 88, Howie Kendrick created 58, etc.  We don’t absolutely know, but we kind of know.  

Runs created estimates are not EVALUATIONS; they are ESTIMATED FACTS.   Two players created 70 runs each, let us say.  One of them might be a near-MVP, the other might be a drain on the offense, due to outs made, parks, defensive contributions and other things.  If we were offering this as an EVALUATION, then we would be saying that a player creating 75 runs is better than a player creating 70, but we’re not saying that, at all.   We are merely saying that here is an estimated fact that you can throw into your evaluation.   It is one fact among many which can be used to evaluate the player’s contribution.  

What if there was a similar estimated fact for each player’s Runs Saved—not his Runs Saved against Average, but his Runs Saved, gross?     Would not this be a contribution to our understanding of his role on the team? 

That is what I am trying to get to here; I am trying to create a way to estimate Runs Saved by each fielder and each pitcher—not Runs Saved against average, which is an end point to the discussion, but rather, Gross Runs Saved or Runs Saved against zero, which is an element of understanding. 

Well. . .but how many runs are Saved by the team?   The 2019 Washington Nationals scored 873 runs, but how many runs did they Save, as a team?

It is not a perfect and unassailable truth that Offense and Defense are perfectly balanced, that Scoring Runs is half the game and preventing them is half the game.   It is not a perfect and unassailable truth, but it is a general and usable truth which can be validated in various ways.   If offense and defense are equal then, on a "league" basis—understanding that the league is no longer a completely self-contained entity—but on a league basis, runs prevented are equal to runs scored.  If there were 11,449 Runs SCORED by National League teams in 2019, there must also have been 11,449 Runs PREVENTED by National League teams—not perfectly, because the league winning percentage was not exactly .500, but we can adjust for that.  The question is, who prevented how many of those 11,449 Runs that were Prevented by Defensive Performance?

Can you understand why THAT question creates a better platform from which to evaluate defensive performance than the zero-based, +/- system?  Does that make sense to you?   Probably it does to some, doesn’t to others, but let’s move on.

How many runs were prevented by the Washington Nationals?

Well, that number we can get to pretty easily.  The Nationals pitched 1,439.1 innings during the 2019 season.  The league average for runs allowed/inning was .5268.   .5268 runs per inning for 1,439.1 innings would be 758 runs.      The Nationals Park Factor was 110, which creates a Park Adjustment Factor of 1.046.  Adjusting for that increases the Expected Runs Allowed to 793.    If Runs Saved are equal to Runs Scored, then every run they did NOT allow below twice that number would be a Run Saved.  Twice that number would be 1,586.   The Nationals actually allowed 724 runs.   That means that their Runs Saved were 862.   That Nationals’ pitching and defense, combined, saved 862 runs on the season.   The team scored 873 runs; they saved 862. 

In the 1970s, I was able to figure out a way to say how many of the runs scored by a team were created by each hitter.  What I am trying to do HERE is figure out a way to say how many of the runs SAVED by the team were saved by each pitcher and each fielder.   It is a very comparable undertaking.

It will take me months to do this; hell, it has already taken me six weeks or so—and there is no guarantee, not even a reasonable expectation, that I will reach the finish line of the effort.  I might get something that works, I might not.   But that’s what I am trying to do.

The FIRST thing you have to do, in that effort, is to establish the zero points, the position of NO effective contribution.   At what point can we say that this number of strikeouts, this number of walks, this number of Wild Pitches, etc.. . . .this number is not even a major league number; it is, rather, the floor against which a major league player may be measured? 

It appears, based on the research that I have done so far, that the floor might be five standard deviations below the norm.  If you’re five standard deviations below the norm, you’re just some guy on the field taking up space.  The worst team in terms of strikeouts, the 2003 Detroit Tigers, was 2.8 Standard Deviations below the norm.  The worst team in terms of walks, the 1915 Philadelphia Athletics, was 4.5 standard deviations below the norm.   The worst team in terms of Hit Batsmen, the 1922 Detroit Tigers, was 4.6 standard deviations below the norm.  The worst team in terms of Wild Pitches, the 1958 Los Angeles Dodgers, was 4.8 Standard Deviations below the norm.    I also know, because of research that I have done but not yet reported to you, that the worst term in terms of Home Runs Allowed (Park Adjusted) was 4.0 Standard Deviations below the norm, that the worst team in terms of DER (Defensive Efficiency Record) was 3.9 Standard Deviations below the norm, and the worst team in terms of turning the double play was 3.0 Standard Deviations below the norm.  It is beginning to look as if 5.0 Standard Deviations below the norm might be a floor, beyond which there are no major league teams. 

But we don’t KNOW that yet; that is, in truth, merely an estimate of where we should begin to LOOK for the floor.   In order for the system to work, the runs saved by the 2019 Washington Senators by all of these things and several more things has to total up to somewhere pretty close to 862 runs—and the pathway toward that number has to be at every step reasonable and consistent with all of the other measurements.   I have no idea, at this point, how I am going to get there; I merely believe that it can be done.  The floor might be 50 (five standard deviations below the norm), or it might be 60 or 70 or 20; I really have no idea.   It might be 50 for one category but 70 for another.  But that’s what I am doing; I am taking measurements, looking for the floor.  I am looking for the zero point.