First Base Hitting

When people think of first basemen they think of hitters first and for good reason. You could argue that they are the best hitters overall in baseball. Certainly, outfielders will have a huge say in that, but most teams have a better hitter at first base than at any other position. As we saw with the catchers, there are different metrics that we use. Some of them compare with the league average, so they can be compare players over time. Others exist on their own, so it is more difficult to compare players across eras.

We will be adding base running runs from Fangraphs this time around. While it may not mean a lot in the grand scheme of things, it does help us differentiate between OPS+ and wRC+. wRC+ includes base running, so it ends up being a lower in most cases. However, there are a few notable exceptions. It may not make a huge difference, but when the overall picture is close, every little thing helps.

Lou Gehrig 179 173 .803 1.205 -27.2
Jimmie Foxx 163 158 .781 1.125 -18.9
Jeff Bagwell 149 149 .722 .975 6.5
Frank Thomas 156 154 .732 1.033 -38.2
Eddie Murray 129 127 .624 .787 3.4
Jim Thome 147 145 .713 1.020 -35.2
Willie McCovey 147 145 .718 .900 -0.4
Harmon Killebrew 143 142 .706 .899 -0.1
Hank Greenberg 158 154 .765 1.086 -2.3
Tony Perez 122 121 .619 .737 -0.7
George Sisler 125 123 .672 .756 19.4

We could break this down in any number of ways, so we will look at the metrics themselves. Four of the five are normalized by comparing the player with the average player. The lone holdout is a statistic called bases per out (BPO). Even though it isn’t normed, it is an incredibly valuable statistic. Sure, the top three guys all played in the Live Ball Era, but you can still compare players from the same period.

Tony Perez didn’t play at exactly the same time as Willie McCovey and Harmon Killebrew, but they were contemporaries. You can see that both were vastly superior to Perez. It’s to the point where you have to question his place in the Hall of Fame. Couple that with his offensive winning percentage and it’s enough to raise the question again. Offensive winning percentage assumes all eight position players produced the same as he did and the team gave up an average number of runs.

The three Live Ball Era first sackers would win well over 120 games if all the hitters produced like they did. A team of Perez’s would win 100 games on the nose. Even a team with that record is usually one of the favorites to win the World Series. Obviously, all eleven guys produce numbers that would make a team full of those guys favorites to win the World Series.

These numbers tend to make a lot more sense when we place the players in groups from the same era. Even though most of the numbers are normed, it is still hard to compare players from the Live Ball Era with the players that played in the 1960s and 1970s. The current era might be comparable to the Live Ball Era, but there is still some separation. We are taking the fielding out of it, so these rankings are not complete, but many would swear by them.

Live Ball Era 

Lou Gehrig 179 173 .803 1.205 -27.2
Jimmie Foxx 163 158 .781 1.125 -18.9
Hank Greenberg 158 154 .765 1.086 -2.3

These numbers demonstrate how good Hank Greenberg really was. Give him his three full seasons he lost to the World War and his career numbers would have been almost as good as Foxx and Gehrig. When you look at the rate statistics you can see that he was just a cut beneath Foxx. Gehrig is in a league of his own. Even when we throw Albert Pujols into the equation, he still winds up better than the rest.

Expansion Era 

Willie McCovey 147 145 .718 .900 -0.4
Harmon Killebrew 143 142 .706 .899 -0.1
Eddie Murray 129 127 .624 .787 3.4
Tony Perez 122 121 .619 .737 -0.7

It’s always interesting to see how similar players are from various eras. You could argue that McCovey and Killebrew are practically the same player based on this profile. Both Murray and Perez enjoyed longer careers, so you could argue that their overall value was suppressed by playing longer. Murray played in over 3000 games (around 400 more than McCovey and 500 more than Killebrew). Perez really only played in about 200 more games than McCovey and 300 more than Killebrew. So, it is harder to make that excuse for him.

Both Murray and Perez drove in more runs than Killebrew and McCovey in that span of time and that is why they are in the Hall of Fame. Of course, in addition to the increased number of games, there is no accounting for the number of opportunities those players had. However, ignoring that we could look at the number of games they played and then take the number of runs and RBI on a per game basis.

  Games Runs RBI TRP TRP/G
Willie McCovey 2588 1229 1555 2784 1.075
Harmon Killebrew 2435 1283 1584 2867 1.177
Eddie Murray 3026 1627 1917 3544 1.171
Tony Perez 2777 1272 1652 2924 1.050

It’s hard to take these numbers at face value. They are raw numbers that don’t account for the hitting environment or the quality of the teams around them. With the exception of Willie Mays, there were no Hall of Famers that surrounded either McCovey or Killebrew. Perez had Johnny Bench, Joe Morgan, and Pete Rose. Murray had Cal Ripken Jr. on his team for most of his time with the Orioles. Even still, this is just another spot where Perez comes out a bit behind his colleagues.

Steroid Era 

Frank Thomas 156 154 .732 1.033 -38.2
Jeff Bagwell 149 149 .722 .975 6.5
Jim Thome 147 145 .713 1.020 -35.2

Again, we notice how similar these players are when we break them down by era. Bagwell’s base-running keeps him in the conversation. He also spent a good portion of his career in the Astrodome where power was suppressed. However, you could put a blanket over all of these guys offensively. As we saw in the last article, the fielding numbers were not necessarily this close.

Daily fantasy baseball is taking the world by storm. While total points (and total points per game) is not as scientific as the sabermetric numbers, it is a lot of fun to play and looking at the historical numbers can be fun. The total points formula changes depending on the source, so we came up with our own.

Total Points= TB + Runs + RBI+ SB + BB + HBP – SO – CS- GIDP

  Games TP TP/G
Lou Gehrig 2164 9661 4.46
Jimmie Foxx 2317 8715 3.76
Jeff Bagwell 2150 7005 3.26
Frank Thomas 2322 7801 3.36
Eddie Murray 3026 8510 2.81
Jim Thome 2543 6982 2.75
Willie McCovey 2588 6626 2.56
Harmon Killebrew 2435 6628 2.72
Hank Greenberg 1394 5436 3.90
Tony Perez 2777 6262 2.30
George Sisler 2055 6726 3.27

These numbers confirm what we already saw from the sabermetric numbers, but they reveal them in another way. Gehrig comes out on top in total points and total points per game. Perez comes out lacking again. Naturally, someone has to be the worst of the group, so it doesn’t mean he doesn’t belong in yet, but it will be interesting to see once we start comparing him with the guys on the outside looking in.

First Base Fielding

When we look at fielding at any position we must differentiate between terms like greatness and value. Even the most discerning fans often interchange those terms as if they mean the same thing. Greatness is an esoteric term that often gets debated at sports bars and on television shows on the MLB Network and ESPN. We can look at a number like the index because all three platforms use like terms. Unfortunately, the fielding numbers compare players with the replacement level player and the average player. Putting those together is problematic.

Even the same platform can treat players differently depending on the metric being used. For instance, total zones appears in multiple platforms, but is primarily used by baseball-reference. It compares players to the average fielder at their position. It makes it easy to understand with zero being average. Their dWAR statistic is intriguing to say the least. It compares players with the replacement level performer, but it doesn’t compare them with the replacement level first baseman. It compares them with an overall replacement level player and since first base is the least valuable defensive position, most first basemen are automatically worse than a replacement level performer at another position.

So, we treat the data like ordinal data. The idea is to see if the various sources agree on who the best fielder is and who the worst fielder is. So, we cannot combine the numbers and get any real idea from them. We just show them all to see if the various sources agree on the value of the fielder.

  Innings TZ DWS DWAR DWS/1000
Lou Gehrig 18831   2 33.0   -8.9 1.75
Jimmie Foxx 16775 21 35.6   -5.5 2.12
Jeff Bagwell 18523 46 32.4   -7.9 1.75
Frank Thomas   8383 -68   6.5 -23.4 0.78
Eddie Murray 21151   -3 36.9 -12.8 1.74
Jim Thome   9539 -24 15.1 -17.2 1.58
Willie McCovey 16718 -65 23.6 -21.8 1.41
Harmon Killebrew   7810   -6 14.0 -18.8 1.79
Hank Greenberg   9999  21 20.5   -4.3 2.05
Tony Perez 14366  16 24.2   -6.9 1.68
George Sisler 17441    6 23.8   -7.6 1.36

We include the innings because the difference between some of these players in terms of productivity is tremendous. The win shares per 1000 innings has a way of evening those numbers out. This is particularly true when looking at the value numbers. Win shares compares with the replacement level first baseman (unlike total zone runs). Before we look at the year by year data we can get a better idea of where these guys are if we look at the rankings in each category. If we can get universal agreement then we have a good idea of what value each player’s fielding adds to their overall value.

Lou Gehrig 6 3 6 4   4.8
Jimmie Foxx 3 2 2 1   2.0
Jeff Bagwell 1 4 5 5   3.8
Frank Thomas 11 11 11 11 11.0
Eddie Murray 7 1 7 6   5.3
Jim Thome 9 9 8 8   8.5
Willie McCovey 10 8 10 9   9.3
Harmon Killebrew 8 10 9 3   7.5
Hank Greenberg 2 9 1 2   4.5
Tony Perez 4 6 3 7   5.0
George Sisler 5 7 4 10   6.5

Here is where the rubber meets the road. Recently, I got into a long drawn out discussion on social media given the need for complex metrics and data. As the usual argument goes, we don’t need complex statistics to tell us who the best player is. Of course, this is true to a certain extent, but it ignores a few things and is certainly not true all the time. More often than not, it is simply an overreaction to developments either we don’t understand or don’t have much need for. We see it in other walks of life and all of us are guilty of that thinking to one extent or another.

Those that argue against the use of statistics often use statistics to argue their point. They use batting average, home runs, RBI, and runs scored. They use fielding percentage, errors, putouts, and assists in fielding. It’s incredibly ironic for anyone to use statistics to argue against the use of statistics. Now, we certainly can argue as to which statistics are the most descriptive and accurate, but we should at least be honest about what we are talking about.

That being said, we don’t need statistics to tell us Jeff Bagwell was a better defensive first baseman than Frank Thomas. You could look at them with a glove in their hand and decipher that much. However, looking at them doesn’t answer the question of how much better one is than the other. What is the relative or precise value of Bagwell’s glove in comparison with Thomas? How does that interact with their overall value? Then, we get to more complex questions like how fielding value compares at positions like first base versus say a shortstop or catcher. Is a team better off with a Thomas type or should they go after a Keith Hernandez type?

Whether this kind of data interests the common fan is certainly debatable. No one really needs to know what someone’s secondary average is to really enjoy the game. However, teams increasingly need to be on the leading edge on data to put together the best rosters and determine how much each player should be paid and whether they should be invested in long-term. So, those of us that enjoy looking at data enjoy looking at it because we want to know not only who was the best, but how much better were than they someone else.

Lou Gehrig 4
Jimmie Foxx 5
Jeff Bagwell 0 1 1
Frank Thomas 0 0 0
Eddie Murray 1 5 3
Jim Thome 0 0 0
Willie McCovey 0 0 0
Harmon Killebrew 0 1 0
Hank Greenberg 2
Tony Perez 1 0 0
George Sisler 1

When we look at awards breakdowns like these we usually see a discrepancy between the total zone and win share awards and the Gold Gloves. Gold Gloves are voted in by the coaches. Rafael Palmeiro won once after a season where he played over 100 games at DH. This isn’t to say that the coaches are always wrong, but given the fact that they may see an opposing fielder a maximum of 19 times during a season, we can’t really take their suggestions all that seriously. It’s not that they can’t identify a good fielder. They know more about baseball than most of us. The problem is a lack of evidence. So, their voting is largely based on reputations that may or may not be deserved.

The problem here is value at first base is overwhelmingly proportioned on the offensive end. The best fielders are rarely ever the most valuable unless they also happen to be great hitters. We don’t see that very often because the skills needed to be a good fielder (quickness, speed) are not present in power hitters. There are notable exceptions, but if you think of the best fielding first basemen in history they usually did not hit for prodigious power. So, while some of us find fielding fascinating and love to see how players are rated with the glove, in terms of value it is not particularly important in finding out which first baseman was the best in history.

Lazy Analysis

Every once in awhile I stumble on to something on the internet that interrupts my train of thought. I’ve been trying to go through the positions in an organized fashion. I posted the first base index earlier in the week and should be moving to fielding shortly. The great thing about having a website instead of a book is that I can respond to things in real time. So, today I’m interrupting the process to respond to something that I read on the interwebs.

It was innocent enough really. Someone posted a link on Facebook to an article they had written about Jimmy Rollins and his chances of getting into the Hall of Fame. I read the article because I am obviously interested in the Hall of Fame and arguments surrounding it. I won’t mention the author or all of the specifics because I don’t believe in snark as a general rule. I highlight the arguments because they are something you see quite often.

The two arguments that came up are similar, but they are enough differences that  we should point them out separately.

  1. In 2007, Rollins won the MVP award with a season where he had 20 triples, 30 home runs, and more than 40 steals. He is the only person in history to produce such a season.

I could call these what my wife calls them (“random ass statistics”) but that would serve to cheapen the argument and add the kind of snark I usually detest. The closest comparison I could make here is the so-called triple double in basketball. If someone scores 12 points, has 11 rebounds, and 11 assists did he have a better game than a player with 28 points, 9 rebounds, and 8 assists?

In the baseball vernacular we could look at that individual season and the shortstops involved. Keep in mind, I’m leaving out WAR and win shares out of this discussion. It’s disingenuous to use a term to justify it’s utility. In other words, we are talking in general about the need for more rigorous analysis. So, Rollins was the only player to put together that particular statistical profile. Unfortunately, that presupposes that each of those events carries the same value. What we have learned is that stolen bases are not all that valuable in comparison with other events. It is much more valuable to steal first base then it is second or third.

Most of you are familiar with OPS. It stands for on base percentage plus slugging percentage. It is said to explain 90 percent of the variance in run production. It is much more descriptive then simply looking at runs scored, RBI, home runs, steals, and triples. Considering his MVP award and high fluting statistical profile we would expect Rollins to be the best shortstop in baseball that season. However, if we look at OPS+ (OPS normalized with home ballpark effects removed and compared to the league average) then we see that is not the case.

  1. Hanley Ramirez: 145
  2. Edgar Renteria: 124
  3. Carlos Guillen: 122
  4. Derek Jeter: 121
  5. Jimmy Rollins: 119

Does this mean he was the fifth best shortstop in baseball in 2017? Of course it doesn’t. We haven’t even looked at fielding and OPS+ doesn’t explain everything. It does explain a whole lot more than that analyst was attempting to explain. In essence, it just means that putting together a list of the number of times a player meets specific statistical markers is an inexact way of arriving at value.

2. Jimmy Rollins had a similar number of hits, runs, RBI, home runs, stolen bases, and Gold Gloves awards as Alan Trammell and Barry Larkin.

I will simply leave Gold Gloves where they are. That requires a whole separate article that we will likely get to someday. That day won’t be today. Suffice it to say that Gold Gloves have about as much to do with identifying fielding greatness as good penmanship has to do with stock car racing. The other comparisons seem compelling and that is why we have to spend more time on it.

Basic statistics have three separate issues that make using them problematic. The most obvious area of bias is one we identified with the first problem. Scouts love to talk about the five tools and love to swoon over guys they call “five tool players.” This is where a guy can hit, hit for power, run, throw, and field. Even ignoring the fact that those tools ignore plate discipline (which might be more important than all of them) we have to recognize that not all of those tools are equally valuable or important. A home run is more valuable than a stolen base. A run scored is more valuable than a hit. So, stacking these numbers next to each other can cloud our judgment as to how valuable a player really is.

The second issue is the issue of place. Where a player produces these numbers can further be divided into two different considerations. First, we have the ballpark the player played in. Coors Field and Petco Park are two very different environments, so we would expect that environment to impact each player differently. You cannot simply stack those counting numbers next to each other without considering how those environments impacted those numbers. Hitting .300 in the Astrodome for instance is far different than hitting .300 in Fenway Park.

The second issue with place is the team that the player played for. Insert any player onto a team like the 1970s Reds, late 1990s Yankees, or 1950s Dodgers will impact their numbers in a very positive way. Do the same for the 1960s Mets, Senators from any period, or the 1930s Phillies and you would see those numbers depressed in comparison with players from the same period that were on good teams. Geography matters. In the instance of Rollins, we have to acknowledge that the Phillies were good throughout that period. Sure, Rollins has a hand in that, but so did Ryan Howard, Chase Utley, Bob Abreu, and others.

This brings us to the bias of time. Time is simple. When did the player play? An average player in 1930 would produce far different numbers than an average player in 1968. This is even if we normalize it for the quality of the team he played on and the ballpark he played in. One cannot compare Alan Trammel, Barry Larkin, and Jimmy Rollins and not account for those three areas of bias. To simply say that they have similar totals in runs scored, RBI, and stolen bases ignores a great deal.

I know I said I would not include WAR or win shares, but I feel the point has been made. What those metrics do is distill the affects of time, place, and the randomness of how much each unit means in terms of helping his team win. They are all included in the secret sauce. Below are the bWAR for each player.

  • Alan Trammell: 70.4
  • Barry Larkin: 70.2
  • Jimmy Rollins: 46.0

I’m not saying that Rollins is not a Hall of Famer. I haven’t done the full analysis on that yet, but it certainly isn’t looking good right now. I find the above a little more compelling than comparing how many bases each stole. In short, this is why something like the index is so valuable. We miss a lot when we only play around with the basic numbers.

Restarting the Index

Some people love to tinker. They tinker with cars in their garage. They tinker by fixing things around the house, or they tinker by building things. Others tinker with a more artistic flare. The women in my life love to sew. Unfortunately, time constraints get in our way and we aren’t able to do the kind of tinkering we would like.

Publishing the index back in 2011 was a difficult process. Writing the book was fairly easy. Statistics have a way of writing the book for you. However, the questions always came up each time I started the writing process: are they the right numbers? The concept of a Hall of Fame Index began a decade before it was published, but it looked far different. The formula kept changing. I’m not even sure if the results were all that different in reality, but they certainly felt different.

The idea of a website became more and more intriguing as I thought about. The concept of the index has changed since the book was published. Sure, some of this can be attributed to the improvement of information in that time, but most of it lies with me. Either my understanding of statistics has improved or my understanding of the game itself has improved. I’m not even sure that’s it.

One of my lasting memories from college was serving on our newspaper’s editorial board. I was the only one that wasn’t a journalism major. The others used to tease me about this and would claim I didn’t know what I was talking about because I wasn’t one of them. The funny thing is that I usually ended up being right. I have no idea whether this was just dumb luck or something else was going on. I’d like to think it was because I had a perspective they didn’t. This story serves to illustrate my relationship with numbers as well.

I’m not a mathematician. I work as a teacher during the day. I help students mostly with writing, but I have been sent to other classes to support the students there. So, mathematical principles often come slower to me. Therefore, my conception of the index continues to change over time. I suppose from the outside looking in it must look like I am indecisive. I tend to focus more in growth than anything else. I love having a website for that reason. It allows me to continue to tinker without having a finite publishing date on my work. My work can continue to grow and the understanding we have of players can evolve with it.

The funny thing about restarting is that it always comes with an abundance of energy. There is new excitement that comes from re-configuring the index. I have always been a big believer in tailoring my conclusions on players after the data has said it’s piece. Many analysts work that backwards. They come up with their conclusions and search for numbers to justify it. There’s no fun in that for me. My analysis of some players has changed over the years because of the changes in data and my methodology. Some would consider that weakness. I consider it to be growth.

So, I welcome everyone to the restarting of the index. Those of you that have followed my work will be surprised to see what opinions change, but most of the time I suspect they will stay the same. Data is data after all and as long as your methods make sense they should produce the same results. Of course, data is only evidence. So, feel free to use the evidence to disagree with me. I would love to hear from you if you do.

via Daily Prompt: Restart

First Base Index

Every time we start a new position we end up rebooting the index. We use the same methodology, but the dividing line between who is in and who is out changes depending on the position. The index was never designed to find a firm dividing line based on a certain number of wins. We look first for gaps in the data and then determine whether there are extenuating circumstances behind those gaps.

So, the first thing we do is look at the BBWAA list of Hall of Fame inductees and see if there are any gaps we can identify. We start with career value and then move on to peak value. Since we have already outlined how this works in earlier articles, we will jump right in and then comment on individual players as we see fit.

Career Value 

  bWAR fWAR WS/5 Total
Lou Gehrig 112.4 116.3 97.8 326.5
Jimmie Foxx 97.4 101.8 87.0 286.2
Jeff Bagwell 79.6 80.2 77.4 237.2
Frank Thomas 73.7 72.0 87.0 232.7
Eddie Murray 68.3 72.0 87.4 227.7
Jim Thome 72.9 69.0 76.6 218.5
Willie McCovey 64.4 67.4 81.6 213.4
Harmon Killebrew 60.4 66.1 74.2 200.7
Tony Perez 53.9 58.9 69.8 182.6
Hank Greenberg 57.5 61.1 53.4 172.0
George Sisler 57.0 51.9 58.4 167.3

We can look at different data points, but it makes most sense to set the dividing line at 200 wins. Three players fall below that point and their stories are all very different. In some cases, we can easily give them a pass and ignore their shortcomings. In other cases we need to look at them a lot more closely. Of course, in practical terms we can’t remove anyone from the Hall of Fame and that would be repugnant to even consider. However, we can consider whether they should be included in the standard moving forward.

This past week, I had the privilege of talking with my local SABR chapter about the index. The issues surrounding the three players on the bottom came up. I love their perspective. None of them are sabermetrically bent, but they all have a keen understanding of the history of the game and their experience and perspective was invaluable. In short, they offered ideas behind some the extenuating circumstances involving specific players.

Hank Greenberg’s case is easiest to explain. He missed three full seasons serving his country in World War II, but he also missed most of the 1941 season due to injury and a part of the 1945 season in service to his country. Those were three prime seasons that would have not only added to his career value (likely in the neighborhood of 40 to 50 index wins) but he also missed out on some peak value as well. As it stood, he won two MVP awards and arguably was the best first baseman in baseball following Lou Gehrig’s retirement. Give him at least those three seasons and he is easily in the middle of the above group.

Tony Perez is a different story altogether. He is a polarizing force amongst those that love the Hall of Fame. For some, he was the glue that held the Big Red Machine together. That was further buoyed by his presence on the Phillies in 1983 when they advanced to the World Series and the fact that the Reds couldn’t seem to advance once he left is further evidence. Of course, that all might be a coincidence or explained through other means.

I tend to be a pretty evidence-based guy. So, let’s look at the evidence. Usually, we would look at the success the Reds have and assume he was integral to that success. In the 1975 and 1976 playoffs (when they won back to back championships) Perez went a collective .258 with four home runs and 17 RBI. That’s pretty impressive production in a combined 17 games. In 1970 and 1972 the Reds advanced to the World Series and lost. He went a combined .221 in those games. He hit only two home runs in those games with eight RBI. So, maybe there is something to that clutch business in terms of his success when the Reds won.

The whole idea behind the definition of clutch is that players somehow play bigger when the chips are down and their team needs them the most. Baseball-reference and other sites have managed to break down performance between low, medium, and high leveraged situations. They do this because sometimes players do perform better under pressure. Sometimes they don’t. Sometimes our memories of players are spot on. Often they aren’t. The people at that meeting seemed to believe Perez was a clutch hitter.

Low .270 .331 .456 149 349 406
Medium .277 .340 .455 145 410 532
High .300 .359 .491 85 508 709

So, chalk one up for those with long memories. Perez really was better when the chips were down and that is one of the metrics that Bill James talked about in his criticism of WAR. When you produce can be very important and the assumption that it all works out in the end can be overly simplistic. Talking about things like chemistry can be overly simplistic, but when a team is looking at the difference between 90 and 95 wins, things like this can be really huge.

On the flip side, George Sisler’s tale is actually fairly common in the history of the game. His career was relatively short, but he had some really brilliant seasons thrown in there. A member asked if anyone that had hit over .400 and not been elected to the Hall of Fame. Fred Dunlap hit .412 in 1884 and led the league in nearly every statistical category. The fact that his name didn’t roll right off the tongue is a testament to his point. Sisler had two such seasons where he virtually dominated the sport. In 1920, he hit .407 and set the modern record for hits in a season with 257. If it weren’t for Babe Ruth and his 54 home runs he would have been the story of baseball.

He was arguably better in 1922 when he hit .420, led the league in steals and triples, and won the Chalmer’s Award for the league’s best player. Yet, what happened to Sisler has happened to so many. He had a debilitating eye condition that caused him to miss the 1923 season. He came back after that, but he was never quite the same. He never had an OPS above .851 in any season following his return. That was after having six straight seasons with an OPS of .843 or higher.

Couple that dip in production with the explosion of production throughout baseball and he really didn’t add a ton of value following his eye injury. These things happen. Every fan has their favorite example of that player that looked like he was on his way to stardom and just didn’t make it. Astros fans have more than their fair share. He had only 6.6 bWAR after that eye injury and he enjoyed more than that in 1920 and 1922 alone. Heck, just one or two other seasons would have made this kind of treatment academic.

Of course, this is why we included a peak value element. This is where there is some separation in the industry. A popular method called JAWS used primarily bWAR and uses a seven-year prime. If you use that kind of system then Sisler comes out looking a lot better. Let’s see what happens with the ten year peak.

Peak Value 

  bWAR fWAR WS/5 Total
Lou Gehrig 90.4 92.5 76.0 258.9
Jimmie Foxx 77.8 78.0 63.6 229.4
Jeff Bagwell 63.0 64.1 57.6 184.7
Frank Thomas 56.4 57.3 58.8 172.5
Jim Thome 52.3 51.9 51.2 155.4
Hank Greenberg 51.5 55.1 47.8 154.4
Harmon Killebrew 47.5 51.4 53.4 152.3
Willie McCovey 49.3 50.6 51.2 151.1
Eddie Murray 49.7 49.2 51.4 150.3
George Sisler 49.9 49.0 46.0 144.9
Tony Perez 45.6 48.1 49.6 143.3

After Thomas we see a very tight distribution. That tends to lend credence to the idea that all of these guys deserve to be in the Hall of Fame. However, we do see some slight differences as compared to the career value. Greenberg for one shoots up the list and that is considering the fact that he lost three prime seasons serving his country. We could conservatively guess that his peak value would have approached Thomas with those three seasons uninterrupted.

This leaves Perez and Sisler on the bottom of the list in both categories. So, your opinion on them depends largely on how you view the Hall of Fame itself. Is it a spot for only the very best of the best or is it a museum that celebrates the game’s history? Both have points in their favor and both have points that go against them. Sisler did not enjoy the same length of success as those above him. Perez enjoyed playing with guys like Pete Rose, Joe Morgan, and Johnny Bench. All three are among the top ten players in history at their position. Depending on the position, all could be in the top five. So, were they that good because of Perez or was Perez made better because of those three? Logic would clearly point to the latter, but as we pointed out, he was a clutch performer according to the numbers, so he can claim to be better than what the index indicates.

Hall of Fame Index 

  Career Peak Index
Lou Gehrig 326.5 258.9 585.4
Jimmie Foxx 286.2 229.4 515.6
Jeff Bagwell 234.2 184.7 418.9
Frank Thomas 232.7 172.5 405.2
Eddie Murray 227.7 150.3 378.0
Jim Thome 218.5 155.4 373.9
Willie McCovey 213.4 151.1 364.5
Harmon Killebrew 200.7 152.3 353.0
Hank Greenberg 172.0 154.4 326.4
Tony Perez 182.6 143.3 325.9
George Sisler 167.3 144.9 312.2

The index works because it preserves the best thing about the Hall of Fame: the presence of debate. Some people see changes of opinion as weakness. I don’t. It means we allow new information or refined arguments to sway our thinking when necessary. Often, the competing views of tangibles vs. intangibles is not necessarily competing. Sometimes, intangibles are just things we were unable to define in the past. Clutch hitting was much that way. It still isn’t perfect, but we have a better sense of what that is.

The same is true of fielding. Of course, we will look at fielding in more detail in another article. Both WAR and win shares have come under fire because their fielding values are not nearly as defined as the offensive ones. At any rate, we will see definite outliers at other positions that make any ones at first base seem tame by comparison.

Active Catchers

We can get carried away when looking at current players and the Hall of Fame. At the catcher position we can say that after two seasons, Gary Sanchez looks like a Hall of Famer. The history of baseball is littered with guys that got off to great starts and for one reason or another (usually injuries) just didn’t sustain it. So, the first rule we follow is that every player must qualify for the Hall of Fame as they currently sit. In other words, they have to have played at least ten years in the big leagues.

That eliminates one very prominent catcher. Buster Posey is the best in the business right now, but he doesn’t have ten seasons in. That might seem like a formality, but we have seen crazy situations before. So, at every position there will be omissions that seem glaring, but we want to profile players that are closer to the end than to the beginning. Secondly, we try to consider players that have a realistic chance for the Hall of Fame. So, fans of Kurt Suzuki and A.J. Pierzynski will have to go somewhere else.

We should keep in mind that the index is not about specific numbers, but we have found that 300 index wins seems to be the general benchmark. So, keeping that in mind we should go ahead and dive on in. Since this is an abbreviated list we will also include the fielding and hitting metrics after we look at the overall index.

  bWAR fWAR WS/5 Total
Joe Mauer 53.4 48.1 58.0 159.4
Yadier Molina 35.4 35.4 48.6 119.4
Russell Martin 36.5 36.6 36.8 109.9
Brian McCann 30.2 36.6 42.0 108.8

There might be a bit of a controversy in that Mauer has been at first base for several seasons now. However, each player is compared with the replacement level player at his position. So, Mauer is now compared to first basemen. So, it is more difficult for him to accrue value offensively and defensively. In other words, while he might accrue more counting statistics than his catching counterparts he is not really accruing additional value.

All four catchers (or players) are in their early to mid-thirties, so each is a lot closer to the end than to the beginning. Reputations obviously go a long way in helping guys in their Hall of Fame bids. Mauer may not have the same cache as Molina because of Molina’s defensive prowess (which we will get to later) but he has more value. Incidentally though, Molina is the only one that could still conceivably add to his peak value because he is in the midst of a strong ten year stretch.

  bWAR fWAR WS/5 Total
Joe Mauer 44.8 43.5 46.8 135.1
Yadier Molina 31.8 29.2 40.6 101.6
Brian McCann 27.8 33.2 37.0 98.0
Russell Martin 33.3 32.9 32.6 97.8

This is where Mauer truly shines. He is the only one of the bunch that has an MVP award and in fact did better across the board in the awards voting. This is important because the BBWAA also votes for the Hall of Fame. It is clear that the other three are coming up short at this point in their careers. If each had two or three more five win seasons they may somehow force their way into the conversation, but that doesn’t seem likely at this point.

  MVP Top 5 Top 10 Top 25
Joe Mauer 1 1 2 1
Yadier Molina 0 2 0 3
Russell Martin 0 0 0 3
Brian McCann 0 0 0 2

Before we take a look at the total index we should probably go here. The BBWAA votes for the MVP award. The BBWAA votes for the Hall of Fame. Anyone see the connection? We could break this down further and see how well the BBWAA pegs the MVP vote according to WAR or win shares, but it really doesn’t matter. This is where they perceive the catchers to be, so we can assert without stretching that they just don’t see Russell Martin and Brian McCann as Hall of Fame quality. Sure, they aren’t done and anything could happen, but it just doesn’t seem likely.

On the flip side, Yadier Molina is in a murky area at this point in his career. He signed a new three-year contract that he says will be his last. If he continues to produce in the last three seasons like he has over the past decade he could have a compelling case. This is particularly true when we start breaking down performance between offense and fielding. Voters often ignore overall value and focus on its parts even when those parts contribute to the overall value. In other words, they love to count fielding twice when it suits them. Before we break into fielding, let’s take a look at the overall index scores.

  Career Peak Index
Joe Mauer 159.4 135.1 294.5
Yadier Molina 119.4 101.6 221.0
Russell Martin 109.9 97.8 207.7
Brian McCann 108.8 98.0 206.8

It should be noted that while Mauer has already eclipsed Gabby Hartnett with his current index score, it does not mean he is definitely a Hall of Famer. He could produce a few more seasons like has the last few and still not get in. He is in what we might call the borderline zone. We have no way of knowing how the BBWAA will react to his career given his switch to first base. It is highly likely that if he plays three or four more seasons at first base then they might see him as a first baseman rather than a catcher.

The others are clearly on the outside looking in. There are a number of catchers living in the zone where those catchers currently are. Some of them enjoyed long careers and were on good teams to boot. So, while we could give Brian McCann and Yadier Molina some extra credit for being on World Series championship teams, that shouldn’t be enough to get them over the top. Of course, other factors will be in play like they are for Molina. So, let’s take a look at the various sources for fielding with defensive runs saved (DRS) thrown in for good measure.

Defensive runs saved is a new one we haven’t used before. That is primarily because it began in the early 2000s (The Fielding Bible), so most of the historical catchers were never rated. Much like baseball-reference’s RTot, it rates players against the average. So, zero would actually mean you were an average fielding catcher. Of course, dWAR and defensive win shares are rated against a replacement level player. So, they are scaled differently. Therefore, we take each metric on its own. Combining them would create all kinds of mathematical issues I don’t have the training to get out of.

Yadier Molina 171 22.1 141 113.7 5 8
Russell Martin 117 14.9 10 66.6 3 1
Joe Mauer 32 2.3 45 65.4 0 3
Brian McCann 22 5.5 -8 63.3 0 0

Let’s start with the obvious. Anyone that bases any analysis on the number of actual Gold Gloves a player wins is a fool. Are we really to believe that Mauer was a better defensive catcher than Martin is because he won two more Gold Gloves? The numbers are a bit across the board and that is due to pitch framing. Some platforms consider it and others don’t. Those that do have different weights for it.

We have not considered Baseball Prospectus for a number of reasons. The primary one is that it does not rate players before World War II according to their WARP (wins above replacement player) statistic. However, they are further along the pitch framing timeline than most of the others and this has thrown McCann’s value up a ton. We don’t see that here.

However, the most interesting thing here is that conflation between value and greatness. Value is built in. Molina is not more valuable than has already been shown because he was such a valuable fielder. Yet, there are those that will consider the greatness and give him extra credit. They are certainly entitled to their opinions and those opinions are not completely out of whack given the data above. The trouble is that players should be evaluated on their ability to help their team win games. We could break that down to producing and preventing runs, but it all winds up in the same place. If we focus on one or the other (hitting or fielding) then we are splitting hairs on exactly how a player helped his team win. That’s cosmetic. As you will see with our hitting information, when Molina gives in one hand, we take away in the other.

Joe Mauer 126 .361 124 .631 .795
Brian McCann 113 .343 112 .558 .740
Russell Martin 102 .333 106 .503 .696
Yadier Molina 98 .323 100 .483 .628

We chronicled what each of these metrics mean in an earlier post about catcher offense. Most of these scores are scaled against a league average. OPS+ and wRC+ are scaled with 100 being average while offensive winning percentage is scaled with .500 being average. wOBA is also scaled, but the average tends to vary with the times. Suffice it to say, Molina is somewhere around average offensively depending on the metric.

It should be noted that average has value. If you put together an average team they will win around 80 games. That’s considerably better than a team full of replacement level players. My hometown Houston Astros were essentially a team of replacement level players at the beginning of the decade. It wasn’t pretty. So, we shouldn’t scoff at being average.

Secondly, when we say average we are talking about the big-league universe. We aren’t talking about the catcher universe. If Molina is average as a big-league player then he is above average offensively for a catcher. So, combine superlative fielding with above average (for the position) offensive performance and you have a very good player. The long and short of it is that I really don’t feel the need to give him more credit for the fielding.

Martin finds himself in a similar circumstance, but his fielding numbers don’t immediately jump off the page. So, he is a little better with the bat and somewhat less valuable with the glove. The end result is that he is not as good as Molina. McCann ends up on the other part of the scale where his fielding is not quite as good as the others and his offense is just a little better. Finally, you get Mauer with his seemingly awesome offense. In reality, that offense most approximates those that are already in the Hall of Fame.

The last thing we will look at are the total points for each of these players. No, total points are not a scientific Hall of Fame tool. It’s something gamblers and fantasy baseball players know about and care about. That being said, it’s interesting and could reveal something we didn’t see before.

Total Points = TB + Runs + RBI + SB + BB + HBP – SO – CS – GIDP 

  Games TP TP/G
Joe Mauer 1731 3883 2.24
Brian McCann 1607 3754 2.34
Yadier Molina 1747 3491 2.00
Russell Martin 1520 3161 2.08

So, does this mean that McCann is a better hitter than Mauer? I really don’t know how much weight you can put in this. Keep in mind this isn’t scaled for home ballparks and some of the data is dependent on the quality of teammates around each player. Also, it presupposes that strikeouts are a negative event. There certainly isn’t universal agreement on that amongst statisticians in the game.

What was interesting last season, is that the Astros limited McCann’s exposure more than the Yankees or Braves had and he produced more total points per game than he had in the previous five seasons. It is always interesting to see how different data sources follow each other. Teams have certainly have relied more on data in recent seasons and simultaneously, the proliferation of gambling in the fantasy world has sparked a need for more data. Often these worlds run parallel to each other, but occasionally we see an intersection. While teams may not use total points per se, they certainly probably borrow some of the concepts. Then again, it might be the daily fantasy sports industry borrowing concepts from the teams.

The Curious Case of Roy Campanella

You can dance around the subject in a number of ways. Roy Campanella did not make his major league debut until he was 28 years old. This wasn’t because he wasn’t good enough. You could credibly argue there was only one catcher in the time period that could hold a candle to Campanella. 28 for catchers is virtually ancient. Catchers reach their peaks a lot earlier than other position players and their peaks don’t last nearly as long. By the time most reach their early thirties they are virtually done.

There are notable exceptions of course and luckily for Campanella he was one. His career lasted ten seasons, but you could see the affects of age on him as well. In three out of his last four seasons he failed to reach one bWAR, 1.5 fWAR, or 2.5 win shares (after the adjustment). He won the NL MVP in 1955, but most people would acknowledge that he really wasn’t the best player in the league that season.

Those three MVP awards serve to cloud his place in the history of the game. Historians correctly assume he would have achieved a lot more had he been called up at the same time as most catchers. When we look at the average of the other Hall of Fame catchers we find that he lost five seasons to racism. We could go wild in our assertions of what he would have done, but a more conservative approach is probably best. So, what we will do is take his first five seasons and assume those would have been replicated in the previous five. However, we will adjust his 1948 numbers because they are artificially low being his first season in the big leagues. He did not play immediately, so he suffered in value. We will instead take the worst of his first four full seasons and assume that level of production.

  bWAR fWAR WS/5
1943 1.7 1.6 2.4
1944 4.4 4.3 4.8
1945 4.1 4.4 4.4
1946 6.7 7.1 6.6
1947 3.7 4.2 4.4
1948 3.7 4.2 4.4
1949 4.4 4.3 4.8
1950 4.1 4.4 4.4
1951 6.7 7.1 6.6
1952 3.7 4.2 4.4
1953 7.1 7.7 6.6
1954 0.1 0.7 2.0
1955 5.3 5.7 5.8
1956 0.6 1.2 2.4
1957 0.7 1.2 2.2
Total 57.1 62.3 66.2

This is pretty simple. We can take this career total and compare it to the other catchers in terms of career value. The total adds up to 185.6. That places him behind Johnny Bench, Yogi Berra, Gary Carter, Ivan Rodriguez, and Carlton Fisk in terms of career value. It places him in the same neighborhood as Mike Piazza. It places him a little in front of Mickey Cohrane, Bill Dickey, and Gabby Hartnett.

I think there are good reasons for that beyond what we see above. There are reasons why he never achieved huge value numbers in any one season. Simply put, the running game throughout the 1920s-1950s were not the same as the 1960s and 1970s. That’s a primary reason why those four catchers find themselves at the bottom. Berra enjoyed a longer peak than the rest or he would have suffered the same fate.

This brings us to the downside of Campanella’s legacy. He was seen at the time as the key cog in the pennant winning teams in 1951, 1953, and 1955. Even if we allow for the biases of the MVP voting at the time (you had to be on the winning team) we would discover those awards came in vain. Win share rankings are easy to track, so let’s track the seasons he actually did play and track his ranking amongst his teammates and the league.

  WS Actual Team LG
1948 12 21 9
1949 24 15 3 8
1950 22 13 3 16
1951 33 1 2 4
1952 22 10 5 17
1953 33 1 2 4
1954 10 11
1955 28 1 2 7
1956 12 11
1957 11 11

Given the relative lack of defensive value of any catcher at the time, the fact that he would have finished in the top ten four times is something. I’m sure if we went back and looked at any of the catchers we would struggle to find any actual MVP awards deserved based on value statistics like WAR and win shares. This has nothing to do with catchers per se. They just have a difficult time matching value with other position players that play far more often during the season.

Even a durable catcher will only play 130 to 140 games in a season where outfielders and first basemen play the full 154 or 162 games. We certainly could boil it down to a wins per game kind of metric and go from there, but that seems to intricate to pick out a player that is the most valuable player in the league.

While we did rain on Campanella’s parade in terms of peak value, the numbers do help him here as compared to his actual ten seasons. The new peak would run from 1944 to 1953 when most catchers would experience their peak performance. The new peak value adds up to 153 wins on the nose. That obviously was a huge boost over his past peak value. Three of those four final seasons killed him in terms of value. It’s something we see from most of the catchers, but he just didn’t have the luxury of a phase out.

  Career Peak Total
Johnny Bench 221.0 174.9 395.9
Gary Carter 206.7 172.2 378.9
Mike Piazza 187.9 167.7 355.6
Yogi Berra 198.2 153.9 352.2
Ivan Rodriguez 204.9 145.0 349.9
Roy Campanella 185.6 153.0 338.6
Carlton Fisk 210.2 120.1 330.2
Bill Dickey 174.7 131.1 305.8
Mickey Cochrane 157.7 142.0 299.7
Gabby Hartnett 172.6 112.0 284.6

All in all these results seem reasonable enough. I think they put Campanella in a historical context that makes sense. We could go overboard and assume he would have produced nine and ten win seasons, but that would be horribly unrealistic. We have to take the player he was an extrapolate that outward. Of course, this is a guess. We cannot assume seasons he did not produce, so we cannot assume he would have been more valuable than Carlton Fisk or not as valuable as Ivan Rodriguez. Still, this seems like a comfortable place to put him when we make some common sense adjustments.

Catcher Offense

One of the frustrating things about looking at advanced metrics is that most of us have a difficult time deciphering what is the in the secret sauce. I talked to a former writer at Baseball Prospectus once. He described the phenomenon very uniquely. He said there were sharp knives and dull knives. The dull knives can use the data and sometimes explain the data to other dull knives, but the sharp knives were the ones that manipulated data. One of the things I can do in this space is break down different numbers as best I can, so you can digest them.

We’ve talked about catcher fielding and there are few things more exciting. We are finding out more and more about pitch framing. So much of what catchers do is wrapped up in that. So, fielding value is likely to be pretty fluid. Conversely, we have a very good handle on offense. We can compare guys with their contemporaries and throughout history. Below are five metrics we will use throughout our look at various players as long as this website is up.

OPS+- OPS stands for on base percentage plus slugging percentage. It is crude, but it is said to explain 90 percent of the variance in what hitters produce. OPS+ compares players with the league average after you distill out the effects of their home ballpark and the league norms that particular season (or length of career). 100 is considered average. Anything over 100 is above average.

wOBA—This stands for weighted on base average. It measures a player’s overall accomplishments per plate appearances and expresses it like an on base percentage. The average varies over time, but usually .330 is a benchmark.

wRC+— This stands for weighted runs created plus. It takes a player’s runs created and measures that against their home ballpark and the era they play in. It then calculates to be against an average of 100, so that it is measured on a per plate appearance basis.

OW%– This stands for offensive winning percentage. It assumes league average offense and league average pitching across the board and creates an offense of only that hitter. The runs created with nine of those players is compared with average runs allowed to create a Pythagorean winning percentage. Obviously a .500 OW% indicates a league average hitter.

BPO—This stands for bases per out. It is calculated by adding total bases with walks and hit by pitches and then divided by outs made. Outs are the life blood of the sport, so the more bases you can produce per out the better offensive player you are.

One of the things we should mention is that there Is a consistent occurrence we see across the board is that the standard deviation is dropping as we advance through history. Some of this is natural given the expansion to 30 teams from 16. Some of this happens through improved scouting and a league wide approach to data. This doesn’t have as much of an effect on statistics like WAR and win shares because those compare to the replacement level player in that era and that’s a sliding scale. The numbers above are more stagnant, so while they do adjust for a player’s era, the earlier players will have a bit of a mathematical advantage.

Mike Piazza 142 .390 140 .669 .897
Mickey Cochrane 129 .413 132 .672 .904
Bill Dickey 127 .396 126 .651 .845
Johnny Bench 126 .362 125 .627 .765
Gabby Hartnett 126 .389 127 .646 .818
Yogi Berra 125 .370 124 .631 .781
Roy Campanella 123 .385 123 .636 .820
Carlton Fisk 117 .354 117 .595 .738
Gary Carter 115 .342 116 .581 .708
Ivan Rodriguez 106 .344 104 .507 .692

You can split these metrics into two categories. There are those were players are more directly compared to players from their own time and there are those where they are compared to all-time. OPS+ and wRC+ are all-time statistics. They are extremely similar except wRC+ includes base running, so it might be a little more accurate. The other metrics more directly compare players with their own time. Cochrane caught during the Live Ball Era when offensive numbers were outrageous. So, his wOBA, OW%, and BPO might be higher than Mike Piazza, but comparatively Piazza was probably better.

I think the big take away though is that Roy Campanella belongs in this group when you start looking at him on a per plate appearance basis. He is fourth in bases per out, fifth in offensive winning percentage, seventh in wRC+, fifth in wOBA, and seventh in OPS+. So, he finishes around the middle of the pack in all of the categories.

We could stop here, but one of the new waves of the fantasy game is daily fantasy sports. It has become a billion dollar plus industry that has even involved government as they decide whether to consider it gambling or not. The idea is that you pick a team with a cap of money and those players compile points for that day. So, instead of a standard five categories like most fantasy leagues, each event is awarded positive or negative points. So, we will show off our own historical example to see which catcher would have been the best daily fantasy baseball player of all-time. Below is the formula we will use.

Total Points = Total Bases + Runs + RBI + SB + BB + HBP – SO – GIDP – CS

To make matters a little easier, we will combine stolen bases, walks, and hit by pitches into a combined positive category. We will combine strikeouts, grounded into double plays, and caught stealing into a negative category. In order to account for differences in career length we will also look at their total points accrued per game.

  Games TP TP/G
Mike Piazza 1912 6595 3.45
Yogi Berra 2120 6448 3.04
Carlton Fisk 2499 6076 2.43
Ivan Rodriguez 2543 5960 2.34
Johnny Bench 2158 5564 2.58
Bill Dickey 1789 5496 3.07
Gary Carter 2296 5483 2.39
Gabby Hartnett 1990 5074 2.55
Mickey Cochrane 1482 4929 3.33
Roy Campanella 1215 3513 2.89

So, while these numbers don’t carry the same weight as the others, they are interesting. Some problems always arise. MLB didn’t always keep official records of statistics like grounded into double plays, so we had to estimate for guys like Cochrane, Dickey, and Hartnett. That beings said, the inclusion of numbers like strikeouts made this a little less predictable than the rest of the numbers.

There can be little doubt that Mike Piazza was the most valuable offensive catcher of all-time, but we should take a minute to acknowledge the greatness that was Mickey Cochrane. While his career was relatively short and his defensive value was not stellar, there might not have been a better hitter at the position in the history of the game.

On the Outside Looking In

The Hall of Fame voting process is ripe with bias and the combined cases of Joe Torre and Ted Simmons represent those biases well. In general, the voters don’t tend to like catchers that move out from behind the dish before the end of their careers. Torre played both third and first base in addition to catcher and enjoyed his best season as a third baseman. So, some want to consider him a third baseman or first baseman. Simmons became a designated hitter late in his career as well.

The natural assumption for both players is that they were moved because they were subpar defensive players. As we know, WAR includes both offensive and defensive elements, so if you are a poor defender you will suffer in the index. It is just as likely that the teams that moved them wanted to take advantage of their bat and moving them from behind the dish allowed them to play more often. We can look at their three systems outlooks on their fielding to test this theory out. In order to do that, we will look only at their numbers as a catcher.

  INN RField UZR FRAA Total
Joe Torre   7432 7 7 5.7 19.7
Ted Simmons 15092 -8 -8 -36.9 -52.9

This is a tale of two cities. Torre was actually a solid defensive catcher. He wasn’t going to make anyone forget about Johnny Bench or Gary Carter, but he was a perfectly good defensive catcher. When he moved to third base he was brutal defensively. So, you could conceivably look at his overall numbers and assume he was a bad defensive catcher. Ultimately, you are responsible for the value you bring to the table, but he would have likely been more valuable had he stayed behind the plate.

Conversely, Simmons wasn’t necessarily a disaster behind the dish, but he wasn’t one of the better catchers either. He also played some at first base and first basemen don’t create the same defensive value as catchers even in the best of conditions. Either way, the facts don’t necessarily match the reputations in both cases.

So, we begin by looking at their career value numbers and comparing that with the lowest guys in the Hall of Fame. Usually, people start using what we might refer to as the “if…then” argument. For instance, if Gabby Hartnett is in the Hall of Fame then these guys should be. That works most of the time, but occasionally you get outliers that only serve to muddy the process.

  bWAR fWAR WS/5 Total
Joe Torre 57.6 62.3 63.0 182.9
Ted Simmons 50.1 54.2 63.0 167.3

Both players are on the right track with these numbers. They both are in the same neighborhood as those other catchers already in the Hall of Fame. We would hate to look at just the index, so we should probably evaluate them on their bread and butter. Offensively, both players were solid and had top reputations. So, let’s compare them directly with the bottom three catchers in the Hall of Fame.

Mickey Cochrane .413 .897 129 1023 132
Bill Dickey .396 .868 127 1164 126
Gabby Hartnett .389 .859 126 1161 127
Ted Simmons .347 .785 118 1283 116
Joe Torre .364 .817 129 1259 129

These things usually become like an SAT exam question. Which one of these does not belong? If you answered Ted Simmons you would be partially right. Yes, his wOBA is much lower and his OPS and OPS+ are lower, but we also have to remember the eras in which these players played. Still, you could use this data as a way to say yes to Torre and no to Simmons. However, to keep both out seems far-fetched when looking at this data alone.

That being said, there is an argument to be made based on the totality of the numbers. Even if we ignore the basic counting numbers, we see that he had more runs created than any of the other catchers on that particular list. Still, this is one of many reasons why we include a peak value element. Not all career value totals are created equal. We have to give the nod to players who achieve some level of greatness in their careers over players that were consistently good, but never great. We can see that by looking at his peak from the point of view of the MVP voters.

  bWAR Rank MVP
1971 3.3 N/A 16
1972 4.5 N/A 10
1973 5.5 8 14
1974 3.6 N/A 13
1975 4.9 10 6
1976 3.4 N/A N/A
1977 5.2 9 9
1978 5.5 N/A N/A
1979 3.6 7 N/A
1980 5.2 7 N/A

It should be noted that Simmons also finished in 19th place in the voting in 1983 when he produced four bWAR that season. There is something to be said for a player that produces three or more wins ten seasons in a row. He had twelve such seasons in his career. Life as a catcher is difficult, so if you can put twelve seasons together like that you’ve done something. If ten of those seasons come consecutively you’ve really done something. However, it isn’t difficult to imagine why he had difficulty capturing the attention of the BBWAA when they never put him in the top five of the MVP voting.

Torre was the MVP in 1971 and had one other top five finish in 1964. While his record doesn’t compare to the likes of the others in the BBWAA, that 1971 season gives him a leg up on Simmons. This is especially true when it comes with a .363 batting average, 230 hits, and 137 RBI. It’s those kinds of seasons that capture the imagination of fans and writers alike. Still, the tale of the tape comes with the peak value numbers.

  bWAR fWAR WS Total
Ted Simmons 44.7 48.4 48.0 141.1
Joe Torre 45.3 49.5 48.0 142.8

I honestly would have never predicted that given the numbers we just looked at, but clearly Simmons was consistently good in comparison with Torre’s occasional greatness. This puts us back into sports bar mode where we have a lengthy debate over whether you would rather have consistent solid production or occasional great production. I suppose if you could predict the great production or couple it with other great production that would be preferable, but life is never that predictable.

When taking a look at the index, both are considerably better than Gabby Hartnett, so your debate comes down to how exclusive you want to make the Hall of Fame. There are those that look at it as a museum and therefore want to include as much as the game’s history as possible. Others want to honor only the very best of the best. I’m sure it doesn’t help when you consider that they generally played at the same time as Johnny Bench, Gary Carter, and Carlton Fisk. There are those that start to feel like an era may be over-represented and that is very prescient when you add in the likes of Thurman Munson and Bill Freehan (who we mentioned in the introduction).

We can blame part of that on expansion. When you move from 16 to 24 (or even 28) teams then you multiply the possibility of having Hall of Fame worthy players at any position. We also can look at the resurgence of the running game and the need for catchers to control that running game. Either way, there are reasons why there were more quality catchers in the era.

  Career Peak Total
Joe Torre 182.9 142.8 325.7
Ted Simmons 167.3 141.1 308.4
Bill Dickey 174.7 131.1 305.8
Mickey Cochrane 157.7 142.0 299.7
Gabby Hartnett 172.6 112.0 284.6

Again, it bears repeating, but this does not mean that Torre and Simmons were better catchers than the other three. It means that their fitness for the Hall of Fame is just as good if not better than the guys already in. Granted, there are reasons to prefer not to put either of them in the Hall of Fame. You could say Torre didn’t spend enough time behind the plate or that Simmons was a subpar defensive catcher. You could certainly claim that Simmons was always good, but never great. The index was never designed to tell people who to vote for. It is a tool that we can use to compare players out of the Hall of Fame with those in the Hall of Fame. If you want to be consistent then you take the two guys and put them in.


Catcher Fielding

One of the things you learn in a basic statistics class is the difference between the kinds of data you can get. While this is rudimentary, it is easy to get them confused when you are looking at fairly complex data. No one really confuses nominal data. These usually take the form of yes or no and tend to be binary in nature. The confusion usually comes when we look at the difference between ordinal data and interval data. This happens when we do something like this. When data isn’t scaled in an identical (or even similar) then the combination becomes ordinal because we simply cannot trust the combination. This is even if each individual source was interval in nature.

Ordinal data is what we use when we want to simply rank one player over another. In our case, we are ranking the ten catchers in the Hall of Fame in terms of fielding. The problem is when we start looking at multiple sources we start looking at wins in some cases and runs in other cases. We are then looking at each one in isolation and standing them next to another that we look at in isolation. When we start combining these we start to get into some serious trouble. This is especially true if we attempt to make any conclusions about the actual value of a player. The differences in value are what we would call interval data. Interval data is the holy grail of data because it not only says who is more valuable than another, but by how much.

When we look at players from different eras and compare their fielding numbers we need to eliminate two words from our vocabulary: greatest and best. Those are conversations best left for the sports bar. We also get into trouble when we start using terms like difficulty and importance. When criticizing fielders, some passionate fans will tend to get defensive and say something like, “well, how much baseball do you play?” or “I’d love to see you go out there and catch.” I’ll admit right now. I never played catcher. When I was a kid I played in the outfield so I am saying nothing about the importance or difficulty of the position. At least I’m not saying it directly.

That being said, the data does say something about the relative importance of the individual skills a catcher must possess. Rating catchers is always more difficult than any other position because they must call the game, block pitches in the dirt, and control the running game of the other team. In the current game we also include pitch framing in that equation. As strategy has changed, the relative importance of all of these skills fluctuates. For instances, controlling the running game was huge throughout the 1960s, 1970s, and 1980s. So, catchers from that period could accrue more value if they were excellent at that particular facet of the game. Conversely, during the live ball era (1920s – early 1940s) hardly anyone stole bases. So, one could be brilliant or not and you wouldn’t see a range of value there.

So, I would have a hard time saying that a Johnny Bench or Gary Carter was a greater fielder than a Gabby Hartnett based on the numbers. Greatness implies that the skills were superior and while that may be true, it is not true based on the numbers. Players are always better understood when they are compared with their own generation. We know more about Hartnett when we compare him with Mickey Cochrane and Bill Dickey then when we compare him with the catchers from the 1970s. Those that saw both could weigh in, but that would be a more qualitative analysis. This becomes increasingly problematic when we include new analysis like pitch framing. The modern catchers become much more understood when we include that, but we can’t include it for the earlier catchers. While we have play by play data going back to the beginning of the century, that play by play data won’t tell us if an individual pitch should have been called a strike or a ball if the catcher were better at framing it.

You undoubtedly did not open this up to hear my caveats about fielding, but it represents the very best reason why I am not comfortable naming a best catcher of all-time. At least, I’m not using the data here to define it. Give me a beer and a good game on television and I’ll throw down on any player, but I’m leaving the charts and graphs at home. We will split the fielding data into two categories. First, we will look at the major career data for our three sources. Keep in mind, since some use wins while other uses runs we will not combine them. Our WAR and WS from the previous article has already embedded that in their totals. Here, we are distilling out the numbers to get a sense as to who was the most valuable fielder of all-time.

Career Fielding 

Johnny Bench 18.7 19.3 97
Yogi Berra 19.9   8.7 33
Roy Campanella 13.7   5.7 17
Gary Carter 24.1 25.5 106
Mickey Cochrane 16.3   4.4 -2
Bill Dickey 19.0   7.6 20
Carlton Fisk 22.4 16.4 30
Gabby Hartnett 21.9   6.6 12
Mike Piazza 12.0   1.0 -25
Ivan Rodriguez 30.1 26.7 135

The above represents my best attempt to normalize the data. The win shares we simply divided by five to make it roughly equal to defensive WAR from baseball-reference. Unfortunately, I struggled to normalize the total zone runs from Fangraphs because it would change the rankings. Typically, we would add ten runs per season to the totals because that would convert from a comparison against the average to a comparison with the replacement level player. In the end, the desire to present the data as those sites/sources wanted ended up winning out.

However, this is where our discussion of ordinal versus interval data comes in. The above numbers still don’t make a whole lot of sense when you put them together because all three sources have considerably different opinions of how much value individual events (or skills) have. Those change between eras as they change between platforms. However, if we show the same table and replace the win totals with their simple rankings in those systems we see something quite remarkable

Fielding Rankings 

Johnny Bench 7 3 3
Yogi Berra 5 5 4
Roy Camanella 9 8 7
Gary Carter 2 2 2
Mickey Cochrane 8 9 9
Bill Dickey 6 6 6
Carlton Fisk 3 4 5
Gabby Hartnett 4 7 8
Mike Piazza 10 10 10
Ivan Rodriguez 1 1 1

I would surmise that we are actually seeing more variance here than at any other position. Different sources treat each individual skill a catcher must possess differently and with the different weights can come wildly different values. Still, only two players are more than two places apart across the board. When win shares is dropped that moves to zero. Four of the players are exactly the same including the most valuable and least valuable.

That being said, this is one of the areas where we have to be careful. Notice how Carlton Fisk drops when compared to the average as opposed to replacement level. He caught for over 20 years, so he managed to accumulate a lot of value. That’s not the same thing as being the greatest or the best, but it is important to note. This is especially true when we start looking at things like Gold Gloves, Win Share awards, and total zone awards. Lacking in those awards does not mean you lack value. It just means you were never the best in any one particular season.

Moreover, this distinction can be seen more at the bottom of the scale than the top. Mike Piazza was universally regarded as a poor defensive catcher (although he rated highly in pitch framing), but depending on the source he still had value when you compared him to the replacement level catcher. This is where we include the remarks of our critics when they say, “I’d like to see you go out there and do it.” Why yes, we aren’t saying any of these guys were a complete buffoon. It’s all comparative in nature and unfortunately you are stacking each up against the very best to play the position. Some aren’t going to come out looking good in every single category.

All that being said, we will finish our journey by looking at individual season honors. We do have some limitations here. Total Zone awards aren’t an official honor, but we only have data going back to 1953. The Gold Glove awards were awarded in both leagues for the first time in 1958. So, some of our players will not have any chance to win anything and others only a limited opportunity. However, win shares awards were added back through all of our players careers. When all things are considered, these are fairly meaningless, but I know many of you will be interested, so we close with this table.

Johnny Bench 10 4 6
Yogi Berra 0 5 3
Roy Campanella 5 1
Gary Carter 3 8 5
Mickey Cochrane 5
Bill Dickey 5
Carlton Fisk 1 0 0
Gabby Hartnett 6
Mike Piazza 0 0 0
Ivan Rodriguez 13 7 9

Win shares is the great equalizer thanks to James’ hard work in breaking down the data for us season by season. When we see how evenly distributed the awards go we can see that greatness is really more evenly distributed than the basic numbers would indicate. It definitely is more evenly distributed than the Gold Gloves themselves. However, we return to our original caveat: greatness and value are two entirely different things. Just because Carlton Fisk won no awards doesn’t mean he has zero value. It’s just that he had the misfortune of catching at the same time as Johnny Bench, Gary Carter, Bob Boone, Jim Sundberg, and Ivan Rodriguez at the end of his career.