Category: Research

One of the rule changes that we saw in 2020 was the introduction of the 3-batter minimum. Unlike other rule changes, this one was planned before the pandemic hit, meaning that we will see it again for sure in 2021. Jayson Stark of The Athletic had an article on this rule change and he quoted several managers who were unified against the change. These managers felt that the rule not only handcuffed them in what they had to do but also kept them from having the chance to run a better bullpen than their counterparts.

Let’s for the moment accept on faith that the latter is true. But let’s take a look at the former. If managers were truly handcuffed by this new rule, we would expect the numbers to reflect that – with offense going up because managers either had to leave guys in the game longer they wanted or because fear of the rule kept them from bringing in a reliever in the first place. In order to check this out, we’ll need to do a preliminary look at the overall numbers, all coming from Baseball-Reference.

In 2019, the overall ERA in MLB was 4.49 and for relievers it was 4.46
In 2020, the overall ERA in MLB was 4.44 and for relievers it was 4.44

That’s pretty remarkable year-to-year consistency and at first glance it’s hard to say that the new rule was a detriment to teams or a boon to offense.

My hope was to show the numbers for all lefty relievers in both seasons but a split off of a split is not so easy to achieve. What was somewhat easy to get was all lefty relievers who did not start a game. And given that it’s not exactly what we want, it’s not awful. What we’re looking to see is if the elimination of the LOOGY guys resulted in a worse bullpen performance. And if you have a lefty reliever that you want to shield from RHB, you’re not likely to give him a start. So, here are the numbers for lefty relievers with zero starts:

2019: 4.34 ERA
2020: 4.02 ERA

The results of the full-time lefty relievers improved with the new rule.

Now let’s shift our focus away from MLB and zero in on the Mets. We know the Mets were absolute zealots when it came to chasing the platoon advantage with their lefty relievers under Terry Collins and it didn’t improve all that much under Mickey Callaway. And we know that all of that jumping through hoops did not make the Mets’ bullpen a good one. Hopefully everyone recognizes that the one year they didn’t have an established LOOGY all season like Jerry Blevins or Scott Rice is the year they went to the World Series.

Anyway, in 2019 the Mets used seven lefty relievers and they combined to allow 38 ER in 84 IP for a 4.07 ERA. In 2020, the Mets used four lefty relievers and they combined to allow 23 ER in 53.2 IP for a 3.86 ERA.

Ending the LOOGY madness and focusing on pitchers who could get all hitters out led to an improvement of nearly a quarter of run – and that’s despite the outstanding results of Justin Wilson in 2019. After posting a 2.54 ERA in 2019, Wilson notched a 3.66 ERA last season. If we take Wilson out of the equation, lefty relievers for the Mets in 2019 had a 5.40 ERA while the 2020 lefties had a 3.97 ERA.

At the heart of the matter, the Mets traded the matchup relievers of Luis Avilan and Daniel Zamora in 2019 for Chasen Shreve in 2020. Avilan and Zamora combined for 23 ER in 40.2 IP (5.09 ERA) while Shreve allowed 11 ER in 25 IP for a 3.96 ERA. And because of the rule, the Mets didn’t look to carry a guy who couldn’t get RHB out at all – like Rice – and didn’t promote lefty relievers who had no business being in the majors, guys like Buddy Baumann who in 3 IP allowed 8 ER.

Let’s look backwards three additional years and see how LH relievers for the Mets performed previously:

2018: 54.2 IP, 32 ER – 5.27 ERA
2017: 152 IP, 74 ER – 4.38 ERA
2016: 134.1 IP, 66 ER – 4.42 ERA

Most teams were moving away from the traditional LOOGY gambit but the Mets were not really one of them. They had to have the new rule make them do what they should have done at least eight years ago – look to carry their best relievers instead of worrying about which hand they used to throw.

In the Stark piece from The Athletic mentioned at the start of the piece, Indians manager Terry Francona said this about the 3-batter rule:

I just don’t like it when they tell you how to compete,” he said. “If they want to tell us that you’ve got to throw the ball within 20 seconds, OK fine. We’ll adjust. But if we handle our bullpen better than other teams, you’d like to think you can get rewarded for it.

The Mets didn’t handle their bullpen better before the rule because of the way they utilized sub-par relievers because they threw with their left hand, which also cost them flexibility in the long-term because those lefty relievers were so limited in what they could do. While Avilan and Zamora combined for 62 games and 40.2 IP, Shreve threw 25 IP in 17 games, which was on pace for 68 innings in a 162-game season. Their overall bullpen ERA dropped 39 points last year.

My preference is for the game to feature strategy over paint-by-numbers managing. My ideal is for the Mets to have a guy in the dugout who, in the words of Francona, gets rewarded for the moves he makes. But we see that the rules in 2020 that reduced strategic thinking – specifically the 3-batter rule and the implementation of the DH – aided the Mets tremendously. It doesn’t give me any joy to say that but the only way the situation improves is when you admit and fix where you have problems.

A few days ago, a story was published here with my grades for the key players on the 2020 Mets. In that piece, Seth Lugo received a D+. There were two comments that specifically mentioned that grade. The first expressed shock – in an approving way, is my guess – that Lugo received one that low. The second comment indicated that my grade was too harsh, with the commenter thinking he deserved a “C” grade, saying he was still good as a reliever.

In 2019, Lugo allowed runs in 13 of his 61 appearances, or 21% of the time. In 2020, he allowed runs in three of his nine relief appearances, or 33% of the time. For a comparison, Justin Wilson allowed runs in nine of his 45 appearances (20%) in 2019 and in five of his 23 appearances (22%) in 2020 while Edwin Diaz allowed runs in 19 of his 66 games (29%) in his first year with the Mets and five of his 26 games (19%) in 2020. In this simple six-season sample, four of the seasons had runs allowed in the 19-22% range. Then we have Diaz with his 29% range in what everyone considered a dismal 2019 and Lugo with a 33% mark in his relief appearances in this shortened season.

That was looking at the good relievers. Let’s take a quick look at the other end of the spectrum. Jeurys Familia allowed runs in eight of his 25 appearances in 2020, which is a 32% rate. Lugo’s was a small sample – absolutely – but when your runs allowed percentage is hanging out with 2019 Diaz and 2020 Familia, that’s not the kind of company you wish to keep. Shoot, let’s do one more. In 2019, Tyler Bashlor allowed runs in eight of his 24 appearances, which as the math majors know is 33%. Wow, this is even more depressing than originally thought.

Regardless, Lugo earned that grade from me more on the results of his starting pitching than his work out of the bullpen. Seven starts and a 6.15 ERA is dreadful, especially from a guy who hasn’t been bashful about wanting to start. Lugo had to know that poor results as a starter would be highly detrimental to his chances of ever being a starter again. That he performed so poorly is pretty shocking, at least to me.

There was a concern that Lugo was tipping his pitches in the Phillies game. He seemed to correct whatever was going wrong in that department in his next outing versus the Rays, when he allowed just 1 ER in 6.1 IP. But he ended the year on the flattest note possible, as he didn’t make it out of the second inning against the last-place Nationals. Lugo ended up with 6 ER in 1.1 IP. And while the Phillies bashed him to death with homers, Lugo did not throw a gopher ball in his start against the Nats.

So, it’s back to the pen and hope he can find his way back to his 2019 performance, right? Well, maybe not.

The best thing working in Lugo’s behalf to remain in the rotation is that the Mets only have two starting pitchers. It’s an expensive proposition to sign three free agent starters and good luck finding someone to trade you a starter cheaply. And it’s not like any of the guys in the farm system appear poised to take a slot. The easiest thing to do would be to tell Lugo he’s got a chance to show he belongs in the rotation until Noah Syndergaard returns to action.

But there’s another thing that makes me think he deserves an additional shot at starting.

The big trend now is to pay attention to when a pitcher is going through a lineup for the third time. Conventional wisdom is that for mid-rotation and back-end starters, you can maximize their success by not letting a guy face him for the third time in a game. And with Lugo sitting with a 6.15 ERA, no one is looking at him as a top-of-the-rotation starter.

Yet Lugo wasn’t running into trouble the third time through the order. Instead, he was getting lit up in the first two innings. In a way, this was just a continuation of his dropoff in performance in the bullpen from earlier in 2020. In seven starts, Lugo allowed runs in the first two innings four times. He pitched past the second inning in five starts and from inning three to inning seven, Lugo allowed 3 ER in 12.1 IP for a 2.19 ERA.

The splits on Baseball-Reference break down how pitchers do by innings. In the first two innings of a game, MLB pitchers allowed 1,656 ER in 3,592 IP for a 4.15 ERA. From innings three thru seven for all pitchers – not just starters – the league ERA was 4.72 over 8,928 innings. Lugo was more than twice as good as the average MLB pitcher in these innings.

Forget about average, let’s take a look at Jacob deGrom. In the first two innings of games in 2020, deGrom allowed 6 ER in 24 IP for a 2.25 ERA. In innings 3-7, deGrom allowed 12 ER in 44 IP for a 2.45 ERA. When Lugo wasn’t getting taken behind the woodshed, he was putting up numbers that were slightly better than deGrom’s in the middle innings of games. This seems relevant to if he can be a starter.

Sure, this has a little bit of, “aside from that, Mrs. Lincoln, how did you like the play?” feel to it. It’s a gigantic problem when he gives up 14 ER in 7 IP in the first two innings of games over four starts. No one should pretend otherwise. The question is if what he did in those four starts in the first two innings is a better indication of how he’s going to perform as a starter than the 12.1 innings in five games from inning three on.

No matter where we look, we have small samples. The one constant, whether in his work as a SP or RP, is that Lugo has a significant number of game where he gives up multiple runs in the first two innings, including eight times as a reliever in 2019. The question we have to ask is: If he pitches for a full season in one role, can he overcome those games and still be a worthwhile pitcher? In 2019, in those eight games, he allowed 23 R (19 ER) in 8.2 IP – which is even worse than this year’s 14 ER in 7 IP. The thing is that Lugo got to pitch in 61 games in 2019 and throw 80 innings. He got the chance to compensate for the awful outings. Lugo didn’t get that chance, either as a starter or reliever, in 2020.

And he didn’t really get the chance in 2017, either. In addition to pitching with the elbow injury for the first time, he missed the first 60 games of the season.

We’ve seen what Lugo can do in a full season as a reliever – the 2.70 ERA and 0.900 WHIP he posted in 2019. It’s really good and a big reason why people are hesitant to make him a starter for a full season, a role where he hasn’t had anywhere near that level of success. Of course, just about any pitcher will perform better in a full season as a reliever compared to a full season as a starter. The question is: At what level does Lugo have to pitch as a starter to make it worthwhile to remove a 2.70 ERA guy from the pen?

There are a number of factors that play into what that number is, including the strength of your starters and relievers. And we know the Mets desperately need starters. But let’s play around some and see if we can come up with what Lugo might do as a starter in a season with 30+ starts.

As mentioned earlier, Lugo gave up runs in the first two innings in 13 of his 61 games as a reliever in 2019, or 21% of the time. So, let’s make him worse as a starting pitcher. Let’s say he gives up run(s) in the first two innings 30% of the time. If he makes 32 starts, that would mean 9.6 games he would give up runs early. We’ll round that up to 10 games. Here are the four games he gave up runs early as a starter in 2020:

9/5 – 2 IP, 1 ER
9/17 – 1.2 IP, 6 ER
9/22 – 2 IP, 1 ER
9/27 – 1.1 IP, 6 ER

That’s two starts where he gave up a run and two starts where he got clobbered. So, let’s give him five starts where he gives up one run and five starts where he gives up six runs. For easier math, let’s assume he pitches at least two innings in all of these starts, getting pulled after two frames in the games he gets clobbered and that he goes five innings in the other games. That would leave us with 20 IP and 35 ER for a 15.75 ERA in the first two innings of these contests.

Now, again to make things easy, let’s assume he pitches six innings in each of his remaining 22 starts. That would give him 167 IP for the season. And we know he didn’t allow a run in the first two innings of those remaining 22 starts. So, we have 64 IP and 35 ER for a 4.92 ERA in the first two innings of games. Now the question remains what he would do over the final 103 innings he pitched.

If he pitched as well as he did in 2020, when he had a 2.19 ERA after the second inning, he would give up 25 ER. That’s 60 ER and over 167 IP, that’s a 3.23 ERA. It seems a no-brainer to prefer a 3.23 ERA over 167 IP compared to his 2.70 ERA in 80 IP as a reliever.

Let’s give him a 3.00 ERA after the second inning. That would be 69 ER for his season and over 167 IP, that’s a 3.72 ERA. Maybe that’s not as clear-cut as the first option but it still seems like you would prefer him as a starter. In 2019, only 61 pitchers in MLB threw 160 innings and only 24 had an ERA better than the mythical 3.72 that Lugo gets in this example. So, check that, this would be a slam dunk, too.

The splits at B-R show Lugo with a career ERA of 3.68 in innings three thru six. If he did this in our example, he would finish the year with 77 ER in 167 IP for a 4.15 ERA. Only 37 starters in MLB in 2019 put up that many innings and had a better ERA. Guys who were close to that innings/ERA combo in 2019 include Max Fried (160.1 IP, 4.15 ERA), Joey Lucchesi (163.2 IP, 4.18 ERA) and Adam Wainwright (171.2 IP, 4.19 ERA).

Ultimately, none of us have any idea how Lugo would fare in a season with 32 starts. Someone could make different assumptions and come up with completely different, more pessimistic numbers than what was presented above. The bottom line for me is that what he did in seven starts in 2020 doesn’t change the narrative to any significant degree. He struggled mightily in the first couple of innings in two starts and gave up a run in two others and had three games without a run. You could say he had five starts where he gave up 2 ER in 10 IP in the first two frames or you could say he had four starts where he surrendered 14 ER in 7 IP.

Do two incredibly poor starts mean he’s incapable of being a starter? If you went into the 2020 season thinking he was a failed starter, you might view that as confirmation. But it’s never good to look at an issue with your mind made up and then only look for confirmation of your belief. And that goes both ways. You can’t say Lugo had a 2.31 ERA in the majority (five of seven) of his starts in 2020, which means he should be guaranteed a spot in the rotation in 2021.

Lifetime, Lugo is 15-10 with a 4.35 ERA in 38 games and 194.2 IP as a starter. Another small sample filled with things that make it tough to take at face value. There were the great results in 2016 when he outpitched his peripherals. There was the delayed start and pitching with the injured elbow in 2017. There were five and seven starts in 2018 and 2020, respectively.

It made sense to me for Lugo to be a reliever in 2019 because the Mets had five starters that were essentially league average or better. It didn’t make sense to me once Syndergaard and Marcus Stroman were out in 2020 for Lugo to be kept in the pen. With the Mets having only two starters right now for 2021, it makes sense to me to utilize Lugo as a starter.

Then we’ll just have to hope he doesn’t go out and make 32 starts and put up a 4.88 ERA – his career mark as a starter since 2017. That’s on the table as a potential outcome. It’s good to go into things with your eyes wide open.

With the Mets stumbling out of the gate in this shortened season, the concern is that they are digging a hole from which they won’t be able to recover. The issue is that while we have a fairly good idea what type of record you need to make the playoffs in a normal year, few know what it’s going to take in a 60-game season. And the other thing to consider is that we are operating with a different playoff format in 2020. Eight teams from each league are going to make the postseason – two from each division plus the next two teams with the best record.

Chris touched on this back before the season started, saying that the Mets would only make the playoffs once in the past five years. But that was before the new playoff structure was announced. So, let’s look at how the entire National League would look after 60 games played in each of the last three seasons.

It’s important to note a few things first. What follows are the teams’ records after 60 games. But teams never have 60 games played at the same date. In a normal year, you can add up the wins and losses for all 30 teams at the end of the year and it will produce a .500 record. That would not be the case in this look. Also, this 60-game look has teams playing wildly different schedules. This year, the schedules will be much more (but still not perfectly) alike for teams in the same division.

2019
Braves	33-27	Brewers	34-26	Dodgers	41-19
Phillies	33-27	Cubs	34-26	Rockies	31-29
Mets	28-32	Cardinals	31-29	Padres	31-29
Nationals	27-33	Pirates	29-31	D’Backs	30-30
Marlins	23-37	Reds	28-32	Giants	25-35

The top three seeds go to the division winners. So, the Dodgers would be the first seed and the Braves would be the third seed. The winner of the Central would be the second seed. There will be no tie breakers played like there would be in a normal year. Instead, the winner of the tie will be chosen based on this formula:

The first tiebreaker is head-to-head record (if applicable). If that’s also a tie, the next tiebreaker is intradivision record. If that’s still a tie, the next is record in the final 20 division games (plus one until the tie is broken).

After 60 games last year the Brewers and Cubs played six games and they were tied 3-3. The Brewers won the season series, 10-9, and so for simplicity’s sake, we’ll call them the winners and the second seed. The next three seeds go to the teams that finished in second place in their division. That makes the Cubs fourth, the Phillies fifth and the Rockies sixth. The final two teams are considered the Wild Card teams and in this look that would have been the Cardinals and Padres. Maybe the Padres would have finished second – honestly did not look.

The playoff format is a 3-game set in the Wild Card round, with the matchups 1-8, 2-7, 3-6 and 4-5. The Division Series is best-of five, with the winners of 1-8 squaring off against the 4-5 winner and the winners of 2-7 and 3-6 also facing one another. The League Championship and World Series will be best-of-seven affairs.

Here’s our 60-game standings for 2018:

2018
Braves	35-25	Brewers	37-23	D’Backs	32-28
Nationals	35-25	Cubs	36-24	Rockies	31-29
Phillies	32-28	Cardinals	33-27	Dodgers	30-30
Mets	27-33	Pirates	30-30	Giants	30-30
Marlins	21-39	Reds	21-39	Padres	26-34

The interesting thing here is a reminder of what a poor start the Dodgers got off to in 2018. The final record looks really good but LA was 16-26 after 42 games. They played great from that point on but their 30-30 record would have left them out of the playoffs.

Our first two years give us fairly “normal” looking results. But 2017 shows us the wackiness that is possible in a 60-game season. Here are those standings:

2017
Nationals	38-22	Brewers	32-28	Rockies	37-23
Mets	27-33	Cubs	30-30	D’Backs	35-25
Braves	27-33	Reds	29-31	Dodgers	35-25
Marlins	27-33	Cardinals	28-32	Giants	24-36
Phillies	21-39	Pirates	26-34	Padres	23-37

The NL West had three really good teams while the NL East had just one. In their first 60 games of 2017, the Mets played the Giants, Brewers, D’Backs, Angels, Padres, Pirates, Angels and Rangers. They actually had two series against both Miwaukee and Pittsburgh. In all, they played 27 games against teams they wouldn’t face under the 2020 schedule. The Mets went 11-16 in those games.

So, there was a 3-way tie for second place in the NL East here. And their records were so bad that the teams that didn’t win the tiebreaker would not make the playoffs. Would the Mets have won? Feel free to jump through all of the scenarios and post the results. The assumption here is that the Mets won. Hey, there’s a one in three chance of it being right!

Overall, the magic number in this three-year look seems to be 31 Wins. In our three-season sample, a team won 31 or more games 21 times and made the playoffs each of those instances. The Mets will have to go 26-20 the remainder of the way to hit 31 wins.

*****

The teams within a division playing fairly equal schedules should lessen the chance of something like the 2017 standings above happening. But it won’t reduce it completely. It’s certainly possible that Interleague play will throw a monkey wrench into things. If nothing else, it should be a little fairer for the Mets, as the rest of the teams will have to face the Yankees, even if not as often as the Mets do. So, instead of the Nationals having a huge built-in edge by playing the Orioles as their national rival, they’ll have to play a team that isn’t 40 games below .500 when they venture outside the NL.

And the other thing that could mess with the results is how teams fare when they have to play multiple doubleheaders to make up games that were lost earlier in the season. Teams aren’t used to playing doubleheaders and now we have the additional uncertainty of how they’ll react playing 7-inning games in those.

Back in 2017 in the middle of the season, Devan Fink from Beyond The Box Score wrote an article about doubleheader results. He found that in the period from 2008 to 2017, there were 231 doubleheaders in MLB and 120 of those resulted in sweeps. However, in 2017 alone there were 14 doubleheaders at the time of the article and 11 of those ended up as being split with each team winning a game. The example he used was flipping a coin. In the long run you expect results to approach 50-50 but anything can happen in a small sample.

Baseball-Reference shows the Marlins with three doubleheaders scheduled right now. But they also only have 58 games on their schedule. So that’s likely two more twinbills coming their way. And a rainout or other Covid cancelations are certainly possible. So, they could be playing even more doubleheaders. Right now, the Marlins are in first place, thanks to an unsustainable record in one-run games. But what happens when the twinbills hit?

One thing that gets lost about the magical 1969 season for the Mets is that they played 22 doubleheaders that year. They swept 11, split 8 and got swept 3 times, giving them a 30-14 (.682) record in twinbill games. In their last nine doubleheaders, they went 14-4. Can any team replicate that type of winning percentage if they play five or more 2020 doubleheaders? If so, that team will be in excellent shape.

Rick Porcello takes the mound tonight versus the Braves, squaring off against the same team that took him behind the woodshed in his first start with the Mets. This gives us a chance to test out a theory of mine. My theory is that whenever a player faces a team that he just pitched against recently, that if he had a particularly good or bad outing against them the first time, he’ll do the opposite in the second go round.

It’s a theory because I’ve never actually looked at the results. So, let’s do that now. First, we’ll have to define some terms. What’s a particularly good or bad start? Let’s use the Bill James Game Score number to determine this, as found on Baseball-Reference. James considers several factors and throws them into equation where an average Game Score is 50. So, let’s consider a particularly good start as one with a Game Score of 60 or higher and a particularly bad Game Score as one with a 40 or worse.

Porcello had a Game Score of 14 for his outing against Atlanta on July 26.

What do we consider recently? For our purposes today, let’s use a definition of two starts in three games within a period of two weeks. So, if a guy faced a club and then went on the injured list for several months and then faced the same club again when he was activated, that won’t count. This actually happened with Noah Syndergaard back in 2017.

Before looking at the data, let me preface this by saying that there weren’t as many of these as expected. Partly this is because you typically only have the opportunities to do this in division games. It’s not like a Mets pitcher frequently faces the Dodgers twice in three starts. And the other is that a lot of starts end up with a Game Score in the 40s or 50s, eliminating them from our consideration. Steven Matz faced the Phillies last year in back-to-back starts late in the season but his first outing against them had a Game Score of 48, so it didn’t make our cut. Here’s our lists, beginning with the particularly good starts:

Date	Pitcher	Opponent	IP	ER	BB	K	GM Score		Date	IP	ER	BB	K	GM Score
81419	Matz	ATL	6	1	1	5	68		82519	6	1	1	6	69
72719	Matz	PIT	9	0	0	7	84		80219	3.2	5	1	4	32
62719	Wheeler	PHI	6	1	2	7	69		70719	5	6	2	7	32
61819	deGrom	ATL	8.1	2	0	10	75		62819	6	3	2	7	53
51119	deGrom	MIA	7	1	1	8	70		51719	5	6	0	3	26
42819	Matz	MIL	7	2	0	4	63		50319	5.2	3	1	3	41
82018	Wheeler	SFG	7	1	1	10	72		83118	7	1	0	9	74
42118	deGrom	ATL	7	0	2	10	77		50218	4	0	0	6	64
40718	Matz	WSN	5	0	2	8	65		41818	4	3	1	6	49
40518	deGrom	WSN	6	1	3	5	60		41618	7.1	3	1	12	65
91717	Gsellman	ATL	7	0	0	3	72		92717	6	1	1	4	59
83017	Montero	CIN	8.1	0	4	8	81		90917	5	1	5	5	55
40917	Syndergaard	MIA	7	1	0	9	70		41417	6	1	0	4	58
Sum			90.2	10	16	94	926			70.2	34	15	76	677
Per Game			7	1	1	7	71			5.1	3	1	6	52

Looking at the 2017-2019 period, there were 13 times that a Mets pitcher had a particularly good start and then faced that team again within his next two starts. In our initial start, the average Game Score was 71 and the follow-up start it was 52. That seems to work in favor of the theory. But let’s dig a little deeper.

There are two factors to consider here that work against our theory. First, you have the possibility of a good pitcher dominating a bad team. The 2018 Giants that Zack Wheeler put up consecutive strong starts against in 2018 went 73-89. This happened in the second half of the year, when Wheeler was lights out and the Giants went 23-41. And, there’s also the fact that pitchers tend to bunch good or bad performances together. You’ll frequently hear that Pitcher X is having a great month.

On the flip side, we have the overall tendency of regression. If a true talent 4.21 ERA guy throws a shutout, like Steven Matz did against the Pirates last year, chances are he’s not going to duplicate that performance the next time out. Essentially, the theory is saying that regression is a stronger factor than either the quality of the team or the streakiness of the pitcher.

Now let’s look at the particularly bad starts:

Date	Pitcher	Opponent	IP	ER	BB	K	GM Score		Date	IP	ER	BB	K	GM Score
81319	Wheeler	ATL	5	5	2	2	23		82419	6	5	4	3	39
61919	Matz	ATL	5	5	4	2	33		62919	2	2	0	3	45
51619	Wheeler	WSN	6	6	2	6	30		52119	7	3	2	6	61
51519	Font	WSN	2.1	5	2	2	25		52019	4	2	4	3	47
41619	Matz	PHI	0	6	1	0	13		42219	6	1	2	6	66
90818	Syndergaard	PHI	6.2	4	5	4	33		91918	4	3	3	6	45
42717	Harvey	ATL	4.1	6	5	1	25		50217	5.1	6	3	2	27
42617	Gsellman	ATL	4	5	3	2	19		50117	5	5	1	0	34
40817	Milone	CHC	4	4	1	2	29		41917	5	1	0	5	62
Sum			37.1	46	25	21	230			44.1	28	19	34	426
Per Game			4.1	5	3	2	26			5	3	2	4	47

There were only nine instances for the Mets in the last three years of what Porcello will be doing tonight. This really surprised me. And there was a tiny bit of unintentional cheating going on to get to nine. The information for Tommy Milone in 2017 was already entered before the realization hit that this came before he joined the Mets. Those two starts both happened when Milone was on the Brewers. Since it was already entered – and we needed the game for a better sample size – it was kept in.

The Game Scores for the particularly bad starts averaged out to 26, which is quite awful. The follow-up start produced a 47 score, a nice increase but not as strong as my expectation going in. Only one-third of our follow-up starts would qualify for our particularly good start list and one of those three was the cheat start by Milone. Maybe a larger sample would have produced different results.

Checking Porcello, he did not have a start the past three years which qualified for our particularly bad grouping and a recent follow-up against the same opponent. You have to go back to 2015 to find a case where it happened. On April 19, he had a lousy game against the Orioles, one that resulted in a Game Score of 14. He faced them the next time out on April 24 and put up a Game Score of 49. In that game, Porcello gave up 4 ER in 6 IP. After he went just two innings in his first start this year, the Mets would be happy with a 6 IP outing tonight.

If asked to name the biggest fluke season by a batter in Mets history, you might answer 1996 Bernard Gilkey or Lance Johnson from the same season. But what if we framed the question in a different way? What Met regular – defined here as someone who amassed at least 2,500 PA in their Mets career – had the most fluke numbers in a season with at least 100 games?

In some cases, fluke isn’t necessarily the right term. Some of these guys were really good players who were very consistent and had a year that was slightly better than others. But some of these guys, the word fluke definitely fits. With that out of the way, let’s run the chart of all of the hitters to amass 2,500 PA with the Mets and their career numbers in blue and oragne.

Name	PA	BABIP	AVG	OBP	SLG	wOBA	wRC+
David Wright	6872	.339	.296	.376	.491	.373	133
Ed Kranepool	5997	.271	.261	.316	.377	.309	95
Jose Reyes	5931	.305	.282	.334	.433	.331	103
Bud Harrelson	5083	.268	.234	.324	.287	.287	79
Cleon Jones	4683	.315	.281	.340	.406	.336	112
Howard Johnson	4591	.268	.251	.341	.459	.348	122
Darryl Strawberry	4549	.283	.263	.359	.520	.378	143
Edgardo Alfonzo	4449	.306	.292	.367	.445	.357	115
Jerry Grote	4335	.285	.256	.321	.329	.295	85
Mookie Wilson	4308	.320	.276	.318	.394	.316	100
Mike Piazza	3941	.296	.296	.373	.542	.385	134
Keith Hernandez	3684	.323	.297	.387	.429	.363	132
Carlos Beltran	3640	.294	.280	.369	.500	.370	127
Daniel Murphy	3619	.314	.288	.331	.424	.327	108
Lee Mazzilli	3496	.288	.264	.357	.396	.340	114
Wayne Garrett	3361	.264	.237	.348	.343	.321	100
Kevin McReynolds	3218	.270	.272	.331	.460	.346	120
Rey Ordonez	3216	.270	.245	.290	.304	.259	53
John Stearns	3081	.274	.259	.341	.375	.325	104
Rusty Staub	2965	.272	.276	.358	.419	.349	119
Felix Millan	2954	.284	.278	.326	.337	.305	90
Todd Hundley	2904	.268	.240	.323	.438	.328	101
Lucas Duda	2895	.287	.246	.343	.457	.347	123
John Milner	2755	.253	.245	.339	.415	.343	115
Wally Backman	2704	.325	.283	.353	.344	.319	103
Tommie Agee	2687	.311	.262	.329	.419	.338	112
Hubie Brooks	2620	.300	.267	.318	.372	.308	94
George Foster	2610	.277	.252	.307	.422	.320	103
Dave Kingman	2573	.235	.219	.287	.453	.329	106

This chart is interesting all by itself. First, 2,500 isn’t all that big of a number – about four years of full-time play – so it’s a tiny bit surprising that only 29 players made the cut. And six players didn’t even reach a lifetime 100 wRC+ with the Mets, with one of them being primarily a first baseman – yuck. It’s interesting how Wright (133), Piazza (134) and Hernandez (132) all had very similar production in their careers with the Mets. Also, it was a surprise to me that Foster had more PA than Kingman. But perhaps the biggest surprise to me was that Doug Flynn didn’t make the list, as he had just 2,269 PA with the Mets. It seemed like he grounded out 1,000 times more than that.

Now let’s run another chart. Let’s take this group of 29 players and instead of looking at their career numbers with the Mets, this time we’ll look at their best individual season in BABIP, wOBA and wRC+. Then we’ll compare that to their lifetime marks in those categories and look for the biggest differences.

NameI	BABIP	wOBA	wRC+	Difference	BABIP	wOBA	wRC+
Wright	.394	.413	156		.055	.040	23
Kranepool	.331	.352	123		.060	.043	28
Reyes	.353	.376	142		.048	.045	39
Harrelson	.300	.313	95		.032	.026	16
Jones	.367	.405	154		.052	.069	42
Johnson	.308	.406	166		.040	.058	44
Strawberry	.306	.412	162		.023	.034	19
Alfonzo	.335	.418	150		.029	.061	35
Grote	.347	.332	112		.062	.037	27
Wilson	.363	.350	129		.043	.034	29
Piazza	.355	.432	168		.059	.047	34
Hernandez	.343	.383	146		.020	.020	14
Beltran	.297	.408	148		.003	.038	21
Murphy	.345	.354	126		.031	.027	18
Mazzilli	.325	.378	137		.037	.038	23
Garrett	.298	.367	122		.034	.046	22
McReynolds	.278	.367	144		.008	.021	24
Ordonez	.285	.282	62		.015	.023	9
Stearns	.277	.353	125		.003	.028	21
Staub	.298	.367	129		.026	.018	10
Millan	.296	.318	100		.012	.013	10
Hundley	.304	.396	143		.036	.068	42
Duda	.326	.368	136		.039	.021	13
Milner	.278	.373	136		.025	.030	21
Backman	.345	.344	119		.020	.025	16
Agee	.345	.363	129		.034	.025	17
Brooks	.306	.333	112		.006	.025	18
Foster	.303	.346	123		.026	.026	20
Kingman	.254	.358	126		.019	.029	20

We should note that these are the best marks in these three categories in a season with 100 games. Often, like with Jones in 1969, all three numbers came in the same season. But it didn’t have to be that way. Agee had his three numbers come in three separate seasons. Also, you’ll see players with best season marks nearly identical to their career numbers. They could be extremely consistent. Or they could have years with fewer than 100 games played with great numbers, which helped bring up their lifetime with the Mets marks. For example, Beltran had a .352 BABIP in 81 games in 2009 and a 150 wRC+ in 98 games in 2011

The Mets regular who had one season where the hits fell in at the biggest difference from his lifetime mark with the club was Grote in 1968. The year before, Grote had a .226 BABIP and the year afterwards, he checked in with a .285 mark. It’s interesting to compare guys who played the same position and see what numbers they put up. While Grote was nearly able to match Piazza in BABIP in their best seasons, Piazza held a 100-point edge in wOBA and a 56-point advantage in wRC+. Grote held a 70-point BABIP advantage on Stearns yet had a 21-point deficit in wOBA and a 13-point shortfall in wRC+ in their best seasons.

Another interesting position comparison is at first base with Duda and Milner. Duda held a 48-point edge in BABIP in their best season but their wOBA and wRC+ numbers were nearly duplicates of one another. And while he’s not a position match, Staub’s best seasons in these numbers falls in the same vicinity as Duda and Milner. My guess is that no one reading this would have considered Duda’s best hitting season was equal, or slightly better, than Staub’s.

The season that had the biggest difference from career mark in wOBA was Jones’ big year in 1969, which just edged out Hundley’s monster 1997 season. Hundley played more games in 1996 and put up better counting numbers in the earlier season. And if you recall from the beginning, ’96 was the year that Gilkey and Johnson had the big fluke years. The Mets that year had a pretty good big three for a team that lost 91 games, as Gilkey had a 155 OPS+, Hundley had a 140 and Johnson had a 125. For a comparison, the 2004 team also lost 91 games and their top 3 OPS+ marks were 119, 111 and 109.

Finally, the biggest wRC+ surplus in an individual season was the 44 points above career average by Johnson in 1989, the only time in his eight seasons where he played 100 games where he had a BABIP over .300, as he had a .308 mark. Just imagine the numbers he would have put up that season if he had Grote in ’68 BABIP luck. Still, Johnson put up a 7.0 fWAR in ’89, tied with ’08 Wright for the seventh-best mark by a hitter in Mets history. Gilkey in ’96 had a 7.6 mark.

For reasons lost to time, my favorite player as a kid was Duffy Dyer. My thoughts turned to Dyer again earlier this week when Jim posted his musical ode to Jerry Grote. My opinion has always been that Grote was overrated, and not just a little. You hear repeatedly that he was the second-best catcher in the NL at the time, behind only Johnny Bench. That never passed the smell test for me. At the very least, Manny Sanguillen and Ted Simmons had a better claim to that feat.

You never know what guys are like in real life. But, no doubt due to my Dyer fandom, my impressions of Grote were always that he was a “get off my lawn” guy, even as a young man. Probably those opinions were shaped by his 1966 Topps card as much as quotes attributed to him where he comes across as a grumpy hard ass.

Regardless, it’s time we look at the stats available to see how Grote and Dyer rate. Offensively, Grote was not very good but Dyer was worse. But it was defensively where Grote was supposed to be a star. So, let’s look at the numbers put up by the Mets’ pitchers when each of these catchers were behind the dish. Dyer caught one game in 1968 so let’s focus on the 1969-1974 seasons and see how they did in those six years.

Dyer

Year	Innings	ER	AB	H	2B	3B	HR	BB	HBP	SF
69	165.1	38	578	113	17	5	6	44	2	3
70	395.2	147	1463	356	63	9	43	151	6	7
71	419.2	140	1541	351	59	13	32	154	9	7
72	814.2	298	3030	736	125	11	69	289	15	26
73	444.1	171	1668	413	50	13	36	165	7	13
74	333.2	114	1251	316	33	9	18	125	9	10
Totals	2573.1	908	9531	2285	347	60	204	928	48	66

Grote

Year	Innings	ER	AB	H	2B	3B	HR	BB	HBP	SF
69	918.2	292	3347	745	138	26	69	335	19	23
70	1064	414	3947	904	164	30	92	424	20	35
71	1042.2	347	3854	874	136	28	68	375	31	28
72	530	187	1964	459	76	18	46	178	12	15
73	684	212	2542	593	104	8	51	221	9	15
74	793	289	3011	782	122	24	50	254	14	26
Totals	5032.1	1741	18665	4357	740	134	376	1787	105	142

Here are those numbers expressed in career (69-74) ERA and triple slash lines:

DD – 3.18, .240/.310/.353
JG – 3.11, .233/.302/.347

For a guy who made his reputation on defense, there’s not really a whole lot separating Grote and Dyer here. If you think – so what – the same pitchers performed remarkably similar, let’s take a look at the 2019 Mets. Mets pitchers had a 3.79 ERA and a .688 OPS when Tomas Nido was catching, compared to a 4.34 ERA and a .746 OPS when Wilson Ramos was the backstop. Well, you might say, that’s an extreme case. Everyone knows Ramos isn’t much of a defensive guy. Okay, let’s look at the 2017 Mets, which primarily featured Travis d’Arnaud and Rene Rivera. The numbers with d’Arnaud were 4.80 and .780, respectively. They were 5.21 and .822, respectively, for Rivera. These differences were much larger than what we saw with Grote and Dyer.

Grote caught just shy of twice as many innings as Dyer in this span. The exact advantage is 1.95557 but that doesn’t mean that they caught the same pitchers a proportional amount of time. The biggest star was Tom Seaver and Grote was behind the plate for 1,039 innings for Seaver while Dyer logged 434.1 innings. Grote caught Seaver 2.4 times as much as Dyer. Jerry Koosman was right in line with their overall numbers, with Grote catching him 1.998 times as often. Jon Matlack is an interesting case. When he first came up in ’71, Dyer caught him 3X as much. Then in ’72, the only year where Dyer caught more than Grote, he still held an edge. But the next two years, Grote leaped ahead, with a final total of 375 innings for Grote compared to 283.1 for Dyer. Grote caught 342.1 innings for Jim McAndrew, while Dyer had 252.2 innings – meaning Grote caught him 1.35 times as often. With Gary Gentry, Grote held a 546.1 to 175.1 (3.1X as often) innings edge while with Ray Sadecki it was Grote 303.1 and Dyer 253.1 (1.2X as often.)

What would the overall numbers look like if Dyer caught Seaver and Gentry proportionally more often than Grote, rather than holding that edge with McAndrew and Sadecki?

Well, what about other areas of catcher defense? Dyer had 14 PB, 95 WP and threw out 39.8 percent (99-249) of opposing baserunners. For Grote, it was 28 PB, 149 WP and 40.1 percent (149-371) of opposing baserunners. The main difference is wild pitches and those are much more dependent on the pitcher than the catcher.

Just to state the obvious, Grote was better than Dyer. But that’s because Grote had an 80 OPS+ in the 1969-1974 period, compared to a 70 OPS+ mark for Dyer in the same span. But no one is ranking Grote up with Bench because of his offense, as Bench had a 135 OPS+ in the same period. If you’re going to say that Grote was a great defensive catcher, you should put Dyer in that exact same category because the overall difference between them was negligible.

Dyer played seven more seasons after he left the Mets, despite being a lousy hitter. Obviously, he was valued in the game for his defense, yet that value was never properly measured by fans of the time. Or now. You still hear today about Grote and his “impact” on the pitching staff but never hear the same thing about Dyer.

It didn’t make sense to a kid at the time and it doesn’t look any better 45 years later.

My preference is for people who finish the year strong, rather than those who start off with a bang and end with a whimper. To the best of my knowledge, there’s no reason to believe this. There’s no credible study that shows if you hoard guys who finished strong in the previous year that you’ll find success in the following season. But wouldn’t it be nice if this was the case?

Part of the issue is defining what a strong finish is. Is September enough? The final six weeks? The entire second half? How you frame the question will impact the results. In 2013, Justin Turner had a .764 OPS after the All-Star break. That’s a solid total but where he really did his damage was in September. That month, Turner had a .929 OPS.

In the first half of the year, Turner had a .637 OPS. In July and August, his OPS was .658 – or pretty much what we would expect from his previous numbers. September is where it all came together for him. But his entire second half consisted of 112 PA and only 42 of those came in September. If we’re going to look at this, we have to accept that we’re dealing with small samples.

So, we’re going to use second-half results to be our strong finish indicator, since that will give us the biggest sample. We’re going to require 100 PA in both the first and second half and we’re going to require an OPS difference of 50 points in either direction. Here are the results from the 2014-2018 seasons. We’ll start with the guys who improved 50 or more points in the second half:

Year	Name	1st Half	2nd Half	Diff
2018	Michael Conforto	0.710	0.895	185
2018	Brandon Nimmo	0.863	0.917	54
2018	Jay Bruce	0.613	0.811	198
2017	Jose Reyes	0.655	0.828	173
2017	Asdrubal Cabrera	0.736	0.830	94
2017	Wilmer Flores	0.753	0.863	110
2017	Yoenis Cespedes	0.822	0.966	144
2016	Asdrubal Cabrera	0.746	0.911	165
2016	Neil Walker	0.753	0.993	240
2016	Wilmer Flores	0.764	0.819	55
2015	Curtis Granderson	0.757	0.898	141
2015	Lucas Duda	0.775	0.956	181
2015	Juan Lagares	0.623	0.709	86
2015	Wilmer Flores	0.682	0.739	57
2015	Michael Cuddyer	0.661	0.811	150
2015	Daniel Murphy	0.736	0.803	67
2015	Ruben Tejada	0.654	0.724	70
2014	Travis d’Arnaud	0.646	0.787	141

That’s a fair number of players who improved in the second half. But with the second-half kicks of 2015, 2016 and 2018, it’s not all that surprising. It’s nice to see that 11 of the 18 people in this grouping improved by over 100 points. Now let’s take a look at the players from this same time period who saw their OPS drop by at least 50 points:

Year	Name	1st Half	2nd Half	Diff
2018	Wilmer Flores	0.782	0.680	102
2018	Jose Bautista	0.830	0.597	233
2018	Kevin Plawecki	0.771	0.621	150
2017	Juan Lagares	0.725	0.624	101
2016	Yoenis Cespedes	0.955	0.778	177
2016	James Loney	0.774	0.656	118
2014	Daniel Murphy	0.755	0.696	59
2014	David Wright	0.765	0.565	200
2014	Curtis Granderson	0.768	0.639	129
2014	Juan Lagares	0.734	0.670	64

There aren’t nearly as many people in this grouping as there were in the first one. But 2014 was well represented here. A tiny bit surprising, as the Mets were five games below .500 in the first half and a game over in the second half. Of course, Jacob deGrom going 6-1 with a 2.18 ERA in the second half influenced things some.

Now the question is: Did the second half performance indicate how they would do the following season? Let’s take a look, starting with the players who improved in the second half. We’ll compare their season-long OPS from one year to the next:

2018 Conforto .797 OPS, 2019 .856 OPS
2018 Nimmo .886 OPS, 2019 .783
2018 Bruce .680 OPS, 2019 .784
2017 Reyes .728, 2018 .580
2017 Cabrera .785 OPS,,2018 .774
2017 Flores .795 OPS, 2018 .736
2017 Cespedes .892 OPS, 2018 .821
2016 Cabrera .810 OPS, 2017 .785
2016 Walker .823 OPS, 2017 .801
2016 Flores .788 OPS, 2017 .795
2015 Granderson .821 OPS, 2016 .799
2015 Duda .838 OPS, 2016 .714
2015 Lagares .647 OPS, 2016 .682
2015 Flores .703 OPS, 2016 .788
2015 Cuddyer .699 OPS, 2016 Did Not Play
2015 Murphy .770 OPS, 2016 .985
2015 Tejada .688 OPS, 2016 .489
2014 d’Arnaud .718 OPS, 2015 .825

At first glance this is an overwhelming refutation of the hypothesis that a big second-half performance is a good indicator of a better follow-up season. But things are never what they seem. We have guys like Nimmo, Cespedes and Duda who saw their playing time the following year drop off significantly due to injuries. Then we have guys like Walker and Cabrera who were traded the next season in mid-year. Plus we have guys like Flores and Lagares who were never able to make the jump into solid starter, regardless if they had a strong finish the previous year or a weak one.

Now let’s look at the decliners:

2018 Flores .736 OPS, 2019 .848
2018 Bautista .727 OPS, 2019 Did Not Play
2018 Plawecki .685 OPS, .2019 .629
2017 Lagares .661 OPS, 2018 .765
2016 Cespedes .884 OPS, 2017 .892
2016 Loney .703 OPS, 2017 Did Not Play
2014 Murphy .734 OPS, 2015 .770
2014 Wright .698 OPS, 2015 .814 OPS
2014 Granderson .714 OPS, 2015 .821
2014 Lagares .703 OPS, 2015 .647

Again, we don’t see much information to support the initial hypothesis.

A normal person would give up on the hypothesis. But no one ever accused me of being normal. My next piece will look at the question in a different way, with an attempt to control for quality a little bit better. The default assumption should be that we don’t find much different than what we found here. But you never know.

Very few people would argue against the notion that experience matters. You’re going to have open heart surgery, you want a doctor who’s done the operation 100 times before. You’re getting new brakes done, you want someone who’s not taking the wheels off the car for the first time. You’re being sued you want a lawyer who’s defended people in the same situation over and over again.

It’s not a great leap to go from experience mattering in those scenarios to holding the belief that experience matters in baseball players. You want the experience and the All-Star resume of Michael Conforto rather than turning RF over to Tim Tebow. Of course that’s an extreme situation. But let’s take another situation, one that’s maybe a little more common. Should you prefer an experienced player like Adeiny Hechavarria over a relatively inexperienced guy like Luis Guillorme?

There’s no right answer to that question.

Coming into 2019, Hechavarria had over 3,000 PA in the majors and a .635 OPS, including a .624 mark in 2018 split among three clubs. He was a good defensive shortstop who had never displayed the ability to hit in the majors. Then, in 151 PA with the Mets, he put up a .611 OPS, or basically what we would expect. But then all of the planets aligned and in 70 PA with the Braves after the Mets cut him rather than pay him a roster bonus, Hechavarria put up a 1.039 OPS.

Did Hechavarria play well with the Braves because of his previous MLB experience? That seems preposterous given that his previous experience with the bat was nearly all bad. Instead, he played well because in the tiny sample of 70 PA, the hits fell in for him. At age 30, he got very lucky in a small sample. Luck doesn’t care if you have experience or not. We saw Kirk Nieuwenhuis come up at age 25 and put up an .821 OPS in his first 114 PA. The rest of the year he had a .618 OPS in 200 PA. The hits fell in right away for the rookie.

We can name other youngsters who had fortune smile on them in small samples before the age of 30. There was Ike Davis in 2010 who in 149 PA put up a BABIP 63 points higher than his career average at the age 24. And there was Josh Satin in 2013 and Eric Campbell in 2014. And we can name guys on the wrong side of 30 who had good luck in small stretches, too. Rod Barajas had a great month in 2010 at age 34. Another catcher, John Buck at age 32 in 2013, had a similar stretch of good play. And who can forget 32-year-old James Loney who hit like Babe Ruth for two weeks back in 2016.

Despite witnessing first hand that luck can happen to young guys and old guys, the Mets seemingly have a preference for veterans. Sure, you can point to Pete Alonso and say the Mets gave the rookie the shot. But it’s kind of hard to say that hitting 53 HR over 693 PA is luck. So, let’s ignore guys who play a full season. Instead, let’s look at guys in part-time roles, guys who are there for depth as much as anything else.

It’s difficult to say Player X was depth and Player Y was not. So, let’s use the blunt tool of playing time, as measured by PA. Let’s go over the past three years and look at the guys who had playing time between 50 and 500 PA. Furthermore, let’s not count guys who failed to top 500 PA because they were traded or spent considerable time on the DL/IL – things that were mostly beyond their control. A healthy Yoenis Cespedes certainly gets more than 500 PA in 2017 and 2018.

We’ll break our guys in the past three years into two groups – those with fewer than 500 MLB PA in one group and those with more than 1,000 MLB PA in the other. And see if there’s a reason to prefer the experienced guys. Let’s start in 2019 and work our way back.

Name	Previous PA	2019 PA	OPS	Name	Previous PA	2019 PA	OPS
J.D. Davis	181	453	.895	Juan Lagares	1834	285	.605
Dominic Smith	332	197	.881	Adeiny Hechavarria	3004	151	.611
Tomas Nido	100	144	.547	Joe Panik	2598	103	.738
Luis Guillorme	74	70	.684	Carlos Gomez	5128	99	.616

This was clearly a win for the young guys. Davis and Smith were hands down the best players in our depth group and only one of our experienced guys did better than Guillorme. And the parameters of our experiment worked in the favor of the experienced guys, as Keon Broxton and his .371 OPS didn’t quite reach our 1,000 PA requirement, while Aaron Altherr and his .458 OPS was kept off the list as he only amassed 35 PA before the Mets sent him away. And ditto for Rajai Davis and his .631 OPS in 29 PA.

Now let’s look at 2018:

Name	Previous PA	2018 PA	OPS	Name	Previous PA	2018 PA	OPS
Jeff McNeil	0	248	.852	Wilmer Flores	1582	429	.736
Dominic Smith	183	149	.675	Austin Jackson	4278	210	.639
Tomas Nido	10	90	.438	Jose Bautista	6845	302	.718
Luis Guillorme	0	74	.523	Jose Reyes	7989	251	.580
				Devin Mesoraco	1300	229	.715
				Adrian Gonzalez	7859	187	.672
				Juan Lagares	1770	64	.765
				Jose Lobaton	1221	57	.470

Another win for the young guys. Again, we see the top youngster blowing away any and all of the veterans. Plus, one can make the case that if Lagares continued to hit as well as he was before he got injured, he would have finished with more than 500 PA and not counted for the veterans. But he was included here because his solid OPS was achieved with a .392 BABIP, one he clearly wasn’t going to keep up for the remainder of the season. My opinion is that expected lousy performance going forward would have kept him from 500 PA, even if the injury didn’t happen.

Now let’s examine 2017:

Name	Previous PA	2017 PA	OPS	Name	Previous PA	2017 PA	OPS
T.J. Rivera	113	231	.760	Wilmer Flores	1220	362	.795
Brandon Nimmo	80	215	.797	Juan Lagares	1498	272	.661
Dominic Smith	0	183	.658	Rene Rivera	1199	187	.669
Amed Rosario	0	170	.665	Nori Aoki	2670	116	.746
Matt Reynolds	96	130	.626
Kevin Plawecki	409	118	.764
Gavin Cecchini	7	82	.529
Travis Taijeron	0	59	.540

This one’s pretty much a draw. Nimmo and Flores had similar OPS totals but with 147 more PA for Flores. The youngsters counter with Plawecki and Rivera both performing similar to Aoki, but combining for over 200 more PA.

Putting all three years together, we have 32 seasons in our sample, with 16 being in the sub-500 PA group and 16 being in the 1,000-and-up group. Six people in the young group put up a season with at least a .750 OPS, compared to just two from our veteran group. On the flip side, five of our youngsters finished with an OPS below .600, compared to just two from our veteran group.

It seems that the Mets will accept mediocre (defined as below average) from their veterans but will cut bait with them if they are horrible. Or perhaps with two of the five horrible seasons from our young-gun group coming from a defensive-minded catcher, maybe they’ll prove more equal in this regard over the long haul, not giving a bigger break here to young guys at all.

Looking at these numbers, it’s hard to come away with the idea that veterans >>>>> youngsters. The 2019 Mets had more experience at Triple-A but those veterans when given a shot in the majors, well, they bombed. So as we look at 2020 and see a Syracuse team with not as much MLB experience as the year before – maybe that’s not a terrible thing.

The model should be to acquire a Davis like J.D., rather than Rajai. The former Davis was a young guy who was blocked in Houston. The latter Davis had a ton of MLB experience but couldn’t get anything more than an NRI. Maybe the fact that the top outfielder at Triple-A this time has only 397 PA in the majors (compared to the 4,581 R. Davis had last year) isn’t anything to fret about at all. While Jarrett Parker only has 397 PA in the majors, he has a .771 OPS in those trips to the plate.

Prior to the start of the 2019 season, my opinion was that signing Wilson Ramos was the best move of the offseason for Brodie Van Wagenen. It seemed much better than paying the freight for J.T. Realmuto in trade or for Yasmani Grandal in cash. By mid May, I was seriously questioning my judgment. But Ramos rebounded and by the end of the year he seemed like a solid enough signing.

By the end of the year, the spin doctors began weaving the narrative that Ramos, stung by the knowledge that the team’s top two pitchers preferred to throw to a different catcher, worked harder on his defense in the second half of the season. In a November article for MetsBlog, John Harper quoted an unnamed scout who opined, “It just looked like he made more of an effort in general defensively. If he does that he’s adequate back there, which is good enough because of what he gives you offensively.”

Whenever you’re reading an article that quotes an unnamed scout – always, always, always take it with a huge grain of salt. Because scouts never go on the record with a reporter, there’s always the chance, however slight, that it’s completely made up out of thin air. Now, my opinion is that Harper actually did talk to a scout. But what’s this scout’s track record in objectively evaluating catcher defense? He’s trotted out as an expert witness but we have no way of discerning if this scout knows what he’s talking about in this area.

What we can do is look at the actual performance of the pitchers with Ramos behind the plate, to see if these subjective opinions of the unnamed scout can be supported by actual outcomes. Let’s look at the starting pitchers, as they were mostly good. It wouldn’t be fair to knock Ramos because the Mets utilized relievers who were terrible.

Here are the 113 games that Ramos started behind the plate for the Mets last season and the results of the starting pitchers:

Date	Opp. W-L	IP	ER	H	HR
3/28	0-0	6	0	5	0
3/30	0-1	6	4	7	1
4/1	2-2	5.1	1	6	1
4/2	2-3	5	2	8	1
4/4	2-3	6	2	1	1
4/6	3-3	5	0	2	0
4/7	3-4	4.2	7	4	0
4/10	6-3	7	4	5	0
4/11	7-4	6	2	4	1
4/12	7-5	6	2	6	0
4/14	8-6	5	3	5	2
4/15	9-5	5	5	9	1
4/16	9-6	0	6	4	2
4/19	10-8	4	1	3	1
4/20	10-9	4.1	5	7	0
4/22	12-9	6	1	3	1
4/23	12-10	7	0	5	0
4/24	12-11	4.2	1	3	0
4/26	13-13	4	5	5	0
4/28	15-13	7	2	5	1
4/29	11-16	6	4	7	0
4/30	12-16	5.1	1	3	1
5/2	13-17	9	0	4	0
5/3	17-16	5.2	3	9	2
5/4	18-16	7	2	6	0
5/6	19-16	7	2	4	1
5/7	20-16	6	4	9	2
5/10	10-27	7	2	9	0
5/14	16-24	8	2	4	1
5/15	16-25	2.1	5	6	1
5/17	10-31	5	6	9	1
5/18	11-31	3.2	2	5	1
5/20	19-27	4	2	3	1
5/21	19-28	7	3	4	2
5/23	19-30	6	1	10	0
5/24	18-30	5.1	6	10	2
5/25	19-30	5	1	5	0
5/26	19-31	7.1	3	5	1
5/28	36-18	6	2	4	1
5/29	36-19	6	3	7	0
5/31	28-29	7	4	7	2
6/2	29-30	6	5	8	2
6/4	24-34	6.2	3	5	0
6/5	25-34	9	0	5	0
6/7	32-29	6	2	6	0
6/8	33-29	6	2	6	0
6/11	41-24	6	3	7	0
6/13	33-33	7	2	6	1
6/14	34-33	6	3	3	1
6/15	35-33	6	4	6	1
6/17	42-30	6	4	10	1
6/19	43-31	5	5	6	2
6/20	40-33	2.1	6	5	0
6/22	41-34	7	1	5	0
6/24	39-38	4.1	7	10	3
6/25	40-38	5.1	4	5	2
6/27	42-38	6	1	2	1
6/29	49-34	2	2	3	0
7/2	54-28	6.1	2	5	0
7/3	54-29	5.1	3	7	1
7/5	45-42	7	2	3	1
7/7	46-43	5	6	8	1
7/12	33-55	5	6	5	2
7/14	34-56	5	1	6	0
7/16	58-34	4	2	5	1
7/17	58-35	6	3	5	2
7/19	48-49	7	0	3	0
7/20	49-49	5	1	5	0
7/21	49-50	6	2	6	0
7/23	47-52	6	0	1	0
7/25	48-53	7	0	4	0
7/26	46-56	5.1	3	5	1
7/27	46-57	9	0	5	0
7/28	46-58	5.2	3	6	1
7/31	46-58	7	1	5	0
8/1	46-59	7	0	4	0
8/2	47-61	3.2	5	6	0
8/3	48-61	4.1	3	7	0
8/5	42-68	4.2	4	8	1
8/6	42-69	8	0	8	0
8/7	42-70	6.2	2	7	1
8/9	61-53	6	4	9	1
8/10	61-54	7	2	7	1
8/11	61-55	5	0	4	0
8/13	70-50	5	5	12	1
8/14	71-50	6	1	2	0
8/15	72-50	5.1	2	4	2
8/17	44-79	7	1	3	0
8/20	74-51	6.1	1	5	1
8/21	74-52	4	1	5	0
8/22	74-53	6	0	2	0
8/24	78-52	6	5	6	2
8/27	69-61	6	4	6	2
8/28	70-61	3	9	9	3
8/29	71-61	7	4	5	2
8/31	69-64	5	2	7	1
9/1	69-65	6	2	7	1
9/3	77-59	7	4	8	1
9/4	78-59	5	1	7	0
9/6	72-67	5.2	2	6	0
9/8	73-68	5	4	6	1
9/9	75-68	7	1	3	1
9/10	75-69	7	1	7	0
9/11	75-70	6	0	4	0
9/13	95-53	5	4	5	1
9/14	96-53	7	0	3	0
9/15	96-54	7	1	6	0
9/17	66-85	7	0	4	0
9/20	72-81	7	0	4	0
9/21	72-82	7	1	7	0
9/22	73-82	4.2	2	3	1
9/23	54-101	5	6	9	2
9/25	55-102	7	0	2	0

And here are the SP ERA for Ramos by month:

4.53 – April
4.17 – May
5.03 – June
3.09 – July
4.16 – August
2.48 – September

This certainly seems to back up the scout’s opinion, as these numbers are better in the second half of the year. But is it because Ramos was doing a better job or was it because of something else? You’re probably aware of how the Mets played better after the All-Star break, in part because the schedule featured more teams below .500 than it did earlier in the year. Here’s the games against teams .500 or above versus teams below .500 by month, represented like a W-L record, with the good teams coming first:

16-6 – April
6-13 – May
14-3 – June
7-10 – July
12-9 – August
11-6 – September

Were the numbers bad in June because Ramos was lazy or were they bad because 14 of the 17 games that month that Ramos started came against the better teams in the league? And we might want to split the July numbers into pre and post AS break. The four games that Ramos started in July before the break were all against teams with a .500 or better record. And the starters had a 4.94 ERA in those games. After the break, the record was 3-10 and the starters had a 2.54 ERA.

When Ramos started at catcher in the first half of the season, the opposing team was .500 or better in 40 of the 62 games. After the break those numbers were 26 and 52, respectively.

The Mets played better against the good teams in September. Yet Ramos is still getting a bounce from the poor teams, as the starters posted a 2.15 ERA that month with Ramos catching versus the sub .500 squads.

Perhaps once you account for the level of competition there’s a tiny bit of evidence that the starting pitchers performed better with Ramos in the second half of the year. But you would still have to account for other factors. Perhaps the Mets did a better job of matching Tomas Nido with pitchers after the All-Star game. Maybe those teams with winning records the Mets played in the second half were fading. It could be that pitchers were battling injuries in the first half that they didn’t have later in the year. Plus, most of the starts from the depth guys, which were just as bad as you feared/remembered, came in the first half of the season.

After looking at all of these numbers, my opinion is that it’s hard to attribute better performance by the club’s starters primarily towards a better defensive performance by Ramos.

*****

Here are the starts by Ramos broken down by individual pitcher, ignoring the depth guys. You may see discrepancies between what’s here and what you would find if you looked at the pitchers’ splits pages on B-R. Here, the only thing that mattered were the starts by Ramos. It’s possible that he caught some of the starting pitchers when he came on as a pinch-hitter and stayed in the game defensively. Anyway, these are presented without comment.

Jacob deGrom

Date	Opp. W-L	IP	ER	H	HR
3/28	0-0	6	0	5	0
4/14	8-6	5	3	5	2
4/26	13-13	4	5	5	0
5/6	19-16	7	2	4	1
5/17	10-31	5	6	9	1
6/7	32-29	6	2	6	0
6/13	33-33	7	2	6	1
7/5	45-42	7	2	3	1
7/14	34-56	5	1	6	0
7/19	48-49	7	0	3	0
7/25	48-53	7	0	4	0
7/31	46-58	7	1	5	0
8/11	61-55	5	0	4	0
8/17	44-79	7	1	3	0
8/29	71-61	7	4	5	2
9/3	77-59	7	4	8	1
9/9	75-68	7	1	3	1
9/14	96-53	7	0	3	0
9/20	72-81	7	0	4	0
9/25	55-102	7	0	2	0

Noah Syndergaard

Date	Opp. W-L	IP	ER	H	HR
3/30	0-1	6	4	7	1
4/4	2-3	6	2	1	1
4/10	6-3	7	4	5	0
4/15	9-5	5	5	9	1
5/2	13-17	9	0	4	0
5/7	20-16	6	4	9	2
5/14	16-24	8	2	4	1
5/24	18-30	5.1	6	10	2
5/29	36-19	6	3	7	0
6/4	24-34	6.2	3	5	0
6/15	35-33	6	4	6	1
8/10	61-54	7	2	7	1
8/22	74-53	6	0	2	0
8/28	70-61	3	9	9	3
9/8	73-68	5	4	6	1
9/13	95-53	5	4	5	1

Zack Wheeler

Date	Opp. W-L	IP	ER	H	HR
4/7	3-4	4.2	7	4	0
4/12	7-5	6	2	6	0
4/23	12-10	7	0	5	0
4/29	11-16	6	4	7	0
5/4	18-16	7	2	6	0
5/10	10-27	7	2	9	0
5/21	19-28	7	3	4	2
5/26	19-31	7.1	3	5	1
5/31	28-29	7	4	7	2
6/17	42-30	6	4	10	1
6/22	41-34	7	1	5	0
6/27	42-38	6	1	2	1
7/2	54-28	6.1	2	5	0
7/7	46-43	5	6	8	1
7/26	46-56	5.1	3	5	1
8/1	46-59	7	0	4	0
8/6	42-69	8	0	8	0
8/13	70-50	5	5	12	1
8/24	78-52	6	5	6	2
9/4	78-59	5	1	7	0
9/10	75-69	7	1	7	0
9/15	96-54	7	1	6	0
9/21	72-82	7	1	7	0

Steven Matz

Date	Opp. W-L	IP	ER	H	HR
4/1	2-2	5.1	1	6	1
4/6	3-3	5	0	2	0
4/11	7-4	6	2	4	1
4/16	9-6	0	6	4	2
4/22	12-9	6	1	3	1
4/28	15-13	7	2	5	1
5/3	17-16	5.2	3	9	2
5/18	11-31	3.2	2	5	1
5/23	19-30	6	1	10	0
5/28	36-18	6	2	4	1
6/2	29-30	6	5	8	2
6/8	33-29	6	2	6	0
6/14	34-33	6	3	3	1
6/19	43-31	5	5	6	2
6/24	39-38	4.1	7	10	3
6/29	49-34	2	2	3	0
7/16	58-34	4	2	5	1
7/21	49-50	6	2	6	0
7/27	46-57	9	0	5	0
8/2	47-61	3.2	5	6	0
8/7	42-70	6.2	2	7	1
8/14	71-50	6	1	2	0
8/20	74-51	6.1	1	5	1
8/31	69-64	5	2	7	1
9/6	72-67	5.2	2	6	0
9/11	75-70	6	0	4	0
9/23	54-101	5	6	9	2

Jason Vargas

Date	Opp. W-L	IP	ER	H	HR
4/2	2-3	5	2	8	1
4/19	10-8	4	1	3	1
4/24	12-11	4.2	1	3	0
4/30	12-16	5.1	1	3	1
5/25	19-30	5	1	5	0
6/5	25-34	9	0	5	0
6/11	41-24	6	3	7	0
7/3	54-29	5.1	3	7	1
7/12	33-55	5	6	5	2
7/17	58-35	6	3	5	2
7/23	47-52	6	0	1	0
7/28	46-58	5.2	3	6	1

Marcus Stroman

Date	Opp. W-L	IP	ER	H	HR
8/3	48-61	4.1	3	7	0
8/9	61-53	6	4	9	1
8/15	72-50	5.1	2	4	2
8/21	74-52	4	1	5	0
8/27	69-61	6	4	6	2
9/1	69-65	6	2	7	1
9/17	66-85	7	0	4	0
9/22	73-82	4.2	2	3	1

The historic rookie season of Pete Alonso almost seemed to have no limits. The decision to bring Alonso to Queens after Spring Training was perhaps the best single move made by the front office this past season. Let’s see, he broke the Mets rookie and single-season home run records, was selected to the All Star Game where he won the home run derby, and claimed the title of MLB record holder for home runs in a rookie season – all of which led to a pile of post-season hardware, the most important being named Rookie of the Year. Alonso’s success came from phenomenal long-ball power, so I wanted to look into the 53 home runs and see what details, if any, could be learned about the slugger beyond face value. Are there obvious location strengths or holes in pitch location? Righty versus lefty pitching? Time of year? Let’s find out.

The first part of this appraisal was to re-watch (at least five times) every full at bat when a homer occurred and chart the pitch sequence as best as possible given limitations of an off-center outfield camera. I used the SNY feed for every home run except where one did not exist. The data were visually plotted in the standard 3×3 location matrix, but then reduced to five locations instead of nine: most fans can see the middle-middle part of the zone pretty well, so I used that field and from there inner-and-outer zones and upper-and-lower zones providing 4 quadrants around “center cut” locations. I recorded the pitch type (fastball or off speed), pitching hand (L/R), and inning. Home runs were charted individually and then grouped by month, which provides enough data for each segment, and still gives six looks across a season.

Most players have a sweet spot or pitch preference or some combination where they have a lot of comfort and so are proficient at hammering the ball, a trait that should be reasonably projected for Alonso. Before getting to the pitch location details, some basic things are worth noting. Thirty-nine of the home runs came against right-handed pitchers, leaving 14 coming against lefty pitching. Rather than a preference to crush RHP, this more likely represents facing more righties than anything else. As far as pitch selection goes, 60 % came from fast balls and 40 % came off of off-speed pitches. Alonso does not seem to care about who he faces or what pitch is coming at him. Talk about an offensive threat.

Assessing the pitch locations as a whole, that is looking at all 53 home runs in one plot showing middle-middle, upper-outer, upper-inner, lower-outer, and lower-inner reveals that not only does Alonso not show a preference for pitch type or delivery hand, he is phenomenal at covering the plate. To my surprise, Alonso homered in the middle-middle (30 %), upper-outer (30 %), and lower-outer (26 %) with pretty much equal devastation, with the remainder coming from the inner half. In all 53 at bats, the overall pitching strategy was to pitch Alonso away, and more down than up.

What approaches were pitchers taking, and is there anything opposing arms might use to get more success in 2020? One thing in the data is clear, Alonso crushed 32 homers (60%) seeing fewer than four pitches, so not much of an approach exists for those. Attacking a batter up with speed is certainly a modern approach to exposing batters prone to eye-level changes, but Alonso is a monster up in the zone, with the majority of fastball homers coming middle and up. Breaking pitches low in the zone and away is another common pitching tactic, although Alonso hit 55% of his off-speed pitches low and away. Alonso’s capacity to cover everything on the outer half, with fastballs or off-speed pitches only adds to his advanced hitting skills.

Given the amazingly comprehensive hitting approach Alonso shows in the home-run-hitting at bats, it seemed reasonable to appraise his consistency across the season and to explore if the home runs came in certain innings. In April, Alonso capitalized on apparently weaker relief pitchers to start his epic run by hitting quite a few late in games, but that was an anomaly. By inning, he hit the most in the first, the fewest in the second and third (as expected giving batting order), then hit them pretty evenly in the fourth through ninth. As far as the season goes, Alonso averaged 8.8 (+/- 1.6) home runs per month, with July really his only departure from the standard deviation; that is not a surprise given the All Star Break and the Home Run Derby blow out.

The data from this season demonstrate that Alonso is much more than a guy who runs mistake pitches out of the park. In virtually all respects of his hitting approach, he could easily be labeled “Mr. Consistency,” not prone to wild streaks like we see with Michael Conforto. Even with the strikeouts, Alonso has a solid eye for the whole strike zone, and capacity to put damage on a ball anywhere in the zone. Sure he hits a lot of home runs, but what the data show is a complete offensive threat that Mets fans should enjoy for a good long time.

Looking to pitchers for next season, it would be hard to offer a plan for succeeding against Alonso. He hit the fewest number of home runs on the inner half, so maybe that’s a plan, but any mistake will have a certain outcome for sure. That Alonso is hunting for good pitches in any count and commonly capitalizes early on, one approach may be to offer him more pitches with the hope of expanding his zone significantly, but refusing to fall behind and serve up the middle-middle pitch for which he is waiting.

Takeaways
1. Pete Alonso was successful at hitting home runs against left- or right-handed pitching, fastballs or off speed pitches.
2. Alonso covered the entire outer part of the strike zone equally well, hitting home runs up and down in the zone. His home run tallies are about equal in each month of the season and in virtually all innings.
3. All the data from the home run at bats in 2019 demonstrate Alonso as a consistent hitter with a strong sense of the strike zone.

Anyone who’s watched the Mets this century has at one point or another lamented the money the club has had on the injured list. Sometimes, like in 2009, it’s just the sheer number and while others, like in 2019, it’s the high-dollar value. This past season, the Mets had three players who combined to produce just 8 PA yet got paid $52.5 million. It’s tough to compete when one-third of your payroll gives you virtually nothing.

But how does that compare to the rest of MLB? Is there an “acceptable” amount of dollars to be unavailable due to injury? Let’s find out. Below is a list of players who missed two months or more due to injury, while making at least $5 million per year. Both the time frame and the dollar value were chosen to be significant. A team could have a highly-paid guy miss six weeks and not show up on this list. But you can miss six weeks and still have 500 PA. You can say that two months is arbitrary and there will be no argument. But you have to put a line in there somewhere. In addition to being somewhat arbitrary, please note that this list is also somewhat subjective.

For instance Fielder is listed for the Rangers and not the Tigers, even though both clubs paid him not to play in 2019. Fielder was traded from Detroit to Texas and they sent some money to offset the salary. Fielder played three years for the Rangers before having his career end early due to neck surgery. Reed is listed because of his thumb injury, although you could make a case that it was ineffectiveness at Triple-A which ended his Twins career. And there are other cases. If you were to complete a list, you might have different people on/off than what’s listed here.

The list was created using the Opening Day payroll number from Cot’s. It was supplemented with Sportrac to get a dollar figure for guys like Gennett, who opened the year on the IL and then was later traded to another team. The Reds were not responsible for 100% of Gennett’s salary. His number was derived from Sportrac.

Team	Player	Salary	Playing Time	Production
BAL	Alex Cobb	$14.00	12.1 IP	(-0.6 fWAR)
BAL	Mark Trumbo	$13.50	31 PA	(-0.3 fWAR)
BOS	Nathan Eovaldi	$17.00	67.2 IP	(-0.3 fWAR)
BOS	Dustin Pedroia	$15.13	21 PA	(-0.4)
BOS	Steve Pearce	$6.25	99 PA	(-0.9)
BOS	Eduardo Nunez	$5.00	174 PA	(-1.0)
NYY	Giancarlo Stanton	$26.00	72 PA	0.4
NYY	Jacoby Ellsbury	$21.14	0 PA	0
NYY	Didi Gregorius	$11.75	344 PA	0.9
NYY	Dellin Betances	$7.25	0.2 IP	0.1
CLV	Corey Kluber	$17.20	35.2 IP	0.6
CLV	Carlos Carrasco	$9.75	80 IP	1
DET	Tyson Ross	$5.75	35.1 IP	0
DET	Jordy Mercer	$5.25	271 PA	0.6
KCR	Salvador Perez	$11.20	0 PA	0
MIN	Addison Reed	$8.50	0 IP	0
HOU	Joe Smith	$8	25 IP	0.4
HOU	Collin McHugh	$5.80	74.2 IP	0.5
LAA	Justin Upton	$18	256 PA	(-0.2)
LAA	Zack Cozart	$12.67	107 PA	(-0.9)
LAA	Matt Harvey	$11	59.2 IP	(-0.3)
LAA	Cody Allen	$8.50	23 IP	(-0.7)
OAK	Stephen Piscotty	$7.33	393 PA	0.6
SEA	Felix Hernandez	$27.86	71.2 IP	(-0.1)
TEX	Prince Fielder	$9	0 PA	0
ATL	Darren O’Day	$9.00	5.1 IP	0.1
ATL	Ender Inciarte	$5.70	230 PA	0.9
MIA	Martin Prado	$15	260 PA	(-1.2)
NYM	Yoenis Cespedes	$29	0 PA	0
NYM	David Wright	$15	0 PA	0
NYM	Jed Lowrie	$8.50	8 PA	(-0.1)
PHI	David Robertson	$10	6.2 IP	(-0.1)
PHI	Tommy Hunter	$9	5.1 IP	0.2
PHI	Pat Neshek	$7.75	18 IP	(-0.3)
WSN	Ryan Zimmerman	$18	190 PA	0.1
CHC	Ben Zobrist	$12.50	176 PA	0.2
CHC	Brandon Morrow	$9	0 IP	0
CIN	Scooter Gennett	$6.57	72	(-0.4)
CIN	Alex Wood	$10	35.2 IP	(-0.2)
MIL	Corey Knebel	$5.13	0	0
PIT	Francisco Cervelli	$11.50	123 PA	(-0.2)
PIT	Corey Dickerson	$5.71	142 PA	0.7
PIT	Chris Archer	$7.67	119.2	0.7
PIT	Gregory Polanco	$6.10	167 PA	(-0.2)
STL	Jedd Gyorko	$5.38	62 PA	(-0.2)
STL	Brett Cecil	$7.75	0	0
STL	Luke Gregerson	$5.00	5.2 IP	0.1
ARI	Taijuan Walker	$5.03	1 IP	0
COL	Mike Dunn	$7	17.2 IP	0
LAD	Rich Hill	$18.67	58.2 IP	0.9
SDP	Garrett Richards	$7	8.2 IP	0
SFG	Johnny Cueto	$21	16 IP	0

By this count, there were 52 players who missed at least two months of the season while pulling down a salary of at least $5 million. That seems surprising to me, as my guess would have been more players to have fit the guidelines. Interestingly, there were three teams – Blue Jays, Rays, White Sox – not to have a player on our list. But not that it was all sunshine and lollipops for these franchises. Toronto paid the Dodgers $16.4 million to take Russell Martin off their hands, Tampa Bay sent $14.5 million to the Giants to do likewise with Evan Longoria and the White Sox spent around $8 million to watch Yonder Alonso stink for them for three months, only to watch him thrive once he was dealt to the Rockies.

As for the Mets, Robinson Cano does not make the list because he missed 6-7 weeks on the IL, missing our two-month guideline. And Brandon Nimmo missed enough time but did not pull down a $5 million salary.

Now that we have the individual players, let’s do another chart, this one showing the effect on a team level. And we’ll add in Opening Day payroll, to see how much of it was lost to significant injury time. These numbers come from Sportrac, since they already had it in one place and Cot’s did not. There are discrepancies between the two numbers but expediency wins out here.

Team	Payroll	Injured $	Percent Lost
PIT	$72.7	$35.98	49.49%
BAL	$73.4	$27.50	37.47%
NYM	$160.5	$52.50	32.71%
NYY	$223.0	$70.14	31.45%
LAA	$161.3	$50.28	31.17%
CLV	$124.9	$26.75	21.42%
MIA	$75.6	$15.00	19.84%
SEA	$144.4	$27.86	19.29%
RSX	$229.2	$43.38	18.93%
PHI	$160.2	$26.75	16.70%
CIN	$128.4	$16.57	12.90%
SFG	$178.6	$21.00	11.76%
KCR	$104.8	$11.20	10.69%
WSN	$172.3	$18.00	10.45%
STL	$174.3	$18.13	10.40%
ATL	$143.9	$14.70	10.22%
CHC	$221.6	$21.50	9.70%
DET	$114.6	$11.00	9.60%
LAD	$201.6	$18.67	9.26%
HOU	$168.8	$13.80	8.18%
OAK	$93.4	$7.33	7.85%
TEX	$130.8	$9.00	6.88%
MIN	$125.3	$8.50	6.78%
SDP	$104.3	$7.00	6.71%
COL	$157.2	$7.00	4.45%
ARI	$118.9	$5.03	4.23%
MIL	$135.9	$5.13	3.77%
TOR	$111.4		0.00%
WSX	$91.4		0.00%
TBR	$64.2		0.00%
Total	$4,166.9	$589.70	14.15%

The Mets had the second-most significant dollars lost to the IL and the third-highest percent of their payroll sidelined via injury. The average percent lost by this accounting was 14.15 % but only 10 of the 30 clubs exceeded this number. Of those 10, only one team made the playoffs, thanks to their huge overall payroll and remarkable play by that team’s reserves who were pressed into action.

Judging by 2019 numbers, the odds are stacked against you making the playoffs if you have an above-average dollars lost to the IL. Certainly the Mets hope that Lowrie will contribute this year and possibly Cespedes, too, although no one should be holding their breath on that one. With Wright a sunk cost and Cespedes likely to be that way, too, the Mets will likely be high on this list again next year. One of their keys will be not adding anyone else, with the possible exceptions of Cano and Jeurys Familia.