The 2019 Mets were done in by their schedule

When the Mets went on their 15-1 run shortly after the All-Star break, many people were unimpressed because it came against teams that were not very good. Yet those very same people failed to hold the Brewers to the same standard. When Milwaukee went on an 18-2 run in September, they were beating the Marlins, Padres, Pirates, Reds and a Cubs team that had quit on itself. But we didn’t hear about that. The talk was how the Brewers were playing great, winning games when it counted.

Conventional wisdom in all sports is that you look to play .500 against the good teams and then clean up against the dregs of the league. But what happens when certain teams get to play significantly more games against the dregs? The A’s got to play 100 games against teams under .500 last year, the most in the majors. By contrast, the Marlins played just 41 games against teams with a losing record, the fewest of any team in MLB.

It should be intuitive that if you’re a sub-.500 team, you’ll get fewer chances to face losing teams. And vice-versa. Of the seven teams that played the fewest games against teams under .500, five of them were under .500 themselves, while a sixth team was exactly .500 on the season. And on the opposite end, the top nine teams with the most games against losing teams all had winning records.

So, who was the only team with a winning record to be in the bottom seven in total number of games against losing teams? Why, the Mets, of course. They faced sub-.500 teams this year 60 times, which tied with the Pirates for the third fewest total in MLB.

So, why did the Mets have such rotten luck with the schedule? The biggest reason was that the NL East was the only division in baseball not to have at least two teams with losing records in it, having just the Marlins.

A distant second is that the Mets always get the short end in interleague play. The Cardinals get to face the Royals as their rival that they play every year and in the 23 years of interleague play, the Royals have been above .500 just four times. The Nationals get the Orioles, who’ve gone 101-223 the last two years. And the Mets get the Yankees, who’ve won 90 or more games 17 times and have never finished with fewer than 84 wins in a season since the beginning of interleague play.

The Mets were 47-55 against teams with a .500 or better record in 2019. That’s a .461 winning percentage, essentially the same as the 101-win Twins, who had a .464 winning percentage. The difference of course was that the Twins played 33 fewer games against those winning teams. The Mets finished strong against these clubs, going 13-6 in their final 19 games against .500 or better teams. But their poor play against the good squads early in the year helped put them in such a big hole.

So, if the difference between the 101-win Twins and the 86-win Mets was essentially all due to the easier schedule of the Twins – buttressed by the fact that the Mets went 3-1 versus Minnesota this year – can we come up with a simple rating system to try to more accurately rank the teams? Let’s try giving double credit for beating a team .500 and above and a double penalty for a loss to a sub-.500 squad.

The Mets were 47-55 against the good teams. So, if we give double points for those wins, they’d be 94-55. They were 39-21 against the poor teams. If we give a double penalty for those losses, they’d be 39-42. Add both splits together and you get 133-97 for a .578 percentage.

Meanwhile, the Twins were 32-37 against the good teams. Double the wins and you get 64-37. They were 69-24 against the poor teams. Double the losses and you get 69-48. Add those together and you get 133-85 for a .610 percentage. The Twins finished with a 92-point advantage in winning percentage. Doubling the good wins and the bad losses cuts their percentage advantage to 32 points.

If we up the multiplier for good wins/bad losses from 2.0 to 2.5, we get the Mets at a .593 percentage and the Twins with a .606 percentage. This 13-point edge for the Twins certainly feels closer to being “right” than the actual 92-point advantage when it comes to describing the relative strengths of the two teams. Let’s do this for all 30 teams and see what we get.

Team Good W Good L Poor W Poor L Wins * 2.5 Loss * 2.5 New Wins New L Pct.
LAD 45 32 61 24 112.5 60 173.5 92 .653
ATL 52 43 45 22 130 55 175 98 .641
NYY 43 32 60 27 107.5 67.5 167.5 99.5 .627
HOU 35 28 72 27 87.5 67.5 159.5 95.5 .625
WSN 48 48 45 21 120 52.5 165 100.5 .621
MIN 32 37 69 24 80 60 149 97 .606
NYM 47 55 39 21 117.5 52.5 156.5 107.5 .593
TBR 38 35 58 31 95 77.5 153 112.5 .576
STL 42 42 49 29 105 72.5 154 114.5 .574
MIL 48 40 41 33 120 82.5 161 122.5 .568
PHI 48 52 33 29 120 72.5 153 124.5 .551
OAK 35 27 62 38 87.5 95 149.5 122 .551
CLE 25 39 68 30 62.5 75 130.5 114 .534
CIN 46 60 29 27 115 67.5 144 127.5 .530
CHC 39 45 45 33 97.5 82.5 142.5 127.5 .528
SFG 42 55 35 30 105 75 140 130 .519
ARI 35 40 50 37 87.5 92.5 137.5 132.5 .509
BOS 28 45 56 33 70 82.5 126 127.5 .497
TEX 31 53 47 31 77.5 77.5 124.5 130.5 .488
COL 38 60 33 31 95 77.5 128 137.5 .482
PIT 30 72 39 21 75 52.5 114 124.5 .478
CHW 35 53 37 36 87.5 90 124.5 143 .465
SDP 40 53 30 39 100 97.5 130 150.5 .463
MIA 38 83 19 22 95 55 114 138 .452
LAA 29 55 43 35 72.5 87.5 115.5 142.5 .448
TOR 35 58 32 37 87.5 92.5 119.5 150.5 .443
SEA 22 59 46 35 55 87.5 101 146.5 .408
KCR 28 60 31 43 70 107.5 101 167.5 .376
BAL 26 67 28 41 65 102.5 93 169.5 .354
DET 19 65 28 49 47.5 122.5 75.5 187.5 .287

In 2019, the average team found themselves playing 53% of their games versus teams .500 and above and 47% versus below-average teams. Yet the Mets played a whopping 63% of their games versus teams .500 and above. By contrast, the Twins played just 43% of their games against the good teams. And that’s not the worst mark this year in MLB. The Astros played just 39% of their games against teams with a winning record and the A’s had a 38% tally.

Under this simple system, the Mets would have the eighth-best percentage in baseball, rather than the 12th-best. And rather than 129 points behind the leader, they trail by 60 points in this ranking. Perhaps the most interesting thing about this ranking is that of the Marlins, who find themselves with a percentage 100 points higher than their actual one. The Marlins played a team .500 or better 75% of the time.

Is this any better than actual W-L records? My take as a Mets fan is yes. But even if this ranking is too simplistic to produce meaningful results, it seems few would argue with the overall point that ease of schedule plays a big factor into final records. Perhaps no better example exists than the 1954 Indians, who went 111-43 thanks to going 89-21 against teams with a losing record. They were 22-22 against teams with a winning record. They played just 29% of their games versus the good teams. Compare that to the Dodgers, who finished 2019 with 106 wins. LA faced a good team 48% of the time this season.

Examining J.D. Davis’ massive home/road split

In the five-year period from 2014-2018, the Mets hit better on the road than they did at home, by an average off 55 points of OPS. In the just-concluded 2019 season, the Mets had a .766 OPS at home, compared to a .774 mark on the road. It wasn’t a home-field advantage but since the hitters had been recently dealing with a significant home-field handicap, it was nice to see this level of production at Citi Field. For a point of comparison, in 2018, the team’s home/road OPS split was .646/.752 – or a 106-point deficit.

Five of the top eight hitters in PA had a home OPS over .800 in 2019. No one enjoyed home cooking more than J.D. Davis, who posted a 1.078 OPS at Citi Field. That’s the highest home OPS for a Met in Citi Field’s 11-year history and just the third time a player with more than a handful of PA topped the 1.000 mark. Davis joined 2015 Lucas Duda (1.000) and 2017 Michael Conforto (also 1.000) in that quadruple-digit club.

Davis was perhaps the biggest surprise of the season for the Mets, in large part because of his home park performance. By contrast, his road OPS last year was .710 – a full 368 points lower than what he produced in Citi Field.

In general, hitting better or worse at home isn’t really a repeatable skill. One year after posting that 1.000 home OPS, Conforto managed just a .682 mark in Citi Field. For his career in Citi Field, Conforto has an .852 OPS. Duda followed up his 1.000 OPS with a .790 mark the following year – and he has a lifetime .815 OPS at home, although the past few years he’s had other home parks besides Citi Field.

On a league-wide basis, players usually hit a bit better at home than on the road. In 2019, the MLB home/road OPS split was .767/.749 – or 18 points. The previous season it was .738/.718, or 20 points. That’s a bit less than what we saw previously. Over the past 10 years, the difference was 29 points. Duda’s lifetime split is 63 points while Conforto’s is 34 points. While both of these are above league average, they’re nowhere close to what Davis experienced in 2019.

And sure, an extreme ballpark is likely to produce some extreme results. Players will typically have a home OPS much higher than their road OPS if they are on the Rockies. But is Citi Field anywhere near that extreme? According to Baseball-Reference, Coors Field has a multi-year batting factor of 118, while Citi Field has a 92 mark.

But with Davis, we’re seeing a player go against the grain in what his ballpark typically produces. Citi Field has been somewhat of a pitcher’s park and Davis hit like gangbusters there last season.

There’s no reason to expect that Davis will put up a quadruple-digit home OPS in 2020. But is there any reason to expect he’ll put up a .710 road OPS next season? Davis spent the entire year with the Mets and amassed 453 PA on the season. But his playing time increased in the second half of the year. In 102 road PA after the All-Star break, Davis had an .821 OPS, compared to a .618 road OPS in the first half.

We have to remember that we’re dealing with a small sample to begin with and then we’re slicing and dicing it into even smaller pieces. That second half road OPS looks encouraging. But it came with a .415 BABIP, so how much weight should we really give it?

And that comes back to the real reason to be concerned about Davis’ 2019. It’s not that he was so good at home and below-average on the road. It’s that he put up his .895 OPS with a .355 BABIP. If he had enough PA to qualify for the batting title, that would have tied Christian Yelich for the fifth-best mark in MLB. And while Yelich runs a super-high BABIP year-in, year-out, you’ll find many more guys who put up an inflated BABIP one year and then return to average or worse numbers immediately.

How many people were convinced that Mallex Smith was a star after his 2018 season where he put up a 3.5 fWAR, when he hit .296 and stole 40 bases? Well, Smith stole 46 bases last year. But he was the textbook definition of replacement player with his 0.0 fWAR. How did it all go so wrong for Smith? Instead of the .366 BABIP of 2018, this past season he had a .302 mark in the category.

Perhaps Davis’ power makes you think he’s more likely to continue his elevated BABIP. That’s not a completely foolish idea. But Mookie Betts went from a .368 BABIP in 2018 to a .309 mark last year. He was still an incredibly valuable player, but his 6.6 fWAR was a noticeable drop from the previous season’s 10.4 mark.

And then there was Lorenzo Cain. In 2018, he was seemingly every Mets fan’s lament. The Mets needed a center fielder, Cain was a free agent and the Mets didn’t give him a second glance. Cain went out and put up a 5.7 fWAR in 2018, powered by a .357 BABIP. Last year Cain put up a .301 BABIP and saw his fWAR drop to 1.5 in nearly the exact same level of playing time.

So, what happens when Davis’ BABIP drops from the .355 mark he put up in 2019? Well, we’re very likely to find out. Maybe Davis can still be a valuable player even with the drop. His playing time should go up and there’s a chance he can improve his defense and baserunning from 2019. The latter two were well below average and all three kept him from posting an fWAR even higher than the 2.4 that he actually did.

A league-average player at a minimum salary is a very good thing. There’s nothing wrong if Davis is installed as the club’s left fielder next year and puts up a 2.0 fWAR over 600 PA. But there’s nothing wrong if the Mets look to sell high on Davis and look for a league-average center fielder that will allow them to play Conforto and Brandon Nimmo in the corners. Davis gives the Mets options and those are good things to have.

Comparing the Mets’ recent SP usage to the 1976 squad

There’s safety in doing things the way every other team in MLB does it. Then there’s the reality of what may be good for some clubs is not good for your team. And the Mets are being served a healthy dose of reality here in 2019, specifically in regards to how they run their pitching staff. By action, the Mets would prefer to keep their starter to under 100 pitches and use relievers in designated one-inning roles to finish the game. But the reality is that the Mets’ bullpen stinks and they need to totally rejigger how they use every pitcher on their staff.

Remarkably, we’ve seen the braintrust ask the starting pitchers to go longer and throw more pitches in games. The bullpen has been such a dumpster fire, they’re ignoring their long-held belief about the 100-pitch boogeyman. Hallelujah!

Starting pitchers generally lose their effectiveness the more times they go through a team’s lineup. But the reality for the Mets right now is a starting pitcher going through a lineup for the fourth time is probably better than just about any reliever facing a hitter for the first time. But even when one accepts that reality, there’s still the stigma of “abusing” the pitcher by letting his pitch count go significantly past 100 pitches.

We’ve developed an entire generation of fans who believe that pitchers going past 100 pitches on a regular basis will definitively result in injury. And what’s remarkable about that is that there’s no proof whatsoever that says 98 pitches equals safety and 115 pitches equals danger. But we’ve developed a fondness for paint-by-number managerial strategy. And when you combine that with an understandable desire to keep pitchers healthy – you get this strict adherence to a pitch count system based on round numbers rather than any concrete science.

There’s a great episode of the TV show M*A*S*H where Col. Blake is consoling Hawkeye after the doctor lost one of his patients. Blake says, “There are certain rules about a war. Rule number one is young men die. And rule number two is that doctors can’t change rule number one.”

The MLB equivalent of that is this – Rule number one is that starting pitchers get injured. And rule number two is that no utilization rule by smart men can change rule number one.

We all want to keep as many pitchers healthy as we can. And certainly, we want to see the best pitchers handled with care. There are two things to keep in the front of your mind. First is that all pitchers are not created equal. And second is that not all pitches are created equal. And what’s crazy is that we had a better understanding of this 40 years ago than we do today.

An idea has built up that before we started tracking pitches that club’s used little discretion on how they employed their starters. If he was pitching well, he was left in the game as long as possible. But the reality is a little different.

Let’s look at the pitch counts for the 1976 Mets. That year’s team was chosen because not only did it have the big three of Tom SeaverJerry KoosmanJon Matlack – it also had fairly stable, well-known guys filling the final two spots in the veteran Mickey Lolich and the youngster Craig Swan. Here are the ages and starts that season for that quintet:

Seaver – age 31, 34 starts
Koosman – age 33, 32 starts
Matlack – age 26, 35 starts
Lolich – age 35, 30 starts
Swan – age 25, 22 starts

It’s not perfect, but it’s a decent ballpark age group for the 2019 Mets, whose rotation guys check in with the following ages:

Jacob deGrom – age 31
Noah Syndergaard – age 26
Zack Wheeler – age 29
Steven Matz – age 28
Jason Vargas – age 36

Pitch counts for 20 games were checked, starting with Seaver’s start on June 9. Worked out pretty nicely as the Mets were trying to get over the .500 mark in this period, too.

The big thing is that we don’t have pitch counts for all games in this era. Instead, Tom Tango’s basic pitch count formula was used. In this estimator, three things are taken into account: batters faced, walks and strikeouts. And the formula to come up with the estimate is as follows:

Total batters * 3.3 + BB * 2.2 + Ks * 1.5

How good is the estimator? Well, we know that Syndergaard threw 105 pitches Saturday night. He faced 27 batters, had 2 BB and 5 Ks.
27 * 3.3 = 89.1
2 * 2.2 = 4.4
5 * 1.5 = 7.5
Total estimate = 101

Saturday night was a bit unusual, as Syndergaard left in the middle of an AB. If he had thrown one more pitch and that PA ended, he would have thrown 106 and the estimate would have been 104. Or if he had thrown two more pitches and walked the batter, he would have thrown 107 and the estimate would be 107. But still, four pitches off from an extremely simple formula is great. Plugging the numbers in, we get the following estimates:

Seaver – 112
Matlack – 142
Kossman – 91
Swan – 139
Lolich – 94
Seaver – 109
Matlack – 119
Koosman – 88
Swan – 118
Seaver – 116
Lolich – 75
Matlack – 73
Koosman- 86
Swan – 111
Seaver – 141
Matlack – 128
Koosman – 108
Swan – 96
Seaver – 90
Lolich – 127

The information was presented this way so you could see that the Mets weren’t slaves to pitching guys every five days. Perhaps that’s another thing that the 2019 Mets could go Back to the Future with in regards to how they handle their starters. Regardless, let’s show these numbers broken down by pitcher:

Seaver – 112, 109, 116, 141, 90
Matlack – 142, 119, 73, 128
Koosman – 91, 88, 86, 108
Swan – 139, 118, 111, 96
Lolich – 94, 75, 127

Not every outing was 140 pitches. And eight outings, when no one was concerned one bit about pitch counts, were under 100 pitches. When Lolich and Swan were going good, they were left in to fly past what would be acceptable today. And four of five starts for Seaver could easily fit in to what they did today. But they didn’t hesitate to let him go to 141 pitches in the other.

Let’s show these pitch estimates one more way. Let’s strip away the pitcher and show the raw estimates, from high to low:

142, 141, 139, 128, 127, 119, 118, 116, 112, 111, 109, 108, 96, 94, 91, 90, 88, 86, 75, 73

This is a 20-game sample of the 162-game season. We get a Mean of 108.15 and a Standard Deviation of 21.06. Now let’s compare that to what the Mets have done the last several games. After Syndergaard was removed in an outing where he had just passed the 100-pitch boogeyman, there has been an apparent shift in allowing starters to go longer in the past 10 games. Now we can use the actual pitch count rather than an estimate. Here they are in that time frame:

117, 107, 112, 120, 98, 94, 93, 116, 113, 105

In this 10-game sample, we get a Mean of 107.5 and a Standard Deviation of 9.77. It’s interesting that the means are fairly close but the SD from our 1976 sample is over twice as large as it is in the sample from today. Does that mean anything? Maybe, although we shouldn’t be quick to make any definitive judgments.

In our 1976 sample, four of the five starters had been in the majors at least four years previously. The outlier was Swan, who had been in the majors parts of three previous seasons and had been in 16 games (15 starts). But Swan was relatively older at age 25.

My opinion is that you need to be careful with younger arms but older arms which have been properly stretched out are capable – in the right circumstance – to go considerably longer than what they typically do here in the 21st Century. Of course, the right circumstances are not so easily defined. If a pitcher is cruising through the first four innings and then has a 22-pitch inning in the fifth and a 32-pitch inning in the sixth – maybe you don’t ask him to come out for the seventh.

But if deGrom has thrown 112 pitches through seven innings and they’ve been relatively stable, or getting easier the last few innings, then perhaps he should be allowed to pitch the eighth, especially given the makeup and results of the 2019 bullpen.

The advantage of the 2019 Mets’ rotation is that they don’t have anyone who necessarily needs to be babied. Syndergaard at age 26 already has 101 starts in the majors under his belt. Matz at age 28 has made 43 starts since his last big trip under the knife. Wheeler at age 29 has made 60 starts in the majors since his last major surgery.

No one would argue that the staff hasn’t been trained – both mentally and physically – to throw 95 pitches in an outing. No one is suggesting that they go from this level to throwing 142 pitches like Matlack apparently did. But they’ve all exceeded 105 pitches in an outing with Matz going as far as 120. They’ve gradually moved to the current level and my opinion is that they can keep gradually extending what they ask from this group.

Right now, my opinion is that we should look at 115 pitches as the new 100-pitch level. Every outing does not have to be 115 pitches. Look at Koosman’s pitch estimate in our 1976 sample, where three of his four outings had an estimate of under 100 pitches. But if a 2019 Mets pitcher is cruising at 112 pitches, we shouldn’t react with horror if he is sent out to start the next inning.

Jeff McNeil continues to provide elevated BABIP

Last year, eight players drew votes in the NL Rookie of the Year voting, including Jeff McNeil, who was one of three players to draw a third place vote and receive one point in the balloting. Ronald Acuna Jr. won in a landslide, getting 27 of the 30 first-place votes. Juan Soto picked up two votes for first place and finished second overall and Walker Buehler grabbed the remaining first-place vote and finished in third place.

Five of the eight players to receive votes were hitters. Let’s look at some numbers last year from that quintet, which also included Brian Anderson and Harrison Bader:

Player Age fWAR PA BABIP
Acuna 20 3.7 487 .352
Soto 19 3.7 494 .338
Anderson 25 3.4 670 .332
Bader 24 3.5 427 .358
McNeil 26 2.7 248 .359

McNeil stands out in a way with the numbers posted in that chart – and not in the way that you would want. He was the oldest player of the group, had the fewest PA and fWAR and had the highest BABIP of the batch. But two of those four were just barely the worst. He’s only a year older than Anderson and his BABIP was just .001 higher than Bader’s and .007 higher than Acuna’s. And of course, playing time and fWAR are related. McNeil had the second-highest fWAR among all players last year with fewer than 345 PA, trailing only Adalberto Mondesi.

But it’s hard not to notice how each club treated their rookies in the offseason. Acuna and Soto are batting cleanup for their team, with Soto getting a lucrative long-term deal. After splitting time between third base and the outfield in 2018, Anderson has settled in at the hot corner and found a home in the second spot in the lineup. And after a trade-deadline deal last season of Tommy Pham, Bader has become the Cardinals starting center fielder.

Meanwhile, after making 52 of his 53 starts at second base last year, the Mets went out and actively traded for a 36 year old 2B with five years and $120 million on his contract. And while it looked like maybe McNeil would move over to 3B, the club went out and signed a 35 year old to a two-year deal. The Mets told their rookie to grab an outfielder’s glove and battle for playing time at a position he hadn’t played since college.

Then the 35 year old offseason acquisition got hurt and McNeil found himself back in the mix at third base. Which may be a blessing, because his work in the outfield in both Spring Training and the regular season looked pretty bad, especially going back on balls. Be that as it may, the end result is that he’s received fairly regular playing time here at the beginning of the season. Let’s run the same chart again, this time for 2019 stats and eliminating age and incorporating walk and strikeout rates:

Acuna 0.9 62 .303 19.4 14.5
Soto 0.2 58 .333 27.6 17.2
Anderson 0.1 59 .306 25.4 11.9
Bader 0.3 50 .217 28.0 16.0
McNeil 0.5 50 .421 12.0 6.0

Once again McNeil stands out, this time for his super elevated BABIP. In the 21st Century, only two of the 2,861 batters with enough PA to qualify for the FanGraphs leaderboards finished the year with a .400 BABIP – 2002 Jose Hernandez with a .404 mark and 2000 Manny Ramirez with a .403 average in the category. It’s extremely safe to say that McNeil won’t finish with a BABIP remotely close to what he has now if he plays a full season.

But while a .300 BABIP is normal for the league it doesn’t necessarily make it normal for the player. David Wright in 6,872 lifetime PA finished with a .339 BABIP and from 2005-2009 posted these rates: .340, .344, .356, .321 and .394 in that cursed year of ’09. You may think it’s sacrilegious to compare any Mets player to Wright but looking at all of MLB, we see 238 individual seasons where a player posted a BABIP of .350 or higher this century. Still only a fraction of our sample – a little over 8% – but at least we’ve gone from “virtually impossible” to “unlikely.”

Unfortunately, BABIP is a stat that takes an incredibly long period of time to “stabilize,” which is where it reaches a point where its “signal to noise” crosses the halfway point. Russell Carleton, before he joined the Mets front office, determined that you needed 820 balls in play for BABIP to reach an R of .7 – what we typically call the “stabilization” point, even if that term makes Mr. Carleton uneasy.

In his major league career to date, McNeil has a .369 BABIP but only 236 balls in play.

The only reasonable conclusion we can draw is that it’s still too early to consider that McNeil has elite BABIP skills. But it’s at least interesting that in the microscopic sample of 2019 that McNeil is continuing, even bettering, the mark he displayed in his small sample of 2018. Meanwhile, three of our other four rookies from last year have seen their BABIP drop off noticeably here in the early going, with only Soto, .338 in ’18 compared to .333 today, duplicating his big season.

While acknowledging that we are dealing with a too small sample size, one other thing we can check is McNeil’s xBABIP. The “x” stats are an attempt to use batted ball data to show what a player’s stats “should” be given his profile, the expected numbers, or x. There are several different versions of xBABIP numbers out there. Here, let’s use Mike Podhorzer’s calculation, both because he’s a friend and also because he has a handy-dandy calculator to use. From his career to date – and making some estimates because we’re combining two different seasons, we get McNeil with an xBABIP of .347 compared to his actual .369 mark. So, no doubt that McNeil has been fortunate, but the hitter he’s been to date has been one that should run an ultra-high BABIP.

Many people discounted McNeil last year because of his age and his elevated BABIP. But he’s not that old and he’s continuing to hit whenever given the chance. His ability to hit the ball to all fields and to make excellent contact are good signs that perhaps he can be one of those guys, like Wright, who consistently run a high BABIP. The only way we’ll know for sure is to see him do it over a longer stretch of time. Here’s hoping the Mets continue to give him that opportunity.

What the Mets need from their starters to make the playoffs

Most everyone expects Jacob deGrom and Noah Syndergaard to be very good. Many of us expect Zack Wheeler to be good, too. However, the outlook is not quite as rosy for Steven Matz and Jason Vargas. The questions become how good does the starting five have to be and can the first three make up for the second two. To answer these questions, let’s look to the playoff teams from the National League since 2012, when the current two Wild Card system was adopted.

That gives us seven seasons, with five teams per season, which works out to a sample of 35 teams. That’s reasonably large enough. However, it’s important to note that these are the raw numbers and the run environments were not necessarily stable in this time period. There were 3.95 runs per game scored in the NL in 2014 and 4.58 rpg in 2017. The hope is that the tradeoff for simplicity isn’t compromised too badly by a swing of roughly half a run between the low and high.

As you might expect, we see all kinds of ways to build a playoff team. We have teams with dominating pitching in both starters and relievers. There are great starting staffs with noticeably worse bullpens. And there are teams where the starters were okay but the relief corps was outstanding. Our 35 playoff teams ranged from starters amassing between 54 and 81 wins, with an ERA between 2.96 and 4.59, which was posted by the 2017 Rockies. The next worst mark for starters’ ERA was also by the Rockies and the 2018 version of the club had a 4.17 mark. Among the 33 non-Rockies teams, the worst mark was 4.05 by the 2017 Cubs. Here are our 35 playoff teams, with their numbers broken down by starters and relievers

Year Team Wins SP Record SP ER SP IP SP ERA RP Record RP ER RP IP RP ERA
2012 Nats 98 72-45 360 953.0 3.40 26-19 185 515.1 3.23
2012 Reds 97 66-43 412 1018.2 3.64 31-22 128 434.1 2.65
2012 Giants 94 71-49 414 998.1 3.73 23-19 179 452.2 3.56
2012 Braves 94 69-54 400 959.0 3.75 25-14 149 486.1 2.76
2012 Cards 88 71-47 398 989.1 3.62 17-27 205 473.1 3.90
2013 Cards 97 77-46 374 984.1 3.42 20-19 182 475.1 3.45
2013 Braves 96 67-51 386 989.2 3.51 29-15 126 460.2 2.46
2013 Dodgers 92 62-46 341 979.0 3.13 30-24 183 471.1 3.49
2013 Pirates 94 64-48 360 925.0 3.50 30-20 175 545.2 2.89
2013 Reds 90 66-48 382 1003.1 3.43 24-24 172 470.1 3.29
2014 Nats 96 70-49 339 1002.1 3.04 26-17 156 468.1 3.00
2014 Dodgers 94 76-44 347 975.0 3.20 18-24 207 489.2 3.80
2014 Cards 90 64-49 371 969.1 3.44 26-23 193 479.1 3.62
2014 Pirates 88 55-49 388 971.0 3.60 33-25 177 485.1 3.28
2014 Giants 88 56-60 406 977.0 3.74 32-14 158 472.0 3.01
2015 Cards 100 72-42 326 979.2 2.99 28-20 152 485.0 2.82
2015 Dodgers 92 64-42 352 978.1 3.24 28-28 203 467.1 3.91
2015 Mets 90 64-51 383 1002.2 3.44 26-21 178 460.0 3.48
2015 Pirates 98 67-48 379 967.1 3.53 31-16 155 522.1 2.67
2015 Cubs 97 60-39 353 946.2 3.36 37-26 193 514.2 3.38
2016 Cubs 103 81-39 325 989.0 2.96 22-19 186 470.2 3.56
2016 Nats 95 72-44 384 960.0 3.60 23-23 187 499.2 3.37
2016 Dodgers 91 59-49 378 862.1 3.95 32-22 220 590.2 3.35
2016 Mets 87 58-55 370 922.0 3.61 29-20 205 525.0 3.51
2016 Giants 87 62-51 405 982.1 3.71 25-24 194 478.0 3.65
2017 Dodgers 104 72-39 333 885.0 3.39 32-19 210 559.2 3.38
2017 Nats 97 72-47 392 973.0 3.63 25-18 232 473.2 4.41
2017 Cubs 92 64-47 400 888.1 4.05 28-23 236 559.0 3.80
2017 D’Backs 93 66-51 378 941.1 3.61 27-18 210 499.2 3.78
2017 Rockies 87 63-56 452 887.0 4.59 24-19 269 550.2 4.40
2018 Brewers 96 54-46 369 847.0 3.92 42-21 237 614.0 3.47
2018 Dodgers 92 57-38 317 896.2 3.18 35-33 240 579.1 3.73
2018 Braves 90 60-46 350 899.2 3.50 30-26 257 557.0 4.15
2018 Cubs 95 59-50 405 888.0 3.84 36-18 219 588.1 3.35
2018 Rockies 91 59-43 432 932.0 4.17 32-29 267 520.1 4.62

We see that the average NL playoff team over the past seven years received 65+ wins and a 3.55 ERA from their starting staff. The two Mets teams from this time period to make the playoffs got 58 wins and a 3.61 ERA in 2016 and 64 wins and a 3.44 ERA in 2015 from their starters. Contrast that with the teams the past two season which fell shy of the postseason. In 2017, Mets starters posted 49 wins and a 5.14 ERA while their 2018 counterparts notched 50 wins and a 3.54 ERA.

Interestingly, the quality – as judged by ERA – just wasn’t there from the rotation in 2017. But last year, the starters posted an ERA that was good enough for a playoff berth. But due to a lousy bullpen and an inconsistent offense, they finished about 15 wins shy of where they needed to be. Reinforcements were made to both the bullpen and offense this year. It’s beyond the scope of this piece to address the offense, so let’s look at the relievers.

Last season, the Mets used 25 guys out of their bullpen and only six of those posted an ERA below the 3.47 mark of the average playoff bullpen. And even that is misleading, as four of those six pitched in nine or fewer innings in the majors for the Mets. Overall, the Mets’ bullpen amassed a 4.96 ERA over 546.1 IP in 2018. Judging by our 35-team sample of playoff teams, they’ll have to cut at least a full run from that mark this season. It will help if they don’t give 130 IP to four guys with ERAs above six like they did last year to the ugly quartet of Tim Peterson, AJ Ramos, Paul Sewald and Anthony Swarzak.

Edwin Diaz had a 1.96 ERA over 73.1 IP and figures to be a big help in this department. Hopefully the team will have Jeurys Familia and Seth Lugo for full seasons out of the pen, too. If those three can give 200+ relief innings with a sub-3.00 ERA, that will go a long way towards stabilizing the pen. In 2018, the Mets received just 129.2 IP by relievers with a 3.00 or better mark.

Getting back to the starters, last year the Mets received 799.2 IP in 136 starts from the five pitchers who begin the 2019 season in the rotation. That’s certainly a strong number of starts, even if you’d like to have 50+ more innings with that many starts. Additionally, the Mets’ top two depth starters – Lugo and Corey Oswalt – combined for 78.1 IP in 17 starts with a 4.48 ERA.

Those are actually pretty good numbers from your sixth and seventh pitchers when you didn’t do a mid-season trade for an upgrade or have a stud prospect waiting in the wings for a promotion. For a comparison, those guys last year for the Nationals – Erick Fedde and Jefry Rodriguez – combined for 89 IP in 19 starts with a 6.06 ERA.

Let’s check in on a computer forecast to see how they project the Mets’ starters. We’ll use Steamer Projections for this exercise. Their forecasts called for the starting five to combine for 138 starts, 822 IP and a 3.65 ERA. That’s a few more starts and innings for the quintet than a season ago but at a worse rate of production.

It’s hard to forecast deGrom to match last year’s 1.70 ERA but Steamer also sees three other Mets starters having their ERA rise compared to 2018. Only Vargas is expected to beat last year’s mark, as it has him going from a 5.77 ERA to a 4.30 mark. That’s a nice improvement, but the Steamer forecast calls for Vargas to pitch in just 105 innings.

Once you factor in the depth starters, the team ERA for SP will certainly rise, probably to the 3.75 range. Recent history shows you can make the playoffs with that type of rotation but you better have a good bullpen and you better have a good offense. Ten of our 35 playoff teams had an ERA of 3.71 or greater from their starters and all 10 of those teams had a bullpen ERA better than their SP ERA. There’s definite room for improvement in the Mets’ bullpen from their 2018 numbers. Can the assembled cast combine for a sub 3.70 ERA?

That seems optimistic.

Spring Training stats and what they mean to you

Baseball is being played again, and it feels great. But do the Grapefruit and Cactus varieties of America’s Pastime really matter? More specifically, do team records and player statistics from Spring Training actually correlate to regular season success?

We all want to go crazy over Dominic Smith’s batting average, or be upset at Zack Wheeler’s ERA, but often these outliers get shrugged off with the phrase, “it’s only spring.” However, these are real baseball games being played, and one may ask how can they not correlate? Let’s just see what the numbers have to say.

You can see that there was a slight positive correlation between a team’s Spring Training record and Regular Season record last year. But we all know correlation does not mean causation, and with the linear regression model only having a slope of 0.35 this is a weak correlation at best. A strong relationship would have a slope closer to a 1:1 ratio. Also, the r-squared value shows that only 9% of the data set is explained by the linear relationship, making this scatter plot more scatter and less of a real trend. The orange dot by the way was the Mets in 2018.

So, team records in spring are not crystal balls to regular season winning percentages, but teams aren’t really playing to win in Spring so this makes sense. Teams are playing to a) practice for the upcoming season and b) to see who makes the team for opening day. Spring is more about the success of individual players than the success of the team.

This proves to be false as well, with another weak relationship between spring success and regular season success. Unfortunately, there were only 39 hitters who qualified for both the Spring and Regular season (the Mets lone representative is the orange dot Brandon Nimmo).

Brandon Nimmo being the only Mets qualified hitter raises a good point about who is playing in these Spring Training games. Paul Goldschmidt did not have enough plate appearances to qualify in last year’s spring season; however, his former teammate Kristopher Negron did. Not heard of Negron? It’s probably because he had a grand total of three plate appearances for the Diamondbacks while playing most of the year in Reno before being shipped to Seattle. These are the type of players playing the bulk of spring games. Remember Nimmo was a 4th outfielder at best this time last year.

Only 40 pitchers reached the number of innings to qualify for 2018 Spring Training, and many of them failed to qualify in the regular season making the sample size for pitching too small. This just further shows that Spring Training is for the 25th men on the rosters, not the stars.

So why do we obsess over these “meaningless” stats? The answer is that while they do not have much of a correlation to regular season success, they matter for the players trying to make the team making them matter to us.

We love rooting for players to succeed because frankly baseball is all too often an overly difficult, unfair, and just plain cruel sport. Players can hit a ball 100 mph directly into the third basemen’s glove, or a pitcher can paint the outside corner but the ball still leaves yard. Freak injuries can happen right as a hot streak starts. But when an unproven player finally overcomes the odds makes it to the big leagues it excites the fans. That might be what we are seeing with Smith, so his 1.2615 OPS is very meaningful in a sense, a sense of hope only found when “it’s only spring.”

Comparing Jason Vargas to other NL fifth starters

Brodie Van Wagenen said that his goal this offseason was to eliminate the “ifs.” Some have wondered if while he was doing this, he should have addressed the rotation and brought in a starter to bump last year’s number five guy to the bullpen. Virtually no one is happy with last year’s fifth starter but how many teams are jumping for joy about the last member of their starting rotation? Do Mets fans have it better than they think? Or are they justified in wanting an upgrade there?

It’s very difficult to determine exactly who the team’s fifth starter is after the fact. Not many teams finish the year with the same five guys in the rotation who began the year in that role. For simplicity, here the fifth starter is identified as the guy with the fifth-most starts on the team. This is far from perfect and you’ll recognize many, many instances where that’s crazy, perhaps nowhere more so than the Cardinals.

But while it’s crazy – is it any more so than finding out who was the fifth starting pitcher of the year for each team and then find out that guy got hurt and only made two starts all year? There are all kinds of wacky scenarios out there and no matter how much we wish, there are some teams where identifying the fifth starter is a fool’s errand. So, simplicity it is.

Here are all of the NL teams, displayed by record by division as presented by Baseball-Reference:

Braves – Brandon McCarthy, 6-3, 4.92 ERA, 15 starts
Fewer than 5 IP – 2X – 13%
Game Score under 50 – 6X – 40%
Why so few starts – Last pitched June 24

Nationals – Jeremy Hellickson, 5-3, 3.45 ERA, 19 starts
Fewer than 5 IP – 7X – 37%
Game Score under 50 – 7X – 37%
Why so few starts – Missed most of June, made just one start after 8/15

Phillies – Zach Eflin, 11-8, 4.36 ERA, 24 starts
Fewer than 5 IP – 7X – 29%
Game Score under 50 – 12X – 50%
Why so few starts – Missed the month of April

Mets – Jason Vargas, 7-9, 5.77 ERA, 20 starts
Fewer than 5 IP – 8X – 40%
Game Score under 50 – 10X – 50%
Why so few starts – Missed most of April and was out from 6-19-7/27

Marlins – Caleb Smith, 5-6, 4.19 ERA, 16 starts
Fewer than 5 IP – 6X – 38%
Game Score under 50 – 8X – 50%
Why so few starts – Did not pitch after 6/24

Brewers – Wade Miley, 5-2, 2.57 ERA, 16 starts
Fewer than 5 IP – 3X – 19%
Game Score under 50 – 7X – 44%
Why so few starts – Missed all of April, out from 5/8-7/12

Cubs – Mike Montgomery, 5-6, 3.99 ERA, 19 starts, 19 relief appearances
Fewer than 5 IP – 5X – 26%
Game Score under 50 – 9X – 47%
Why so few starts – worked out of pen until 5/28

Cardinals – Carlos Martinez, 8-6, 3.11 ERA, 18 starts, 15 relief appearances
Fewer than 5 IP – 5X – 28%
Game Score under 50 – 8X – 44%
Why so few starts – Missed from 5/8-6/5, moved to pen 8/21

Pirates – Chad Kuhl, 5-5, 4.55 ERA, 16 starts
Fewer than 5 IP – 4X – 25%
Game Score under 50 – 6X – 38%
Why so few starts – last start 6/26

Reds – Anthony DeSclafani, 7-8, 4.93 ERA, 21 starts
Fewer than 5 IP – 6X – 29%
Game Score under 50 – 12X – 57%
Why so few starts – missed all of April & May

Dodgers – Ross Stripling, 8-6, 3.02 ERA, 21 starts, 12 relief appearances
Fewer than 5 IP – 8X – 38%
Game Score under 50 – 7X – 33%
Why so few starts – worked out of pen in April, made only one appearance 7/29-9/7

Rockies – Chad Bettis – 5-2, 5.01 ERA, 20 starts, 7 relief appearances
Fewer than 5 IP – 5X – 25%
Game Score under 50 – 10X – 50%
Why so few starts – moved to pen and made only one start after 8/12

D’Backs – Clay Buchholz, 7-2, 2.01 ERA, 16 starts
Fewer than 5 IP – 1X – 6%
Game Score under 50 – 2X – 13%
Why so few starts – first start 5/20, missed 6/24-7/24

Giants – Dereck Rodriguez, 6-4, 2.81 ERA, 19 starts, 2 relief appearances
Fewer than 5 IP – 2X – 11%
Game Score under 50 – 5X – 26%
Why so few starts – made debut 5/29

Padres – Robbie Erlin, 4-7, 4.21 ERA, 12 starts, 27 relief appearances
Fewer than 5 IP – 4X – 33%
Game Score under 50 – 7X – 58%
Why so few starts – moved to rotation 8/2

Vargas finished tied for fourth in most starts, even in his injury-riddled campaign. But his percentage of early exits and below-average performances were near the bottom of the league. His eight games with fewer than 5 IP was tied for the most in the league and only two pitchers had a percentage of below 50 Game Scores higher than Vargas.

But in 11 starts after returning from his second stint on the DL, Vargas was 5-3 with a 3.81 ERA. His percentage of games of 5 IP or fewer dropped to 27% and his percentage of Game Scores under 50 fell to 36%. Both of those totals would put him in the top half of the league for fifth starters.

Do you believe that what Vargas gave the club after his second DL stint is the “real” guy? It doesn’t appear to be a slam dunk either way, but believing that is the case is certainly a reasonable position to take.

Of course the issue is what happens if either that isn’t the “real” Vargas or if he or another starter gets injured and has to miss a bunch of time. Who do the Mets turn to then? Seth Lugo is a strong replacement, although that opens a hole in the bullpen. They also have two internal candidates in Chris Flexen and Corey Oswalt, although neither of those excelled in previous chances. And there’s newly-acquired Walker Lockett, who turned in a strong season last year in the hitter-friendly Pacific Coast League.

While the Mets have options, they don’t seem to have the depth that they do at, say, second base. With the money that’s already been spent, it seems unlikely that they’ll bring in a pitcher to push Vargas to the bullpen. But perhaps they’ll bring in another reliever who will make moving Lugo to the rotation an easier pill to swallow.

Or maybe we see the “real” Vargas all season instead of just for the last 11 starts and the real worry is if Steven Matz can ever be the guy he appeared to be in 2015-16.

A three-year look at September records and following year performance

The 2018 Mets finished the season strong, going 18-10 (.643) in the month of September. That was the sixth-best record in the majors and four of the five teams that finished ahead of them went to the playoffs. Even the non-playoff team was the Rays, who finished the year with 90 wins. A lot of people prefer to dismiss numbers put up in September, saying that they’re meaningless. They hold this opinion for a variety of reasons. But let’s put some recent real time results to the test, to see on a team-wide basis if September records had any predictive qualities for the following season.

What follows is a three-year sample for the years 2015-2017, giving us 90 teams to examine. In the chart, the columns labeled “W” and “L” and “W-L%” are what the clubs did in the month of September. The “Overall” column is their full-season winning percentage and “Diff” is the difference between what they did in September compared to what they did for the full season. The chart is sorted from greatest positive to greatest negative difference. The last two columns are the winning percentage for the following season and the difference in winning percentage from our initial year to the following season.

Year Team W L W-L% Overall Diff W% (Y+1) Year over Year
2017 CLE 26 4 .867 .630 .237 .562 -.068
2016 ATL 18 10 .643 .422 .221 .444 .022
2015 MIA 18 12 .600 .438 .162 .491 .053
2017 PHI 16 13 .552 .407 .145 .494 .087
2017 NYY 20 9 .690 .562 .128 .617 .055
2017 OAK 17 12 .586 .463 .123 .599 .136
2015 CHC 23 9 .719 .599 .120 .640 .041
2015 LAA 20 11 .645 .525 .120 .457 -.068
2016 MIL 16 13 .552 .451 .101 .531 .080
2017 HOU 21 8 .724 .623 .101 .636 .013
2016 SEA 18 11 .621 .531 .090 .481 -.050
2017 CHC 19 10 .655 .568 .087 .583 .015
2017 CHW 15 15 .500 .414 .086 .383 -.031
2016 NYM 18 11 .621 .537 .084 .432 -.105
2015 TEX 20 12 .625 .543 .082 .586 .043
2016 BOS 19 10 .655 .574 .081 .574 .000
2015 BAL 18 13 .581 .500 .081 .549 .049
2015 BOS 17 14 .548 .481 .067 .574 .093
2016 LAA 15 14 .517 .457 .060 .494 .037
2015 CLE 17 14 .548 .503 .045 .584 .081
2017 STL 16 13 .552 .512 .040 .543 .031
2017 MIL 16 12 .571 .531 .040 .589 .058
2015 TOR 19 12 .613 .574 .039 .549 -.025
2016 CLE 18 11 .621 .584 .037 .630 .046
2016 BAL 17 12 .586 .549 .037 .463 -.086
2015 COL 15 18 .455 .420 .035 .463 .043
2017 BOS 17 11 .607 .574 .033 .667 .093
2017 ARI 17 11 .607 .574 .033 .506 -.068
2017 TOR 14 14 .500 .469 .031 .451 -.018
2015 SEA 15 15 .500 .469 .031 .531 .062
2015 ARI 16 15 .516 .488 .028 .426 -.062
2017 SFG 11 15 .423 .395 .028 .451 .056
2015 LAD 19 13 .594 .568 .026 .562 -.006
2016 LAD 17 12 .586 .562 .024 .642 .080
2016 ARI 13 16 .448 .426 .022 .574 .148
2015 WSN 17 15 .531 .512 .019 .586 .074
2016 CHW 15 15 .500 .481 .019 .414 -.067
2016 SDP 13 17 .433 .420 .013 .438 .018
2016 CIN 13 17 .433 .420 .013 .420 .000
2017 KCR 15 15 .500 .494 .006 .358 -.136
2015 ATL 13 18 .419 .414 .005 .422 .008
2016 STL 16 14 .533 .531 .002 .512 -.019
2016 WSN 17 12 .586 .586 .000 .599 .013
2015 NYM 17 14 .548 .556 -.008 .537 -.019
2017 MIN 15 14 .517 .525 -.008 .481 -.044
2015 DET 14 17 .452 .460 -.008 .534 .074
2015 TBR 15 16 .484 .494 -.010 .420 -.074
2017 ATL 13 17 .433 .444 -.011 .556 .112
2015 MIN 16 16 .500 .512 -.012 .364 -.148
2016 OAK 12 17 .414 .426 -.012 .463 .037
2017 TBR 13 14 .481 .494 -.013 .556 .062
2015 CHW 15 18 .455 .469 -.014 .481 .012
2015 MIL 13 19 .406 .420 -.014 .451 .031
2017 NYM 12 17 .414 .432 -.018 .475 .043
2016 CHC 18 11 .621 .640 -.019 .568 -.072
2016 NYY 15 15 .500 .519 -.019 .562 .043
2016 MIN 10 19 .345 .364 -.019 .525 .161
2016 TBR 12 18 .400 .420 -.020 .494 .074
2017 COL 15 14 .517 .537 -.020 .558 .021
2015 PHI 11 19 .367 .389 -.022 .438 .049
2017 SDP 12 17 .414 .438 -.024 .407 -.031
2017 CIN 11 17 .393 .420 -.027 .414 -.006
2015 PIT 19 14 .576 .605 -.029 .484 -.121
2016 DET 14 14 .500 .534 -.034 .395 -.139
2017 PIT 12 16 .429 .463 -.034 .509 .046
2015 SFG 15 16 .484 .519 -.035 .537 .018
2016 SFG 15 15 .500 .537 -.037 .395 -.142
2017 WSN 16 13 .552 .599 -.047 .506 -.093
2016 TEX 15 13 .536 .586 -.050 .481 -.105
2017 SEA 12 16 .429 .481 -.052 .549 .068
2016 PHI 11 18 .379 .438 -.059 .407 -.031
2016 MIA 12 16 .429 .491 -.062 .475 -.016
2017 TEX 12 17 .414 .481 -.067 .414 -.067
2015 NYY 15 17 .469 .537 -.068 .519 -.018
2016 HOU 13 16 .448 .519 -.071 .623 .104
2015 CIN 10 22 .313 .395 -.082 .420 .025
2016 COL 11 18 .379 .463 -.084 .537 .074
2016 KCR 12 17 .414 .500 -.086 .494 -.006
2015 OAK 10 20 .333 .420 -.087 .426 .006
2017 MIA 11 18 .379 .475 -.096 .391 -.084
2015 HOU 13 17 .433 .531 -.098 .519 -.012
2017 LAA 11 17 .393 .494 -.101 .494 .000
2016 TOR 13 16 .448 .549 -.101 .469 -.080
2015 KCR 15 17 .469 .586 -.117 .500 -.086
2016 PIT 11 19 .367 .484 -.117 .463 -.021
2015 STL 15 16 .484 .617 -.133 .531 -.086
2015 SDP 10 21 .323 .457 -.134 .420 -.037
2017 DET 6 24 .200 .395 -.195 .395 .000
2017 LAD 13 17 .433 .642 -.209 .564 -.078
2017 BAL 7 21 .250 .463 -.213 .290 -.173

So, looking at the info from the first row, the 2017 Indians went 26-4 in September, for an .867 winning percentage in the final month of the season. Overall in 2017, they had a .630 winning percentage. Their difference of .237 from September to their year-long winning percentage was the greatest of all 90 teams in our sample. The following year, 2018, the Indians had a .562 winning percentage. While that was good for 91 wins, it was a fall from what Cleveland did in 2017. So what the Indians did in September of 2017 was not necessarily a harbinger of things to come in 2018.

But eight of the next nine teams with the best difference between their September record and their overall record saw their season-long winning percentage go up in the following year. Our top 10 in positive September differential run the gamut, from a .407 winning percentage (66 wins) to a .630 winning percentage (102 wins).

If we expand to the 20-best teams in September differential, we see that 15 of them improved their winning percentage the following season.

Perhaps just as importantly, we see that teams that put up the worst September differential generally performed worse the following year. Eight of the bottom 10 teams in September differential saw their winning percentage go down in the following season. And when we expand to the bottom 20 teams, we see that 14 of them turned in a worse winning percentage the next year.

Of the middle 50 teams by September differential, 29 of them saw an increase in winning percentage the following season compared to 21 that saw their winning percentage go down.

So, the top 10 teams saw their winning percentage increase 80% of the time the following season
The top 20 teams saw their winning percentage increase 75% of the time
The middle 50 teams saw their winning percentage increase 58% of the time
The bottom 20 teams saw their winning percentage increase 30% of the time
The bottom 10 teams saw their winning percentage increase 20% of the time

No doubt that there is likely a more sophisticated way to look at the data. And that more sophisticated way may show something entirely different. But this first glance seems to indicate both a fairly strong correlation between teams that played significantly better in September than they did overall increasing their winning percentage the following season, as well as teams that played significantly worse in September showing a decrease in winning percentage the next year.

The 2018 Mets had a September differential of .168, which would be the third-best mark if eligible for our 2015-17 sample. Another thing our sample shows is that seven of the eight teams that finished with an overall winning percentage under .500 that had a winning record in September had an overall increase in winning percentage the following season. Only the 2015 Diamondbacks, who had an overall winning percentage of .488 and a September mark of .516, saw their fortunes decline the following year.

Of course, we should also note that the 2016 Mets had a .084 September differential, the 14th-best mark in our sample, and they saw their following season winning percentage decline by .105 – one of just five clubs in the top 20 to go backwards and the largest faller of the five.

The BABIP comps for Jeff McNeil

If asked to name three rookies from the 2018 season, chances are you would name Ronald Acuna Jr., Juan Soto and Shohei Ohtani. If asked about their performance, you’d probably say that they lived up to and even exceeded the hype. If asked about their future, you’d probably say that if they stay healthy, the sky’s the limit. For what it’s worth, the respective BABIPs for that trio were .352, .338 and .350 yet no one bats an eye at those.

But when we examine Jeff McNeil, we say he exceeded all expectations and if we ask about his future, we wonder what will happen next year when he doesn’t run a .359 BABIP. The former is certainly true and the latter is a prudent point of view. But how much is the second half of that sentence shaped by the first half? Do fans in Atlanta, Washington and Anaheim fret about a sophomore slump from their rookies?

That trio all brings more power to the table than McNeil does. But the Met brings other things that surpass our heralded rookies. McNeil made much better contact, striking out fewer than half the times on a rate basis compared to the NL guys and nearly one-third as much as Ohtani. He was a far superior baserunner and because he’s an infielder, he brings more defensive value. With that last point in mind, let’s compare McNeil to other rookies this century who played the same position and see what we find.

Setting the minimum level of PA to 200, here’s where McNeil rates in various categories out of the 124 people who qualified for our list:

AVG – .329 (2nd)
OBP – .381 (4th)
SLG – .471 (11th)
K% – 9.7 (8th)
BB% – 5.6 (T 77th)
BABIP – .359 (6th)
wOBA – .368 (5th)
wRC+ – 137 (3rd)
BsR – 5.5 (T 3rd)
fWAR – 2.7 (T 9th)

There are 11 people from our list who posted a .350 or better BABIP. In addition to McNeil, Tampa’s Joey Wendle also did it in 2018. So, let’s look at the other nine guys and see what they’ve done for an encore after their big rookie season. The numbers in parentheses are rookie year, rookie BABIP and rookie fWAR. All WAR numbers will come from FanGraphs.

Trea Turner (2016, .388, 3.3) – Slowed by injuries in 2017, Turner put up a .329 BABIP and a 2.8 WAR in his sophomore season. Last year he played in all 162 games and had a .314 BABIP and a 4.8 WAR.

Esteban German (2006, .388, 1.6) – The following season he had a .307 BABIP and a 1.1 WAR in 405 PA. He only received 321 PA over four partial seasons in the majors after that.

Scooter Gennett (2013, .380, 2.0) – He followed up with .321 and 1.7 marks in 2014 and struggled mightily the next two seasons. But Gennett has had a major revival the past two years. In 2018, he had a .358 BABIP and a 4.5 WAR.

Enrique Hernandez (2015, .364, 1.5) – Suffered a mighty drop in BABIP, falling to a .234 mark and replacement level in 2016. He rebounded to a 1.3 WAR in 2017 and this past season he had a 3.3 WAR, despite a .266 BABIP.

Whit Merrifield (2016, .361, 1.4) – In 2017, he fell to a .308 BABIP but with roughly 300 extra PA he posted a 2.9 WAR. This past season, Merrifield rebounded to a .352 BABIP and recorded a 5.2 WAR.

Martin Prado (2008, .357, 1.1) – In 2009, he had a .331 BABIP and a 2.6 WAR, getting a nice bump thanks to the extra 248 PA. From 2010-2016, he amassed 19.7 WAR. Has been slowed by injuries the past two seasons.

Donovan Solano (2012, .357, 1.3) – Solano managed just a .287 BABIP the following season and with his BsR numbers also taking a big tumble, managed just a 0.2 WAR. He rebounded some in both categories in 2014, but only enough for a 0.8 WAR. He recorded just 117 PA in the majors after that.

DJ LeMahieu (2012, .353, 0.4) – He fell to a .328 BABIP and a 0.3 WAR the following year and turned in a similar season again in 2014. But the next three years, he once again posted ultra-high BABIPs and amassed 8.2 WAR in that time frame, mostly on the strength of a 4.4 WAR in 2016. Last year his BABIP fell to a .298 mark but he was able to post a 2.0 WAR thanks to the finest defensive season of his career.

Jemile Weeks (2011, .350, 1.8) – His BABIP fell to .256 the following season, when he posted a (-.02) WAR. He posted just 120 PA in the majors after that.

Well, that’s certainly a mixed bag. If you want to be bullish on McNeil, you can point to Turner, Gennett, Merrifield and Prado. But if you want to be bearish, you can cite German, Solano and Weeks. But is there something else we can add that might bring more clarity to the issue? Let’s take these nine guys and add back in McNeil and Wendle.

Name Age PA BsR Def WAR
Turner 23 324 5.6 (-1.8) 3.3
German 28 331 (-1.3) (-9.3) 1.6
Gennett 23 230 2.9 1.2 2.0
Hernandez 23 218 (-1.3) 0.9 1.5
Merrifield 27 332 3.6 2.9 1.4
McNeil 26 248 5.5 1.9 2.7
Prado 24 254 (0.7) (-3.6) 1.1
Solano 24 316 2.7 0.8 1.3
LeMahieu 23 247 (-1.6) 3.3 0.4
Wendle 28 545 3.0 4.9 3.7
Weeks 24 437 0.2 (-2.8) 1.8

Here’s where McNeil ranked in each category:

Age – 8th
PA – 8th
BsR – 2nd
Def – 4th
WAR – 3rd

Most of the players on this list were in either their age 23 or age 24 season. Interestingly the two guys who made the list in 2018 were older than that. The only two other players older than 24 were German and Merrifield, one bust and one success story, so not a lot to go by examining age.

None of the players amassed a full season of PA. The pre 2018 leader was Weeks, who turned out to be a bust. There’s a dropoff to the next group, which had four players amass between 316 and 332 PA. That had two busts and two successes. The ones at McNeil’s level were more of the success side but it’s hard to put a lot of faith in that.

McNeil is neck and neck with Turner as the best baserunner of the group. The pre 2018 baserunners who were noticeably above average were mostly in the success group. Solano was the only good baserunner to be among the busts. Weeks was barely in positive numbers. Only Prado was a poor baserunner from our success group.

The Def category is another mixed bag. LeMahieu and Whittfield were the best of the pre-2018 bunch while German, Prado and Weeks were the worst.

It’s hard to look at these extra categories and see any home run for future forecasting. The seemingly best addition was the BsR category, which if indicative of future success would bode well for both McNeil and Wendle.

Let’s try one more thing. Much like mathematically you shouldn’t add OBP and SLG but we do it anyway to get an easy answer, let’s add BsR and Def and see what turns up. Here are the pre-2018 leaders among our high-performing BABIP group:

6.5 – Merrifield
4.1 – Gennett
3.8 – Turner
3.5 – Solano
1.7 – LeMahieu
(-0.4) – Hernandez
(-2.6) – Weeks
(-4.3) – Prado
(-10.6) – German

Maybe we have something here. The top three guys are our successes and two of the bottom three guys are our busts. McNeil (7.4) and Wendle (7.9) would hold the top two spots on this made-up list.

When we look at comps, the hope is that we find a strong conclusion, regardless of which direction it’s ultimately in. When examining the comps of rookie second basemen with ultra-high BABIPs, we simply don’t get that. But the fact that there are multiple success stories in our small sample is still encouraging for looking ahead with McNeil.

Perhaps the best comp of these nine pre-2018 guys is Merrifield. Both he and McNeil were older than the majority of players in our sample and they both posted positive numbers in BsR and Def. Merrifield took a hit in BABIP in his sophomore season but still put up a strong 2.9 fWAR thanks to a full season of playing time. And in his third year, Merrifield put up a terrific 5.2 fWAR. Let’s hope McNeil can follow a similar path.

The 2018 Mets’ bumper crop of six-week hot streaks

Regardless of what a player’s final numbers are at the end of the year, he didn’t accumulate them by putting up the same production each month of the season. All players have streaks, stretches where the hits fall in as well as periods where they simply can’t buy a hit. But even knowing that, we still find players whose final numbers are propped up by a stretch that was completely out of whack with what they did previously or subsequently.

In rough terms, these unusual streaks comprise six weeks or 30 games or 120 PA. Now, maybe it’s 37 games or maybe it’s 98 PA. But this is the general ballpark and there are enough examples of it in the recent past. This list is not exhaustive; it’s merely the players that jumped immediately to mind. Here are a dozen players in the past decade who fit the six-week pattern:

2010 Rod Barajas – From April 5 to May 20, a stretch of 33 games and 124 PA, Barajas put up a .276/.306/.586 line. For the remainder of the season, he had a .637 OPS. In 2009, he had a .661 OPS and in 2011 it was a .717 mark. But Mets fans will always remember that glorious streak he had to start his only year with the club.

2012 Kirk Nieuwenhuis – There’s nothing better than for a young guy to come up and hit right away and that’s exactly what Nieuwenhuis did. From April 7 to May 11, a stretch of 31 games and 119 PA, he posted a .308/.381/.442 line. For the remainder of the season, he had a .612 OPS. The following year he had a .615 OPS. There would be a couple of other six-week stretches in his career, which allowed him to play in the majors for parts of six seasons, despite his tendency to strike out nearly one-third of the time he came to the plate.

2013 Juan Lagares – While he did not enjoy immediate success in the majors, Lagares did have his streak fairly early in his rookie campaign. From June 18 to August 1, a stretch of 35 games and 121 PA, Lagares posted a .333/.375/.505 line. In his first 32 games that year, he had a .507 OPS and in his final 54 games, he notched a .545 OPS. The next season was his high-water offensive season with a .703 OPS. The next three years saw marks of .647, .682 and .661, respectively.

2013 John Buck – An All-Star in 2010, Buck had put up a .683 OPS in 2011 and a .644 mark in 2012 before joining the Mets. But he took a page from the Barajas book, getting off to an incredible start. From April 1 to May 3, a stretch of 25 games and 102 PA, Buck had a .263/.294/.611 line, thanks to 10 HR in 95 ABs. In his final 305 PA with the Mets, he managed just a .564 OPS. In nine games with the Pirates at the end of ’13, he had a .667 OPS and the next year, his final one in the majors, he had a .570 OPS split between two clubs.

2013 Josh Satin – After cups of coffee with the Mets in 2011 and 2012, Satin got a mid-season call and hit right away. From June 18 to July 22, a stretch of 23 games and 82 PA, he had a .364/.488/.545 line. The rest of the year, Satin had a .655 OPS. It was enough for him to make the club out of Spring Training the following season but he had a dreadful go of it, managing just three hits in 43 trips to the plate before he was sent out. And he never returned to the majors.

2014 Eric Campbell – When the Mets sent down Satin in the second week in May, he was replaced by Campbell. From May 11 to July 12, a span of 40 games and 107 PA, he recorded a .347/.393/.459 line. For the rest of the season he had a .508 OPS. He played parts of two more seasons with the Mets, with a .607 OPS in 2015 and a .511 mark in 2016.

2014 Matt den Dekker – A cup of coffee in 2013 saw den Dekker put up a .546 OPS in 63 PA. He got a mid-year promotion in 2014 but was sent back to the minors after a .424 mark in 49 trips to the plate. But when he returned on August 10 until the end of the season, a stretch of 36 games and 125 PA, den Dekker had a .290/.392/.374 line. He was traded right before the start of the 2015 season and in 178 PA since, he’s managed just a .652 OPS over the following four seasons. In his hot streak, he traded power for OBP. Since then, his OBP has been just .278 albeit with a .170 ISO.

2015 Travis d’Arnaud – In his first 533 PA in the majors, d’Arnaud put up a .683 OPS over the 2013-14 campaigns. He got off to a good start in 2015, although it was severely limited thanks to two different trips to the DL. But from August 3 to September 15, a span of 32 games and 133 PA, d’Arnaud put up a .319/.398/.612 line. The final two weeks of the season saw a .376 OPS and the following three years – all derailed by injuries – he’s combined for a .690 OPS over 668 PA.

2016 James Loney – An early injury to their starting first baseman put the Mets on the lookout for a replacement. Campbell was given a shot but didn’t do anything with it. So the Mets turned to Loney, who was languishing in Triple-A with another club. A one-time solid player who hit for a good average and was a plus fielder, Loney had been in decline. He had a .778 OPS in 2013, a .716 mark in 2014 and a .680 mark in 2015, which prompted the low-budget Rays to release him despite being the second-highest paid guy on the team. The Padres gave him a chance but a .797 OPS in the Pacific Coast League was no one’s idea of a guy earning another shot in the majors. His first 15 games with the Mets, Loney had a .629 OPS – pretty much what was expected. But from June 17 to August 1, a span of 38 games and 148 PA, Loney had a .299/.358/.493 line. Then from August 2 to the end of the year, he managed just a .598 OPS over 155 PA. And that was his last season in the majors.

2018 Adrian Gonzalez – Having had such great luck with Loney, the Mets turned to another ex-Dodger in decline in Gonzalez. From 2015-2017, Gonzalez’ OPS marks went from .830 to .784 to .642, the latter in an injury-plagued season. Gonzalez started the year with a two-hit game and through May 7, a stretch of 26 games and 94 PA, he had a .256/.330/.463 mark. But in his final 93 PA, Gonzalez posted a .556 OPS before being released. He did not sign with another club, although allegedly he turned down opportunities and now hopes to play again in 2019.

2018 Jose Bautista – Like Gonzalez, Bautista had been in decline the past few years. His 2015-17 OPS marks were: .913, .817 and .674 in his final season with the Blue Jays. He hooked on with the Braves, who wanted him to go back to the infield and play 3B. But Atlanta gave up on him after 40 PA and a .593 OPS. The Mets picked him up and from May 22 to June 30, a stretch of 35 games and 105 PA, he put up a .266/.438/.506 line. But in the rest of his tenure with the Mets, Bautista had a .606 OPS in 197 PA. He bounced back with an .870 OPS in 57 PA with the Phillies and allegedly wants to return to the majors next season.

2018 Austin Jackson – This is a career that has been all over the map, both literally and figuratively. Drafted by the Yankees, traded to the Tigers and he also spent time in the majors with the Mariners, White Sox, Indians and Giants before joining the Mets during the 2018 season. He was lousy in 2015-16, putting up a .687 OPS over those two seasons. A bounceback year in Cleveland got him a two-year contract with San Francisco. But Jackson put up a .604 OPS in 165 PA with the Giants before they cut bait on him. The Mets picked him up and from July 29 to August 29, a span of 31 games and 124 PA, he put up a .322/.371/.443 line. But in his final 85 PA, Jackson managed just a .396 OPS as he had a whopping 44.7 K% down the stretch. He better hope September stats are meaningless.


Anyone who reaches the majors has incredible talent. It shouldn’t be a shock that they can be red-hot for a six-week stretch. The key is not to be fooled about a hot streak that lasts this long, especially when the performance that surrounds it is not particularly good. Of course that’s easier said than done. And sometimes these hot stretches are the springboard to better things. That’s what we’re hoping for with Amed Rosario, who came up with such a good reputation but who in his rookie season in 2017 notched a .665 OPS and his first 383 PA in 2018 saw a .619 mark. But from August 10 to September 14, a span of 32 games and 142 PA, he had a .333/.366/.526 line. The final two weeks of the season, Rosario had a .538 OPS.

How the Mets stack up against playoff teams using fWAR

The Mets finished the year 77-85, 13 wins behind the division-leading Braves. Not all wins are created equally. Some wins are due to “talent” and others are due to “luck.” And it’s not always easy to tell where one of these things ends and the other starts. The Braves were 23-12 in one-run games while the Mets were 16-26. We can argue over how much of that difference is due to talent and how much is due to luck. But hopefully no one would argue that there isn’t some percentage of both factors involved here.

So, how can we look to isolate talent from luck on a team-wide basis? There’s probably not a correct answer to that question. For better or worse, fWAR will be used here, primarily because with FIP used as a pitcher input, it frames things not on fortunate bounces or poor defense but with a focus on things within a pitcher’s control and assuming average results otherwise. Here’s the 2018 fWAR by team:

Yankees 29.4 26.6 56.0
Astros 24.7 30.7 55.4
Dodgers 33.0 20.5 53.5
Indians 27.1 23.3 50.4
Red Sox 29.6 20.6 50.2
Athletics 31.1 13.4 44.5
Brewers 26.6 16.5 43.1
Braves 25.7 15.0 40.7
Rays 23.9 16.4 40.3
Cubs 27.1 12.9 40.0
Cardinals 25.0 14.8 39.8
Nationals 24.9 14.6 39.5
Angels 24.4 11.0 35.4
Mariners 18.2 16.8 35.0
Rockies 14.8 19.0 33.8
Diamondbacks 16.7 16.8 33.5
Mets 16.4 16.9 33.3
Pirates 19.1 13.6 32.7
Phillies 12.5 19.6 32.1
Twins 15.2 12.5 27.7
Reds 19.3 6.1 25.4
Rangers 15.0 8.1 23.1
Blue Jays 11.5 9.6 21.1
Padres 7.8 12.7 20.5
Giants 7.3 12.2 19.5
Royals 13.5 5.1 18.6
White Sox 9.9 7.3 17.2
Tigers 8.0 8.8 16.8
Marlins 9.9 3.3 13.2
Orioles 2.7 5.4 8.1

Nine of the top 10 teams by fWAR made the playoffs and the Rockies finished 15th. Colorado exceeded its Pythagorean Record by six games, one indication of how “lucky” they were this season. The Rockies also were fortunate with a 26-15 mark in one-run games. Their overall winning percentage was .558 and they had a .634 mark in one-run games. If there was a playoff team to brand as fortunate, the Rockies seem like a good choice.

So, humor me and grant that fWAR is a reasonable proxy for talent on hand. Where and how can the Mets increase their talent level to be a legitimate playoff club? Before we answer that, let’s post another chart. This one has the Mets and the 10 clubs that made the playoffs. It also includes, in descending order, the top 10 individual fWAR marks on each team, along with the sum of the top three marks, the top five marks and the top 10.

                        Top 3 Top 5 Top 10
Indians 53.5   8.1 7.6 6.1 5.6 5.3 4.3 3.5 2.8 2.2 2.1 21.8 32.7 47.6
Red Sox 53.3   10.5 6.5 5.9 4.9 4.3 2.8 2.7 2.7 1.6 1.5 22.9 32.1 43.4
Astros 52.7   7.6 6.8 6.3 3.6 3.1 2.9 2.5 1.6 1.6 1.5 20.7 27.4 37.5
Yankees 52.8   5.7 5.0 4.9 4.6 4.2 2.7 2.6 2.5 2.5 2.1 15.6 24.4 36.8
Dodgers 51.7   5.2 4.2 3.6 3.6 3.5 3.3 3.3 3.1 2.7 2.6 13.0 20.1 35.1
Mets 39.6   8.8 4.5 4.2 4.1 3.0 2.7 2.2 1.5 1.5 1.5 17.5 24.6 34.0
Athletics 43.7   6.5 4.9 3.7 3.6 3.4 3.0 2.6 2.1 2.0 2.0 15.1 22.1 33.8
Rockies 39.0   5.7 5.0 4.5 4.2 2.8 2.7 2.0 2.0 2.0 1.7 15.2 22.2 32.6
Braves 41.6   5.2 3.9 3.8 3.7 3.3 2.9 2.6 2.4 2.0 1.9 12.9 19.9 31.7
Brewers 40.4   7.6 5.7 3.6 3.1 2.4 1.9 1.4 1.3 1.2 1.1 16.9 22.4 29.3
Cubs 39.4   5.3 3.6 3.2 2.9 2.6 2.3 2.0 1.6 1.5 1.5 12.1 17.6 26.5

It seems to me that the Mets have the top level talent to compete for a playoff spot. Buoyed by Jacob deGrom, they had the fourth-best mark when we look at either the top three or the top five. But when we extend to top 10, the Mets fall to sixth. And when we include the whole team, and the whole league, the Mets fall to 17th. Everyone has their sights set on Bryce Harper and Manny Machado. And no doubt, adding a player of that caliber would be great. But more so than one superstar, the Mets need a better supporting cast, more guys to bump up from the “bad” to “mediocre” level and from “mediocre” to “average” ballpark.

Will deGrom be able to duplicate his magnificent season? Probably not. But the hope is that whatever amount he falls off by is made up by a full season by Noah Syndergaard and two productive halves by Michael Conforto. Will Jeff McNeil and Brandon Nimmo be able to repeat their 2018 performances? Perhaps not but the hope is that neither will Jay Bruce nor Todd Frazier.

Nowhere is this type of upgrade more available than in the bullpen. Last year the Yankees had the top bullpen in the majors with a 9.7 fWAR and the Royals had the worst with a (-2.2) mark. The Mets ranked 28th with a (-0.6) total.

For those who think catcher could use some kind of major upgrade, it’s possible that would come from more playing time for guys who were already on the roster and no playing time in 2019 for Jose Lobaton. Both Devin Mesoraco (0.7) and Kevin Plawecki (0.6) had a positive fWAR and the Pirates, the team with the best catching fWAR numbers in the majors, had a 5.3 mark. Meanwhile, Lobaton and Tomas Nido were both in negative numbers.

However, Nido did some fine work handling Syndergaard down the stretch. Some clamor for a strong defensive backstop to work with the pitchers. When Nido was behind the plate, Syndergaard had a 1.97 ERA in 73 IP. With all other catchers, he had a 3.98 ERA in 81.1 IP. While beyond the scope of this piece, if Nido could shave two full runs of ERA off Syndergaard, it’s hard to imagine that’s not worth putting up with his massive offensive liabilities.

Finally, it’s not so easy to isolate CF production from overall OF production, as the Mets used both Conforto and Nimmo in multiple outfield positions. Neither player had good offensive numbers when used in center, although that’s a correlation/causation issue. Conforto played CF a lot early in the season, when he wasn’t as good as he was post All-Star break. It could be the demands of center were too much on him. Or it could be that he wasn’t fully healed from his shoulder injury. Nimmo’s rough patch lined up closely with when he was used in center. Was it the difficulty of playing center that dragged down his offense or was it an unfortunate-timed 50-plus points of BABIP drop?

My opinion is that it’s harder to argue for the defensive approach in center than it is at catcher. And with Juan Lagares likely still to be on the roster next year, he can easily enough be used in CF. At least until he goes on the DL again. And if that does happen, we’ll get a different chance to see Conforto and/or Nimmo in center and see if their offensive struggles repeat when asked to play in the middle.

MLB trends younger and the Mets need to do likewise

As we near the end of the second decade of the 21st Century, the game is different than it was back in Y2K. Everyone focuses on the increase of strikeouts, especially as it compares to hits. But one thing that doesn’t get nearly enough attention is how the game is trending younger. Baseball-Reference includes age-related numbers, breaking down into four age groups. Here are the PA for batters in each of those groups, in the year 2000 and 2018:

Ages 2000 2018
25- 38,683 51,175
26-30 81,171 85,758
31-35 58,493 43,054
36+ 11,914 5,152

MLB today has fewer than half the PA by guys age 36 and older than it did 18 years ago. There’s also more than 15,000 fewer PA by guys in the next highest age bracket. The majority of these lost PA by baseball senior citizens are going to guys 25 and younger, although there’s been an uptick in the 26-30 class, too. Overall, there are over 5,000 fewer PA here in 2018 than there was in 2000.

Percentage wise, the 2018 numbers break down as follows:

25-: 27.6
26-30: 46.3
31-35: 23.3
36+: 2.8

With the National League not using the DH for the vast majority of their games, the expectation is that there are fewer guys age 36+ in the senior circuit. Overall this year, there were 92,885 PA in the NL and 1,890 of those came by guys 36 and older. That’s 2.0 percent or fewer than the 2.8 MLB number for this age bracket.

Furthermore, players in the age 36+ bracket had the lowest OPS (.711) of any of the four age groups. The league OPS was .722 in the NL this season. So, the overall trend is for fewer players at this age, there are fewer guys in this age bracket in the NL than in the AL and the baseball senior citizens are the least productive as a whole.

Now let’s look at the Mets. Here is their PA breakdown by age:
Youngest – 2,216
Prime – 1,521
Older – 1,951
Senior – 489

The Cubs led the National League with 520 PA by players age 36 and up. The Mets were second and the Brewers were third with 273. The reason the Cubs had so many is because 37-year-old Ben Zobrist – a one-time Mets target in free agency – is still very good. Zobrist posted an .817 OPS in 520 PA this year. Zobrist was the only senior player used by the Cubs.

The Mets had a lot of PA in the first category thanks to Michael Conforto, Brandon Nimmo and Amed Rosario. And with their emphasis on adding guys on the wrong side of 30 through free agency the past few seasons, the older grouping was well-represented, too. But the senior category was what was so frustrating.

In the first two-plus months of the season, Adrian Gonzalez was in the lineup nearly every day. And shortly after they cut ties with him, Jose Bautista became a regular until he was dealt to the Phillies. Bautista gave the club six good weeks but unfortunately he played over twice that long with the Mets. These two combined for a .701 OPS.

The Mets gave more opportunities to baseball senior citizens and they responded with worse production than league average. And the only reason the numbers weren’t even worse is because Jose Reyes and his .580 OPS just missed the cutoff at age 35.

The five oldest players on the Mets – Bautista, Gonzalez, Reyes, David Wright and Jose Lobaton – combined for 799 PA and put up a .202/.303/.342 line. Bautista really saves this group but even that comes with an asterisk. After a great start, he put up a .585 OPS over his final 177 PA with the Mets. If there’s any justice in the world, not one of these five guys will be back next season.

The hope is that the new GM will not have the same allegiance to older players that the previous GM did. It would be a step forward if the oldest guys to get a PA for the Mets in 2019 were Yoenis Cespedes and Todd Frazier, who will both be in their age 33 season.

It’s unknown what the Mets’ budget will be next year. COO Jeff Wilpon intimated the other day that it will be up to the new GM if the Mets are active players in the free agent market this offseason. Generally, if you want to avoid adding guys on the wrong side of 30 to your team, then free agency isn’t the place to shop. But the new GM will have the option – if not necessarily the financial resources – to go after Bryce Harper and Manny Machado, who both will be in their age 26 season in 2019.