Photo by Roberto Carlo

Note: All swing statistics cited in this article are current through October 2nd, 2022

Despite a disappointing final weekend of the regular season in Atlanta, the Mets finished with one of their best records in franchise history. Jeff McNeil’s turnaround season has been one of the key factors to their success. After an uncharacteristically poor 2021 campaign, McNeil won the batting title and finished with a top-ten single-season batting average in Mets team history.

A quick glance at McNeil’s numbers might indicate that he has simply reverted to the prolific hitter he was during the 2018-2020 seasons. A deeper look, however, reveals that a significant change in hitting philosophy is likely facilitating this 2022 success.

As outlined in this 2019 MMO piece, Statcast Swing Take is a nifty tool that helps better clarify the value added or subtracted by a hitter’s tendency to swing at or take pitches in different areas inside or outside of the strike zone. The tool defines four zones: the ‘heart’ of the plate (pitches that are close to the center of the plate and definite strikes), the ‘shadow’ zone (pitches that are on the edge of the strike zone or just outside of it, and have a 50/50 chance of being called a strike), the ‘chase’ zone (pitches that are clearly not strikes but still within several inches of the zone), and the ‘waste’ zone (pitches that are not at all close to being a strike).

Each hitter in the league records a ‘run value’ for each zone, based on whether they swing at or take each pitch they see in all four zones, as well as the game context of each pitch. The context refers to the count when the pitch was thrown, the number of runners on, and the number of outs; all dictate the amount of run expectancy gained or lost by a swing or take on every pitch a batter faces during a season. Aggregating all of those run expectancies into the four regions of the plate can be a convenient way to quantify how much a hitter’s aggressiveness and patience helps or hurts his team, with a positive run value indicating he was above the league average, and a negative number demonstrating below average performance.

Comparing Jeff McNeil’s 2021 and 2022 Statcast Swing Take profiles provides some fascinating insight into his dramatic turnaround. McNeil’s 2021 rate stats were paltry compared to his career numbers, especially his .251 batting average and .319 OBP, both of which were over 60 points below his career marks before that season. Nevertheless, his 2022 batting average and on-base percentage were very much in line with his 2018-2020 averages.

McNeil’s 2021 Swing Take profile explains the steep downturn in his fortunes that season: He had a cumulative run value of -13, rating well below league average, and correlating well with his disappointing OPS+ of 87, with his swing/take numbers indicating a significant drop in production in both the heart and shadow zones.

In 2021, McNeil was well above league average in the chase and waste regions—with run values of +11 and +14, respectively. That success was primarily because he was able to lay off those pitches, which are clearly balls, at a better rate than average, thus saving himself from either a swing and a miss or weak contact likely to result in an out.

However, in the shadow zone, those pitches that are near the edges of the strike zone and have a roughly 50/50 chance of being called a strike, McNeil’s run value was -25, completely offsetting the +25 gained in the chase and waste regions. McNeil’s unusual aggression led to outcomes of weak contact and swinging strikes. Furthermore, McNeil netted an additional -13 run value on pitches in the heart zone, close to the middle of the plate, also primarily because he swung at those much more frequently than average, and generally had poor outcomes.

In 2022, though, McNeil has completely changed his Swing Take profile, and through October 2, has compiled a robust +23 run value. While his ability to lay off pitches outside of the strike zone in the chase and waste regions has remained relatively consistent, a huge shift has occurred in the shadow region. As outlined in his 2021 recap, the shadow area, with a -25 run value, was by far McNeil’s weakest region. Yet in 2022, McNeil has turned the shadow region into a net positive, with a run value of +5 runs. Only two other qualified hitters have improved their Run-Value in the Shadow Zone as much as McNeil, year over year.

In 2021, he swung at pitches in the shadow region 64% of the time, well above the major league average of 53%. However, in 2022, McNeil has only swung at pitches in the shadow region 59% of the time, which is now much closer to the league average of 54%.

McNeil’s rapidly improved ability to lay off pitches around the edges of the strike zone in 2022 has allowed him to improve from solid to elite levels in several key Statcast batting metrics. While McNeil does not hit the ball particularly hard, ranking well below average in metrics like exit velocity and hard-hit percentage, his ability to find holes is exemplary. However, a disciplined approach is necessary to have the best chance of doing so, and McNeil simply was too aggressive on mediocre pitches in 2021 to be able to do so. The renewed patience in 2022, though, has allowed him to place balls where he wants to with much more frequency, as evidenced by his expected batting average being in the 92nd percentile.

Whether due to a personal shift in hitting philosophy or the impact of coaching, Jeff McNeil has retooled his approach at the plate in the 2022 season. The change has been quite a success, culminating with a standout series versus the Braves, where he batted .539 (7-for-13), while the rest of the Mets’ offense generally sputtered. Given those struggles being experienced by many Mets batters at present, Jeff McNeil’s ability to generate hits will be of vital importance to the team as they embark on their first postseason run since 2016.

Matt Mancuso also contributed to this article

The post Looking Under the Hood of Jeff McNeil’s Rebound Season appeared first on Metsmerized Online.

Gary A. Vasquez-USA TODAY Sports

Note: All statistics cited in this article are current through the All-Star Break.

Over the past several seasons, the Mets have earned the reputation of being a below average defensive team. Indeed, while defensive metrics are notoriously fickle, the underlying numbers back up that sentiment. For example, from 2017 through 2020, the Mets ranked 29th, 26th, 26th and 25th, respectively, in Defensive Runs Saved (DRS).

DRA is a metric used to objectively assess a player’s defensive skill. The formula is a bit complex, but in a nutshell, DRS rewards fielders for turning batted balls into outs and punishes them when they cannot do so. The reward is greater for more difficult plays, as adjusted to the league average for that defensive position, while the punishment is also greater when a simpler play is not made.

Perhaps in response to their persistent defensive shortcomings as a team, the Mets opted to begin shifting much more frequently in 2021. From 2016, when MLB Statcast began tracking shift frequency, through 2020, the Mets ranked in the bottom third of the league each season in shifts per plate appearance, including 27th during the 2020 season. In 2021, they jumped all the way up to second in the league for shifting, while also moving up to ninth in DRS. It could certainly seem possible that there is a relationship between the two, though there are also other explanations for the improvement in team defense, such as the addition of Francisco Lindor at shortstop.

Nice play from Francisco Lindor to help Adonis Medina throw a four-pitch inning. pic.twitter.com/X3b6GXSZu0

— Metsmerized Online (@Metsmerized) July 2, 2022

One unassailable fact of this change in defensive philosophy is that it comes from the front office. Despite a new manager in 2022, the Mets continue to shift aggressively, currently ranking third in all of MLB. So far during this campaign, however, the frequent shifting has not fully offset some of New York’s below average fielders. After the big leap forward last year, the Mets have regressed a bit defensively this season, and currently rank in the middle of the pack — 14th — in DRS. An important culprit in the step backwards has been the Mets lack of defensive range. When they do get to balls, the Mets are generally around average at avoiding errors, but the reaching the balls part has been an issue.

There are several different ways to measure a player’s defensive range, that is, how many nearby batted balls that a fielder can make a play on. Generally, this will correlate to a fielder’s speed, quickness and athleticism, which determines how quickly they can react and cover the necessary ground to make a play on a ball. A popular range-based fielding metric that is similar to DRS is Outs Above Average (OAA). An OAA of 0 generally correlates with average, and the Mets as a team are at just 2 OAA, ranking 18th in the league. There is of course variability across the squad. In addition to Francisco Lindor, Brandon Nimmo and Luis Guillorme also rate well above average for OAA, as evidenced many times so far this season.

Most Outs Above Average (OAA) by an infielder on plays coming in this season:

Tommy Edman: +6
Ke'Bryan Hayes: +6
Manny Machado: +6
Bobby Witt: +6
LUIS GUILLORME: +5@Metsmerized #Mets #LGM pic.twitter.com/zqEv38awV1

— Mathew Brownstein (@MBrownstein89) June 29, 2022

Brandon Nimmo draws another “oh wow” from Gare on this tremendous catch in center. pic.twitter.com/41A70FhNwJ

— Metsmerized Online (@Metsmerized) July 8, 2022

Where the Mets’ greatest defensive weakness lies is at the infield corners. Pete Alonso at first base and Eduardo Escobar at third base each check in at -5 OAA, despite a few nice plays by Escobar in a recent series versus the Cubs. Thus, shifting the infield is an important tool for attempting to limit the impact of multiple regulars with below-average range.

Nevertheless, there are innumerable situations where shifting does not make sense, and therefore a lack of range can be more easily exposed. For instance, back in May at Citi Field, the Mets and Cardinals were tied in the top of the ninth. With the go ahead run on third and two outs, Adam Ottavino induced a weak ground ball to third from Tyler O’Neill. O’Neill has one of the top sprint speeds in baseball, so Eduardo Escobar knew he had to hurry, especially given his limited range. Since it took him awhile to get to the ball, Escobar had to quickly field it and throw across the diamond, but in his haste, briefly bobbled the ball. In doing so, O’Neill beat the throw to first, and what turned out to be the winning run crossed the plate. Had Escobar been able to retrieve the weak grounder more quickly, he still might have been able to make the play to end the inning, even with the bobble, but instead, had no room for error.

With the trade deadline looming, third base is a potential spot for the Mets to seek an upgrade, thanks to Eduardo Escobar’s offensive struggles. His lacking defense, however, has probably been more detrimental to the team. New York’s other internal option at third base, J.D. Davis, has a similar profile, with underwhelming offensive numbers in 2022, and a very poor track record defensively. Thus, if the Mets wish to improve their defense at third base, they will have to look outside of the organization. Even if they do so, the Mets should still be expected to shift frequently, but improving their fielding at the hot corner should make their defense more effective overall.

The post Analytics Corner: Mets Defense and Shifting appeared first on Metsmerized Online.

Brad Penner-USA TODAY Sports

The 2021 campaign did not go according to plan for Carlos Carrasco and the Mets. Carrasco was a key piece of an off-season trade in which the Mets also acquired Francisco Lindor. However, due to injuries, Carrasco’s Mets debut did not occur until July 30, and he never looked quite right on the mound. The 2022 season, though, has been a completely different story for Cookie.

Carrasco’s traditional statistics look good so far in 2022, including 10 wins (tied for third in the National League) and 100 strikeouts in 99 innings. However, when considering a slightly more advanced metric— Fielding Independent Pitching (FIP)— Cookie’s bounce back is even more impressive.

Unless it is hit towards them, a pitcher has no control over what happens once a batted ball is put into play. Instead, he relies on the skill, positioning, and acumen of the eight fielders behind him, and even his own, as a pitcher is counted as just another fielder when computing stats like earned runs. Therefore, a batted ball entering the field of play introduces a considerable amount of luck that can influence a pitcher’s statistics. Commonly used metrics such as ERA, however, do not account for the variability of fielding. Indeed, for the purposes of that measure, all outs recorded by fielders are equal, whether it’s a routine fly ball or one of the greatest catches of all time.

ERA counts Endy Chavez‘s legendary catch as equal to a routine pop-up.

Further complicating matters, a pitcher’s ERA can also be influenced by sequencing, another factor not truly indicative of their skill. For example, we can think about two pitchers, A and B, both of whom allow six hits over six innings pitched, all of them singles, and no other baserunners. Pitcher A scatters those hits equally over the six innings, one per inning, and allows zero runs. Pitcher B, however, allows three consecutive singles in the second inning, and again in the fourth innings, with a run-scoring in both instances. Therefore, while Pitchers A and B were equally effective in preventing baserunners, Pitcher A winds up with a perfect 0.00 ERA for the start, while Pitcher B earns an ERA of 3.00. FIP is therefore a handy tool because it only considers outcomes that a pitcher has complete control over, namely strikeouts, unintentional walks, hit-by-pitches, and home runs. It simply ignores balls hit into play, since those results are not believed to be as accurate a reflection of a pitcher’s performance.

How is FIP calculated? According to the MLB Glossary, the formula is ((HR x 13) + (3 x (BB + HBP)) – (2 x K)) / IP + FIP constant. The ‘FIP constant’ at the end of the formula is simply a number used to put FIP on the same scale as ERA and adjust the league average FIP to match the league average ERA. This allows us to look at a FIP value and interpret it the same way as an identical ERA value. For example, a FIP below 3.00 would generally be quite strong.

Since, like ERA, a higher FIP is bad, and a lower one is good, we can now understand more clearly how the formula works. As homers are quite damaging to a pitcher, FIP punishes them quite severely for allowing one, to the tune of 13 for each. Walks and hit-by-pitches are also bad for pitchers, but not nearly as much as a home run, so each one only counts as 3 in the FIP calculation. Meanwhile, strikeouts are a huge asset for pitchers, often representing a best-case scenario, since they prevent bad things from happening, not only hits, but also other events that can harm a pitcher, such as errors, misplays, and sacrifice flies. This makes strikeouts a crucial component of the FIP formula, and they are assigned a negative number, given their many benefits to a pitcher.

Jumping back to the topic of Carlos Carrasco’s rebound, it turns out that solely considering ERA actually understates his improvements in 2022. Cookie’s 4.27 ERA, inflated recently by a few tough starts versus Houston, may seem a bit underwhelming. His FIP, however, is a solid 3.51. Why is there such a large gap between ERA and FIP? Well, this tells us that Cookie has overall pitched quite effectively, and perhaps received some bad luck. In terms of what he can control, Carrasco ranks among roughly the top 25% of pitchers in walks issued, is allowing fewer homers than the league average and is striking out more batters than average. Thus, Carrasco has likely been victimized by some poor luck this season.

Indeed, a few big innings have inflated his ERA, such as a six-run inning for the Phillies, in which five of the runs were charged to Cookie, the final two on a Garrett Stubbs three-run homer allowed by Chasen Shreve. Indeed, had some of the batted balls that inning been turned into outs, or had Shreve not allowed the inherited baserunners to score, Carrasco’s season ERA would be lower. As a result of all this, we can conclude that if he continues to throw the ball this well and stay healthy, Cookie should continue to pitch like an upper-end starter for the Mets, and his ERA should decline.

Like all statistics, FIP has its limitations. By ignoring batted balls, we are removing some key metrics that can predict a pitcher’s success, namely the quality of contact he allows. If a pitcher consistently gives up hard hit balls, they are more likely to fall as hits, which should result in more runs allowed and a poorer outcome for his team. It can be challenging, however, to distill batted ball statistics down into a single number, as several metrics, including average exit velocity, expected batting average, and barrel percentage, all contribute. So, if you are trying to quickly assess a pitcher’s effectiveness, FIP is an excellent tool to use. Plus, for Mets fans, here is a handy way to end a debate: The active leader in career FIP, by a wide margin? One Jacob Anthony deGrom.

The post Analytics Corner: Carrasco’s Rebound Season Showcases Context of FIP appeared first on Metsmerized Online.

Mandatory Credit: Vincent Carchietta-USA TODAY Sports

Note: All statistics cited in this article are current through the games of June 6, 2022

Advances in statistical analysis have transformed the game of baseball in recent years, informing everything from free-agent contracts to defensive alignments. Driving this revolution has been a dizzying array of numerical tools, which typically share the goal of removing random chance from more traditional measures of success or failure.

For much of the 20^th century, so-called ‘counting statistics,’ such as RBIs and pitcher wins, drove baseball’s numerical discourse. However, during the second half of the 1900s, and especially in the new millennium, close observers of the game began to realize the flaw of that approach: Counting stats can easily be skewed by luck or other circumstances outside of a player’s direct control. RBI totals, for example, can be inflated simply by hitting in a strong lineup or happening to come to bat with many runners on base. A seemingly more objective way to assess a batter’s skill is through On-base Plus Slugging (OPS), which has the advantage of being a rate statistic, adding on-base percentage and slugging percentage to yield a combined measure of how frequently a batter reaches base and hits for power.

OPS, though, also has its flaws. For example, as any Mets fan knows, Citi Field has typically favored pitchers since its opening, but OPS alone does not consider ballparks. Moreover, offense has notably been down across baseball so far in the 2022 season, but once again, a hitter’s OPS does not reflect that trend. However, there is an increasingly cited and straightforward statistic that does take ballpark factors and leaguewide offense into account: OPS+, which should be one of the first tools that any baseball fan uses when evaluating the performance of an individual batter.

Let’s take a quick dive into how OPS+ is calculated and what it means.

According to the MLB Glossary, “OPS+ takes a player’s on-base plus slugging percentage and normalizes the number across the entire league. It accounts for external factors like ballparks. It then adjusts so a score of 100 is league average, and 150 is 50 percent better than the league average.” For example, in 2021, the league-average OPS was .728. So, if we ignore ballpark effects for a moment, a player with a .728 OPS would hold an OPS+ of 100, or league average, and a batter with an OPS over .728 would also have an OPS+ of above 100.

To account for ballparks in OPS+, an index called ‘park factor’ is used. Park factor is also scaled to 100, with higher numbers indicating that a ballpark is more friendly to offense, and lower values implying that it favors pitchers. In the 2021 season, Citi Field’s park factor was 98. Therefore, Mets hitters, by virtue of playing half of their games at a pitcher-friendly park, would have a higher OPS+ than a player on a team such as the Red Sox, whose home field, Fenway Park, had a park factor of 105 in 2021, the third-best for offense.

Indeed, during the 2021 season, both New York’s Pete Alonso and Boston’s Xander Bogaerts had an OPS of .863. However, Alonso’s OPS+ was 133, while Bogaerts’s was only 127.

Statistically, Citi Field favors pitchers, which makes OPS+ an important tool for giving Mets hitters their due.

Turning our attention to the current 2022 campaign, the Mets have received plenty of attention for their offensive turnaround so far this season—but the raw numbers do not even tell the entire story. The Mets as a team are second in the National League with a team OPS of .749, behind only the Dodgers, who have an OPS of .761. However, the Mets are tops in the league with an OPS+ of 115, well ahead of the Dodgers, and their 111 OPS+ in second. The difference can be explained by park factors, as Los Angeles’s home field, Dodger Stadium, has thus far been among the top ten most hitter-friendly stadiums in 2022, as have the park of two divisional rivals: Coors Field and Oracle Park. Meanwhile, Citi Field is currently exactly league average for offense in 2022.

For Mets fans, OPS+ also can open up an intriguing debate about the top offensive seasons in franchise history.

Unsurprisingly, Mike Piazza holds the Mets franchise record for single-season OPS, with a mark of 1.012 in 2000. While impressive, it is important to remember that 2000 was MLB’s highwater mark for offense in the modern era, with a league-record average OPS of .782.

The 2000 season occurred during the height of the steroid era, sandwiched between the home run chases of 1998 (Mark McGwire vs Sammy Sosa) and 2001 (Barry Bonds). With that context, Piazza’s performance, while still historic, loses a bit of its luster. Indeed, his OPS+ that season was 155, tied for ‘only’ sixth-best in team history. Mets fans might be surprised to learn that the franchise record for OPS+ belongs to Howard Johnson, who notched an OPS+ of 169 during the 1989 season. HoJo’s .928 OPS was almost 100 points lower than Pizza’s mark, but the league average OPS was a paltry .695—more closely matching the league average so far during this season of diminished offense. In fact, given the suppression of offense thus far in 2022, Pete Alonso’s current OPS of .906 yields an OPS+ of 157, which would displace Piazza for number six on the Mets’ all-time list.

Some might be surprised to learn that Howard Johnson holds the Mets single-season record for OPS+, a normalized measure of offensive productivity.

While the normalization aspect of OPS+ helps to make it a more powerful statistic, it is not without its limitations. OPS, and by extension OPS+, can still be impacted considerably by luck.

A common instance of good luck benefiting a batter’s OPS/OPS+ is when a hitter makes soft contact but still reaches base via outcomes such as an infield single or pop-up that falls in front of an outfielder. Fortunately, additional advanced statistics can help to flag circumstances where OPS+ is being skewed by good luck. For example, there are numerous ways to measure the quality of contact a hitter is making, such as exit velocity.

Harder hit balls generally have a better chance of falling for hits, so if a batter has consistently above-average exit velocity, it would be likely that his high OPS/OPS+ is not just a result of good fortune. Pete Alonso, for instance, has a career average exit velocity of 91.0 miles per hour, well above the MLB average of 88.4 miles per hour over that time. Unsurprisingly, Pete’s career OPS+ is a robust 140—a direct result of his propensity to hit balls very, very hard.

#OTD in 2019, Pete Alonso tattooed Jonny Venters' 0-1 fastball to straightaway center field for his 6th HR of the season.

Alonso's HR had an exit velocity of 118.3 mph – which is still the hardest-hit home run of his major league career.@Metsmerized @Mets #Mets #LGM pic.twitter.com/G251LYniiy

— Mathew Brownstein (@MBrownstein89) April 11, 2021

OPS+ is readily available on statistical websites such as Baseball-Reference and has even appeared on stadium scoreboards with increasing frequency. So, the next time you are at Citi Field, or arguing with an elder about who was the better hitter ‘back in the day,’ you might just have the perfect opportunity to impress your companions with a detailed explanation of OPS+!

The post Introduction to Advanced Baseball Analytics: OPS+ appeared first on Metsmerized Online.