The issue that plagues the valuation of late-inning relievers is the generalized treatment of runs.

WAR is the most accepted player evaluation metric and wins are determined by run value. Run value is determined in a generalized sense; it’s too perilous and unwieldy to predict, or evaluate performance, based upon the sequencing of events.

However, late-inning relievers do not pitch in a general situation. Unlike many other players we know when they will perform. They are unique; they pitch in particular situations: the late innings of a baseball game.

They are not vulnerable to give up a home run in a large range of innings like a starting pitcher. They are vulnerable to giving up runs in the innings their role demands them to appear in; most notably the 7th, 8th, and 9th innings.

Therefore, reliever value should be measured by a more specific run value. This run value, and ultimately win value, cannot be measured in a general sense. Their valuation must account for the specific times they appear in a game.

I set out to do this with those principles in mind.

First, I used Baseball Reference’s Play Index to determine the amount of runs scored in between the 7th and 9th innings of all games in 2015. There were 13,448 7th, 8th, and 9th innings played last year. That is the equivalent of 1,494 full 9 inning baseball games. In sum, 5,968 runs were scored in the 7th, 8th, and 9th innings of baseball games in 2015. On average, that is 3.99 runs per “game”, where “game” signifies 9 innings of 7th-9th inning performance.

Second, also using Baseball Reference’s Play Index, I looked at the 300 pitchers with the most appearances in the 7th, 8th, and 9th innings. This does not represent every pitcher that pitched in the 7th, 8th, and 9th inning, but it gets us to Trevor Cahill, who pitched 16 innings in the 7th inning or later.

I then split this list of pitchers into two groups. Theoretically, the 90 best relievers in the league would be pitching in the 7th, 8th, and 9th innings (30 teams; 3 relievers each). Therefore, the first group is the first 91 pitchers with the most appearance (Tony Sipp and Blaine Boyer each appeared in 43 innings between the 7th and 9th innings, so there is one more than 90 in this case). The other 209 pitchers represent the “replacement” pool.

The average performance of the “replacement” pool was taken to determine the performance of a replacement player. Here is what that looks like:

Andruw Jones is likely to announce his retirement from Major League Baseball sometime in the very near future. Jones hasn’t been on the MLB radar since his last season in the big leagues back in 2012, when he played 94 games with the New York Yankees but hit just .197/.294/.408. He played 2013 and 2014 with the Tohoku Rakuten Golden Eagles in the Japan Pacific League and hit 26 and 24 home runs, while combining to hit .232/.393/.441. He’ll turn 39 years old in April, so he is likely to hang up his spikes after a 17-year Major League career.

In this column at FanGraphs, David Laurila made an apt comparison between Jones and Jim Edmonds with these numbers showing the similarity:

.254/.337/.486, 1933 hits, 434 HR, 10 Gold Gloves, 67.1 WAR—Andruw Jones

.284/.376/.527, 1949 hits, 393 HR, 8 Gold Gloves, 64.5 WAR—Jim Edmonds

It’s a good comparison. They were nearly equal in value in their careers and both hit many home runs and won numerous Gold Gloves.

Another interesting player to compare Jones to is more similar when you look at the arc of their careers. Both came up to the big leagues at the age of 19 and were very good players until the age of 30, then experienced a significant drop-off in value from that point on. That other player is Ken Griffey Jr. More on him later.

Andruw Jones came up with the Atlanta Braves in 1996, making his Major League debut on August 15^th. He only hit .217/.265/.443 in 31 games in his rookie year but helped the Braves make it to the World Series. He hit two home runs in Game 1 against the Yankees, becoming the youngest player to ever hit a home run in the World Series. The Braves lost the series four games to two, but Jones hit .400/.500/.750 and made his presence known on a national stage.

Jones established himself in center field for the Braves in 1997 at the age of 20. He hit .231/.329/.416, which was below average for a hitter in an era of high offense (96 wRC+), but he was so good defensively that he was worth 3.7 Wins Above Replacement. The following year was the first in an impressive stretch of nine seasons from 1998 to 2006 during which Jones averaged 6.4 WAR per year. Not only did he excel on defense during this nine-year stretch, he averaged 35 home runs per season, 99 runs scored, 104 RBI, 12 steals, and a .270/.347/.513 batting line (119 wRC+). He was a five-time All-Star and won nine straight Gold Glove Awards (he would win a 10^th in a row the next year). If Jones had played in the first part of the 20^th century, his nickname might have been “Death to Flying Things.” Instead, he was just Andruw Jones. Jones’ best season was a 7.9 WAR year in 2005 when he hit .263/.347/.575 with 95 R, 51 HR, 128 RBI and finished second in the voting for National League MVP. This stretch was the essence of Andruw Jones—a power-hitting center fielder with 35 home runs a year and terrific defense. There were only four players in baseball worth more WAR during this nine-year stretch: Barry Bonds, Alex Rodriguez, Randy Johnson, and Pedro Martinez.

Jones was an above-average player again in 2007. He was worth 3.3 WAR thanks primarily to still excellent defense. His hitting dropped off considerably, though. After hitting .262/.355/.553 with a combined 92 home runs over the two previous seasons, Jones hit just .222/.311/.413 in 2007. His 26 home runs were his lowest total since 1999. This would be his last season in Atlanta and his last season with a WAR above 2.0. It was also his last excellent season on defense. Jones would play with four different teams over the final five years of his Major League career and hit .210/.316/.424. His once-great defense dropped off precipitously and he averaged just 0.6 WAR per season.

Those last five journeyman years for Jones could make it hard for people to remember how great he was in the first part of his career. Through the first seven years of his career, Andruw Jones was nearly the equal of Ken Griffey Jr. Both Jones and Junior reached the Major Leagues as 19-year-olds and were power-hitting center fielders. Griffey started winning Gold Glove Awards in his second year in the bigs and won nine Gold Gloves over the next 10 years. Jones won his first of 10 consecutive Gold Glove Awards in his third year in the Major Leagues. While both were considered good fielders, the truth was that Jones was significantly better than Junior for an extended period of time and held more of his defensive value in the latter years of his career. Jones was an elite fielder through his age-30 season, then became more of a slightly-below-league-average fielder in his last five years. Griffey, on the other hand, was rarely at the elite level as a fielder that Jones reached and when he declined, it was a significant decline to well-below-average defense in his late 30s.

Griffey was the better hitter, of course, but in terms of overall value, they were very close into their mid-20s. The chart below shows each player’s cumulative WAR by age. Griffey’s WAR advantage after each player’s first seven years was slim, just 38.2 to 36.5.

In a similar number of plate appearances, Jones and Griffey had a similar number of home runs, runs scored, and RBI. Griffey had a significant edge in batting average, on-base percentage, and slugging percentage. Jones was much better on defense. As mentioned above, they were very close in overall value.

Griffey took his game to another level in his age 26 and age 27 seasons, when he averaged 9.4 WAR per year while hitting 105 home runs and slugging .637. Jones averaged 5.2 WAR in his age 26 and 27 seasons, which is great — just not at the same level as Griffey.

The five-year stretch of seasons when Jones and Griffey were 26 through 30 years old makes up the bulk of the difference in career WAR between the two players. During this stretch of ages, Jones accumulated 27.7 WAR and Griffey had 35.6. Again, Griffey was a much better hitter, with a significant edge in average, on-base percentage, and slugging percentage, along with a large edge in runs, home runs, and RBI. Jones made up some of that difference with his still excellent defense.

This is not to say that Jones wasn’t an elite player. He was. Over the five-year stretch from age 26 to 30 (2003 to 2007), Andruw Jones was seventh in baseball in WAR.

If you expand the range to the first 12 years of his career, from 1996 to 2007, Andrus Jones was also seventh in baseball in WAR, behind Barry Bonds, Alex Rodriguez, Chipper Jones, Pedro Martinez, Randy Johnson, and Curt Schilling. In his first 12 seasons, Andruw Jones averaged 87 runs scored, 31 home runs, 93 RBI, and a .263/.342/.497 batting line with excellent defense.

And that was it. Those first 12 seasons make up nearly 96% of Jones’ career WAR even though he continued to play for another five years. He signed with the Dodgers as a free agent prior to the 2008 season and had the worst year of his career. He hit .158/.254/.249 and his defense went from excellent to average. His WAR for that season was -1.1. He rebounded on the hitting side over the next three seasons but was no longer the defensive stud he’d once been and became a part-time player. Over his last five seasons, he was worth just 2.9 WAR total.

Of course, Ken Griffey Jr. did not age well either. He was injured in 2001 at the age of 31 and finished with the lowest WAR of his career to that point (1.8). From 2002 to 2004, he played an average of just under 70 games per year and had 0.5 WAR per season. He continued to hit well (117 wRC+), but on defense he struggled. From 2004 to 2009, no outfielder in baseball with more than 2000 innings in the field had a worse Ultimate Zone Rating (UZR) than Griffey. He was even worse than Manny Ramirez and Adam Dunn.

The graph shown earlier reveals the similar arcs of the careers of Andruw Jones and Ken Griffey, Jr. They both were great players through the age of 30 and below average players from age 31 on. Griffey did have more truly elite seasons. He had three seasons with eight or more WAR, which were better than any season Jones had, but they were very close in the number of seasons with four or more WAR (Griffey had 10, Jones had 9).

It will be interesting to see what Hall of Fame voters think of Andruw Jones in five years. Admittedly, there was a 10-WAR difference between Jones and Griffey over the course of their careers, but they don’t seem all that different when you look at their similar career trajectories and their distribution of WAR, particularly in the number of great seasons they each had. Jones played 17 years, while Griffey played 22. But in Griffey’s final five years, his value was below replacement level. It didn’t seem like it because he hit a respectable-looking .247/.340/.444 and had nearly 500 hits and almost 100 home runs, but his defense was a killer that greatly affected his value.

Ken Griffey, Jr. was just voted into the Hall of Fame with 99.3% of the vote, the highest percentage ever. Jim Edmonds was on the same ballot and is now one-and-done with just 2.5% of the vote. How will Andruw Jones fare?

The Pirates brought in Ryan Vogelsong this winter, and most Pirates fans believed it was a depth move, and that another move would follow and net them a better option. Recent comments by GM Neal Huntington hint that perhaps they are done, and that Pirate fans should resign themselves to seeing Vogelsong as the #5 starter, at least to start the season.

Vogelsong has not been good for a while. In 2011-12 he threw 369 innings for the Giants with a 3.68 FIP, adding 4.6 WAR.  Since then, 423.1 very mediocre IP with 4.33 FIP generating 0.8 WAR.  Steamer projects 109 IP at 4.38 FIP & 0.6 WAR.

With that in mind, I decided to do a little keyboard coaching to find a path to improvement.

The first unusual thing I noticed on Vogelsong’s Brooks Baseball card is that he throws five pitches in fairly equal proportion:

http://i1028.photobucket.com/albums/y344/Arik_Florimonte/f36b430c-4284-4886-9df7-b8e626e0b8a3_zps0duyggiw.jpg

Let’s look at RHB first. In 2015, he was average, his wOBA against sitting right at .300. Here is the SLG against by pitch for the last five seasons:

http://i1028.photobucket.com/albums/y344/Arik_Florimonte/dee0a538-a64e-4b45-af60-b7aa65dedd74_zpsoij52g6d.jpg

His 4-seam, sinker, cutter, and curve all yielded good-to-excellent SLG, yet in 2015 the changeup got hammered, to the tune of .563 SLG (up from the .370 range), and he got less than 5% whiffs (down from 7% previously, and far below the league average of 11.9%). Yet he still uses it 5% of the time. The curve on the other hand, has been good, producing a SLG under .300 in four of five years, and GB% and SwStr% right around average. I’d suggest it’s time to ditch the change against RHB, and rely more on the curve.

Against LHB, he gets torched. Batters mashed a .383 wOBA against him in 2015 and a .346 wOBA in 2014. Here are the numbers by pitch:

http://i1028.photobucket.com/albums/y344/Arik_Florimonte/b541ef80-4eab-4c67-8f88-effa9416754f_zpswkjfiakc.jpg

His only pitches with decent SLG against in 2015 were the 4-seam (.390) and the cutter (.265), while the other three pitches all have SLG over .630. (The curve at least has a 16% whiff rate). Those three “bad” pitches are used over 60% of the time. While I’m sure he needs to mix those pitches them in sometimes to keep lefty hitters honest, they are simply getting destroyed. So, I suggest he could have some more success if he stopped trying to throw his sinker and changeup — or at least cut way back — and occasionally mixed in the curve as the pitch to keep them honest.

While Vogelsong’s problems likely require a solution more sophisticated than “throw your bad pitches less” (and undoubtedly his coaches have a better view on this than some guy behind a monitor looking at numbers), his recent results suggest he won’t be much above replacement level unless he changes *something* vs. LHB. Obviously, resurrecting his once-good changeup would be the preferred option, but failing that: ditch the changeup and stop throwing the sinker to lefties. Boost 4-seam and cutter usage against lefties, and mix in the curve to keep them honest. Maybe a 4-seam/cutter/curve mix would be enough to get him through the order twice, and if not, his results with his “good” pitches may be good enough for the bullpen.

This week the strike zone has been getting a lot of attention. If you’ve been paying any attention to baseball (and I’m sure you have since fantasy baseball leagues are starting to open up) there have been a few articles/releases suggesting that MLB may be considering raising the strike zone from the hollow beneath the kneecap to the top of the kneecap. It seems like a good idea since strikeout rates are on the rise, but was this a result of (1) pitchers getting better or (2) hitters getting worse or (3) have strikes been getting called differently? I’ll give you a hint; it’s neither of the first two suggestions, at least not directly. No, instead let’s focus on the strike zone and more specifically two things: (1) visualizing the strike zone from 2008 to 2015 and (2) using a standardized set of pitches look at how those pitches have been called over time.

Let’s go through the methods I used before we get to the plots. I used the pitchRx package in R to gather and store the data and used many of the functions included in the package. Next I went through the data and subset the PITCHf/x data by year since I was interested in looking at annual changes. Now due to a combination of time restraints and lack of computing power I didn’t run all of the pitches thrown in each year so I did some subsetting instead. I downloaded a CSV from the FanGraphs leaderboards of all qualified pitchers from 2008 to 2015. In each year I randomly selected 20 pitchers from the list of qualified starters to represent how the strike zone was called for that given year. Finally I ran the data through a general additive model (seen here) which was used to create the “heat maps” for the probability of called strikes in the plots below. I also tested the probability of five standard pitches being called strikes, but that is addressed a bit more later one so I won’t bore you with the details twice. Added note: if anyone actually wants a copy of the R code leave a comment below and I’ll get in contact with you.

Below I’ve included a GIF of the strike zone from 2008 to 2015 . If you watch it a few times you’ll begin to notice the gradual changes to the bottom of the strike zone, plus when it flips from 2015 to 2008 you can really notice the difference. It’s not surprising that there are inter-annual differences between the zones since I’m sure MLB makes a few minor tweaks every off-season and maybe there is a changing of the guard over time for the umps. I also need to apologize about the 2010 plot, the left (L) and right (R) are reversed and I can’t seem to switch them. We will just have to deal with that one plot being different. In all plots the label “L” refers to left-handed batters and “R” to right handed batters.

Now I wanted to find a way to quantify changes to how pitches were being called and I decided on using a set of standardized pitches. Below is a plot showing the locations I chose for my test pitches. I went with five different locations. The pitch right down the middle was my control of sorts, just to make sure things were getting called consistently over time. The remaining locations were the ones I was really interested about; three of those pitches were all located on the lower edge of the strike zone and the final pitch was located 0.2 feet or 2.4″ (the metric system would be more useful here, just sayin’) below the bottom edge of the strike zone. When I initially began this simulation I expected that the lowest pitch would be a second control pitch that would consistently be called a ball, but the results were pretty surprising. Also, I’d like to include that the strike zone to lefties is slightly shifted so that more outside pitches are called strikes.

OK so we are almost at the exciting conclusion. Using those standardized pitches from the plot above I used the general additive model to predict the probability of that pitch being called a strike in a given year. The results are summarized in the plot below. We can see that the pitch being thrown at coordinates 0, 2.5 (the one down the middle) the probability of being called a strike is basically 100% every year. Well that’s a good thing at least that call is consistent. The low pitch thrown down the middle on the bottom edge of the strike zone, coordinates 0, 1.7 (green line), has increasingly been called strike since 2008 to both right- and left-handed batters. Pitches down and in to righties increased pretty significantly this past season where the probability crept above 50%; to lefties that pitch is down and away and it’s been called pretty consistently since 2011 (red lines). Pitches thrown down and away to righties or down and in to lefties (coordinates 1, 1.7 — purple lines) haven’t changed all that much over the time period.

Now we get to what I think is the most interesting pitch. The low fastball down the middle (coordinates 0, 1.5) the one that should be out of the strike zone. This pitch is represented by the gold/yellow lines on the plots. In 2008 these pitches had a chance of being called a strike ~10% of the time to both righties and lefties. Over the last eight seasons that number has trended upwards and in the 2015 season settles in somewhere around 36-40%, which is not an insignificant proportion.

Based on this data it certainly appears as though MLB is justified into looking at raising the strike zone. Pitchers that live down in the zone have been given an increasing advantage in a relatively short amount of time. Hopefully this sheds some light onto the debate on whether or not to raise the strike zone in the coming seasons or maybe the umps will be able to make some adjustments for the upcoming season.

During the past three seasons in Pittsburgh, Ray Searage has worked his magic to rejuvenate the careers of struggling pitchers. From increasing the usage of a two-seam fastball to induce ground balls and having a pitch framing expert behind the dish, the Pirates rotation has raised a few eyebrows. A few key pitchers that benefited from Searage were AJ Burnett, Francisco Liriano, Edinson Volquez, JA Happ, and many more. After seeing the success of these four pitchers, it becomes very difficult to doubt a pitching acquisition made by the Pirates. Therefore, who will be Searage’s next project?

On December 9, the Pirates agreed to send soon-to-be-free-agent second basemen Neil Walker to the Mets in exchange for veteran left-hander Jon Niese. Niece was drafted by the Mets in the 7th round in 2005 out of Defiance High School in western Ohio. Since 2010, he has been a consistent innings eater for the Mets rotation known for inducing a ton of ground balls. In 2012, Niese sported a 13-9 record with a 3.40 ERA and a career-high 2.6 WAR. From 2010 to 2014, Niese was consistently a 2 WAR pitcher, which would project as an average to above-average mid-rotation starter. However, in 2015, he struggled at times and posted a career low 0.9 WAR. Even though he posted a career high 55% ground ball percentage, he was not missing bats much with his 5.8 K/9. It’s safe to say that Niese is seeking a rebound in 2016 and he has come to the right place.

Heading into the 2016 season, I am very high on Jon Niese and believe he fits perfectly in a Pirates rotation managed by Ray Searage. Niese has a repertoire that includes a sinker, cutter, and four-seam fastball that will induce many ground balls. With their statistical findings on defensive alignments outlined in Big Data Baseball by Travis Sawchik, the Pirates could use Niese to their advantage. When looking into some of Niese’s pitch-usage data, I found his situation comparable to that of J.A. Happ. After acquiring a struggling Happ from Seattle during last summer’s trade deadline, Searage noticed a decrease in the usage of his fastball and encouraged him to be more aggressive. Happ adjusted his approach almost immediately and put up an impressive 7-2 record with a 1.85 ERA in 11 starts. So how does Jon Niese’s struggles compare to Happ’s? I found my answer after referring to brooksbaseball.net for pitch-usage data in his 2012 season and 2015 season. In 2012, Niese threw his four-seam fastball at 35.7 percent compared to 20.2 percent in 2015. This is a significant difference in a matter of only four seasons. I would also like to note that he reduced his cutter usage by almost 7 percent in that time span.

Upon his return to Pittsburgh, I am expecting Searage to take a similar approach with Niese as he did with Happ. Increasing the fastball usage and being more aggressive will only benefit Niese with an even better defense behind him. Steamers projects Niese to repeat at a 5.8 K/9 in 2016. However, Fans projections sees him returning to a 6.4 K/9. Let’s not forget that he is throwing to pitch-framing extraordinaire Francisco Cervelli, which may work in his favor to get more strikes. While I believe that he will be able to miss a few more bats than last year, his main strength will be pitching to contact and inducing ground balls into the many defensive alignments behind him.

While the Pirates’ projected rotation may seem a bit top-heavy at the moment, look for Niese to be a solid #3 behind Gerrit Cole and Francisco Liriano. By mid season, the Pirates rotation could be a force with the debuts of top prospect Tyler Glasnow and former second overall draft pick Jameson Taillon. In October, while many may disagree now, watch for the Pirates to be declared the winner of the offseason swap with the Mets.

Some know him as “The Freak”, while others like myself know him as “Big Time Timmy Jim“. Tim Lincecum is planning on showing if he’s got anything left in the tank sometime next month. This year he had some problems with his hip and ended up getting surgery in mid-September. Here’s a link to a some info about hip labrum surgery for those who are interested. Early in his career he was one of the most dominant starters out there and you could make an argument that for a short period he was the most dominant pitcher in baseball. Over the last four years he’s become a dependable 4th or 5th starter, but the 2015 season was one of the worst of his career.

Age has seemingly caught up with another pitcher. Lincecum is yet another example of a pitcher whose velocity peaked early in his career and has been on a decline ever since. We don’t have PITCHf/x data for his rookie 2007 season, but we have the data for the rest of his career. Besides the 2011 season where he regained some form, he’s shown a pretty consistent decline in velocity over time.

To me, the obvious outlier is the most recent season where he saw his average fastball velocity dip below 88 MPH and about 2 MPH slower than the 2014 season. This is where we can see how his hip issues affected his velocity on the mound. Below is table with his peripheral stats (excluding his rookie season). To give a quick overview, K/9 has been trending downward, possibly relating to his diminished velocity. It doesn’t look like his BB/9 or HR/9 has any significant trend, but FIP has almost always been more generous than ERA.

As I said before, Lincecum recently had hip surgery and I assume he is nearing the end of his rehab since he’s planning a February showcase to try and secure another contract. Given his uncertain injury status, and his performance over the last four years, he’s likely only going to be able to secure a 1-year contract possibly with some performance bonuses. Teams are definitely taking a risk if they decide to sign him, since over the last two years he has been just slightly above replacement level, accumulating o.1 WAR in 2014 and 0.3 WAR in 2015. I’ll also mention that as a starter in 2014 he was worth 0.3 WAR, and he was worth -0.2 WAR as a reliever.

He’s certainly not the most imposing pitcher to ever set foot on the mound, standing 5′ 11″ and weighing in at 170 lbs (maybe with a wet towel wrapped around his waist); he’s one of those pitchers who needs to use his whole body to gain the necessary momentum to get those 90+ MPH fastballs. If you go back and look at the fastball velocity chart above it’s pretty clear that there was a significant drop in velocity this previous season. I think it’s pretty fair to think that his hip issues had something to do with that phenomenon. Here’s a link to an article from MLB Trade Rumors with some info about his surgery. I remember reading a more in-depth article earlier in the off-season saying that his hip issues were screwing with his mechanics, but I’ve been unable to find a link to that story. But the takeaway should be that he wasn’t healthy. He wasn’t able to generate the necessary power due to his hip issues and his velocity suffered as a result.

So the question becomes, if the surgery was a success and his rehab goes well, what can we reasonably expect from him for the upcoming season? Well that is definitely a tricky question since he’s almost 32, he’s two years removed from throwing in the 90s, and there’s the possibility that he won’t be back with the team that drafted him. I think in the best-case scenario we could see him start hitting his 2012-2013 velocity (~90.3 MPH) and if that’s the case we could start to see his K/9 creep up to around the 9.0 mark again. But that’s just my opinion and my opinion means basically nothing, so I’ll include a comparison.

I was only able to find one example of a pitchers who’d undergone the same type of surgery as Lincecum and that was Charlie Morton. In October 2011 he also underwent the hip surgery. You can check out his velocity chart below. He also had Tommy John the following June so if you’ll humour me and ignore the elbow issues you’ll see that his velocity over the 2011 season dropped from 94 to just under 92, only to return to 95+ after recovery from TJ.

Over the last two years Lincecum has amassed 0.4 WAR and made $35 million. There is no doubt that the Giants overpaid for his service over the last couple of years and I can’t see him getting anywhere near that annual salary. If we go by the market rate of ~$8 million/WAR, on a bounceback contract where a team expects a 0.5 WAR season we could see a contract in the ballpark of $4 million. Even that seems high to me; if I were to venture a guess I would put it around the $2-million mark with incentives. I’m definitely not saying he’s going to be the pitcher from five years ago, but a dependable 4th or 5th starter with the potential to strike out almost 200 batters sounds pretty awesome to me. You’ve always got to wonder if he’s got any magic left in him. Baseball is better with The Freak in it and hopefully he gets back on the mound soon.

The game is on the line. It’s the bottom of the 9th inning, runners on first and second with two outs for the Mets. Justin Turner drives a fly ball off the bat at a speed of 88.3 mph. All hope for the Braves looks to be lost. In a blink of an eye or just .02 seconds Jason Heyward reacts and races out of center field traveling 18.5 mph to make an incredible diving catch to save the game.

This data set was one of the earlier Statcast recordings released to the public. It shows how important such information could potentially be to clubs in the future. Statcast can record data such as Acceleration, Route Efficiency, Reaction Time, Max Speed, Distance Covered and more. Although not all of their data is available to the public, I wanted to further explore how a baseball club would benefit by using this technology to research defensive analysis on improving a player’s abilities and a club’s defensive positioning.

First off, a team could compile this data and separate each player’s metrics by direction. Players move differently when heading in different areas of the field. It’s obviously easier to move forward than running backward, so having this data would allow teams to identify key information and make comparisons down the road. This can be done so by separating a fielder’s range into eight different quadrants (see graphic below). Once that is done, averages are created based for each quadrant. For instance, on average, what is Brett Gardner’s route efficiency when moving right? When moving in quadrant 6, what is Charlie Blackmon’s average reaction time?

Quadrants

#1: Forward

#2: Right Forward

#3: Right

#4: Back Right

#5: Backwards

#6: Back Left

#7: Left

#8: Left Forward

All this information, separated into different quadrants, will help in visualizing and breaking down defensive ability. When we have averages of acceleration, max speed and reaction time it can create a visual graphic or “Statcast Range” to witness how much distance a player could potentially cover in a certain amount of time. For example, lets say Jason Heyward’s average reaction time, acceleration and max speed when going left was .02 sec, 15.1 ft/s^2 and 18.5mph respectively. We know using this information Heyward could cover approximately 81 feet in 4 seconds. Time can help us represent a player’s estimated “Statcast range.” Each player’s range will look differently as they may show in which directions they are better at fielding. We can then use this analysis to compare fielders and also adjust defensive positioning.

This information will help guide a team in improving its players’ abilities. Teams can compare players much easier and understand what flaws coaches must look into fixing. For example, if a fielder has below-average route efficiency or reaction time to a certain part of the field, this information can be relayed to the coaching staff to further improve a player’s ability over time. In order to put this in perspective, Eugene Coleman of the University of Houston found that the average major-league ballplayer ran 24 feet per second. Using this number, having 0.04 more seconds means the average major leaguer can cover 11.5 more inches of ground. That’s almost a foot more and within only .04 seconds. If a ballplayer cuts down his reaction time, improves his route efficiency, and more, he would be able save time in covering several more feet of ground and thus improving his defensive ability.

To adjust a player’s defensive positioning, a team would have to combine its knowledge from this analysis with the understanding of a hitter’s batted balls. If they know a certain player is a pull hitter and hits to certain parts of the field, they can track his batted-ball locations, hang time and exit velocities to project areas in the field to which he may hit. Using what we know about a fielder’s Statcast metrics and “Statcast Range “ a player’s positioning could be adjusted. Doing so would lead to more accuracy. Improving the range of a team’s fielders will help save distance and time. The ability to increase production of more outs will provide a club with a better advantage for winning the game.

Being a Rockies fan for most of my life, I’ve had my fair share of discussions about how a ballpark can affect not only the performance of the home team, but also that of the visiting team. At this point, I don’t think anyone has any doubt that Coors Field is a hitter’s park. However, there are a couple of questions regarding this park I’d like to address. First of all, is Coors Field alone in its capacity of enhancing offense, or is it comparable to other parks around the league? And secondly, is this effect stronger among Rockies’ hitters than it is for hitters from other teams?

To answer the first question, let’s compare offensive production at home versus on the road for each team, so we can see where the Rockies stand among the rest of the league in this regard. I selected a time frame from 1995 to 2015, simply because it is the same time frame that Coors Field has been hosting baseball games. For teams that moved to a new park during that time, we’ll consider only the seasons played in the newest stadium. The comparing stat we’ll use is OPS. I chose OPS instead of runs scored (which many park factors out there use) to take sequencing out of the equation. The order in which individual events occur in baseball can depend on things like lineup construction or managerial in-game decisions, but mostly it’s just random chance. I could have chosen a sounder, more sophisticated stat like wOBA, but OPS is more readily available, and a wider array of audiences are familiar with it.

After constructing a table for each team, consisting of year by year home and away OPS, I calculated the percent change of the two means, using the away value as the base. But simply comparing means can be very misleading. Randomness will always create a difference between two means, even if there is no actual effect causing it. In order to have some confidence that the differences we observe are statistically significant, I ran a Student’s t-test to each set of data (i.e. yearly home and away OPS for each team). The threshold of significance was set at 0.10, which means that there would be a 10% chance of seeing these differences if there were no real effect.  Anything above that value was considered not significant.

The following table contains the percent change for every team, along with its p-value. Red values don’t satisfy the significance criterion.

According to these numbers, 19 out of 30 ballparks have a statistically-significant positive effect on the home team’s offense, while 10 of them can be considered “neutral” due to the non-significant nature of the data, and just one (San Diego) has a significant negative effect on the home team’s offense.

At first glance, Coors Field seems to be in a league of its own when it comes to enhancing the home team’s offensive production. A common rule of thumb is that in a normally distributed data set, 99.7% of its values fall within three standard deviations of the mean. Any value outside of that range is considered an outlier. In this case, that range goes from -12.43% to 20.96%. Colorado, with its variation of 27.01%, falls way outside these limits, making it the only outlier of the group. This answers our first question, confirming that there’s no park that increases offense for the home team quite like Coors does. Which takes us to the second question: does it have a similar effect on visiting teams? Let’s crunch some numbers and see what they tell us.

The idea is to repeat the same process we used for answering the first question, only this time we’re going to use opponents OPS or OPS against, instead of the team’s own OPS. Basically, what we’re trying to do is compare how opponents’ offenses as a whole, change when they visit a particular park. In other words, and using Colorado as an example, we want to know how the league’s OPS against the Rockies is affected by playing at Coors Field as opposed to anywhere else.

Using the same methodology, here’s the opponents OPS change by park:

There are a couple of things to digest from of this table. First off, the fact that Colorado has the only park in which visiting hitters significantly increase their offensive production is pretty mind-blowing. It seems to me that we’ve been using the term “hitter’s park” way too lightly. Out of the 30 ballparks actively housing an MLB team, 19 have a statistically-significant negative effect on the visiting team’s offense. Just like in our first analysis, 10 of them can be considered “neutral”, with p-values above 0.10, and just one (of course, Coors Field) has a positive effect with a good degree of significance.

This seems to contradict the numbers showed in our first table. In fact, out of the 19 parks that enhanced offensive performance for the home team, 10 of them also have a negative effect on visiting hitters. How can this apparent contradiction be explained? Well, it probably has a lot to do with the all-encompassing concept that is Home Field Advantage. For whatever combination of reasons (familiarity with the park, sleeping in their own beds, having dinner with their families), playing at home seems to get the best out of most players. If you think of the visiting teams’ OPS as a pitching stat for the home team (which it is), then you can interpret the numbers in the second table as having 19 out of 30 parks with a positive effect on the home-team pitching staff, 10 being neutral, while just one of them having a negative effect. Coincidentally, that’s precisely a mirror image of the results we got when analyzing the first table.

Going back to the second question, does Coors Field have a greater impact on Rockies’ hitters than on the rest of the teams? The short answer is yes. The variation in OPS for Colorado players is 27.01%, while the equivalent for non-Rockies players is “just” 9.00%. So by just comparing these two values, it seems evident that the effect is in fact greater among Rockies’ hitters. The explanation could be again simply Home Field Advantage, but the difference is just too big. If we merge both tables in one, and consider the visiting hitters as a control group, then a simple subtraction should give us a rough estimate of the net effect of Home Field Advantage on home-team hitters.

Here’s that table. Red values were not considered in the subtraction since they were deemed non-significant.

Coors Field sits comfortably at the top, way ahead of Minute Maid, the second park on the list. Applying the same criteria for outliers we used before, Colorado’s Net Effect of 18.01% is not within the range of three standard deviations around the mean (-1.60% , 16.74%), once again being the lone outlier. It doesn’t look like that this is simply a result of Home Field Advantage; it seems there’s something else. This brings up a new question, one for which I’m not sure I have a definite answer: Does Coors Field undermine the Rockies’ ability to have a healthy offense on the road?

Let’s go back for a moment to the 27% increase in OPS for Rockies’ hitters at home. That number could mean a huge spike in offensive production when they play at Coors Field or a massive collapse when they hit the road; it depends on how you see it. Colorado ranks dead last in the majors in OPS away from home in the same time span we’re studying, so either they have been the worse offensive team in two decades (which is certainly an option) or something is causing them to consistently under-perform on the road. Of course, it doesn’t help that almost half of their games away from Denver are played in places like San Diego, Los Angeles, and San Francisco. In fact, according to the numbers in the second table presented in this piece, Colorado’s division rivals have the toughest combination of parks for visiting hitters. The average drop-off in opponents OPS in NL West parks (excluding Coors Field) is -7.15%. The following table shows that value for every team in the majors (for the purpose of this exercise, Houston was considered an NL Central team).

This definitely helps explain, at least partially, the abnormal home/away splits that Rockies’ hitters have had historically. Not only do they play their home games in the biggest, if not the only true hitter’s park in the game, but they also play a big chunk of their road games in three of the toughest pitcher’s parks in MLB.

The last question remains unanswered; the thesis of a Coors Field Hangover effect is largely unproven. Still, there’s a good amount of circumstantial evidence that points to the existence of something like it.

The Los Angeles Dodgers have five prospects in MLB’s Top 100, good for a sixth-place tie in the majors. These prospects include two in the top five (SS Corey Seager and LHP Julio Urias) as well as three RHPs in the top 60 (Jose DeLeon, Frankie Montas, and Grant Holmes). Two other Dodger prospects are among the top 10 at their respective positions (C Austin Barnes and 2B Micah Johnson). All this from a team that’s had just one losing season in the last 10, and currently has an eye-watering payroll of around $230 million, roughly the same as the Yankees. At that price you could field almost four whole Brewers teams.

All the Dodgers’ big dollar contracts (which I”m loosely defining here as $7 million AAV or more) except Clayton Kershaw’s will come off the books after the 2018 season, leaving the Dodgers with … a younger, cheaper roster that still wins a lot of games. Or maybe not — after all, prospects are gambles, and many crap out. But the Dodgers have put themselves in a position to at least have a reasonable expectation of success in the near future with a roster quite different from today’s, without taking up extended residence in the damp, roach-infested divisional cellar.

The five teams with more top-100s than the Dodgers have lost a lot more games while accumulating those prospects. Colorado, Atlanta, Cincinnati, Minnesota and Philadelphia have had a combined 25 losing seasons since 2006. Each has six top-100s except the Rockies, who have eight. (And this doesn’t include MLB’s current tank commanders, the Astros and Cubs, who have four and five top-100s, respectively, and have assembled playoff caliber rosters while each having seven losing seasons in the last 10.)

Most of the teams on the above list will be good again in four or five years. The Twins are already on the upswing, and the Phillies’ brilliant Cole Hamels trade may help to shorten their rebuilding project. But all of these teams have been varying degrees of bad for some time, while Dodger fans have not had to endure anywhere near that kind of punishment. How did the Dodgers do it?

In one sense, they simply used ordinary items found around the typical baseball household: They made their first-round picks count (Seager, 18th overall, and Holmes (22nd)). They effectively scouted internationally (they signed Urias out of the Mexican League when he was just 16, for a relative pittance). They effectively scouted other teams (Montas and Johnson came from the White Sox thin, and now thinner, farm system). They at least arguably scouted their own roster effectively (coughing up Jose Peraza to get Montas and Johnson — a fascinating trade in which the future of all three players is the subject of considerable debate; the Dodgers also surrendered Scott Schebler, a player less subject to debate, or indeed to discussion of any kind). And the Dodgers developed their own talent well: Seager, DeLeon, and Barnes (a 9th rounder acquired from the Marlins for Dee Gordon) would all go higher in a hindsight draft, as perhaps would Holmes. The players deserve most of the credit for their own development, but the team deserves at least some.

Nothing about baseball is inevitable, least of all the prior or continuing development of the Dodgers top prospects. Three years ago no Dodgers official would have been able to name these players and say “we know we’re building around these guys.” Three of the key players weren’t even with the organization at the time.

But the Dodgers may have established an organization that enables them to, perhaps even makes it likely that they will, produce a handful of front-line, cost-controlled players every four or five years, whether the major-league team rains or shines. That hypothetical Dodger official of three years ago could have said “we’re pretty sure we’ll be building around maybe four or five guys from our organization, we just don’t know exactly which handful yet. Once we know that, we’ll fill in the details.” That exciting moment is now fast approaching. The Dodgers of 2019 will likely be a hell of a lot of fun to watch, and the three years in between may not be too bad either, if the rickety overpaid veterans don’t break down too quickly.

And my guess is the Dodgers’ secret sauce isn’t brilliant scouting or next generation analytics or superb coaching or excellent training regimens. Well, it may be all of those things, but the only thing that can get all of those things is money.

Money doesn’t help a farm system, at least not like it used to. With the restrictions on draft and international spending, it’s not like the Dodgers can bully the other 29 teams on an amateur level.

Thus Grant Brisbee in 2014, discussing Andrew Friedman’s arrival in LA. The article is still interesting and worth clicking through, but I disagree to some extent with Brisbee’s assertion quoted above. It’s true that restrictions on player salaries and international signings are limiting the ability to buy players. But there is no restriction on the amount that can be spent on locating, evaluating, and developing players. At least some of the rivers of cash that are no longer flowing into players pockets are flowing to these activities instead.

It’s possible that in their obsession to rein in Scott Boras, Jerry Reinsdorf and his allies created a far more dangerous enemy: zombie armies of  number-crunchers, scouts, coaches, and trainers, all laboring, knowingly or not, to produce those next five top-100 prospects. Boras used to facilitate inefficient spending by large-market teams, enhancing smaller-market teams’ ability to compete in the non-payroll space. Those days are waning, Chris Davis notwithstanding.

Maybe the Dodgers have just gotten lucky. Four of their top five prospects are pitchers; the Dodgers are just a couple of snapped UCLs or torn labrums away from disaster. (The recent signings of Scott Kazmir and Kenta Maeda are in part insurance against fire, theft, or loss of the four young arms mentioned above.) Joc Pederson may devolve into a second-division starter. Yasiel Puig may become a reality TV star. Bad things happen even to the best run organizations.

So these Dodgers may signify nothing. But the Dodgers’ owners disagree; that’s why they hired Andrew Friedman. The Dodgers aren’t going to have a $200-million payroll after 2018, maybe never again, and they wanted somebody who understood how to run a team that, like an iceberg, has the vast majority of its substance lying below the surface. Friedman wrested production from the infertile Tampa soil of with the obsessive frugality of a medieval Russian peasant. He’ll apply those same skills in LA, but mostly in ways that won’t be immediately visible on the field, using money that won’t be visible on the payroll.

This is Borg baseball. It could be baseball’s future. And for lower-revenue teams, resistance may well be futile.