Throughout each level of the minor leagues, a lot of time and effort is devoted to travel. A more productive model would be for an entire level playing in one location. Spring training’s Grapefruit and Cactus Leagues are a great example. Like spring training, the goal of the minor leagues is to develop, not to win. In this system, players would have more time to work on strength, durability, and skill development. This system could be in effect until the prospect reaches Double-A. At that level, players could start assimilating themselves to playing ball all over the map. However, this is merely a pipe dream. The more realistic option to improving the minor leagues would be to raise each player’s salary.

In 2014, three ex-minor-league baseball players filed a lawsuit against Major League Baseball, commissioner Bud Selig and their former teams in U.S. District Court in California. Sports Illustrated attorney and sports law expert, Michael McCann, explained their case.

“The lawsuit portrays minor league players as members of the working poor, and that’s backed up by data. Most earn between $3,000 and $7,500 for a five-month season. As a point of comparison, fast food workers typically earn between $15,000 and $18,000 a year, or about two or three times what minor league players make. Some minor leaguers, particularly those with families, hold other jobs during the offseason and occasionally during the season. While the minimum salary in Major League Baseball is $500,000, many minor league players earn less than the federal poverty level, which is $11,490 for a single person and $23,550 for a family of four….

The three players suing baseball also stress that minor league salaries have effectively declined in recent decades. According to the complaint, while big league salaries have risen by more than 2,000 percent since 1976, minor league salaries have increased by just 75 percent during that time. When taking into account inflation, minor leaguers actually earn less than they did in 1976.”

Like many big corporations, MLB teams would never increase minor-league salary just because it is the right thing to do. What’s in it for them? Think about it like this.

At point A, when the average MiLB player has a wage set at W2, the player will take Q2 hours out of the day to work toward baseball. As you can see, there is room to improve, as point B is optimal. Accomplishing point B would mean increasing a player’s salary to W1. In turn, players could afford to take Q1 hours out of the day toward baseball. With most minor-league players needing to find work in the offseason or even during the baseball season, a raise in salary would give them the opportunity to be full-time baseball players. These prospects would spend more time mastering their craft, speeding up the developmental process.

With a season as long as 162 games, there is no telling how much depth could be needed in a given year. Just ask the Mets. That’s why it is important to maximize the development in a team’s farm system. At the end of the day, this is merely a marginal benefit. It will not take an organization’s farm system from worst to first. However, it only takes one player that unexpectedly steps up in September to alter a playoff race, proving worth to the investment.

I tried to come up with a witty preamble to this but all I could come up with was a lame story about playing RBI Baseball 4 against my older brother. And unless you have mistakenly come to FanGraphs while trying to get to Farmers Almanac (no judgments, Google auto-complete can be weird sometimes) then you probably don’t care about that. So let’s dispense with the amusing introduction and get right to the question. (Or did I just subversively come up with a witty preamble by explaining how I did not have a witty preamble?!)

Scenario:

Runner on first with two out. 0-2 count.

Now anyone who is even slightly familiar with baseball will tell you that this is not a good situation for the offence. Those who are very familiar with baseball to the point that they read things like this post will probably even quote the run expectancy matrix to demonstrate how bad of a situation this is for the offence.

So, yeah, not looking good for the offence. The chance of scoring a run from that base/out state is 0.127. And that is without even accounting for the 0-2 count which obviously makes things worse. MLB as a whole slashed .155/.187/.237 with a 47.6 K% and a 10 wRC+ last year through two-out, 0-2 situations with runners on. In other words, the batter made the third out ~80% of the time. Even Mike Trout, who is Baseball Jesus, strikes out over half the time in 0-2 counts and is running a tOPS+ that is almost single digits. For all intents and purposes, the inning is likely over when it hits that situation.

But the team at the plate is not totally powerless. It can still decide how to end the inning, and they could do it in a way that gives them a more favourable outcome. Which brings me to the crux of this argument;

Why not have the guy on first just take off running?

Before the pitcher even comes set, just take off for second. Worst case, they tag him out and the inning ends (which was the most likely outcome anyway), but now the guy at the plate leads off the next inning in a fresh count, which is obviously a much more favourable scenario for a hitter. And best case, the defence screws up and the runner is now on second. Granted, that is an extreme outcome, and even two out and runner on second is still not a great scoring scenario. But referring back to the run expectancy matrix, it’s ~50% higher than when he was standing on first.

If the outcome of the scenario is almost overwhelmingly going to be an out, then you are not really giving away an out as much as you are just deciding who takes the out. If you have a good hitter at the plate, why have him continue to hit in what is a pretty futile situation, and waste one of his limited PAs, when you can reset the situation and give him what amounts to an extra PA by having the runner take the out instead?

Let’s look at Mike Trout’s career as an example since, well, since it’s fun to look at Mike Trout’s numbers.

No surprise, Mike Trout is a much, much, much better hitter overall than he is in 0-2 counts. Every hitter is. Now let’s also check back in with our friend, the run expectancy matrix.

So right off the bat (no pun intended), we see that the chances of scoring a run at the start of any inning are considerably better than scoring a run with two outs and a runner at first. Add in the fact that you have a very good hitter leading off in Trout and things have seemingly changed significantly for the better, simply by having your base-runner act like an 11-year-old exchange student on the base paths.

If Trout does anything to get on first (single, walk, HBP, dropped third strike, coming to the plate and performing a stand-up routine that is so good the opposing team just awards him first as a thank you, etc etc), now all of a sudden the chances of scoring a run in the inning have gone up to 0.416. Given that Mike Trout got on base nearly 45% of the time last year and is around 40% for his career, it seems like a fairly reasonable outcome. So by having your base-runner deliberately make an out to end the previous inning and saving Trout from doing so, you have gone from a situation where you had a .127 (or lower given the fact that the 0-2 count is not accounted for in the matrix) chance of scoring a run and your best hitter producing an out to a situation where you very likely have a 0.416 chance of scoring a run. And that does not even account for all the other things Trout might do new in this new PA. If he hits a lead-off double, your chances of scoring a run in the inning are now 0.614. If he hits a lead-off home run, your chances of scoring a run are….hold on, where is my calculator? Plus, you have also avoided what was highly likely an out for your best hitter and having to wait two or three innings for him to bat again.

Last year, MLB teams averaged 219 PAs where they had runners on and an 0-2 count. As stated above, in that situation the hitter wound up making the third out ~80% of the time. So that is ~200 innings that could have started with a different guy at the plate and ~200 outs at the plate that could theoretically have been something other than an out. How many innings would have been different by simply giving up the runner for the third out and letting the hitter lead off the next inning in a more favourable count? If you have a good hitter at the plate and he is down 0-2, it might be worthwhile strategy to just tell your base-runner to take off and let your hitter try again the next inning.

Or maybe I have had too much coffee today.

Last month, I introduced the xFantasy system to these venerable electronic pages, in which I attempted to translate Andrew Perpetua’s xStats data for 2016 into fantasy stats. The original idea was just to find a way to do that translation, but I noted back then that the obvious next step was to look at whether xFantasy was predictive. Throughout last season, I frequently found myself looking at players who were performing below their projection, but matching their xStats production, or vice versa, and pondering whether I should trust the xStats or the projections. Could xStats do a better of job of reacting quickly to small sample sizes, and therefore ‘beat’ the projections? Today, I’ll attempt to figure that out. By a few different measures, Steamer reliably shows up at the top of the projection accuracy lists these days, and so in testing out xFantasy, I’m going to pit it against Steamer to see whether we can beat the best there is using xStats.

First, a quick note on the players included in this dataset. The original xFantasy model was trained on 2016 data for all players with >300 PA. For the comparisons made here in ‘Part III’, player seasons are separated into halves, and all players with >50 PA in a half are originally included. Some have been eliminated due to either changing teams, or lack of data somewhere in 2015 or 2016 (for instance, if they missed an entire half due to injury). Some players have inconsistent names, and since I’m a bad person who does things incorrectly, I indexed my data on player names instead of playerID’s. That means everyone’s favorite messed up FanGraphs name, Kike/Enrique/“Kiké” Hernandez, isn’t included, along with a couple others.

To recap from last time, the inputs I use to calculate each of the xFantasy stats are:

HR: xISO
R: xAVG, xISO, SPD*, Team_R+RBI, Batting Order
RBI: xAVG, xISO, SPD*, Team_R+RBI, Batting Order
SB: xOBP, xISO, SPD, Team_SB/PA, Batting Order
AVG: xAVG

(*SPD score has been added to R and RBI calculations since the original xFantasy post)

For both years of xStats data, 2015 and 2016, I’ve separated players into first half (1h) and second half (2h) production. I also have pulled old Steamer projections from the depths of my computer from roughly the All-Star break each year (i.e. early July). All data used today is posted up in a Google spreadsheet here. Anyway, that means our three competitors will be…

1. Prorated 1h production: Take each player’s 1h pace in the five categories and prorate it to their 2h plate appearances.
2. 1h xStats (xFantasy): Take each player’s xStats production from the 1h and project the same production over their 2h plate appearances.
3. Steamer: Take each players Steamer projection and adjust based on actual 2h plate appearances.

Option #1 would be our absolute lowest bar, we should hope xStats can do a better job predicting future performance than the raw ‘real’ stats over that same time period. And I’ll go ahead and say that we’re expecting option #3 is probably the highest bar — Steamer is a much more complex system, using several years of player history (where available), adjusting for park factors, and certainly using many more variables. For xFantasy, it’s just Statcast data, and just over a fairly small sample. This same idea was brought up recently by Andrew:

“Both of these methods use a very, very different process to evaluate players.  xStats uses Statcast data and nothing else, it clings to batted-ball velocity and launch angle. ZiPS is quite different, and there are many resources you can look at to learn more about it.  At the end of the day, though, you see very similar results.  Eerily similar, perhaps.”

– Andrew Perpetua, “Using Statcast to Project Trea Turner”

I hope anyone reading this has already seen that post, as Andrew is using xStats in exactly the way I’m considering here — look at a guy with small major-league sample size, with a recent change in skills (more power for Turner), and see what xStats projects for him.

So first, to set the standard, here are our so-called lower and upper bounds for coefficient of determination (R²) values when predicting second-half (2h) stats:

It’s maybe surprising that using first-half stats does a fairly decent job, but that’s largely due to using the known second-half playing time. Steamer is significantly better across the board, though it’s worth noting that AVG is nearly impossible to predict, with Steamer doing a bad job (R²=.143) but 1h stats doing a far worse job (R²=.067). Before we get to xFantasy, I also wanted to test how my slash-line conversion models were working (i.e. the method used to translate xStats into xFantasy). To do so, I took the rate stats predicted by Steamer (AVG, OBP, ISO) and plugged them into the xFantasy equations to arrive at what I’ll call ‘xSteamer’:

And hey, it looks like very little change. That means Steamer’s relationships between the rate stats and HR, R, and RBI are fairly similar to the ones I’ve come up with. Steamer’s models are still (obviously) better for the most part, though xSteamer somehow beats the original Steamer model when it comes to HR! SB is where we see something completely different, where my model is coming up with significantly worse predictions (R²=.494) than the original Steamer (R²=.671). I would guess that means that historical SB stats are more useful predictors of SB than a player’s current SPD score (actually, a simple check will tell you that’s true, 1h SPD and 2h SPD do not correlate well). In any case, it’s finally time to see where xFantasy falls on this spectrum we’ve set up:

If I’m being honest, I was really hoping to see xFantasy fall closer to Steamer on AVG and HR. But at least for R/RBI, we can definitively say xStats are much more useful for projecting future performance than 1h stats. In the case of SB, it’s a bit of a split decision — xFantasy is doing a poor job, but Steamer does a similarly poor job (both with R² of approx .49) if using the same inputs as my model.

Now I have to acknowledge an obvious weakness of xFantasy in terms of predictive ability: Team_R+RBI, Team_SB/PA, Batting Order, and SPD…we could likely project each of these much more accurately than just using recent history. Rather than pulling real stats from the first half for each of those, I could have pulled projections or longer historical averages, and likely improved the outcomes significantly. As a shortcut, let’s just eliminate those variables and try again. For this next set of data, I’ve plugged in the *actual* second-half performance for each player in Team_R+RBI, Team_SB/PA, Batting Order, and SPD. For the most direct comparison, I’ll show xFantasy vs. xSteamer:

Now that’s looking pretty good! Gifted with the power to know a few things about actual second-half team performances, xSteamer sets the bar with the highest R² in each of the five categories. And xFantasy is not far behind! One of the most obvious areas for potential improvement is already a work in progress, with the next version of xStats including park factors. Beyond that, I think this stands as good evidence that xStats could be the basis of a successful projection system, especially if combined with additional historical info or team-level projections. To back that claim up, I’ve come up with one final comparison. Using 2015 xStats, along with the first half of 2016 xStats, we can come up with 1.5 years of xAVG/xOBP/xISO to make predictions of second half 2016. For completeness’ sake, I’ll use a 1.5-year average of all other inputs (i.e. team stats, order, and SPD).

Exciting! It turns out that having more than one half (AKA < 300 PA) of stats leads to much better results. Until we have another year of xStats data to play with, this is the best test we can do for the predictive ability of xStats, but I’m personally quite impressed that this very simple model built on top of xStats is nearly matching the much more complex Steamer system.

At the outset of this whole study, I was hoping to show xFantasy/xStats were at least marginally useful for projecting forward, and I think we’ve seen that. So now I’ll return to the original question: Might xFantasy actually beat Steamer when major-league sample size is small? The easiest possible comparison would be to break down the projection accuracy by player age…

And…yes! xFantasy does a better job projecting the second half for players under 26. Using just Statcast data, 1h SPD score, 1h team stats, and 1h batting order, xFantasy is able to beat the Steamer projection in HR, RBI, and AVG, along with an essential tie in R. The SB model is still quite bad, but I suspect pulling a longer-term average of SPD score (would have to include minors data) would push it up to Steamer’s level. Of course, Steamer is still kicking butt in both the other age ranges. On a mostly unrelated note, both systems do a great job projecting HR/R/RBI for old players, but a surprisingly poor job of projecting SB!

Next time…

So far I’m impressed with how useful xStats and xFantasy can be. I’m looking forward to integrating the further upgrades that Andrew Perpetua has been working on! I’ve also done some initial work on xFantasy for pitchers, using Andrew’s xOBA and xBACON allowed stats, along with Mike Podhorzer’s xK% and xBB% stats. If I can get it to a place of marginal usefulness, I’ll return for a part IV to look at that!

As I said last time, it’s been fun doing this exploration of rudimentary projections using xFantasy and xStats. Hopefully others find it interesting; hit me up in the comments and let me know anything you might have noticed, or if you have any suggestions.

What do Mike Trout, Josh Donaldson, Dustin Pedroia, Corey Seager and Manny Machado all have in common? Besides the numerous accolades that they share between the Rookies of the Year, the Silver Sluggers, the MVP awards and the combined 16 All-Star appearances, they all share one less obvious trait: they have more career plate appearances batting second in the lineup than anywhere else. Gone are the days of your team’s best player batting third or fourth. The new normal is now MVP-caliber players batting second. It has worked for Pedroia and the Boston Red Sox, Machado and the Baltimore Orioles, Donaldson and the Toronto Blue Jays and Seager and the Los Angeles Dodgers. Not for nothing, but those teams all made the postseason last year with large contributions from their second-hole hitters AND Trout was the AL MVP for the second time in his career on a last-place Los Angeles Angels team. And as more teams continue to adopt this trend, the New York Yankees should also look to bump up their best hitter.

In an appearance the other week on a YES Network interview, GM Brian Cashman has stated that the Yankees have kicked the tires on splitting Brett Gardner and Jacoby Ellsbury in the lineup. This makes a lot of sense when looking at their game; they both rely on their ability to get on base and set the table more so than their ability to drive in runs. Additionally, both players have slowly, but noticeably, been in decline in recent seasons, primarily due to age and injury. Gardner has been the subject of trade rumors over the past few seasons and Ellsbury has been the ire of the New York media for largely failing to live up to the seven-year, $153-million deal he signed before the 2014 season. River Ave Blues has already had a look at how the Yankees would approach this situation and they have provided a solid solution, but they almost immediately toss out the idea of Gary Sanchez batting there for one reason or another, while Sanchez is most deserving of the promotion.

Sanchez has established himself as the Yankees’ most dominant hitter after bursting on the scene last year. The Yankees, their fans, and the nation all expect Sanchez to hit in the third spot in the lineup, a prestigious position considering the history of the franchise, but moving the young slugger to second would not only better suit the team, but would also play to his strengths. Sanchez, despite the short sample size of 231 plate appearances, has proved to be a pretty good fastball hitter. Of the 294 fastballs he has seen, he has connected for a .328 AVG and .781 SLG, and nine of his 20 home runs. Why does this matter? Traditionally, number-two hitters have seen more fastballs than elsewhere in the lineup, and to further cement his commitment to the fastball, per Brooks Baseball, Sanchez had an exit velocity of 94.3 MPH against the heater (Sanchez ranked in the top 10 in overall exit velocity last year). Young players are also traditionally late to adapt to major-league breaking pitches. Can you blame them when they’re up against this or this?

Secondly, it has been proven that two-hole hitters collect more plate appearances per season than the three through nine spots. This is not new information, but the exact number of plate appearances has been up for debate for years. Beyond the Box Score might’ve ended the debate while also examining how the two hole has changed, stating that “[e]ach drop in the batting order position decreases plate appearances by around 15-20 a year,” which might explain why MVPs Trout and Donaldson have made a living there over the past few seasons. An extra 10-20 plate appearances could mean an extra home run or two over the course of the season. Baseball is a game of inches, but it’s also a game of runs.

With a lineup bereft of veteran power and more intent on utilizing the “Baby Bombers,” as they’ve been so aptly named, moving Sanchez up to second could and should give the lineup a much-needed boost if the reliance on Greg Bird and Aaron Judge should go somehow awry. Veterans Matt Holliday, Chase Headley and Starlin Castro have had good seasons and impressive resumes, but they need to return to All-Star form to carry a team of youngsters and a questionable starting rotation. No one really expects Sanchez to produce at the same rate that he did last year, but perhaps a bump up would allow him to produce at an above-average level again.

The GM of the Philadelphia Phillies has been accumulating the players to potentially pull off the greatest single–season heist in the history of baseball.

How will they do this, you might ask?

By utilizing the 3-3-3 rotation.

I will explain why recent rotation alterations by the 1993 Athletics and 2012 Colorado Rockies were not successful. Then I will show how the Phillies version of the 3-3-3 will change the baseball world. But first, let me explain the 3-3-3 rotation and its benefits.

The classic 3-3-3 rotation uses three groups of three pitchers each, pitching once every three games.

Game 1 – Innings 1-3 (Pitcher#1) Innings 4-6 (Pitcher #2)  Innings 7-9 (Pitcher #3)

Game 2 -Innings 1-3 (Pitcher #4)Innings 4-6 (Pitcher #5)Innings 7-9 (Pitcher #6)

Game 3  – Innings 1-3 (Pitcher #7) Innings 4-6 (Pitcher #8) Innings 7-9 (Pitcher #9)

Ideally, each pitcher will throw three innings or 30-50 pitches per appearance. By the end of the season each pitcher will pitch about 162 innings over 54 appearances.

This rotation will help pitchers succeed by:

1) Allowing hitters only one plate appearance against each pitcher

2) Eliminating fatigue by keeping pitch counts down

The more opportunities a hitter has against a pitcher, the better success he has. Dave Fleming of Bill James Online provided statistical evidence from 2008 supporting this fact:

 PA  BA OBP SLG OPS

1st PA in G 108606 .255 .328 .398 .727

2nd PA in G 44505 .270 .334 .431 .765

3rd PA + in G 34520 .282 .346 .453 .800

Notice how every hitting statistic increases with each at-bat. To make a few comparisons, Eduardo Nunez was an All-Star last year, and his OPS was .758. All-Star Xander Bogaerts had an OPS of .802. So if you leave a pitcher in past the third AB (generally 7th or 8th inning) you’re facing a lineup full of 2016 Xander Bogaertses. Not exactly a winning formula.

A similar pattern was echoed in pitch counts:

PA BA OBP SLG OPS

Pitch 1-25 87685 .261 .333 .410 .743

Pitch 25-50 39383 .257 .326 .400 .726

Pitch 51-75 31791 .270 .333 .429 .763

Pitch 76-100 24261 .277 .344 .450 .795

The fact that pitches 1-25 were less effective than 25-50 is due to lineup construction. The rest of the numbers clearly show that pitchers are exponentially worse after the 50th pitch.

In this post, I will explain:

1) Why the 3-3-3 rotation did not work for La Russa in 1993

2) Why the Rockies’ alternative rotation wasn’t accepted in 2012

3) The benefits the 3-3-3 rotation will provide the Phillies in 2017 and beyond

Before we begin, there a few concepts we must accept:

1) Baseball is not archaic; it is ever-changing

2) Categorizing pitchers as only “starters”, “relievers” or “closers” is limiting to the pitchers’ value and abilities. We have to look beyond these inadequate labels. I will use these terms in this article, but attempt to focus on these underlying meanings:

a) Starter – Pitcher trained to throw 5+ innings

b) Reliever – Pitcher trained to throw 1-2 innings

c) Closer – Pitcher with experience throwing the last inning

3) There is no one system that produces winners or losers. You must utilize your personnel to the best of their abilities and limitations

Why the 3-3-3 rotation did not work in 1993

1) The Athletics did not have the personnel to execute the strategy

2) The experiment lasted one week

First, the Athletics had one of the worst pitching staffs in the league in 1993. They were in last place when they implemented the 3-3-3 rotation and had lost nine of their last 12 games. Here is a list of their ERAs in ascending order:

Name                      Training      ERA    Synopsis

Bobby Witt                 SP           4.21     97 ERA +

Goose Gossage          RP           4.53    Age-41 season

Todd Van Poppel      SP           5.04     21-year-old rookie

Ron Darling               SP           5.16       79 ERA+

Bob Welch                  SP           5.29     Age-36 season

Mike Mohler          RP / SP     5.60     Started 9 of 42 appearances

Kelly Downs           RP / SP     5.64     Started 12 of his 42 appearances

Shawn Hillegas      RP / SP      6.97    Started 11 of 18 appearances

John Briscoe             RP            8.03    Started 2 games in 139 IP in career

Only Bobby Witt and Goose Gossage had an ERA under 5.04. Witt was by far their best pitcher and his 97 ERA+ shows he was below average.

The second reason it did not work is the experiment only lasted one week. The public and media backlash from the switch to this rotation was so great that La Russa was forced to abandon the experiment after one week. One week! I don’t care what you do in baseball, if it only lasts one week, then you didn’t give it a real chance. Buster Posey hit .118 in his first week in the MLB in 2009, but the Giants wisely kept him around for 2010.

Why the Rockies’ alternative rotation did not work in 2012

1) They did not have the right personnel

First, let’s describe the specifics of the Rockies’ new rotation. It was a four-man rotation of Jeff Francis, Jeremy Guthrie and rookies Drew Pomeranz and Christian Friedrich. In each start, these four pitchers were given a strict 75-pitch limit. Three rotating pitchers called “piggybacks” would then relieve them.

Game 1 – Francis (75 pitches) Piggyback #1 Reliever #1 Closer #1

Game 2 – Guthrie (75) Piggyback #2 Reliever #2 Closer #1

Game 3 – Pomeranz (75) Piggyback #3 Reliever #3 Closer #1

Game 4 – Friedrich (75) Piggyback #1 Reliever #1/2 Closer #1

Similar to the 1993 A’s, the Rockies made their switch out of desperation. When implemented on June 20th, the Rockies were 18 games below .500 and in a 6-15 slump, on pace to lose over 100 games. Here is a look at the top six Rockies pitcher stats by the end of the year, with ERAs in ascending order:

Name                       Training         ERA       ERA+     IP

Jhoulys Chacin           SP               4.43        105         69

Drew Pomeranz         SP               4.93         94         96.2

Alex White               SP/RP           5.51          84          98

Jeff Francis                 SP               5.58          83          113

Christian Freidrich    SP               6.17          75           84.2

Jeremy Guthrie          SP               6.35          73          90.2

Only one of these starters was even an average pitcher. Three of the four rotation mates were at least 27% worse than the average pitcher in 2012. The issue with the 1993 A’s and the 2012 Rockies are they made these moves in the middle of last-place seasons. They were desperate to change what were the worst pitching staffs in the league. No team heading for a last-place finish is going to respond well to a complete overhaul of the staff in the middle of the summer.

The good news for this particular experiment, however, is that the Rockies pitching staff performed much better after the change was made. In the first 21 games that it was implemented, the starting pitchers improved from a league-worst 6.28 ERA to a league-worst 5.22 ERA. That’s more than an entire one-run improvement! Still the league worst (control your laughter), but that’s a major improvement.

I believe that gives us hope that an alternative and better rotation can be found in the correct circumstances. With the right rotation mates and the correct distribution of pitch counts, I believe there is room for improvement. The key is to train and implement the rotation before the season begins. No pitcher is going to be motivated to try a new system if it is implemented in the middle of a terrible season. It has to be the game plan to begin with, and everyone must be on board. Below you will see why the Phillies have the perfect staff for a 3-3-3 rotation. I have used the 3-3-3 rotation as my basis, but implemented some changes inspired by the 2012 Rockies to ensure success.

How the 3-3-3 Rotation will benefit the Phillies

1) Utilizing the perfect personnel

2) Peak value from assets

3) Health (Physical and Mental)

Personnel

The Phillies have eight middle-of-the-rotation MLB-ready starters who have demonstrated the ability to get MLB hitters out for multiple innings per appearance. The Phillies have five quality relievers who have demonstrated the ability to get MLB hitters out for one inning+ per appearance. Let’s take a look at the 2016 Phillies stats in order of ascending ERAs:

Name             Training    MLB IP 2016    ERA 2016      MLB service

Asher                 SP                27.2                    2.28              0.061 years

Neris                 RP                 80.1                   2.58               1.104 years

Benoit            RP / CP           48                      2.81                Final Year

Neshek          RP / CP            47                     3.06                Final Year

Eickhoff             SP                 197.1                  3.65                1.045 years

Hellickson       SP                 189                     3.71                Final Year

Ramos             RP                 40                       3.83               0.101 years

Buchholz         SP               139.1             Career 3.96          Final Year

Velasquez        SP                131                       4.12                1.086 years

Nola                  SP                 111                      4.78                 1.076 years

Gomez          RP/ CP           68.2             4.85 w/ 37 SV       Final Year

Eflin                   SP               63.1                     5.54                  0.111 years

Thompson        SP               53.2                     5.70                 0.058 years

Asher, Eickhoff and Hellickson were MLB starters with ERAs under 3.71 last year. Buchholz has the ability to be a front-line starter coupled with a career 3.96 ERA. Velasquez and Nola showed great promise despite rather average ERAs in the 4s. Velasquez sported a 10.6 K/9 ratio while Nola’s curveball has the best horizontal movement in the Majors (9.3 inches, beating out Gerrit Cole). The only two pitchers who disappointed were Eflin and Thompson, two young starters getting their first crack at the majors. Let’s count on them performing better next year.

The best reason why this personnel is perfect is because all of the trained starters have generally similar projections. From a projection and performance standpoint, all of these pitchers are middle- to back-of-the-rotation guys with upside. Nola and Velasquez are projected #2/#3 guys while Eflin, Thompson, Asher and Eickhoff are #3 to back-of-the-rotation guys (Though Eickhoff did have an impressive year in 2016). There is no Kershaw or Verlander or Bumgarner or Cueto who are expected to dominate and throw eight innings every start.

By only allowing them up to 50 pitches and one time through the lineup, the numbers listed in the introduction illustrate that the 3-3-3 rotation puts these players in the best possible position to succeed. Since the numbers are now in their favor, pitchers will have a refined focus and confidence. They can make a structured game plan on how they’re going to attack each hitter. This will limit extended innings under duress and ultimately build confidence in the minds of these young pitchers.

You may ask, Kevin, the Phillies aren’t going to contend in 2017. Why go through such a drastic change to get marginally better?

The answer is using the 2017 season as a stage for their assets to increase in value.

Asset Valuation

The Phillies are not in line for a winning season in 2017. They most likely won’t win 80 games in 2018. But 2019 is their year. That amazing 2018-2019 class of Kershaw, Donaldson, Machado, Harper, Pollock, LeMahieu, Keuchel, Harvey, Wainwright, Corbin, Smyly and Shelby Miller will be theirs for the taking, as the only money they have tied up is to Odubel Herrera. Even the 2017-2018 class of Arrieta, Cobb, Darvish, Duffy, Pineda, Tanaka (option), and Cueto (option) could insert an ace or #2 into their staff.

That is why they need to act now. They must increase their pitchers’ values now and acquire better assets with 2019 in mind. The free-agent market will be booming from 2017-2019, thus lowering trade-market value of any player after this year’s deadline. Instead of trading away prospects to get the guys they need, teams will simply open their pocketbooks. Now is the time to trade these middle-of-the-rotation guys away. Especially because they are not all in the 2019 plans.

“Utility Pitchers”

What is the most overpriced asset on the market right now? Relief pitching. More specifically, pitchers who can pitch multiple innings in relief in tough situations. See: Andrew Miller, Kenley Jansen, and Aroldis Chapman. By utilizing the 3-3-3 method, you are training your starters to pitch multiple innings in different scenarios and relieve in later innings. The 3-3-3 method trains your pitchers to achieve the greatest possible value by becoming what I like to call “utility pitchers.”

What makes players like Ben Zobrist, a .266 career hitter, and Ian Desmond, a .267 hitter, worth $60-70 million? They are utility players. Teams these days love utility players and are willing to pay big money for them. They are more valuable now than they have been in all of history. The same can be said for utility pitchers.

If you have ever been to the Arizona Fall League, it is used as a stage for the game’s top prospects. Starting pitchers generally pitch three innings, and relief pitchers will pitch 1-2 innings each for the remainder of the game. They do this to give teams’ top minor-league players exposure to higher competition with an added benefit of raising prospect value in the eyes of other teams. By sending their players to compete with top minor-league competition for all scouts to see, a good showing will raise potential trade interest. For example, this year the Giants sent a young catcher named Aramis Garcia, a former second-round pick. Garcia doesn’t fit into the Giants MLB plans with a player like Buster Posey entrenched at catcher until 2022, but they used him as one of their eight player selections anyway. I can surmise they did this to boost his stock for potential trade scenarios. The Phillies do not have all their current pitchers in their 2018-2019 MLB plans, so why not show them off to other teams?

By using the 3-3-3 method in the MLB as a stage for their abundance of young pitching talent, their pitchers will:

1) Get experience against the top talent in the world

2) Potentially increase their trade value

3) Limit innings to 130 – 160 IP

4) Give young pitching the best chance to succeed at the MLB level

5) Keep their innings down and arms fresh

The Phillies 2017 3-3-3 rotation, which you will notice is a quasi version of the 3-3-3 that I referenced above, would look like this:

1st Group – Hellickson (3) Asher (3) Eflin (2) Neris (1)

2nd group –  Nola (3) Eickhoff (3) Thompson (2) Gomez (1)

3rd Group –  Velasquez (3) Buchholz (3) Benoit (1) Ramos (1) Neshek (1)

Why this particular grouping?

1. Ability to sell three of what we call “closers” at the deadline. They can also switch Benoit and Ramos to the closer role on any particular day, giving Klentak five pitchers with closing experience to sell.

2. Give Eflin and Thompson only 2 IP per appearance because of their struggles last year. This should increase their confidence by decreasing their perceived pressure.

3. Since the Phillies signed two relievers to one-year deals in the offseason, it is apparent that Klentak wants to sell them off at the deadline. This is why I chose the quasi 3-3-3 system.

Imagine Klentak’s bargaining power at the deadline if he has even three of these newly trained utility pitchers pitching well, especially if one is a guy like Asher, Eflin, or Thompson? He could promise 5+ years of control of a utility pitcher who can be a traditional starter or a multi-inning reliever out of the bullpen.

Some people will read this and think that this would be a “demotion” or “devaluation” from being a “starter.” This is not true. All of these pitchers made it to the MLB as what you would call “starters.” They have excelled at pitching 6+ innings per game. This experiment would simply add value to all of them. Just as playing Ben Zobrist at LF, RF and SS doesn’t take away his ability to play 2B.

Most relief pitchers don’t get drafted as closers or relief pitchers. They are given chances at various roles and stick with whichever role suits their strengths best. Look at Chapman and Andrew Miller. Look at Joe Blanton! Terrible pitcher as a labeled “starter” but excelled in a set-up role for the Dodgers last year. General managers won’t trade for a guy for a postseason run if he hasn’t proven that he is going to be a solid contributor in the specific role they need for their team. So by using 2017 as a value-booster, you train all of your pitchers for multiple roles so you can have the leverage to trade any of your guys to any team. Every postseason team needs pitching. The 3-3-3 rotation will give Klentak unlimited options to acquire talent that will help the 2019 team be successful. GMs are most vulnerable at the deadline, and it is time to take full advantage.

Some people might argue that bringing up all of these pitchers at once would be a waste of MLB service time. But what is more important to a GM who has multiple pitchers with middle-of-the rotation ceilings? An option year or service time? This experiment is exactly that, an experiment. It is a trial run for one half of a season to ramp up current asset valuations to acquire a lot of quality pieces for the future. Since all of these pitchers are already on the 40-man roster, sending them to the minors would waste an option year anyway. So why not give this a try? The worst thing you could lose is half a season of MLB service time on a few guys who have served less then 20% of one year in their career.

HEALTH

In an arm-health study by Dr. James R. Andrews the following chart is comprised:

Ages 14 and under – 66+ Pitches (4 days rest) 51-65 (3) 36-50 (2) 21-35 (1) 1-20 (0)

Ages 15 and over – 76+ Pitches (4 days rest) 61-75(3) 46-60 (2) 31-45 (1) 1-30 (0)

These pitchers are prized assets. Millions of dollars coupled with thousands of hours of prep, coaching and playing time are used per arm. Why don’t we take better care of these players?

As a kid, your parents told you to eat your vegetables, sleep eight hours a night and stay in school while getting 60 minutes of exercise a day. But as we grow older we continually skip our vegetables, sleep five or six hours a night, forget to keep our brains active, and rarely exercise. We feel that we can still function this way, but more importantly, we feel we have to function this way. This is because we put too many responsibilities on ourselves at the expense of our own well-being. I’m arguing that we are giving these pitchers too many responsibilities, at a detriment to their peak physical health. Why? Because traditional baseball knowledge tells us that a five-man starting staff is the right way to go in 2017. But look back at history: there used to be one-man, two-man, three-man and even four-man rotations. Those proved to be unsuccessful. I am saying that the five-man rotation isn’t working either. It’s time to make a change.

What if we treated these valuable multi-million-dollar arms with the care that we take with our Little League arms? I propose a hopeful plan of three innings finished for each starter, but an absolute maximum of 36-50 pitches no matter what. These pitchers will then receive two days of rest for every 36-50 pitches, thus receiving the care a child under 14 would receive (see chart above). It is impossible to argue that this wouldn’t be a healthier system than the one we have now. Finally, let’s shift back to trade value. If Klentak is making deals on July 31 and a playoff contender is asking him how his players can help them win a championship, health is another big concern! If he can say that his pitchers have been put on a stricter regimen than any other team in the league, and that his players’ arms are healthier and more fresh than any other team in July in the history of baseball, that is going to increase his bargaining power. Remember, keeping players healthy, putting them in the best position to succeed and increasing trade value all are focused on the 2019 season. Klentak’s initial plan has always been focused on the 2019 season. And this plan will add tremendous benefit to that goal.

Conclusion

Now I am not saying that every team should utilize this strategy. I am not saying this is the future of baseball for eternity. I am saying that with the Phillies assets, at the perfect time in their development, this will be a great strategy to use. A Double-A or Triple-A prospect is worth much less than an MLB-proven prospect. A pitcher who can relieve, start and spot-start is worth more than just a conventional “starter” or “reliever.” More utility is always better than less utility. Healthier arms are better than overused arms.

I am saying the Phillies should give this a try for half of a season in which they won’t win more than 80 games. There is nothing to lose. And hey, if everything goes to plan, maybe this starts a revolution. If not, then they seamlessly revert to a five-man rotation in August. The goal of business is to buy low and sell high, looking for the most reward for the least amount of risk. This is about as high-reward as you can get in a sub-.500 season with about as little risk as I can imagine.

A new idea is always crazy before it makes sense. In the 1920s and 30s it was a rule that star pitchers had to throw 10-20 relief appearances in addition to their normal starting roles. In the 1880s, catching a ball on one bounce was an out. It even used to be legal for a first baseman to grab a runner by the belt so he couldn’t steal second! It is time for a new discussion about the modern-day pitching staff. It is time for rebuilding teams to try new things to get an edge on the competition. It is time for the game of baseball to go through yet another change. We owe it to the fans, to the players, and to the history of our beloved game. We owe it to ourselves to put our reputations on the line for the greater good of baseball.

Happy Holidays. A new year is almost upon us. Just around the corner, pitchers and catchers will be gearing up to report. Spring-training facilities are prepping for an early start in anticipation of the World Baseball Classic, added excitement for any baseball fan ready to brush the cold off. Every new year brings change. Some more than others. This year, the new CBA was agreed upon. As the real game changes, so too does the fantasy world. Our league is entering its twelfth year, which is mind-blowing to me, considering we now represent six different states in four different time zones. Part of our longevity is attributed to adapting to the ever-changing landscape of baseball. Sabermetrics are slowing creeping into our stat categories — power is relied on less, and relievers more so. All that to say, we have changed again.

Our constant struggle has always been how to reflect the real game as best as possible without drastically changing the landscape of the league during one offseason. Recently there has been a trend toward an arms race. Pitchers were going ridiculously early in drafts and trades were featuring first- and second-round draft picks for non-keeper-eligible starting pitchers. Our solution to reduce the value of starting pitching in our league was to move from strikeouts to K/9 so as to reflect our six stat categories: Wins, K/9, ERA, WHIP, Net Saves, and Quality Starts.

Enough about our incredibly awesome keeper league. With all the talk of the winter meetings, the World Baseball Classic, and a new year, the jump on pitching is long overdue. So, the top 155 pitchers were ranked accordingly.

Method

Steamer has released their 2017 projections. These projections, of some 4000-plus pitchers, were exported to Microsoft Excel. Pitchers were then sorted by WAR: highest to lowest. The top 155 pitchers were then selected. In a 10-team standard league, no team should roster more than 15 pitchers, giving justification for cutting off the sample at 155. Five stat categories were then selected. Steamer does not project quality starts or blown saves. Therefore, to balance the importance of SP vs RP, innings pitched was selected in addition to Wins, K/9, ERA, and WHIP.

A table was then created with the stat categories on the x-axis and the pitching running down the y-axis (if you will). Each pitcher was given a positional value based on where that pitcher ranked within each stat category. For example, Max Scherzer is projected to have an outstanding 10.93 K/9 rate, which ranks sixth in the top 155. Scherzer was therefore given a value of 6 for the K/9 category. Scores were summed for each pitcher. Pitchers were than ranked by final score. Finally, a correlation using the summed scores and pitcher rank was executed to examine the relationship between stat categories and pitcher ranks.

Results

A complete list of the top 155 pitchers can be found at the end of this document. Below is a list of the top 20. Of note are Lance McCullers and Robbie Ray, who rank at 17 and 19, respectively. Not surprisingly, Clayton Kershaw is number one.

A correlation was then performed to explore the relationships of stat categories on pitcher total scores. Table 3 highlights K/9, ERA and WHIP as very strong correlations, with ERA being the strongest. Innings pitched had the weakest correlation.

Discussion

The goal of this exercise was to explore the impact on the changing landscape of pitching stat categories in fantasy baseball. The top 20 pitchers remain starters. However, within the top 20, one can see the impact of the change to K/9 from strikeouts. Both McCullers and Ray rank inside the top-15 projected K/9, according to Steamer. This led to the question, just how much of an impact will K/9 have on total scores?

The correlation revealed a strong relationship, but not the strongest. Therefore, the answer is, it has a strong impact, but in the end not as much as ERA and WHIP. What does strong mean? Statisticians usually agree that a correlation above .75 is considered a very strong relationship. To explore this meaning, let us take a look at an extremely early positional ranking done by ESPN.

Below, we’ll play the guessing game.

The above numbers appear somewhat similar. In a standard league, you may be inclined to lean toward Player 2. Indeed, according to ESPN, Player 2 is ranked 38^th at his position and Player 1 is ranked 62^nd. However, when scored using the methodology in this study, Player 2 ranks 49^th while Player 1 ranks 19^th. Two things when considering this. Table 4 are stats from 2016. The aforementioned rankings are based on 2017 projections. It could be that Player 1 has more room to grow. However, the change from strikeouts to K/9 is evident. Player 1 (10.11) has a much better K/9 than Player 2 (8.35). Therefore, the K/9 relationship to player ranking is correctly strong, and ranking Player 1 higher than Player 2 is logical. If you were wondering, Player 1 is Robbie Ray, and Player 2 is Drew Smyly.

Limitations

Steamer does not project quality starts or blown saves, therefore the correlation could be skewed toward starters or relievers. These results should only be taken into consideration when these five stat categories are in play. The sample size of starting pitchers is large enough, but not for relief pitchers. Only five relievers were projected in the top 155 pitchers ranked by WAR. Results of the correlation, then, could look different had more relievers been incorporated.

Future research

Future research should then include additional relievers. Expanding the pitcher rankings to the top 300 would include most relevant pitchers according to Steamer. Furthermore, additional stat categories should be explored. Would adding saves and quality starts affect the rankings? Certainly, the more variables added, the more complicated the results become. However, finding a balance between starters and relievers, reflective of the real game, should be further explored.

Conclusion

A great importance is placed on starting pitching, both in the real and fake game. However, relievers seem to have a growing importance. In 2016, three months of Chapman cost the Cubs two of the game’s best prospects, a trade usually reserved for starting pitching. How to value starting pitching compared to relief pitching is left open to interpretation, especially in the world of fantasy. A reduction on starting pitching value was in order for our league and for standard leagues. How to go about this should reflect the real game. For 10 managers, the decision was to move from strikeouts to K/9.

This initial research demonstrates that this change does not swing the pendulum too far toward relievers and away from starting pitching. A correlation demonstrates the strongest relationship to pitcher ranking is ERA. Given a head-to-head matchup, with an innings limit, having multiple starters with a good ERA will still be favorable to deploying strong relievers. The top 155 pitcher rankings further confirm this fact. Initial conclusion is that a move to K/9 is a positive switch that reflects the growing importance of a good reliever, while still favoring starting pitching.

Appendix A

Top 155 Pitchers

A good friend of mine with whom I argue about baseball often once posed a very interesting question to me.  He asked me, if I were to build a team completely devoid of one tool, which tool would I want to be missing?  In the ensuing argument, I was asked to rank the tools from least to most important for team success.  I put the order as arm, speed, fielding, contact, and power.  It was not until later that day that it struck me just how great a question he had asked.  Now, several months later, I will attempt to quantify the tools.

The rules for this study will be simple.  Two teams will be assembled for each of the five tools.  Each team will be considered league-average in every tool but the one for which they are being evaluated.  One of the teams for each tool will be the best possible in that one area, and the other will be the worst possible.  The runs lost from league-average by the worst possible team will be subtracted from the runs gained by the best possible teams.  The larger the difference, the more important the tool.  The teams will have one player for each position (minimum 250 PA, 450 Inn).

Note:  Pitchers are not included.  Losing arm does not mean losing value from pitchers.

Power

The players on the teams for power will be determined using isolated power.

Best Possible Team:  C) Evan Gattis (.257); 1B) Chris Carter (.277); 2B) Ryan Schimpf (.315); 3B) Nolan Arenad0 (.275); SS) Trevor Story (.296); LF) Khris Davis (.277); CF) Yoenis Cespedes (.251); RF) Mark Trumbo (.277)

This group has a combined ISO of .276, which would put their team OPS+ at about 115.4.  An average team has 6152.6 PA in a season.  Using these figures, they would score 836 runs as a team, compared to the 725 of an average team.

Worst Possible Team:  C) Francisco Cervelli (.058); 1B) Chris Johnson (.107); 2B) Jed Lowrie (.059); 3B) Yunel Escobar (.087); SS) Ketel Marte (.064); LF) Ben Revere (.083); CF) Ramon Flores (.056); RF) Flores

The combined ISO for this team was only .072, making the OPS+ about 87.8.  Runs scored for this team would then be 636.

Difference between BPT and WPT:  200 runs

Contact

The players on the teams for contact will be determined using K%.

BPT:  C) Yadier Molina (10.8); 1B) James Loney (10.1); 2B) Joe Panik (8.9); 3B) Jose Ramirez (10.0); SS) Andrelton Simmons (7.9); LF) Revere (9.1); CF) Revere; RF) Mookie Betts (11.0)

Collectively, this team would strike out in 9.7% of their plate appearances.  League average in 2016 was 21.1%, meaning the BPT is 11.4% better than league average.  The team would score 807 runs.

WPT:  C) Jarrod Saltalamacchia (35.6); 1B) Chris Davis (32.9); 2B) Schmipf (31.8); 3B) Miguel Sano (36.0); SS) Story (31.3); LF) Ryan Raburn (31.3); CF) Byron Buxton (35.6); RF) Sano

This high swing-and-miss team would strike out in 33.9% of plate appearances.  This is 12.8% higher than average.  The team would score 632 runs.

Difference between BPT and WPT:  175 runs

Fielding/Arm

As it turns out, there are really not stats for exclusively measuring a fielder’s arm.  Baseball-Reference has Arm Runs Saved, but that is not for infielders.  Additionally, the stat I originally wanted to use for Fielding, UZR/150, is not available for catchers.  To remedy both of these problems, I elected to use DRS.  DRS is available for all positions, and it takes a fielder’s arm into account.  Because I will not be taking values for fielding and arm on their own, fielding will receive about 60% of the total difference in the category.  The remaining 40% will be attributed to arm.

BPT:  C) Buster Posey (23); 1B) Anthony Rizzo (11); 2B) Ian Kinsler/Dustin Pedroia (12); 3B) Arenado (20); SS) Brandon Crawford (20); LF) Starling Marte (19); CF) Kevin Kiermaier (25); RF) Betts (32)

Kinsler and Pedroia tied for the lead at second base, so I just listed both of them.  The brilliant defensive team would be 162 runs better than the average in the field.  Of these, 97 will be attributed to fielding and 65 to arm.

WPT:  C) Nick Hundley (-16); 1B) Joey Votto (-14); 2B) Schimpf/Daniel Murphy/Rougned Odor (-9); 3B) Danny Valencia (-18); SS) Alexei Ramirez (-20); LF) Robbie Grossman (-21); CF) Andrew McCutchen (-28); RF) J.D. Martinez (-22)

The team of these players, who look like pretty good players, would have a -148 defensive value.  The value to fielding is -89 runs, and -59 for arm.

Difference between BTP and WPT (Fielding):  186 runs

Difference between BTP and WPT (Arm):  124 runs

Speed

Speed presents a problem.  It is valuable on the basepaths, obviously, but it is also valuable in the field.  More speed means more range.  Speed Score is a stat that represents the importance of both, but it does not translate well into value.  I decided to go with FanGraphs BsR, even though it does not measure speed in the field.  That value can be circumvented by routes and reactions anyway.

BPT:  C) Derek Norris (1.8); 1B) Wil Myers (7.8); 2B) Dee Gordon (6.2); 3B) Ramirez (8.8); SS) Xander Bogaerts (6.1); LF) Rajai Davis (10.0); CF) Billy Hamilton (12.8); RF) Betts (9.8)

This speed roster is a team that anyone would like to run out every day.  It is a young and athletic team.  Even so, based on speed alone, the team is just 63 runs above average.  That is the lowest value above average for any BPT.

WPT:  C) Molina (-8.7); 1B) Miguel Cabrera (-10.0); 2B) Pedroia (-4.5); 3B) Escobar (-5.6); SS) Erick Aybar (-3.9); LF) Yasmany Tomas (-5.5); CF) Jake Smolinski (-3.4); RF) Tomas

The lead-foot team is 47 runs below average.  That is the closest to average of any WPT.  Speed clearly has the least impact of the five tools.  I regret not putting it last.

Difference between BPT and WPT:  110 runs

Conclusion

I will admit that I was wrong.  Arm actually has some real value.  My excuse, I guess, is to say that it slipped my mind that arm is important for infielders as well as outfielders.  That should not have happened, and I am a little upset I made that mistake.  Fielding also beat out contact, which I did not expect.  I do not even have a defense for this one, as I do not know what I was thinking.

In all honesty, this post was written to win an argument.  However, it does have a deeper purpose.  This answers the question posed so many years ago in Moneyball.  If a general manager can afford to buy players with only one tool, which tool should it be?  This information is probably not new to any front office in baseball, but it is something to remember when considering small-market strategy.

Anyway, here is the official list of the five tools by importance, at least for 2017.

1.  Power

2.  Fielding

3.  Contact

4.  Arm

5.  Speed

It was in November, yet we may already have the most fascinating free-agency signing of the offseason. Traditionally, free agency is for contending major-league clubs looking to overpay players in hopes that they can deliver a championship. The Milwaukee Brewers went off the beaten path and may be using free agency as a vessel to help their rebuild.

This year’s free-agent class, headlined by Edwin Encarnacion (34) and Carlos Beltran (39), has a shortage of quality bats. The 2016-2017 free-agent class will more than likely be defined by complementary players rather than typical studs who will impact a pennant race. This lack of possible assets forced the Milwaukee Brewers to get creative. The Brewers’ signing of KBO baseball star Eric Thames, four years removed from his last MLB at-bat was…genius?

First, let’s see how we got here.

The Brewers were unhappy with Chris Carter manning the first-base position. It is not often a team will cut a player after he hit 41 home runs, but that is exactly what happened. Carter’s overall lack of production outweighed the power output. Posting a .218 batting average, coupled with a 33.1% strikeout percentage, Carter performed slightly better than a replacement-level player. After cutting ties with Carter, Milwaukee looked at its free-agent options.

With his coming off a 47-home-run season, it is unrealistic for the Brewers to sign All-Star Mark Trumbo (30). The only other impact bat would be Mike Napoli (35). Napoli should benefit from the scarcity of sluggers this offseason. In 2016, Napoli had a nice bounce-back campaign, launching 35 home runs and making headlines such as “Party at Napoli’s.” However, the party stops at first base. Napoli is a below-average baserunner and defender, causing his VORP (Value Over Replacement Level Player) to total just 1.0.

The Brewers would have to be in love with Napoli’s ability to swing the stick for the club to decide to pull the trigger. But a 35-year-old slugger with poor defense is likely not a good fit for any National League team, let alone the rebuilding Brewers.

As for the rest of the free agents, there is a theme of mediocrity. Moreover, each of them will be over the age of 30 by opening day. Even if the remaining players are able to defy the odds and maintain their levels of performance, it will be nothing more than a stop-gap signing.

After a 73-89 campaign in 2016, the Brewers are not in “win now” mode. Over the past two years, the Brewers have sold, sold, and sold some more. Each trade Milwaukee made brought in quality talent, and according to MLB.com Milwaukee now has MLB’s #1 farm system. Milwaukee has eight players cracking the top-100 prospect list that will be making themselves known as soon as next year. So for a team in rebuilding mode, why sign Eric Thames? Low risk; high reward.

Per Adam McCalvy, Thames will make $4 million in 2017, $5 million the year after, and $6 million in 2019. The team also holds an option on his contract for 2020 for $7.5 million, with a $1-million buyout. That totals out to $16 million guaranteed. Fiscally, it boils down to this: Approximately $25 million for two years of Carter or 3-4 years of Thames for $16-$24.5 million, including bonuses.

In 181 major-league games, Thames posted a .250 batting average with 21 home runs. He had a respectable .727 OPS in that time. This bodes well in comparison to recent Cubs signee Jon Jay who had a similar .774 OPS in his first two seasons. Thames found himself out of the league, while Jon Jay continued his successful career. After 2012, Thames found work in the aforementioned KBO. Over three seasons, Thames averaged 42 home runs while hitting .347 and earned an MVP award in 2015. Oh, and there’s a 30-minute highlight reel of just home runs.

Pitching in Korea cannot be compared to the talent in Major League Baseball. There is a big difference between putting up numbers in Korea and doing so in MLB. However, Jung Ho Kang and Hyun Soo Kim are supporting evidence that succeeding can be done. One thing is evident when watching Thames swing: he has raw power to all fields.

If Thames performs similarly to his 2011-2012 form, then Milwaukee has lost nothing. The deal would simply mean they swapped two replacement-level first basemen while simultaneously saving money. But if Thames shows that he truly is a new player, Milwaukee will once again be front and center during the trade deadline. Thames could be the premier left-handed bat on the trade market while also having a dream contract for contending clubs. The value of his bat along with contractual control over him through 2020 at only $16 million guaranteed could bring in multiple top prospects. This is the dream scenario of course, but hey, it can’t hurt to dream.

The Los Angeles Dodgers’ search for a second baseman continues. The solution might lie in the most obscure of places — a 1B/OF free agent. On many other websites there has certainly been a lot of debate about who the Dodgers should pursue to occupy second base. Ian Kinsler is clearly the focal point, and there’s good reasons for it. The Detroit Tigers publicly state they’re trying to get younger and the Dodgers need a second baseman. That’s about as natural a fit as possible.

And Ian Kinsler’s a really good second baseman. He just produced 5.8 WAR as a 34-year-old. That mark was the 11th-best WAR total in an offensively potent American League. In fact, the season he just turned in was a historic one as far as second basemen go.

There’s no denying Kinsler is a good player, and he’s definitely cost-controlled. He’s slated to earn $11 million in 2017 and he has a $10-million option for 2018 which would obviously be picked up by whatever team he plays for at that point. All this means that Kinsler is going to cost a fortune in a trade. Cody Bellinger’s name is being thrown around as the centerpiece. However, there have also been reports that Kinsler will not waive his no-trade clause should the new team not give him an extension. This wrinkle would make it easier to acquire Kinsler, but it would also undermine his best asset — a tiny contract.

But with everyone’s minds on trades, there’s another option the Dodgers could pursue and that option is named Steve Pearce.

In a way, Pearce reminds me a lot of Justin Turner. Both began their careers off slowly. Pearce never played more than 100 games until 2014 when he was 31 years old. Although Turner played more games earlier in his career, they weren’t that productive. The similarities start to come together when you notice they began to rake at the plate late in their careers. We all know about Turner’s numbers, but Pearce isn’t a slouch either.

In 2016, Pearce slashed .288/.374/.492 in 300 AB. Pair that with 13 home runs and a K% of 17.9% and you’ve got yourself a solid offensive player. He hits for average, gets on base, hits for moderate power, and doesn’t strike out much. So why hasn’t anyone been talking about him?

To begin with, people really only think of him as a platoon partner. Last year, FanGraphs even came out with an article stating “Orioles Reacquire Lefty Masher Steve Pearce.” As much as I love the writers there, I don’t buy the argument he only hits lefties. Just this year, Pearce posted a 176 wRC+ against lefties but still hit a well above-average 118 wRC+ against righties. Basically he goes from being a god against left-handed pitchers to being above-average against right-handed pitchers. And that’s crazy valuable!

With that problem being solved, he’s still not getting talked about. And that’s because he finished 2016 hurt. He actually underwent successful elbow surgery this year. So I’ll give the critics that.

But there’s another reason why no one’s talking about Steve Pearce as a second baseman. That’s because he doesn’t really play second base.

From 2007 to 2016, Pearce has played 33 games at second base, totaling a measly 242.2 innings at the position. But before you say signing a guy who has barely played second base to play second base all year long is dumb, I’ll explain. In those 242.2 innings, Pearce averaged 1.7 UZR/150 innings. Basically, he’s been about average defensively. Sure, it’s a ridiculously small sample, but it’s better than no data.

All I’m saying is that the Dodgers would be smart to look into Steve Pearce. MLBTradeRumors projects he’ll receive a 2 year, $20 million offer. With that price tag, he’ll be a lot cheaper than acquiring Ian Kinsler. And there’s definitely a reason for that. He’s coming off elbow surgery and isn’t a natural second baseman. But offensively speaking, Pearce is better than Kinsler. It’s just a matter of whether he could play second.

As Cubs fans and non-Cubs fans alike celebrate the end of the 108-year drought, we have overlooked the fact that in winning, the Cubs also bucked two trends in major league baseball:

1. 100+ win teams struggle in the postseason and rarely win the World Series, especially since the wild-card era began in 1995
2. Losers of the ALCS and NLCS (Cubs lost 2015 NLCS) historically decline the following season, both in win total and playoff appearance/outcome

Below is a table to quantify a team’s performance in the playoffs:

*The LCS was a best-of-five-game series from 1969 through 1984

It is important to acknowledge how close a team comes to winning a particular round. Based on a 0 to 4 scale, with 0 indicating the team missed the playoffs and 4 indicating the team won the World Series, the table credits fractions of a whole point for each playoff win. For example, in a best-of-seven-game series, each win (four wins needed to clinch) is worth 0.25. In a best-of-five-game series, each win (three wins needed to clinch) is worth 0.333 (1/3). Any mention of playoff result or average playoff result in this article is derived from this table.

THE STRUGGLE OF 100+ WIN TEAMS IN THE POST-SEASON

Playoff baseball, due to its small sample size and annual flair for the dramatic, historically has not treated exceptional regular season teams well. Jayson Stark recently wrote an article for ESPN titled, “Why superteams don’t win the World Series.” He noted that only twice in the first 21 seasons of the wild-card era had a team with the best record in baseball won the World Series (1998 and 2009 Yankees). Those two Yankee teams are also the only two 100-win ball clubs in the wild-card era to win the World Series. Research in this article will span the years 1969 to 2015, with 1969 being the first year of the league championship series (LCS).

Entering the 2016 season there had been 47 100+ win teams since the start of the 1969 season. Of those, 10 (21.3%) won the World Series. Other than those 10 World Series winners, how did 100+ win teams fare in the post-season?

Below are the average playoff results for 100+ win teams in each period of the major league baseball playoff structure from 1969 to 2015. The playoff structures were as follows:

1969-1984: LCS (best of 5 games) + World Series (best of 7 games)

1985-1993: LCS (best of 7 games) + World Series (best of 7 games)

1995-present: LDS (best of 5 games) + LCS (best of 7 games) + World Series (best of 7 games)

The wild-card game (2012-present) is omitted because a 100+ win team has yet to play in that game, although it certainly would be rare if we ever see a 100+ win team playing in the wild-card game.

As the data shows, 100+ win teams during the 1969-1984 period on average made a World Series appearance. This could be partly due to the fact there was only one round of playoffs (the LCS) ahead of the World Series, with the LCS being a best of five games. It was certainly a much easier path to the World Series once a team made the playoffs, yet on average 100+ win teams were finishing with a World Series sweep.

Changing the LCS from a best-of-five-game series to a best-of-seven-game series had a negative impact on team post-season performance, as 100+ win teams during the 1985-1993 span on average lost a deciding Game Seven in the LCS.

When the league added the wild card and LDS in 1995, it expanded the opportunity to make the playoffs but made the path to a World Series title more difficult, for a team now had to win 11 games to hoist the trophy. In the wild-card era, 100+ win teams are on average losing 4-1 in the LCS. This period also has the lowest percentage of 100+ win teams winning the World Series.

Using average playoff result standard deviation and a normal distribution, we can also see that the likelihood of a 100+ win team to win the World Series has had a significant decrease over the past several decades, left at under 7% during the wild-card era. The longevity of 100+ win teams in the playoffs has been trending downward over the past several decades. Despite being on the verge of a World Series defeat, the Cubs were able to successfully break through and buck a trend that had haunted outstanding regular-season teams for decades, especially since the wild-card era began in 1995.

THE CURSE OF THE LCS DEFEAT

The 2015 Cubs lost to the Mets in the NLCS yet bounced back in 2016 to have an even better regular season and win the World Series. This, however, was a rare feat. Teams that lose in the LCS historically win fewer regular-season games and perform worse on average in the post-season (if they make it) the following year. Below are two charts (1969-2015 and 1995-2015) that display average win differential, average playoff result, likelihood win differential is greater than +5 (2016 Cubs were +6), and the likelihood of winning the World Series.

Due to the 1981 and 1994 strikes, a few data points for win differential and playoff result are not included in the calculation. The data set includes 82 LCS losers for win differential and 88 LCS losers for average playoff result. The 1980-81, 1981-82, 1993-94, 1994-95, 1995-96 win differentials are not included for LCS losers in both leagues. The 1994 and 1995 playoff results are not included for LCS losers in both leagues because there was no post-season in 1994, hence no LCS loser. Regardless, there is a notable trend among LCS losers to perform worse the following season.

The 2016 Cubs not only won six more regular-season games than in 2015, but they became only the seventh team in history to lose the LCS one season and win the World Series the following season (1971 Pirates, 1972 Athletics, 1985 Royals, 1992 Blue Jays, 2004 Red Sox, 2006 Cardinals). Two of the previous six teams repeated as champions: 1973 Athletics and 1993 Blue Jays. Most recently, the 2005 Red Sox lost 3-0 in the ALDS and the 2007 Cardinals failed to make the playoffs.

LOOKING FORWARD

The Cubs have already been pegged favorites to win the 2017 World Series, which isn’t surprising given the fact nearly every key player is under team control. Is history on their side? Winning back-to-back titles is difficult in today’s competitive league, as new baseball thinking has somewhat evened the playing field and the small sample size of post-season baseball has the ability to lend unexpected results.

The 10 100+ win teams who have won the World Series since 1969 historically have not been successful in their attempts for back-to-back titles. Below are the average win differentials and average playoff result for these teams in the season following their championship:

Only three of these 10 teams (1975-76 Reds, 1977-78 Yankees, 1998-99 Yankees) have repeated as champions. Can the 2017 Cubs be the fourth? No matter the numbers, the 2017 Cubs still have to perform on the field. They were on the brink of losing the World Series in 2016, so we must not take anything for granted. But despite this, there’s no doubt the 2017 Cubs will be in a good position for a repeat. The Cubs are expected to be MLB’s best regular season team in 2017, according to FanGraphs and Jeff Sullivan’s analysis in his November 11, 2016 article. Only time will tell.