Archive for January, 2017

Playing Roulette with Danny Duffy and Wil Myers

January is notoriously slow for baseball activity, but the other week gave us two interesting extensions to digest. Wil Myers was extended for six years and $80 million, while Danny Duffy received five years and $65 million. Both of these players have had interesting careers thus far. Wil Myers has been polarizing in various ways since he was traded for James Shields. The most recent development has been his transition from playing OF to 1B, and seeing if he would be a valuable asset. As for Duffy, he spent part of the season in the Royals’ bullpen before sinker/slidering his way to potential ace status. If you look at both of their production over the last four years you see the following:

Year Duffy Myers
2016 2.8 3.8
2015 1.2 0.6
2014 1.9 -0.1
2013 0.5 2.3
Total 6.4 6.6
Average 1.6 1.7

The table above doesn’t exactly inspire much confidence paying these two individuals the approximate GDP of Qatar. Obviously, the Royals and Padres liked what they saw this past year and were ready to buy into the future. Both Duffy and Myers have youth on their side at 28 and 26 so the teams are buying recent improvements and prime years. Steamer, too, is optimistic about both players, projecting Duffy for 3.1 WAR and Myers for 2.4 WAR.

These deals are not without risk and there is real concern about the inconsistency of both players. As illustrated above, both Duffy and Myers have had years of above-average production and also years where they barely scratched replacement level. These deals may be seen as opportunistic for both player and team, but let’s take a look to see where the value may lie. First, we need to look at how much the team paid and the expected breakeven value.

 Name Contract Value Expected War War Per Year
Duffy 65,000,000 8.1 1.6
Myers 83,000,000 10.4 1.7
Assumes 8M / 1 WAR

Based on this analysis, the teams are paying these players to be exactly what they have been over the past four years. At first glance, this seems like a steep price for the pair who have had middling results but players who have shown superstar upside, even inconsistently, have immense value. A similarity both players share is signing these contracts under team control. Each presumably would have done better on the open market but decided to sell after career years.

Given the significant swings in performance, these contracts are unique because the total value of the contracts may be recouped over 1-2 years. Just this past season, Duffy and Myers were worth 2.8 and 3.8 WAR, respectively. Using the 99th percentile outcome for both these players, a 5 WAR outcome seems to be the absolute ceiling for these two players. Using the same 8M per WAR valuation, a 5 WAR season would produce a value of $40M. This would account for 62% of Duffy’s breakeven WAR and 48% of Myers’. If they were to return to their previous form and be worth 1.6 WAR each year for the remainder of the contract, the team would still enjoy a significant amount of surplus value. If you think 5 WAR is optimistic and prefer to think of their ceiling as closer to 4 WAR, the math still favors the teams’ side of these deals.

Danny Duffy and Wil Myers represent players who offer youth and inconsistency, and they have shown glimpses of stardom. Their respective contracts build both optimism and risk into the final dollar value. The unique part of these deals is quantifying the risk associated with these players. Given their inconsistencies, the teams should potentially expect to receive most of the value in one year while receiving middling results in the others. The Padres and Royals are betting on talent and recent improvements. Teams generally extend players with the idea of receiving consistent year-to-year value. Duffy and Myers portray a more boom-or-bust scenario. Generally, we have an idea of how a contract will go after Year 1; given these two players, we won’t know the result of the deal until the very end.

xFantasy, Part III: Can xStats Beat the Projections?

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:

R: xAVG, xISO, SPD*, TeamR+RBI, Batting Order
RBI: xAVG, xISO, SPD*, TeamR+RBI, Batting Order
SB: xOBP, xISO, SPD, TeamSB/PA, Batting Order

(*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: TeamR+RBI, TeamSB/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 TeamR+RBI, TeamSB/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.

Gary Sanchez Should Bat Second

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.

wRC+ by Leverage: the Good, the Bad, and the Funky

So I got a little carried away with the new splits leaderboard when I was looking up some wRC+ data. I was curious about which players performed the best/worst in high-leverage situations and one thing led to another and it led me to looking at top performers across the three leverage situations (low, medium, and high). If you want to know more about how leverage is calculated there is an old article in The Hardball Times here.

I used the splits leaderboards to gather 2016 hitter data by leverage situation and I only included players who had a minimum of 20 PA per split. Once I gathered all the data I converted each player’s wRC+ by leverage situation to a percentile and calculated each player’s mean percentile rank along with the variation around the mean using standard deviation to produce the following plot.

The blue line is just a LOESS line showing the general trend of the data. What the line is telling us is that players on the extreme end of the percentile ranks also seem to have the lowest variation or, more simply put, good players seem to be consistently good and bad players seem to perform poorly across all leverage situations. Using that plot as my baseline, I started exploring the data to answer some question about player performances in 2016. I included the top 10 players in ordered tables going from from least interesting to most interesting, at least in my opinion. First, let’s look at the top performers from this year.

Players who ranked highest in wRC+ across all leverage situations
Leverage Rank
Name Low Medium High Mean Rank SD
Mike Trout 98 97 99 98 1
Freddie Freeman 97 88 94 93 4.6
Josh Donaldson 97 94 88 93 4.6
Anthony Rizzo 93 88 97 92.7 4.5
Joey Votto 96 98 84 92.7 7.6
David Ortiz 96 99 77 90.7 11.9
Matt Carpenter 91 82 94 89 6.2
Paul Goldschmidt 88 86 88 87.3 1.2
Tyler Naquin 93 81 87 87 6
Ryan Schimpf 80 86 93 86.3 6.5

Boring, Mike Trout leads the way as the top performer. Apparently it doesn’t matter when he comes up to the plate; he is going to smash the ball. But I’m not going to focus on Trout, as I’m not qualified to write about him and he’s above my pay grade, so let’s leave him to the professionals. Like I said before, least interesting first and hopefully it’ll get more exciting as we go. Here’s a fun fact to keep you going: In high-leverage situations among players with a minimum of 20 PA, Ryan Howard led the league in ISO with a 0.640 mark. Ryan Schimpf was second with an ISO of 0.542. And Howard did that with a 0.118 BABIP, too.

Second, let’s take a look at the worst performers of the season.

Players who rated as the worst performers across all leverage situations
Leverage Rank
Name Low Medium High Mean Rank SD
Yan Gomes 17 23 0 13.3 11.9
A.J. Pierzynski 17 21 9 15.7 6.1
J.B. Shuck 25 16 11 17.3 7.1
Nick Ahmed 15 35 3 17.7 16.2
Jake Marisnick 37 19 6 20.7 15.6
Ramon Flores 21 20 21 20.7 0.6
Gerardo Parra 33 29 1 21 17.4
Juan Uribe 19 38 11 22.7 13.9
Adeiny Hechavarria 20 34 15 23 9.8
Alex Rodriguez 19 30 22 23.7 5.7

After a pretty impressive career, although it also came with its fair share controversy, we see A-Rod make this list. And it doesn’t look like he is going to be playing again this year, which casts some doubt on whether he is going to make it to 700 career home runs (he’s currently at 696).  But more importantly, our poorest performer of 2016 looks to be Yan Gomes. I was inclined to say A.J. Pierzynski should actually be considered the poorest performer of the year since his standard deviation was about half of Gomes’, but then I noticed that Yan Gomes was in the 0th percentile in high-leverage situations — literally the worst. Not all-time worst, but still pretty bad! And I guess if you want to argue that the worst percentile should actually be 1, as in the 1st percentile, then you could make that argument, but the value was rounded to 0 when Yan Gomes registered a whopping -72 wRC+ in high-leverage situations. The second-worst was Gerardo Parra at a -59 wRC+; that’s a pretty significant gap between first and second. Fun-fact time: In high-leverage situations, Mike Zunino ran a 30.8% walk rate, although he also struck out 30.8% of the time too. Yasmani Grandal had a 30.4% walk rate to go with a much smaller 13% K%.

Everyone always seems to be looking for players who are on the extreme ends of the leaderboards, but let’s give some love to the unsung heroes of the world, the completely average performers! I wasn’t sure if I simply wanted to use mean percentile rank as a measure for averageness, so I decided to go with what I called Deviation in the table. Deviation is calculated by adding the standard deviations (SD) of a players percentile ranks to the Δ50 column. The Δ50 column is calculated as the absolute value of a players mean rank minus 50.

The most average performers of 2016 in wRC+
Leverage Rank
Name Low Medium High Rank SD Δ50 Deviation
Scooter Gennett 55 46 49 50 4.6 0 4.6
Ezequiel Carrera 46 44 51 47 3.6 3 6.6
Leonys Martin 44 54 47 48.3 5.1 1.7 6.8
Matt Duffy 41 49 49 46.3 4.6 3.7 8.3
Avisail Garcia 45 51 42 46 4.6 4 8.6
Howie Kendrick 46 59 52 52.3 6.5 2.3 8.8
Johnny Giavotella 40 44 42 42 2 8 10
Jason Castro 47 49 62 52.7 8.1 2.7 10.8
Jonathan Schoop 62 53 54 56.3 4.9 6.3 11.2
Brandon Phillips 55 48 63 55.3 7.5 5.3 12.8

And Scooter Gennett comes away as the most average performer of the season! He also ran a 0.149 ISO on the season and I think 0.150 is usually considered average. Look how wonderfully average these guys were; we should all take a minute to enjoy the little things in life. I realize this may not be the sexiest table, but it’s still interesting. You might not be getting a whole lot out of these guys over an entire season, but they are going to go up there and do average things whether you like it or not.

Two tables left — hopefully you’re still with me here. Let’s look at consistency. People always say consistency is key. I guess that’s good advice except when you’re on the terrible end on the spectrum.

Table looking at the most consistent performers based on percentile rank
across the 3 leverage situation (low, medium and high)
Leverage Rank
Name Low Medium High Mean Rank SD
Ramon Flores 21 20 21 20.7 0.6
Ivan De Jesus 32 32 33 32.3 0.6
Mike Trout 98 97 99 98 1
Paul Goldschmidt 88 86 88 87.3 1.2
Johnny Giavotella 40 44 42 42 2
Yunel Escobar 66 69 65 66.7 2.1
Hunter Pence 79 76 80 78.3 2.1
Wilson Ramos 81 80 85 82 2.6
Alexei Ramirez 26 32 28 28.7 3.1
Austin Jackson 43 38 37 39.3 3.2

Ramon Flores and Ivan De Jesus both had extremely consistent seasons; it’s just too bad they are on the wrong end of the spectrum. But I have to say Ramon Flores beats out Ivan De Jesus as he registered on average 12 percentile ranks poorer. In third we see Mike Trout showing incredible consistency while being the top performer in the league, followed closely by Paul Goldschmidt. It’s interesting see the top four players on this list from opposite ends of the spectrum, but the rest of this list bounces back and forth as well.

And here we are, the last one or as the title says “the Funky”. I found that volatility was the most interesting question, or which players showed the most boom or bust in 2016. Most of the players in this list performed best in low- and medium-leverage situations, often above the 90th percentiles.

Looking at players who showed the highest volatility based on percentile rank
across the 3 leverage situation (low, medium and high)
Leverage Rank
Name Low Medium High Mean Rank SD
Sandy Leon 96 84 2 60.7 51.2
David Peralta 95 29 1 41.7 48.3
Dansby Swanson 23 99 15 45.7 46.4
Yangervis Solarte 93 73 5 57 46.1
Mac Williamson 59 95 4 52.7 45.8
Alex Avila 36 99 12 49 44.9
Jarrod Saltalamacchia 41 9 97 49 44.5
Pedro Alvarez 86 85 9 60 44.2
Ryan Zimmerman 21 85 1 35.7 43.9
Kris Bryant 98 91 19 69.3 43.7

After perusing though the list, one of the most interesting names that jumps out should be Jarrod Saltalamacchia and his 97th percentile rank in high-leverage situations last year. And here’s another twist, would it surprise you to hear that in 2016 Miguel Cabrera was the least-clutch hitter among all Tigers qualified hitters? Check out the Tigers leaderboard here. But the 2016 volatility award goes to Sandy Leon, who absolutely mashed balls in low-leverage situations, was no slouch in medium-leverage spots, but dropped off the map in high-leverage situations. I have no idea how BABIP relates to wRC+, but with Sandy Leon it looks like his BABIP reflects what was happening in the different situations (0.434, 0.393 and 0.190). There is probably some combinations of descriptive stats that would explain some of the variance, and BABIP may very well be included, but I’m not going to go into that here.

Hope you enjoyed this. If anyone wants a copy of the R code I used to make the graph and tables, leave a comment below and I’ll pass it along. I ended up finding a pretty cool library to create html tables in R so you don’t have to mess around with formatting and manual inputs. As long as you’re willing to put a little work into understanding css you can basically customize the look of your tables.

The Major Impact of Edwin in Cleveland

Recently, the Cleveland Indians signed slugger Edwin Encarnacion in a bold move to get their formerly middle-of-the-pack offense kick-started.  The deal, which pays $65 million to Encarnacion over three years, can’t be considered a good or bad deal yet — that is still to be determined.  If Edwin, who is, like everybody in the world, constantly getting older, performs like he did for the past two years, then the deal will be a steal for the Indians.  Yet if Edwin begins to show his age at the plate, then the deal will hardly be worthwhile.  Most likely, though, he will accumulate 25-35 (on an overly optimistic side) home runs, while batting for a not noteworthy .280.

Looking over the signing, one can easily come to the assumption that Cleveland will be better with Edwin.  Certainly, any level-headed person wouldn’t consider him to be a minus.  However, nobody has really come out and said that Edwin is the difference between a good team and a great team.  Yet from looking through the depths of Cleveland’s roster, one sees something uncontrollably powerful occurring slowly but surely in Cleveland.  Something that has been in development every since the Indians brought Jason Kipnis to the big leagues in 2011.  And now, with the addition of Edwin Encarnacion, they seem to be done.

What the Indians have done through the past five years is that of a front-office masterpiece.  Last year, they came within a game of winning of the World Series, and this year, they are poised to make a run for the trophy again.  As mentioned before, it all started with the arrival of a noncommittal prospect named Jason Kipnis in 2011.  Kipnis had played well, but definitely not worth a mention in any top-prospect lists.  In the majors, he took a few years to blossom, but he’s been on the rise ever since.  He is now a solid second baseman with speed and power, the second-sacker of every team’s envy.

That same year, Francisco Lindor entered the rookie team of the Indians.  Unlike Kipnis, he became a highly-touted prospect, and his first appearance in the major leagues, in 2015, was widely watched.  And ever since that first game, Lindor has not looked back, joining Kipnis in the ranks of the best middle infielders in the league.

This past year, 2016, was when all the front office’s hard work finally blossomed.  At first, the season did not start out very well.  Stalwart right fielder Michael Brantley got injured early on, and the season’s prospects looked slim.  Yet about a third of the way through the season, something amazing happened.

The Indians were not doing badly, but were definitely not excelling in the season.  So, in a radical move, they decided to see how a prospect would fare in the bigs.  So they summoned Tyler Naquin from the farm system and immediately implanted him in center field.  Thankfully, the lanky Naquin performed above and beyond anyone’s expectations.  He finished the season in the contest for Rookie of the Year, despite missing a good chunk of the season.  Meanwhile, a player who had spent a few years in the bigs yet never really got to play was coming into his own just about the time when Naquin came up.  Jose Ramirez had been drafted by Cleveland after the 2010 season and was called up in 2013.  He didn’t get much playing time, and was sent back down to the minors the next year.  He was called up again in 2015, and played poorly.  However, he wasn’t ready to ruin his big-league career.  At around the time Naquin came up, Ramirez became hot.  He started playing like he hadn’t ever in his career.  Somehow, someway, a switch had been flipped inside him.  Somehow, someway, the Cleveland Indians were in business.

Although the Indians had failed to win the World Series, the season had still been a wild success.  They had built a powerful machine, and with Brantley back in right field for the 2017 season, who knew what could happen?  But still, they seemed to be missing something.  Even with the amazing midseason reinforcements and Cleveland’s powerful lineup (Napoli, Santana, Lindor, Kipnis), the Indians were 18th in the majors in runs scored.  They were getting many runners on base, as their .329 OBP (tied for seventh-best in the MLB) testified.  They just needed one more piece, a guy who could get those many baserunners home.  And although Napoli was big and strong and hit majestic homers, he just wasn’t the guy the Indians needed.  So they signed Encarnacion.  With him on the team and Brantley back, possibilities are boundless.  Their lineup (shown below) will be incredibly potent.

1.  Francisco Lindor;  Shortstop

2.  Jason Kipnis; Second Base

3.  Edwin Encarnacion; DH

4.  Michael Brantley; Right Field

5.  Carlos Santana; First Base

6. Jose Ramirez; Left Field

7.   Lonnie Chisenhall; Third Base

8.  Roberto Perez; Catcher

9.  Tyler Naquin; Center Field

Although the order could be debated on, its potency and presumed consistency are undeniable.  There are only 1.5 holes in the lineup (Roberto Perez=1, Chisenhall=.5), and other than that, the rest of the lineup is stocked with really good players. That’s seven really good players in one lineup.  That is something special.  The lineup is also well-rounded.  There are Lindor, Kipnis, Brantley, and Ramirez providing consistency, while Encarnacion and Santana provide the dingers.  Of course, the four who provide consistency can be relied on to produce at least 15 homers a year.  And although the batting order looks very impressive, the pitching rotation is what really makes the Indians special.  The pitching rotation made it to the World Series minus two of their best pitchers — Salazar and Carrasco — and almost won it!

The addition of Encarnacion will, in my opinion, prove to be great.  The Indians will leap from 18th to fifth in offense in the majors, and they will have a very good regular season.  Again, this is just my opinion, but the Indians do look awfully dangerous come the 2017 season.

Which Pitchers Got Burned on the First Pitch in 2016?

It’s always good to get ahead in the count.  The difference in average run expectancy between a first-pitch strike and a first-pitch ball is over .07 runs.  However, trying to get ahead can go horribly wrong for a pitcher.  With 2016 now in the rear-view mirror, I wanted to take a look back at this past season to see which pitchers got hurt more than most on the first pitch.

First, we have to decide on what it means to be “hurt” on the first pitch.  A pitcher could give up a bunch of singles on the first pitch but not get hurt too bad, depending on the situation.  If a starting pitcher is given a five-run lead, he could give up a few first-pitch hits here and there simply from laying fastballs in the zone trying to get some quick outs.  If he gives up a two-out double with no one on up five runs, the win expectancy isn’t going to change much.  Similarly, in a tie game late with a runner in scoring position, a base hit could give the opposing team the lead for good, leading to a huge change in win expectancy.  Since we are interested in context-dependent numbers, we will use a context-dependent statistic.  Win probability added (WPA) will do the trick.  Adding up WPA will tell us the general story of what happened on the first pitch. Mainly, who got burned.

Using 0-0 count data taken from Retrosheet and Baseball Savant, we will rank the top five relievers and top five starters by their WPA to determine who got hurt on the first pitch in 2016.  However, adding the WPA for all pitchers in 2016 is a daunting and unnecessary task as there are over 600 pitcher stats to comb through.  To get a better sense of the worst performers on the first pitch, we will make a simple rate stat of runs allowed on the first pitch divided by the amount of at-bats the ball was put in play on the first pitch. This stat will allow me eliminate a majority of the pitchers for our list.  If a pitcher has a low to average runs to at-bat ratio, they probably didn’t get burned too much on the first pitch. To make our sample size even smaller, we will use the thresholds of 25 ABs for relievers and 50 ABs for starters to qualify for the data set. This gives us 31 qualified relievers and 48 qualified starters.  Additionally, I have included the at-bats throughout the season that I have deemed to have “hurt the most” for a particular pitcher.  These at-bats led to the largest swing in win expectancy last season for each pitcher.  So, for 2016, here are the pitchers that would like more than a few first pitches back:


#5 Tyler Lyons

(Okay, there were relievers with worse WPAs than Lyons, but when you get hit this hard on the first pitch, I have to make you #5. Hey, it’s my list! I can do what I want!)

Lyons actually had a positive WPA on at-bats where the ball was put in play on the first pitch.  However, most of these ABs happened in garbage time which is the major reason why his WPA is close to average.  He lands on this list due to a .480 batting average on the first pitch, including seven extra-base hits.

2016 breakdown

  • ABs: 25
  • Hits: 12 (3 doubles, 4 home runs)
  • Runs Allowed: 8
  • Runs Allowed per AB: .320
  • WPA: 0.075
  • AB that Hurt the Most: May 6th vs Pirates
    • Tasked with keeping the Cardinals close down one run in the 6th, Jung Ho Kang gives the Pirates a commanding 3-0 lead heading into the late innings
    • Cardinals Win Expectancy before AB: 34%
    • Cardinals Win Expectancy after AB: 13.7%
    • Swing in Win Expectancy: 20.3%

#4 Casey Fien

Fien had rough go of things in 2016, finishing with 6.43 FIP and a -0.8 WAR.  Causing most of that damage were the 13 homers he allowed in just 39.1 innings, including five allowed in 25 ABs on the first pitch.  However, teams will continue to take chances on him as he possesses well above-average spin rate (2,504 RPMs) on his fastball, which makes him a cheap potential bounce-back candidate.

2016 breakdown

  • ABs: 25
  • Hits: 15 (3 doubles, 5 home runs)
  • Runs Allowed: 10
  • Runs Allowed per AB: .400
  • WPA: -0.304
  • AB that Hurt the Most: April 4th at Orioles
    • With no score in the bottom of the 5th,  The Orioles get on the board when Adam Jones doubles off the right-center field wall to bring in two runs
    • Orioles Win Expectancy before AB: 72.9%
    • Orioles Win Expectancy after AB: 85.4%
    • Swing in Win Expectancy: 12.5%

#3 Tony Cingrani

There isn’t a whole lot of mystery as to what Cingrani is going to throw toward the plate.  In 2016, he threw his fastball over 89% of the time.  The first pitch of the at-bat was a huge nemesis for Cingrani last season as nearly half of his runs allowed came as soon as the hitter stepped in the batters box.

2016 breakdown

  • ABs: 25
  • Hits: 10 (4 doubles, 1 triple, 1 home run)
  • Runs Allowed: 13
  • Runs Allowed per AB: .520
  • WPA: -0.547
  • AB that Hurt the Most: May 1st at Pirates
    • Down 3-1, Sean Rodriguez gets the Pirates within one with an RBI triple.  The Pirates tie the game two batters later with an RBI single by Matt Joyce.
    • Pirates Win Expectancy before AB: 19.8%
    • Pirates Win Expectancy after AB: 45.3%
    • Swing in Win Expectancy: 25.5%

#2 Justin Wilson

Good news? Wilson posted an average leverage index of 1.48 in 2016, which was a career high. Bad news? Wilson posted a WPA of -0.84, which was a career low.  The first pitch of the at-bat was no exception.

2016 breakdown

  • ABs: 25
  • Hits: 13 (2 doubles, 3 home runs)
  • Runs Allowed: 10
  • Runs Allowed per AB: .400
  • WPA: -1.108
  • AB that Hurt the Most: August 9th at Mariners
    • Three-run homers hurt.  They really hurt when your team is up three runs in the bottom of the 8th with two outs and one swing later, its a tie game.
    • Mariners Win Expectancy before AB: 8.8%
    • Mariners Win Expectancy after AB: 52.4%
    • Swing in Win Expectancy: 43.6%

#1 Tom Wilhelmsen

Like most of the pitchers on this list, 2016 was a year to forget for Wilhelmsen.  He posted a career low 6.38 FIP and was worth a -1.0 WAR.  Its not all bad, though, as Wilhelmsen pitched better upon his return to Seattle in June, but didn’t pitch well enough to warrant a roster spot at this point for  2017 when he was released in November.  He is currently a free agent.

2016 breakdown

  • ABs: 27
  • Hits: 15 (4 doubles, 2 home runs)
  • Runs Allowed: 10
  • Runs Allowed per AB: .370
  • WPA: -1.149
  • AB that Hurt the Most: May 15th vs Blue Jays
    • Tied a 2 in the 6th, Jose Bautista hits a bases clearing, three-run double to put the Blue Jays in a commanding position to win the game.
    • Rangers Win Expectancy before AB: 47.1%
    • Rangers Win Expectancy after AB: 15.2%
    • Swing in Win Expectancy: 31.9%


#5 Dallas Keuchel

After a Cy Young season in 2015 where he was worth nearly six wins, Keuchel regressed a bit in 2016.  Even with his struggles, Dallas was still an above-average pitcher with a 92 FIP-.  Shoulder issues cut his season short in late August but look for Keuchel to come back stronger next year for what projects to be a strong Astros club, hopefully with better results on the first pitch.

2016 breakdown

  • ABs: 81
  • Hits: 27 (6 doubles, 1 triple, 7 home runs)
  • Runs Allowed: 21
  • Runs Allowed per AB: .259
  • WPA: -0.938
  • AB that Hurt the Most: April 21st at Rangers
    • Here’s that devastating three-run homer again.  Given a 1-0 lead in the top of the first, Keuchel gives the lead right back and then some when Ian Desmond hits the first pitch he sees out to give the Rangers a 3-1 advantage.
    • Rangers Win Expectancy before AB: 44.4%
    • Rangers Win Expectancy after AB: 70.1%
    • Swing in Win Expectancy: 25.7%

#4 Felix Hernandez

Sometimes a pitcher can end up on this list simply due to a larger amount of at-bats against him throughout the season.  This could be the case with King Felix as he had 92 at-bats where the batter put the first pitch in play.  Between the starters and relievers, Hernandez had the lowest batting average against him on the first pitch at .326.  Unfortunately, a majority of these hits came at inopportune times for Hernandez, which influenced his WPA more than most.  Ultimately, this is what we are looking for as timely hitting can hurt a pitcher quite a bit.

2016 breakdown

  • ABs: 92
  • Hits: 30 (8 doubles, 4 home runs)
  • Runs Allowed: 23
  • Runs Allowed per AB: .250
  • WPA: -1.087
  • AB that Hurt the Most: July 20th vs. White Sox
    • One out away from getting out of the first with no damage, Todd Frazier gives the White Sox a huge boost early with a first-pitch three-run shot to left.
    • Mariners Win Expectancy before AB: 50.4%
    • Mariners Win Expectancy after AB: 23.7%
    • Swing in Win Expectancy: 26.7%

#3 Sonny Gray

2016 was an injury filled season for Gray as he had two separate stints on the DL before making a final appearance on September 28th after nearly two months on the shelf.  Injury issues to his right trap and forearm clearly played a role in his poor performance last season.  Did those same injuries also play a role in being hit hard on the first pitch?  While I can’t say for sure, it is possible.  Hopefully those injury issues are behind Gray in 2017 as he looks to return to being one of the best starters in the American League once again.

2016 breakdown

  • ABs: 60
  • Hits: 28 (4 doubles, 5 home runs)
  • Runs Allowed: 17
  • Runs Allowed per AB: .267
  • WPA: -1.322
  • AB that Hurt the Most: May 15th at Rays
    • With the score tied at 1, Brandon Guyer gives a huge advantage to the Rays on the first swing with a three-run shot to left.
    • Rays Win Expectancy before AB: 54.4%
    • Rays Win Expectancy after AB: 82%
    • Swing in Win Expectancy: 27.6%

#2 Kyle Gibson

Right shoulder issues early, home runs and a .330 BABIP could sum up Kyle Gibson’s 2016.  As far as the first pitch goes, homers were again an issue as he allowed eight of them throughout the season. I wouldn’t expect the same in 2017.

2016 breakdown

  • ABs: 75
  • Hits: 35 (9 doubles, 8 home runs)
  • Runs Allowed: 23
  • Runs Allowed per AB: .307
  • WPA: -1.962
  • AB that Hurt the Most: August 17th at Braves
    • With Gibson given a 2-0 lead into the 3rd inning, Freddie Freeman altered the game back into Atlanta’s favor with one swing.
    • Braves Win Expectancy before AB: 26.6%
    • Braves Win Expectancy after AB: 51.2%
    • Swing in Win Expectancy: 24.6%

#1 David Price

When David Price signed last winter with the Red Sox for $217 million, he was expected to be the ace of the staff right away.  That didn’t happen as quickly as Red Sox nation would have liked in 2016 as Price posted his highest FIP- since his rookie season in 2009.  This continued into the postseason with another subpar performance against Cleveland in the Divisional Series.  Ten home runs allowed on the first pitch last season surely didn’t help the below-average season — for David Price standards — in 2016.  With Chris Sale now in the fold to take some pressure off, I fully expect Price to return to being the dominant pitcher we have seen in the past in 2017.

2016 breakdown

  • ABs: 109
  • Hits: 43 (6 doubles, 1 triple, 10 home runs)
  • Runs Allowed: 26
  • Runs Allowed per AB: .239
  • WPA: -2.218
  • AB that Hurt the Most: June 8 at Giants
    • What a time for your first career homer!  With the score tied at 1 in the bottom of the 8th, Mac Williamson unties it on the first pitch of the inning giving the Giants a one-run advantage late.
    • Giants Win Expectancy before AB: 59.3%
    • Giants Win Expectancy after AB: 88.5%
    • Swing in Win Expectancy: 29.2%

**Things to keep in mind**

  • I ranked these two lists by WPA. This doesn’t exactly mean that David Price performed more than twice as bad as Dallas Keuchel on the first pitch. Due to its additive nature, Price could have a higher WPA because he had more opportunities to perform worse than Keuchel.  WPA was just a way to rank the pitchers.
  • There is no predictive nature to WPA. If I were to do this list again for 2017, I would expect a completely different list.
  • This was just a fun list to see who got hit hard on the first pitch in 2016. By no means is this an analysis of why a pitcher performed poorly in 2016. While poor performance on the first pitch certainly can aid in an overall poor season, just because a pitcher performs poorly on the first pitch one at-bat doesn’t mean we can expect him to keep performing poorly the next at-bat.
  • It’s not fun getting burned on the first pitch…


The 2017 Phillies Can Change Baseball Forever

The GM of the Philadelphia Phillies has been accumulating the players to potentially pull off the greatest singleseason 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:


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:


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)


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.


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.


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.

Is Ivan Rodriguez Going to Be Part of the Class of 2017?

Is Ivan Rodriguez going to make the Hall of Fame when the results are announced today? In my opinion, it’s close to a toss-up.

Does the man they called Pudge deserve to be enshrined, though? Most would agree he does, whether it’s because he’s the all-time games played leader at catcher, the all-time hits leader among catchers or because of some combination of his traditional stats (311 HR, 2844 H, .296 AVG, 13 Gold Gloves, 14 All-Star appearances) and sabermetric stats (+68.9 career fWAR). Will he be? Who knows! At least, this year, that is. If he doesn’t go in this year he’ll almost certainly be close enough to make his enshrinement in 2018 a mere formality.

What are his chances for this year, though, since that’s what’s important right now?

I’ve been following how Rodriguez has been doing via Ryan Thibodaux’s amazing BBHOF Tracker (check out his twitter handle @NotMrTibbs if you have yet to do so). A few dozen ballots in, I realized that Rodriguez was tracking extremely well with voters who checked off fellow superstar catcher Mike Piazza’s name last year. From that point on, I decided to follow along as Ryan tracked ballots and see if I could get an accurate bearing on whether Rodriguez would join the ranks of first-ballot Hall-of-Famers.

Over at the Tracker, there’s a row labeled “Estimated Net Gain Needed” for returning candidates. I wanted to see how many Piazza voters Pudge could afford to lose, so I used the same assumptions to calculated Pudge’s allowable net loss that were used there:

Total voters: 435

Returning voters: 415

New voters: 20

I used Piazza’s percentage for the returning voters (83%) and new voters (8/10 on new voters in 2016, 80%) to calculate Piazza’s expected votes if he were put back on the ballot — or, essentially, how I expected Pudge would do if he exactly cloned Piazza. All the math worked out to -33.45, meaning Rodriguez could afford to lose 33 Piazza voters, but 34 would presumably put him just under the assumed 327 votes needed (327/435 is just over 75%).

How has I-Rod actually fared, though?

Through 247 ballots tallied in the Tracker, 208 voters have voted for whom information is known about their 2016 voting. 180 of them voted for Piazza last year, whilst 28 withheld a vote. Of the 180, Pudge has picked up 157 of them. He’s also picked up a stunning nine of the 28 who didn’t vote for Piazza last year, which is significantly better than how I’d expected he’d do there, and is partially what’s kept him afloat.

All in all, with about 57% of the approximate vote total counted, Pudge has 158 votes from these 200 returning voters, 14 fewer than what Piazza had. That net -14 is about 40% of his allowable net loss. What does all that mean for his chances, though?

To answer that, I delved a little deeper into the numbers, breaking down how Pudge has done based on size of ballot combined with if they voted for Piazza.

I also utilized the information at the bottom of Thibodaux’s Tracker to see who we knew for sure did not vote in 2016 and how that affected the percentage of Piazza’s voters who were available to vote for Pudge.

So far, there have been 12 confirmed, eligible 2016 Hall-of-Fame voters who did not cast a 2017 ballot, 11 of whom did vote last year. Of those 11, 10 had public ballots last year and Piazza went 8/10 on them, a touch below his overall 83% mark. There are actually nine voters who have publicly revealed their ballots this year who didn’t vote last year, too — Jeff Blair, Steve Dilbeck, Lynn Henning, Kevin Modesti, Jim Reeves, John Romano, Gary Shelton, Willie Smith and Clark Spencer. Those nine went 7/9 on Pudge, essentially cancelling out the lost Piazza voters.

The projected net-loss-allowed figure also used an 80% assumption for Pudge and new voters, but Pudge has actually fared a little better, going 13/14. If six more first-time voters cast ballots and Pudge was named on five of them, he would go 19/20 on new ballots, gaining a much-needed three-vote cushion. At that point, he’d essentially be able to lose as many as 35 of Piazza’s voters.

There are two key ways to look at how Pudge will do on the remaining ballots. Most players do worse when private ballots are tallied; they tend to skew more anti-PED and put fewer names on their ballots. Last year, Mike Piazza finished the pre-results portion of ballot-tracking at 86.3% and fell 3.3% from pre-results to the final tally.

According to Thibodaux’s tally, Piazza got 102 of the 129 fully private, untracked votes last year, or 79.1%. He got 81 of the 100 public ballots released after the announcement, meaning he got 79.9% of all votes not released at the time of the announcement.

Going back a bit, Pudge has gotten 166 votes on the public returnees, whereas Piazza had 180. If that ratio were to hold on the remaining ballots, taking Piazza’s post-results percentage and multiplying it by 92.2%, the percentage of Piazza voters Pudge has gotten, would leave Pudge around 73.8% on private ballots. Thibodaux’s estimates say Pudge needs to get 130 of the remaining 187 ballots, which translates to 69.52%. All in all, much of this is good news for Pudge. Bagwell dropped about 6% from pre-results to final last year, but Bagwell hadn’t done quite as good against Piazza voters and had more certainty among his private voters. Pudge is on his first try; people can estimate how he’ll do all they want, but most voters seem to consider him a clearly superior candidate to Bagwell, despite the fact that Bags was likely the better player.

Rodriguez, I don’t believe, drops quite as much as Bagwell did. Does he drop as little as Piazza, though? Who knows! And that’s exactly the issue. One projection system I’ve seen (authored by Nathaniel Rakich, @baseballot) estimated Pudge’s public/private differential will be an 8.5% drop-off. From where he is right now, he can more than afford that differential and still survive and be elected.

One last thing I’ve looked at is how small-Hall voters who had Piazza have voted on Pudge. Last year, pre-announcement, there were 11 voters who voted at max five players and selected Piazza. Eight of these selected Rodriguez this year, which isn’t fantastic, but is still very good. Additionally, two others who had five-or-fewer ballots and didn’t vote for Piazza chose to vote for Rodriguez. Some have been afraid that Rodriguez’s chances would be ruined by small-Hall ballots, but I’d counter that this research has made me feel the exact opposite. At announcement-time last year, Piazza had votes on 11 ballots of five-or-fewer names. Right now, Piazza currently has votes on 10 of last year’s five-or-fewer voters.

It’s going to be close. It could go either way. However, there are plenty of reasons to be optimistic about Pudge’s first-ballot chances when the results are announced at 6 p.m. ET.

Note: While writing this, another previously private voter released his ballot and voted for Pudge despite not voting for Piazza last year, meaning Pudge is now -13 at the 57% mark.

Trying to Put PEDs in Perspective

One of the most controversial issues in baseball history has arisen recently in regards to the Hall of Fame. Historically the debates over a player’s worthiness of enshrinement have focused almost squarely on a player’s career statistics and in fact for many that remains the case today. But recently a new trend among candidates has begun to emerge. The morality of their careers now seems to matter more than ever and not just with borderline candidates. Players like Barry Bonds and Roger Clemens have numbers that put them in the conversation for being the greatest position player and pitcher of all-time, respectively, but because people question the means by these players attained their numbers, they have been thus far shut out of receiving the game’s highest honor. The issue? PEDs, or, more simply put, any drug concerning the use of anabolic steroids or HGH.

I feel that it is important to differentiate the difference between PEDs and these two families of drugs, because in a technical sense over-the-counter aspirin could be considered a PED. The idea of punishing a player for taking a simple Tylenol seems utterly ridiculous even under the most draconian of PED rules.

It is specifically the use of anabolic steroids and/or HGH that has been the square focus of people’s outrage and it’s the attitudes people have about these two drugs which I will be focusing on.

The Health Risks

To date no long-term study on the long-term health effects of these drugs has ever been done, mainly because no scientific institution of significance would ever approve of such a thing, but the anecdotal evidence seems to be mixed at best and not good at worst.

Countless athletes across various sports have contracted various health issues during and after their playing careers, some of which no doubt stem at least in part from steroid use. Others however have had minimal to non-existent side effects of use and with the rigors of sports such as football it’s almost impossible to make the distinction between issues that were caused by the side effects of drug use and the side effects of the physical toll caused by playing the sport itself.

Another issue as it relates especially to sports like football and other combat sports like MMA and boxing is the issue of facing opponents who are on steroids or HGH themselves. Regardless of any physical effects Barry Bonds’ PED use may have had on himself, it’s hard to correlate any direct negative health consequences this would have on another player because of the non-physical nature of the sport. Taking steroids or HGH to add more power to your jab would seem to be a different story, especially over time.

Less mentioned is the role that illegal or hard drugs may have played in many of the more notable cases of steroid-attributed health issues. Steroids rose to prominence around the same time as the drug cocaine did and while the long-term health effects of steroids may not be very well documented, the long-term health effects of cocaine use certainly are.

Unlike the scare-tactic view of Len Bias’ allergic reaction death, the more likely health outcome of cocaine use, especially long-term, is a premature heart attack. Sadly there is probably no greater evidence of this than among some of the more recent celebrity deaths. In addition to having and or succumbing to serious heart-related issues, Carrie Fisher, Whitney Houston and Robin Williams were all noted heavy cocaine users and I have no doubt that use played a significant part in the health struggles all three went through in their later years.

Ken Caminiti was both a steroid and cocaine user. How much of an impact each had on his health independent of the other is impossible to say. It is worth noting though that Caminiti’s off-field lifestyle may help explain why he is now dead and why others like McGwire are still alive even though their total HGH and/or steroid use over the course of their lives may have been close to equal.

How serious the side effects of drugs like steroids and HGH are from one person to the next is impossible to say, but I haven’t seen any anecdotal evidence yet to suggest that aside from the most extreme cases that steroid use on its own can cause your mortality to be lowered by 20-30 years.

As it relates to a sport like professional baseball, in the grand scheme of health risks related to the sport, I actually don’t view the direct side effects of steroids or HGH as being that significant of a health risk. If a player suffers an injury such as an ACL tear, it is just assumed that player will undergo major knee surgery and 6-9 months of serious rehab like it’s nothing.

Guys have developed lifelong chronic pain by trying to play through injuries that they wouldn’t have had they taken better care of themselves instead of playing baseball. Derek Jeter broke his ankle in the 2012 ALDS trying to play through a bone bruise and even received a cortisone shot to alleviate the pain. In terms of long-term health effects, I would put that injury up there with any side effects that could come from a steroid cycle.

Many may claim we take these types of health issues players go through to get on the field for granted because it’s what they want to do and they are getting financially compensated, but I think the real reason is because most people can’t really relate to it. Most jobs aren’t entirely dependent on your physical well-being. If you have a debilitating injury you’re probably not given the options of getting it fixed ASAP just so you can have the opportunity of getting your old job back, or find a new line of work.

For a player like Joe Mauer, the financial implications of this decision may not be nearly as severe as, say, a nine-year backup catcher who needs one more year in the league to get a full pension, but the expectation to handle these serious injuries that require surgery to fix them is all the same.

All this being said, the biggest health risk I see with steroid and HGH use as it relates to baseball isn’t any direct side effect stemming from use, but rather the long-term effects from the increased amount of injuries caused that can be attributed at least in part to steroid or HGH use. As always, it is important to note that your mileage may vary with this statement. In a sport like baseball, it may mean a chronically sore elbow or shoulder caused by overuse. With football, it could be a more damaged brain caused by being hit by a 280 pound player who added on an extra 40 pounds of muscle by taking steroids.

Looking at the issue through that lens I think should give people a different understanding of how the risks of these drugs should be viewed. The conventional argument of “no player should have to choose between using a drug that could potentially cause them harm and playing” can still hold true, but the long-term consequences can be very different. Say what you want about Roger Clemens, but aside from Mike Piazza, I didn’t see him trying to cause bodily harm to any opposing player he came across. For someone like former defensive end and noted steroid user Mark Gastineau, causing physical harm to the opposing player is pretty much in the job description. The amount of physical strength he gained from those drugs was used against his opponents in a way that caused more adverse harm to them than would otherwise exist.

This may not quite be the boogeyman “all drugs can kill you” view that many anti-drug advocates have taken, but it takes a far more honest look at the risks associated with these types of drugs not just on an individual level, but a league-wide one as well. Looking at it through that lens makes the type of sport a lot more relevant to the discussion and I think subsequent punishments.

However strict the MLB and NBA are with regard to steroids and HGH, sports like the NFL and UFC should be far stricter and far more serious about talking about this problem. Regardless of how you feel about steroids in sports morally, I don’t see how anyone using steroids while playing baseball is causing any type of increased health risk for any other player beyond the increased incentive for that other player to use just to keep up with the rest of the pack. I can’t say the same when it comes to combat sports and for that reason I can’t say the issue should be viewed the same across all sports.

The Attitude

At the heart of the media and fan outrage toward the issue of steroids, I think, is what simply boils down to the idea of an otherwise perfectly healthy person taking a potentially dangerous drug purely for the benefit of making themselves a better athlete.

I add in the word “perfectly healthy” because when it comes to players trying to play through injuries and ailments, people tend to be far more understanding. A cortisone steroid for instance can have health effects that are just as adverse as an anabolic steroid, but because cortisone steroids are almost entirely associated with players recovering from injury, their use is tolerated largely without question.

Which is actually worse for your health is tough to say, but it is fascinating to me that the same people who would condemn the use of blood doping by Lance Armstrong having no issue giving praise to the athlete who “gutted it out” through an injury that would have kept most people on the bench. One is viewed as a selfless action, the other selfish, yet aside from the issue of one athlete being perfectly healthy and the other not, the dynamic between doing what’s best for your career or team versus doing what’s best for your health is almost identical.

What seems to bother people more than anything when it comes to PEDs is when athletes are able to obtain results through these drugs that would be impossible to achieve without them.

In all, 52 players have been suspended under the MLB’s adopted drug policy, which began in 2005. Of those 52 players, most fans of the sport could only name a small handful of players who have actually been caught, but even the casual fan will have no issue recalling that Álex Rodríguez was one of the 52 players that have been suspended. When it comes to Bartolo Colón, his ability to provide entertaining at-bats appears to have caused most fans to come down with a case of amnesia.

The amount of outrage a player receives over using PEDs appears to be more correlated to their overall ability and whether or not they are likeable than it is to the act of using itself. It may not be a coincidence that at the same time that David Ortiz is getting more and more attention for being a serious Hall-of-Fame candidate, the overall view towards known PED users seems to have softened somewhat. It certainly hasn’t been lost on me that some of the same writers who have been pounding the table how they would never vote in a person with a PED cloud around them tend to get a bit quieter on the subject matter when it comes to David Ortiz. A 2003 failed drug is now seen as a “minor association” with PEDs.

Is this is a sign of writers softening their stance on the issue, or is it simply the case that a lot of this “outrage” over PEDs is really just an excuse to perform a litmus test on a player’s “likeability”? Each writer will have a different answer to this question, but as a whole the group probably falls somewhere in the middle.

What Should The Hall of Fame Be About?

“Voting shall be based upon the player’s record, playing ability, integrity, sportsmanship, character, and contributions to the team(s) on which the player played.”

That is the official verbiage by which all Hall-of-Fame players should be voted on, yet the phrase itself is incredibly vague and unspecific as far as how much weight should be placed on each characteristic.

If you are to view all of these attributes as being equal though, then a player like a player like Carlos Delgado should have a slam-dunk case for getting into the Hall of Fame. In addition to his three Silver Slugger Awards and his 473 career home runs, and having no PED-related issues; Delgado was also one of the most active players in baseball when it came to charity work, activity trying to increase exposure of the game, while also being a spoken advocate of things like improving education in his native home of Puerto Rico.

On the flip side of the argument though is Roberto Alomar, who spit in umpire John Hirschbeck’s face during an argument over balls and strikes in 1996. It was a disgraceful moment in baseball history for everyone involved, and thankfully it didn’t prevent Alomar from getting into the Hall of Fame, but it was amazing to me how many fans and writers were willing to completely disregard Alomar’s 17-year career over one 30-second incident. Anyone who thought this incident alone was bad enough to warrant being kicked out of the Hall needs to pick up a history book.

I bring up these two players because it offers proof that the whole case with character and integrity is really a one-way street. As honorable as Delgado’s off-field actions were/are my guess is very few voters even know about how extensive Delgado’s charity work is, let alone care enough about it to the point where it could actually influence their vote.

If I were voting it would not influence my vote one bit and in my opinion Delgado is not a Hall-of-Famer, but I’m not someone who also claims to put a great deal of weight on things like “character” and “integrity” when it comes to voting.

I’m not asking for anti-PED voters to vote for Carlos Delgado. What I’m asking is for them to be consistent about what it is they want from a player and not look at things like character as someone thing that can only be a pure negative.

The Hall of Fame doesn’t necessarily need to honor the best players who ever played the game. I would be all for honoring players that weren’t just great on the field, but also people who anyone associated with the game could be proud of, but that would seem to change the Hall-of-Fame worthiness for some of the players who are now in.

A player like Joe DiMaggio who easily has the numbers and the reputation to warrant induction would not be someone I would consider worthy of induction under this new system because of his off-field transgressions and general personality which was not described as all that friendly, to put things mildly, based on numerous accounts.

It’s impossible to talk about baseball in the 1940s without mentioning Joe DiMaggio, and some may feel that his service in WWII should go a ways in overcoming some of his more off-putting attributes, but is DiMaggio’s situation really all that different from someone like Curt Schilling, who is being held out for a lot of the same reasons that DiMaggio was given a free pass on? (i.e. Just not being a nice or good human being.)

These are the unintended consequences of morality run amok. You wind up doing things you don’t agree with or don’t support, but have to do anyway, else risk contradicting yourself and calling into question how strongly held your principles on this really are. The alternative is what I think we have now. A double standard where players like David Ortiz have their PED issues overlooked or brushed under the rug simply because they were a popular player who voters would very much like to get in, while less popular players like Kevin Brown are treated like almost chopped liver because they didn’t get well with the media and weren’t marketed very much during their career.

Some may think putting players like Bonds and Clemens in will damage or even ruin the integrity of the institution itself. That may in fact be true, but the alternative to that is the Hall of Fame loses credibility as an evaluation tool for confirming a player’s greatness. This aspect was already damaged during Frankie Frisch’s reign as head of the Veteran’s Committee. Guys like Chick Hafey and Jesse Haines may be Hall of Famers, but have nowhere near the kinds of numbers you need to have in order to view their enshrinement as a serious endorsement of their greatness.

But Roger Clemens does not need a plaque in Cooperstown in order for people like me to consider him the greatest pitcher of all time, just like Pete Rose doesn’t need a plaque to be considered one of the greatest hitters of all time. But if the Hall of Fame wants to be viewed as the foremost authority on player greatness, the Hall of Fame needs these players to be members way more than those players need the Hall of Fame.

Just speaking for myself, I’ll take a morally ambiguous Hall of Fame that that can be viewed as an authority of player greatness over a morally righteous Hall of Fame that can’t.

Searching For Undervalued Pitchers

When looking to the future, there are countless ways to try and find undervalued pitchers.

One such way is to look at which pitchers’ FIPs outperformed their ERAs last year. This is a good approach, but it isn’t enough. For one, there will be players who consistently underachieve on their metrics, like the ever-teasing Michael Pineda. He sits second on the 2016 leaderboard in ERA-FIP, but his ERA is more than half a point greater than his FIP for his career and over a point greater each of the past two seasons.

The other problem with this approach is that FIP has become mainstream enough that everyone will be doing this same thing. Players who outperformed their FIP will be be common targets on draft day, driving up their prices and eliminating any sleeper potential that they had. This, too, is the downside of projections and other easily accessible data.

A different approach is then needed. In that spirit, I decided to create a linear regression model to predict a subsequent year’s ERA based on the difference in first- and second-half splits from the previous year, as well as that year’s ERA. This would help find the players who improved the most from the beginning of the year to the end, and perhaps players who are likely to carry over those improvements into the next season.

The model was generated using data from 2002 to 2015 obtained from FanGraphs’ splits leaderboard, with only pitchers with at least 50 IP in each half-season being considered so as to remove potential outliers. Non-significant variables were removed, and a final model was created. The resulting model was then used with 2016 data to predict ERA in 2017. The following graph shows those predictions, after being rescaled, plotted against 2016 ERA:

For the most part, the predictions line up with their 2016 counterparts. The labeled data points, though, are the ones I want to focus on. Based on this model, each of them are expected to see their ERA drop significantly from last season to this one and could help provide value in the latter rounds of drafts.

Jeff Samardzija
2016 ERA: 3.81
2017 Projected ERA: 3.40

The Shark has had a rough career. Since becoming a starter in 2012, he’s only had one season in which he’s beaten last year’s mark of a 3.81 ERA. He’s played for four different teams in those five years, he’s on the wrong side of 30 and his name is at the same spot on the pronunciation scale as Jedd Gyorko’s. But he does have a few things going for him. He’s struck out over 20 percent of the batters he’s faced in all but one year since 2011, and he’s pitching in a park where home runs go to die. His average fastball velocity is holding steady above 94mph and it was only two years ago where he had a sub-3 ERA with the estimators to back it up. He’s proven he can put up solid numbers, so the predicted improvement isn’t unreasonable. He had a 3.66 FIP in the second half of 2016 that exactly matched his ERA, a substantial drop from his first half numbers. The biggest contributors were his strikeout rate, which rose from 18.9 to 21.9 percent, and his HR/9, which dropped from 1.15 to 0.94. There’s no reason to think the rates are unsustainable either — his HR/FB dropped to a near-league average (in a normal year) 10.8 percent, and his strikeout rate improved almost directly with an increase in his O-Swing%:

Samardzija was able to get batters to swing at pitches out of the zone more frequently as the season went on, and consequently was able to produce more strikeouts. Steamer projects him for a 3.66 ERA, which isn’t all that far off this model’s prediction. If he can bring his strikeout rate back to what it used to be, and AT&T Park does its job, Samardzija could provide some sneaky value in 2017.

Ivan Nova
2016 ERA: 4.17
2017 Projected ERA: 3.72

Moving to the NL seemingly agreed with the former Yankees second-round pick. After posting an unsightly 4.90 ERA in 21 games (only 15 starts) in pinstripes, he turned his season around in Pittsburgh with a 3.06 ERA and 2.62 FIP in his final 11. Switching leagues undoubtedly helped, but there are more reasons behind his improvement. For one thing, he increased his strikeout rate while decreasing his walk rate — just doing those two would be reason to expect a lower ERA. Perhaps more significant, though, is that he halved his HR/9. Much of this is due to a change in scenery — his HR/FB dropped from 21.3 percent before his trade to just 7.8 percent afterward. Of course, he can’t be expected to repeat his performance. He walked just three batters in 64 2/3 innings, good for a 1.1 percent walk rate and a 17.33 K/BB. While Nova is probably better than Phil Hughes, it’s unlikely that even he can replicate that kind of walk rate. Look for Nova to improve on his ERA from last year, but don’t expect him to be as good as his second half. He’ll fall somewhere in the middle, but even that will be more than useful.

Wily Peralta
2016 ERA: 4.86
2017 Projected ERA: 4.35

Don’t look now (unless you promise to come back), but Peralta had a 2.92 ERA in the second half of 2016. Part of this was admittedly due to an inflated 81.7 percent strand rate, but even accounting for that, he managed a 3.75 FIP and 3.59 xFIP during that stretch. His success can be due largely in part to his increase in strikeout percentage, which jumped from 13.6 percent to 20.8 percent. It’s difficult to determine the exact reason behind this, but one explanation might be his increase in velocity. At the start of the year, his fastball was only averaging under 95 mph, a continuation of his 2015 trend and a disgrace to fireballers everywhere. By August, he was closing in on 97 mph, and presumably striking out batters as a consequence. Here’s his velocity by month since 2014, via BrooksBaseball:

Not only did Peralta see an increase in his strikeout rate, but his walk rate improved as well from 8.7 percent to 6.5 percent, which is the lowest to reasonably expect given his career numbers. His WHIP dropped from 1.88 to 1.15, his HR/9 from 1.64 to 1.02 and his wOBA against from .421 to .295 — seemingly everything improved except his age, but I’ll give him a pass on that account. The secret behind his success? His ability to limit hard-hit balls and induce soft contact. Take a look at the trends for each type of contact rate:

In case that doesn’t do it for you, here’s his Statcast exit velocity broken down by game date, via Baseball Savant (with a linear regression line added for those last few skeptics who aren’t convinced):

Peralta’s not an ace, but he has the potential to help out teams this season. Monitor his velocity during spring training, and buy him for a discount on draft day.

Clay Buchholz
2016 ERA: 4.78
2017 Projected ERA: 4.02

Of all pitchers who threw at least 50 innings in each half of the season, Buchholz improved his FIP the most — his first-half FIP was 6.02, so he gave himself quite an advantage, but he still brought it down to 3.74 following the Midsummer Classic. He’s already proven himself to be a capable pitcher, with four sub-3.50 ERA seasons in his past seven seasons, and now he goes to Philadelphia, where pitchers go to be reborn (see: Hellickson, Jeremy). Also, he moves from the AL East to its NL counterpart. Besides going up against a pitcher instead of a designated hitter, he will be facing the likes of the Braves and Marlins instead of the Blue Jays and Yankees.

Despite the difficulty of his former division, Buchholz still managed to improve as the 2016 season wore on. He marginally increased his strikeout and walk rates, doubling his K-BB% to a still-mediocre 9.3 percent in the second half of the season. While that’s not exactly comforting, it’s worth noting that his walk rate in the first half the season was higher than anything he’s put up since 2008, so it’s not likely to approach that number anytime soon. Furthermore, he was able to bring down his bloated HR/FB rate, despite the league’s general struggle to do so. In the first half of the season, 15.9 percent of Buchholz’s fly balls resulted in home runs, which would have been higher than any single season in his career. In the second half that number improved to 5.1 percent, which was much more reasonable given his average rate of 6.5 percent over the previous three seasons. Steamer projects him for a 4.07 ERA, but it’s not difficult to envision a scenario where he does better than even that.

With all that being said, not all of the pitchers on this list are going to live up to their projections. No model is perfect, and none of these guys have exactly had exemplary careers. But they all showed significant improvement over the course of last year, and that’s a strong indication for what to expect from them in 2017.