Spelunking through the FanGraphs archives as one does, today I ran across Dave Cameron’s 2008 post entitled “The Worst Season in Recent History.” At the time, Dave found that in 2002, Neifi Perez was worth 3.5 wins below replacement for the Kansas City Royals.

Since then, changes to the way that WAR is calculated have revised Perez’s WAR for 2002 to -2.9, opening the door for some other contenders to the throne of worst season in recent history. Taking “recent” to mean the last 25 years, three seasons are virtually tied for that crown. Cristian Guzman in 1999, Jose Guillen in 1997, and David McCarty in 1993 each were worth 3.1 wins below replacement. But Guillen’s season holds a special place in my heart, and I’d like explore it a bit more as we come up on its twentieth anniversary.

The Pirates, having performed one of the more drastic teardowns that the major leagues will see, rushed the 20-year-old Guillen to the majors from A-ball and made him their opening-day right fielder. For the season, he had 526 plate appearances in 143 games, slashing .267/.300/.412. This was good for a wRC+ of 82, as his reasonable batting average and isolated power were vitiated by a 3.2% walk rate. His bat was worth 12.2 runs below average; acceptable from a middle infielder or catcher, not at all good from a corner outfielder.

But Guillen was not just any corner outfielder that year. According to the defensive metrics we have for 1997, he was one of the worst fielders there was, worth 29 fielding runs below average even for a right fielder. Combined with the positional adjustment of -5.7 runs for playing right field, this gave him an eye-popping -34.7 Def rating. This is the sixth-worst of the last 25 years, ahead of such luminaries as Adam Dunn and Brad Hawpe, as well as 36-year-old Dante Bichette and the tragedy that was Ken Griffey Jr.’s last full season in Cincinnati.

Dave’s article about Neifi Perez estimated that his negative value reduced Kansas City’s effective payroll from $47 million to $34 million. In 1997 a win above replacement was not worth as much; according to Lewie Pollis’ estimates, a win in 1997 was worth $1.65 million, giving Guillen a value of negative $5.115 million dollars. But…remember where I mentioned that the 1997 Pirates had performed a drastic teardown? Notoriously, in 1997 their entire opening-day roster was making less money than Albert Belle. According to Baseball Chronology’s numbers, over the course of the season the Pirates increased their total payroll to $15.12 million, but Guillen’s negative value ate up a full third of that.

You may also remember that, despite their scanty payroll, the 1997 “Freakshow” Pirates unexpectedly contended for the division crown late into the season. They finished the season 79-83, five games behind the Houston Astros. If they had been able to replace Guillen with the mythical replacement player, they would have finished 82-80, short of the division crown, but delaying the onset of their streak of losing seasons five more years, and saving long-suffering Pirate fans the brunt of many jokes about a streak that became almost old enough to drink.

If they had replaced Guillen with the players who actually wound up filling in in their outfield, they might well have won the division, as Mark Smith and Turner Ward combined for 2.7 Wins Above Replacement in 414 plate appearances. (Or, well, they could have given more plate appearances to some of the rest of the collection of below-replacement villains you can find at that link. And there’s no reason to think Smith and Ward would have continued to produce that well given more appearances. But let me dream.)

Guillen continued to frustrate despite his evident talent, moving to the Devil Rays, Diamondbacks, and Reds before breaking out for a few decent years at age 27. In all, the decision to promote him from A-ball before his 21st birthday was one of the poorer recent decisions.

Jose Guillen finished seventh in the 1997 NL Rookie of the Year voting. Tied for 11th, with one third-place vote, was Neifi Perez.

It was when I was in sixth grade that I first began to seriously examine baseball.  I made my first annual Top 100 MLB players list that year.  Of course I didn’t know about advanced stats at the time, so Miguel Cabrera was atop that list.  Ironically that was before his Triple Crown.  Brian Kenny had educated me by then, and Trout has been first on every list since.  Anyway, back to the point, I also received the Bill James Historical Abstract that year, and became obsessed with his all-time rankings.  There was his all-time Top 100, and a Top 100 at each position.  Thinking about this the other day, it occurred to me how unusual the second-base rankings were.  Far be it from me to question the Godfather of Sabermetrics, but they seem wrong to me.  Here is the Top 10:

1. Joe Morgan
2. Eddie Collins
3. Rogers Hornsby
4.  Jackie Robinson
5. Craig Biggio
6. Nap Lajoie
7. Ryne Sandberg
8. Charlie Gehringer
9. Rod Carew
10. Roberto Alomar

Again, this seems wrong, but it is Bill James I’m refuting, so some research is probably required.  First, let’s rank the group by career rWAR:

1. Rogers Hornsby 128.7
2. Eddie Collins 122.2
3. Nap Lajoie 104.8
4. Joe Morgan 99.6
5. Charlie Gehringer 79.6
6. Rod Carew 76.7
7. Craig Biggio 65.5
8. Roberto Alomar 65.2
9. Ryne Sandberg 64.2
10. Jackie Robinson 59.4

Career rankings are tricky, because at some point a great peak is better than a long career.  Volume does matter.  Players like Robinson, who played only 10 seasons, suffer in career totals.  Let’s see the players ranked by the total fWAR from their four top seasons.  The group is ranked here by four-year peak:

1. Hornsby 45.6
2. Morgan 38.7
3. Collins 38.0
4. Lajoie 36.4
5. Robinson 33.2
6. Gehringer 30.8
7. Carew 28.7
8. Sandberg 28.1
9. Biggio 26.9
10. Alomar 25.7

That’s nice.  We now know who the best among the group were for their career and for condensed excellence.  However, simply having a long career doesn’t mean a player is the best, nor does having the best brief period of dominance.  Luckily, there’s JAWS.  JAWS is a system used for ranking players that combines career WAR and WAR over a player’s seven-year peak.  It is often used for analysis of Hall of Fame candidacies.  Let’s check out our group when using the JAWS system:

1. Hornsby 100.2
2. Collins 94.1
3. Lajoie 83.8
4. Morgan 79.7
5. Gehringer 65.6
6. Carew 65.4
7. Sandberg 57.2
8. Robinson 56.8
9. Alomar 54.8
10. Biggio 53.4

After seeing these three lists it is evident that only four of the ten are in the running for the title of being the top second baseman of all time:  Collins, Hornsby, Lajoie, and Morgan.  So far all I’ve used to evaluate these players is WAR.  Now, WAR is definitely a great tool, but it is not the only tool.  How about comparing the remaining four players in a few other ways?  Let’s see career wRC+ and Def for starters.

• Collins:  144, 68.3
• Hornsby:  173, 126.5
• Lajoie:  144, 86.3
• Morgan:  135, 14.0

Hornsby is the top-rated player in both wRC+ and Def.  He lead all three lists of WAR metrics.  This doesn’t really look close.  Why then did Bill James have both Morgan and Collins ahead of Hornsby?  He was clearly the best hitter of the three, so then why?  He led both of them in defensive value, so that can’t be why either.  Maybe it’s baserunning?  Let’s check out these three players (sorry Nap Lajoie) in BsR.

• Collins 42.3
• Hornsby -1.8
• Morgan 79.0

Here we go!  Finally, a reason to question Hornsby as the greatest second baseman.  Morgan was first for Bill James, so clearly he believes that the mediocre baserunning of Hornsby and the tremendous baserunning of Morgan makes a huge difference.  Let’s concede hitting to Hornsby, and focus on the two final candidates in just fielding and running the bases.  For their careers the difference in fielding was 112.5 runs, while in baserunning it was 80.8 runs.  Hornsby still wins.  No matter how it is examined, Hornsby always comes out on top.  The greatest second baseman in baseball history is Rogers Hornsby.

Every offseason I do a top 100 MLB players list.  Around the new year is when I start to consider this list seriously, beginning by naming the best player at each position.  Usually, about half of the 10 positions (excluding DH) are close, and the other half are runaways.  This year there is a position that goes beyond even calling it close: third base.

The hot corner currently claims three of the probable top five players in baseball in 2016 NL MVP Kris Bryant, 2015 AL MVP Josh Donaldson, and three-time AL All-Star Manny Machado.  Mike Trout and Clayton Kershaw would of course round out the top five, with players like Mookie Betts and Jose Altuve just missing.  Ranking all of the top players against each other, however, will be discussed in a later article.  For now the focus will stay on the three incredible third basemen.  On the top 100 prior to the 2016 season, Donaldson was the highest-ranked 3B, coming in at #2 overall behind only Trout.  Machado was close behind Donaldson at #9 overall, while Bryant was third at the position in the #18 slot.  But 2016 has now come and gone, and all three of these players had spectacular years.  Now how do they rank?

Let’s start with WAR over the last two seasons, since that’s how long Bryant has been in the league.  For purposes of being fair, we’ll use rWAR.

1. Josh Donaldson 16.3
2. Kris Bryant 14.3
3. Manny Machado 13.5

Well, according to WAR, Donaldson is the clear champion of the position.  He has been worth far more than his competition over the past two seasons.  Just for the record, two players whom I am certain people will try to argue belong with this group in the comments, Adrian Beltre and Nolan Arenado, finish well behind Machado in rWAR.  As useful as WAR is in comparing players, it is not a be-all-and-end-all ranking.  How do the three title players of this article order in OPS+?  This will be the last two seasons as well.

1. Donaldson 151
2. Bryant 142
3. Machado 130

Donaldson wins handily again.  Baseball is about more than just hitting.  How about baserunning?  I’ll rate by XBT% and BsR.

1. Bryant 51% XBT%, 14.4 BsR
2. Donaldson 39%, 4.2
3. Machado 46%, 0.7

Here we go — a list that isn’t topped by Josh Donaldson.  Of course Kris Bryant is a very good baserunner, so this was to be expected.  What’s interesting to me is the edge Donaldson has over Machado despite taking the extra base 7% less of the time.  This can be attributed to Donaldson being on base more often.  Aside from hitting and baserunning, there is defense.  How are these three by the top metrics there?  DRS and UZR/150 should serve this purpose well, again using the past two seasons.

1. Machado 21.0 UZR/150, 27 DRS
2. Donaldson 15.5, 13
3. Bryant 13.1, 7

Bryant is hurt in DRS by his flexibility in positions, but the UZR/150 makes up for that.  Machado is in another world when compared defensively to these competitors.  He is simply incredible on defense.  This, however, does not make up for his being behind both Bryant and Donaldson in hitting and baserunning.

It seems that Donaldson should place first in the position, with Bryant second and Machado third.  One thing is bothering me about this entire analysis, though.  The 2015 and 2016 seasons are being counted as the same in terms of importance.  That should not be.  I’ll re-rank the group by rWAR, weighting 2016 over 2015.  A weight of 1.75:1, or 7:4 in whole numbers.

1. Donaldson 21.88
2. Bryant 20.34
3. Machado 18.50

Well, the order is the same as the original list using WAR, even if the two leaders are much closer.  How about using wRC+?  The weights will remain at 1.75:1.

1. Donaldson 425.3
2. Bryant 396.8
3. Machado 359.8

Donaldson is still the best offensive player.  He still is the best at the position.  One factor is still not being taken into consideration: age.  Donaldson will be in his age-31 season in 2017, meaning he should be entering into a decline.  Bryant will enter his age-25 season, and Machado his age-24.  They should both be improving.  Steamer projections clearly buy into this improvement, at least for Machado, who is projected to have the highest WAR of the three.  Until this actually comes to fruition, however, Bryant’s superior numbers will keep him above Manny Machado.

How to handle the age factor?  In the WAR lists I included, Donaldson’s an average of 10.8% better than Bryant, and he’s 19.5% Machado’s superior.  It seems unlikely that a combined Donaldson decline and Bryant or Machado improvement would make up this gap.  Even if it was more likely, the numbers that have already occurred would take precedence over the numbers that may occur.  Donaldson is still the champion of the hot corner.  The top three third basemen in the MLB right now are:

1. Josh Donaldson, Toronto Blue Jays
2. Kris Bryant, Chicago Cubs
3. Manny Machado, Baltimore Orioles

Despite an influx of information resulting from the advent of Baseball Info Solution’s batted-ball data and the world’s introduction to Statcast, surprisingly little remains known about pitchers’ control over the contact quality that they allow.  Public consensus seems to settle on “some,” yet in a field so hungry for quantitative measures, our inability to come to a concrete conclusion is maddeningly unsatisfying.  In the nearly 20 years since Voros McCracken first proposed the idea that pitchers have no control over the results of batted balls, a tug-of-war has ensued, between those that support Defensive Independent Pitching Statistics (DIPS) and those that staunchly argue that contact quality is a skill that can be measured using ERA.  Although it seems as if the former may prevail, the latter seems resurgent in recent years, as some pitchers have consistently been able to outperform DIPS, hinting at the possibility of an under-appreciated skill.

It is also widely assumed that a hitter’s BABIP will randomly fluctuate during the season, and that changes in this measure often help to explain a prolonged slump or a hot streak at the plate.  Hitters’ BABIPs can also vary drastically from year to year, making it difficult to gauge their true-talent levels.  Research in this field has been done, however, and there have been numerous attempts to develop a predictive model for this statistic, one that projects how a player should have performed, or perhaps more succinctly, his expected BABIP, or xBABIP.  Inspired by the progress, and albeit limited, success of these models, I embarked upon a similar project, instead focusing on the BABIP allowed by pitchers, rather than that produced by batters.  What began as a rather cursory look at exit velocity evolved into a much deeper look, and with this expansion of scope, I achieved some success, though not as much as I had hoped.

My research began with a perusal of Statcast data, and I began to use scatter plots in R to visualize each statistic’s relationship to BABIP.  Most of the plots looked something like this:

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In the majority of plots, it seemed as if there may have been some signal, but there was quite a bit of noise, making it difficult to detect anything of significance.  This perhaps explains the lack of progress in projecting BABIP: after looking at these plots, it appears quite simply difficult to do.  Despite these obvious challenges, I remained hopeful that I could perhaps develop something worthwhile with enough data.  Therefore, I began aggregating information, collecting individual pitcher-seasons from FanGraphs, Baseball Savant, Brooks Baseball, and ESPN, then manipulating and storing the data in a workable format using SQL.  Since Statcast data only became available to the public in 2015, my sample size is unfortunately a bit limited.  I also wanted to incorporate the defense that pitchers had behind them along with park factors when creating my model, so I removed all pitchers that had changed teams mid-season from my records.  This left me with a grand total of 641 pitcher-seasons (323 from 2015, 318 from 2016), and 188 pitchers showed up in both years.  For the remainder of my study, I used the 641 pitcher-seasons to develop the model, but when checking its year-to-year stability and predictive value, I could only use the 188 common data points.

To begin, I fed 29 variables into R: K/9, BB/9, GB%, average exit velocity, average FB/LD exit velocity, average GB exit velocity, the pitcher’s team’s UZR, the pitcher’s home park’s park factor, his Pull/Cent/Oppo and Soft/Med/Hard percentages, and an indicator variable for every PITCHf/x pitch classification.  (Looking back on this, I wish I included more data in my analysis to truly “throw the kitchen sink” at this problem, perhaps including pitch velocity, horizontal and vertical movement, and interaction terms to more accurately represent each individual’s repertoire.  Alas, I plan on keeping this in mind and possibly revisiting the topic, especially as more Statcast data becomes available.)  This resulted in an initial model with an adjusted R-squared of about 0.3; I then ran a backwards stepwise regression with a cutoff p-value of 0.01 to determine which variables were most statistically significant.  Here is the R output:

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For clarity, the formula: xBABIP = -0.157 + 0.005684 * BB/9 + 0.0009797 * GB% + 0.003142 * GB Exit Velocity – 0.0001483 * Team UZR + 0.005751 * LD%

I again obtain an adjusted R-squared of about 0.3, and I don’t find any of these results to be overly surprising, but to be fair, I had little idea of what to expect.  Before examining the accuracy of my entire model, I checked each variable’s individual relationship to BABIP, along with the year-to-year stability of each.  These can be found below in pairs:

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I was most perplexed by the statistical significance of BB/9, and even after completing my research, I still find no entirely compelling explanation for its inclusion.  Typically, BB/9 is considered a measure of control rather than command, but intuitively, these skills seem to be linked, and perhaps pitchers with better command and control are able to paint edges more effectively, thus avoiding the barrel and preventing strong contact.  I was disappointed that its relationship to BABIP appeared so weak, but because of its relative year-to-year stability, I hoped that it would retain some predictive power.

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Previous research has indicated that ground-ball hitters are able to sustain higher-than-average BABIPs, and thus, its inclusion in my model should not come as a shock.  Again, it would have been nice to see a stronger correlation between GB% and BABIP, but there is obviously quite a bit of noise.  However, it does seem that generating ground balls is a repeatable skill, which lends itself nicely to the long-term predictive nature of an xBABIP model.

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Again, as previous research has suggested, the inclusion of GB exit velocity is to be expected.  However, its correlation with BABIP is not as high as I would have hoped; I suspect this may be a result of the unfair nature of ground balls.  In a vacuum, one would expect that low exit velocities are always superior, yet a fortunately-placed chopper may actually have better results than a well-struck ground ball hit right at a fielder, and thus, exit velocity’s signal may be dampened.  There does appear to be some year-to-year correlation though, which offers some promise of an unappreciated skill.

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Here, I’m surprised by the lack of correlation between UZR and BABIP; I collected this data to control for the quality of the defense behind a pitcher, assuming that this could be a pretty significant factor, and although it did remain in my model, the relationship appears to be quite weak.  We should expect a very low year-to-year correlation between UZR, as pitchers that changed teams in the offseason were included in my study, and even if they remained on the same roster, teams’ defensive makeups can change drastically from one season to the next.  Thus, the latter graph is rather useless, but I chose to include it for consistency.

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Unsurprisingly, LD% has the strongest relationship to BABIP, checking in with an R-squared of about 0.15.  I obviously wish that there were a stronger correlation between the two, yet despite the noise, when looking at the data, I think it is fairly evident that there is a signal.  And although I have read that LD% fluctuates wildly from year to year, I was shocked by the latter graph.  It seems as if this is entirely random, and that this portion of a pitcher’s batted-ball profile can be simply chalked up to luck.  This revelation is a bit discouraging, as it suggests that my model may struggle with predictive power, since its most significant variable is almost entirely unpredictable.

I anticipated that more variables would be statistically significant, and I am surprised by their disappearance from the model.  I assumed that Hard% would be highly correlated with BABIP, but it disappeared from my formula rather quickly.  I also assumed that pitchers who generated a high true IFFB% would exhibit suppressed BABIPs, but nothing turned up in the data.  And finally, I thought that K/9 may have been significant; it can be considered a rough estimate of a pitcher’s “stuff,” and I speculated that pitchers with high K/9 probably throw pitches with more movement than usual, perhaps making them harder to square up, but my model found nothing.

After considering each of the significant variables individually, I wanted to examine the overall accuracy of my entire model.  To do so, I plotted pitchers’ xBABIPs vs. their actual BABIPs, along with the difference:

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As mentioned earlier, after incorporating all of the statistically significant variables in my model, I achieve an R-squared of about 0.3, a result that I find satisfying.  I obviously wish that my model could have done a better job explaining some of the variation in the data, and I suspect my model could be improved, although I have no idea by how much.  There is an inherent amount of luck involved in BABIP, and it is entirely plausible that pitching and defense can in fact account for only 30% of the observed variance, and the rest can only be explained by chance.  Despite the lower-than-desired R-squared, I do believe it still verifies the validity of my model, if only for determining which pitchers over- or under-performed their peripherals, saying nothing about why they did so or if they can be expected to do so again in the future.  The lack of correlation in the difference plot indicates that pitchers have been unable to systematically over- or under-perform their xBABIP from year to year, and along with the residual plot, suggests that my model is relatively unbiased and doesn’t appear to miss any other variables that obviously contribute to BABIP.

After determining that my metric had some value in a retrospective sense, I set out to determine whether it had any predictive power.  Because of the lack of year-to-year correlation for most of the statistically significant variables included in the model, I was quite pessimistic, although still hopeful.  I first checked the year-to-year stability of both BABIP and xBABIP:

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It seems that both measures are almost entirely random, although xBABIP is perhaps just a bit more stable from season to season.  Despite this, comparing 2015 BABIP to 2016 xBABIP revealed that, as expected, my model holds little to no predictive power:

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Again, although disappointing, this result was to be expected, as the most powerful variable in my model, LD%, fluctuates wildly.  Despite this lack of predictive power, I stand by my model’s validity when considering past performance, and as more data accumulates, perhaps it can be adopted in a stronger predictive form.

Even after concluding that my metric has little predictive value, I thought it would be interesting to look at some of the biggest outliers.  2015’s biggest under- and over-achievers (with their 2016 seasons included as well), along with 2016’s luckiest and unluckiest pitchers can be found below:

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Although the model holds no predictive power after quantitative analysis, anecdotally, it appears to do a decent job.  Each of the 10 pitchers featured as an over- or under-achiever in 2015 saw the absolute value of their difference fall in 2016 (although the sign did change in some cases); in no way am I suggesting that the model is predictive, I just find this to be an odd quirk.  I also find it perplexing that George Kontos appears an over-achiever in both years and can think of no explanation for this.  Along with outperforming xBABIP, his ERA has also beaten FIP and xFIP in each of the last two seasons and five of the last six, suggesting a wonderful streak of luck, or perhaps hinting that the peripheral metrics are missing something.

Ultimately, although it would have been nice to draw stronger conclusions from my research, I am mostly satisfied with the results.  When developing his own model for hitter BABIP, Alex Chamberlain achieved an R-squared of about 0.4 when examining the correlation between BABIP and xBABIP, the highest I have found.  However, his model included speed score, a seemingly crucial variable that I was unable to account for when analyzing pitcher’s BABIPs.  With this in mind, I find an R-squared of 0.3 for my model entirely reasonable, and despite its lack of predictive power, I consider it to be a worthy endeavor.  As the sample size grows and more Statcast data is released, I plan to revisit my formula in coming offseasons, perhaps refining and improving it.

In baseball analysis, we often use comparable players or “comps” to discuss what we think the player is likely to do in the future. Prospects are the most comped players because the general baseball public does not know much about minor leaguers. Comparing these young players to major leaguers allows fans to imagine what these prospects could someday become. Comps are also often used in projection systems. Data analysis has found that similar players often perform similarly throughout their careers. Thus, using former players who compare well with current players aids projection systems in forecasting what a particular player is likely to do in the coming years. Comparable players are also used in contract negotiations and arbitration battles. Players at similar ages with similar careers can expect to get roughly the same contract. In fact, the arbitration process is almost solely interested in comparing similar players and their wages.

Sometimes, players aren’t viewed as being similar when in reality they are actually quite alike. Recently, I found that Julio Teheran and Jose Quintana top each other’s similarity score lists on Baseball Reference. I had usually thought of Quintana as one of the game’s best pitchers and a true ace, while Teheran was at least a rung below that and probably more of a number 2 or 3 starter, so I did some research and found that these two pitchers are more alike than many probably realize.

Both pitchers are from Colombia and they were actually born only miles apart. Colombian-born baseball players are actually quite rare as there have only been 19 such players in MLB history, and this includes at least one set of brothers and a set of cousins. In fact, just this past season Teheran and Quintana became the first Colombian-born pitchers to ever start against each other in the same game. The two are apparently also quite good friends off the field and even work out together in the offseason. They each have also decided that they will pitch for Colombia in the upcoming World Baseball Classic. That will make for a formidable 1-2 punch for the Colombian pitching staff and will be hard for any other team in the tournament to match up against.

These two pitchers also match up quite well statistically, as their numbers look quite similar in a multitude of categories.

You might be able to find two pitchers with more similar numbers, but it wouldn’t be easy. They were both virtually 5-win pitchers according to Baseball-Reference, and the difference there likely comes from Quintana throwing a few more innings than Teheran. Their ERA, FIP, and xFIP are all almost identical and they both achieved their numbers in similar ways, too. Neither pitcher allows many baserunners, and they both strike out about eight batters per nine innings. In 2016, they both also had nearly identical ground-ball rates, and they suppressed homers to the same degree. Both pitchers had incredible seasons in 2016 and were both deserving All Stars, and while Jose Quintana did have a slightly better year and has been the better pitcher for the past several years, Julio Teheran has considerably closed the gap on his fellow statesman.

After seeing how closely the two pitchers’ 2016 stats aligned, I wanted to see how closely their styles of pitching matched up as well. While the approaches are not quite as similar as the statistics, you can see by the pitching styles how the stats could end up so similar. Using PITCHf/x data from Brooksbaseball.com I found that the biggest similarity in their repertoires is their four-seam fastballs. They both rely heavily on this pitch while throwing them about as hard and with similar amounts of movement.

These fastballs are not particularly special for two pitchers with such pedigree. They are each thrown with just average velocity and with roughly an average amount of downward and horizontal movement. They produce roughly the same amount of ground balls as the average pitcher and miss about as many bats as the average fastball. The most unique aspect of either of these pitchers’ fastballs is that Jose Quintana induces an exorbitant amount of pop-ups, which are basically as good as a strikeout. This allows his otherwise average fastball to play up better than the average starter.

After the four-seamer, their repertoires begin to deviate quite a bit. Quintana relies heavily on his sinker and his curveball as secondaries and mixes in a changeup occasionally. He throws his sinker just as hard as his four-seamer, but he gets more movement from the sinker. Julio Teheran uses his slider as his main secondary, throwing it over 26 percent of the time, while he mixes in a sinker, a changeup, and a curveball as his tertiary offerings. His slider is a plus pitch and he uses it to miss bats, while the other pitches are basically used as change-of-pace offerings to keep hitters off of his fastball and slider combination. Both of these guys get by with just average or better stuff, but command of their arsenal coupled with their mastery of the art of pitching have made them two of the upper-echelon pitching talents in the game.

It would only make sense that two players this similar would have similar contracts, but these contracts go way past similar — they are borderline identical. They are each under team control for the next four years. Teheran will make $37,300,000 and Quintana will make just a few hundred thousand more at $37,850,000, assuming that their respective option years are picked up, which is a pretty safe bet. Their yearly salaries are basically identical as well:

Neither player’s salary ever deviates more than just a few hundred thousand dollars in any year under these current contracts. It only makes sense that two players with so many similarities would be compensated so similarly, but should they actually be valued the same?

Probably not; while they did have virtually the same season statistically this year, Quintana’s track record for this level of success is longer. Teheran does also have a successful track record, but he did struggle in 2015, and Quintana just seems to be the surer bet at this point. Steamer projects Quintana to be worth over a win more than Teheran in 2017. However, I do believe that their values should be a great deal closer than public perception. Teheran is two years younger than Quintana and could just be hitting his prime, he is signed to the same contract as Quintana, and his stuff may actually be better. Quintana is currently being aggressively shopped and the asking price is said to be roughly the same as the Chris Sale package. Julio Teheran is not worth that kind of package, but it might be closer than you think.

The Seattle Mariners finished second in the AL West with an 86-76 record. With a strong offense — they scored the sixth-most runs per game during 2016, led by Robinson Cano, Kyle Seager, and Nelson Cruz — the Mariners starting pitching lagged behind. With fans hoping for a bounce-back performance from Felix Hernandez, the King waned further, seeing an increase in ERA, FIP, and walk rate with decreasing number of strikeouts, first-pitch strikes, and swinging strikes. Hernandez was worth only 1 win above replacement, and at 30, it is unlikely the King will ever become the dominant pitcher he once was.

Despite logging only 121 innings, James Paxton pitched well, leading to a 3.5 fWAR, the highest among all Mariner pitchers. Paxton has always shown some upside, having strung along a 3.43 ERA and 3.32 FIP in 50 starts across four seasons. The 28-year-old has struggled to remain healthy, having only pitched 286 innings since 2013. Throughout the 2016 season, Paxton showed his best form.

Paxton averaged the highest fastball velocity for left-handed pitchers at 96.7 MPH. It was almost 3 MPH faster than the lefty ranked second, Robbie Ray. Among pitchers with 100 innings pitched, Paxton had the fifth-best FIP-, 17^th-best SIERA, and 21^st-best strikeout-minus-walk percentage. Furthermore, Paxton threw strikes. This was evident in his first-pitch-strike rate — 62.4% — and with the Mariners pitcher posting an elite 4.7% walk rate. Throughout the season, Paxton was unlucky, with a .347 BABIP and a strand rate hovering close to 66%. Paxton’s average exit velocity on line drives + fly balls was slightly above average. Couple that information in with a Deserved Run Average (DRA) of 3.09, and it is fair to say Paxton pitched fairly well and should have an impressive 2017 campaign.

One of the reasons for Paxton’s success? He changed his release points:

James Paxton Release Point Changes

In addition, Paxton’s cutter became one of his main pitches. Having reluctantly thrown it in years past, Paxton’s cutter was his second-most-used pitch and was quite effective. Among pitchers who threw 200 cutters, Paxton’s had the best whiffs per swing rate. Batters kept swinging, and they kept missing. It also boasted the lowest wOBA allowed in his arsenal.

James Paxton Cutter Vertical Movement

The big change in Paxton’s cutter, aside from the 1-mile increase in velocity: less rise (In 2014, Brooks Baseball classified the cutter as a slider). As the season wore on, Paxton also got more rise in his fastballs, leading to a greater induction of pop-ups. Paxton’s curveball was second in velocity among left-handed pitchers who threw at least 200. It featured an above-average ground-ball rate and swinging-strike rate.

Paxton showed significant growth during the 2016 season. With Felix Hernandez unlikely to return to his previous form, Paxton has the tools and ability to become the Mariners’ ace. The key for him will be to stay healthy in a pivotal season for both the Mariners and the 28-year-old pitcher.

If you paid any attention at all to the 2016 Giants, you noticed that the bullpen was pretty terrible. When the game was on the line – when all the cards were on the table – the bullpen came in and ruined everything. Need I remind anyone of NLDS Game 4 against the Chicago Cubs? I didn’t think so.

Anyway, that’s old news. The Giants did something about this problem, inking closer Mark Melancon to a four-year, $62MM contract on December 5.

Some in the baseball industry think that the contract is risky. There are two main reasons: first, Melancon relies heavily on limiting home runs, and was helped by playing half his games at PNC Park in Pittsburgh, an extreme pitcher’s park. Indeed, his HR/FB ratio over the last four years (5.9%) has been much better than the league average (10.1%), and if it regresses, Melancon is in trouble. The obvious counterargument is that Melancon is moving move from one of the most pitcher-friendly ballparks in baseball (PNC Park) to the most pitcher-friendly ballpark in baseball (AT&T).

The second knock on Melancon is that his strikeout rate is just mediocre. This makes him risky because if he suddenly starts walking people or losing command within the strike zone, there’s no buffer of dominant stuff to fall back on to sustain the success he’s had for most of his career. Before delving into that success, it’s worth understanding where the Giants are coming from.

Somehow, the Giants bullpen wasn’t dead last in Win Probability Added (WPA) last year. They were 10th-worst in baseball at -0.01. The bullpen essentially broke even in terms of increasing or decreasing the team’s chances of winning.

For example, if a starter went six-plus innings, leaving the game with two on and one out in the 7th with a team win probability of 80 percent, the Giants bullpen (as a whole, for the entire season) sustained those odds. Of course, in reality, things don’t quite play out that way in individual games, since the odds at the end of a game are always 100 or 0 percent. Essentially, they blew some games and they saved some games. Compared to other teams in baseball, the Giants were significantly worse. They were the only playoff team with a negative bullpen WPA. When the dust all settled, the bullpen was pretty bad, both in and out of context, and the breakeven WPA reflects that.

Enter Mark Melancon. Over the last four seasons, no relief pitcher has a better WPA. He’s put up 13.25 WPA in 290 innings. While WPA isn’t necessarily a sustainable skill, it’s hard to argue that the following players lucked their way onto the top 10 WPA leaderboard among relief pitchers since 2013: Melancon (13.25), Zach Britton (12.97), Andrew Miller (10.94), Wade Davis (10.41), Tony Watson (10.31), Craig Kimbrel (9.32), Aroldis Chapman (9.21), Dellin Betances (9.00), Kenley Jansen (8.98), and Joaquin Benoit (8.92). Those are some of the very best relievers in the game.

Notice that Melancon is way ahead of Britton, and way, way ahead of everybody else. Melancon’s stellar WPA basically means that, since 2013, he’s been the best reliever in baseball at increasing his team’s chances of winning. That seems significant.

On a broader scope, Melancon has been among the best relievers in the game in other key areas:

Relative to his peers, Melancon has pitched a ton of innings, been among the best in baseball at preventing runs, limited baserunners extremely well, and induced plenty of soft contact. While he may not be the most dominant relief pitcher out there, the results speak for themselves, and the Giants are clearly expecting those results to continue.

Melancon will remain in an extreme pitcher’s park. He’s a ground-ball guy who has a tendency to allow weak contact, and he will have an excellent infield defense behind him. He has a track record of success (albeit not the kind that’s always sustainable).

The Giants seem to covet pitchers like Melancon who induce weak contact, instead of guys who routinely strike out 10+ batters per nine. Johnny Cueto is like that. Matt Cain was like that. Those two perfectly illustrate the risk and reward with players of their statistical profile.

Cueto took a step forward in what was already a brilliant career when he moved to the wide open spaces of AT&T Park with stellar infield defense behind him. Matt Cain, however, lost the control that enabled him to be so successful early in his career, and his ability to induce weak contact and limit home runs disappeared, and he suddenly became one of the worst pitchers in baseball.

Any large commitment to a baseball player is risky. Melancon is arguably a type of pitcher who comes with some added risk. Despite it, Melancon has a tremendous track record, will play in a great ballpark for his skill-set, and will be helped by San Francisco’s superior infield defense. There are no sure things in baseball, but continued success for Melancon is well within the realm of possibility, and it’s exactly what the Giants expect and need.

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony La Russa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

Assessment

The 1985 Montreal Expos 

OWAR: 55.8     OWS: 320     OPW%: .556     (90-72)

AWAR: 37.5      AWS: 252     APW%: .522     (84-77)

WARdiff: 18.3                        WSdiff: 68  

The “Original” 1985 Expos claimed the National League Eastern division title with a 90-victory campaign, outpacing the Mets by five games. Tim “Rock” Raines swiped 70 bases in 79 attempts, registered 115 runs, batted .320 and set a career-high with 13 triples. Gary “Kid” Carter (.281/32/100) established personal-bests in home runs and placed sixth in the NL MVP balloting. Tim Wallach clubbed 36 doubles and merited the first of three Gold Glove Awards at the hot corner. Andre “The Hawk” Dawson swatted 23 big-flies and knocked in 91 baserunners. Vance Law ripped 30 two-base hits for the “Actuals”.

Gary Carter (catcher) and Tim Raines (left field) ranked eight at their respective positions in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” Expos teammates chronicled in the “NBJHBA” top 100 ratings include Andre Dawson (19^th-RF), Tim Wallach (27^th-3B), Andres Galarraga (42^nd-1B), Larry Parrish (53^rd-3B) and Tony Phillips (66^th-RF). “Actuals” first baseman Dan Driessen ranked seventy-eighth while third-sacker Hubie Brooks placed eighty-ninth.

  Original 1985 Expos                                  Actual 1985 Expos

Bob James locked down the late innings for Montreal, saving 32 contests with a 2.13 ERA and a 1.027 WHIP in 69 appearances. Shane Rawley fashioned a 13-8 record with a 3.31 ERA at the top of the rotation. Fellow portsider Joe Hesketh posted a 2.49 ERA to complement a 10-5 mark during his rookie campaign. Bryn Smith (18-5, 2.91) paced the “Actuals” in wins and WHIP (1.052). Tim Burke (9-4, 2.39) and Jeff Reardon (3.18, 41 SV) anchored the “Actuals” bullpen.

  Original 1985 Expos                                Actual 1985 Expos 

 Notable Transactions

Gary Carter 

December 10, 1984: Traded by the Montreal Expos to the New York Mets for Hubie Brooks, Mike Fitzgerald, Herm Winningham and Floyd Youmans. 

Bob James 

June 10, 1982: Sent to the Detroit Tigers by the Montreal Expos as part of a conditional deal.

May 4, 1983: Returned by the Detroit Tigers to the Montreal Expos as part of a conditional deal.

December 7, 1984: Traded by the Montreal Expos to the Chicago White Sox for Vance Law.

Tony Bernazard

December 12, 1980: Traded by the Montreal Expos to the Chicago White Sox for Rich Wortham.

June 15, 1983: Traded by the Chicago White Sox to the Seattle Mariners for Julio Cruz.

December 7, 1983: Traded by the Seattle Mariners to the Cleveland Indians for Jack Perconte and Gorman Thomas.

Shane Rawley

May 27, 1977: the Montreal Expos sent Shane Rawley and Angel Torres to the Cincinnati Reds to complete an earlier deal made on May 21, 1977. May 21, 1977: The Montreal Expos sent players to be named later to the Cincinnati Reds for Santo Alcala.

December 9, 1977: Traded by the Cincinnati Reds to the Seattle Mariners for Dave Collins.

April 1, 1982: Traded by the Seattle Mariners to the New York Yankees for a player to be named later, Bill Caudill and Gene Nelson. The New York Yankees sent Bobby Brown (April 6, 1982) to the Seattle Mariners to complete the trade.

June 30, 1984: Traded by the New York Yankees to the Philadelphia Phillies for Marty Bystrom and Keith Hughes.

Honorable Mention

The 2008 Washington Nationals 

OWAR: 37.2     OWS: 243     OPW%: .500     (81-81)

AWAR: 18.3      AWS: 177     APW%: .366     (59-102)

WARdiff: 18.9                        WSdiff: 64  

The “Original” 2008 Nationals played .500 ball and finished fourth in the division. The “Actuals” dreadful results placed them 22 games off the “Originals” pace. Grady Sizemore (.268/33/90) produced a 30-30 season, successfully stealing 38 bags in 43 attempts while eclipsing the century mark in runs scored for the fourth straight season. Left fielder Jason Bay (.286/31/101) tallied 111 runs and drilled 35 doubles. Vladimir Guerrero (.303/27/91) topped the .300 mark for the 12^th consecutive year and supplied 31 two-base knocks. Milton Bradley (.321/22/77) clubbed 32 doubles, paced the circuit with a .436 OBP and merited his lone All-Star appearance. Orlando Cabrera contributed 33 two-baggers while double-play partner Brandon Phillips blasted 21 dingers and pilfered 23 bases. Cliff P. Lee (22-3, 2.54) achieved Cy Young honors and led the League in ERA. Armando Galarraga (13-7, 3.73) finished fourth in the Rookie of the Year balloting.

On Deck

What Might Have Been – The “Original” 2008 Mariners

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive