Going back to January 2016, Dave Cameron wrote an article detailing the breakdown of money owed to Chris Davis over the life of the deal he signed last year. For myself, this provided insight into how teams value long-term contracts, but more importantly it led me to more questions about how money depreciates over time. Fast-forward to the present and we start to see some articles and comments with people speculating about how much money teams are going to throw at Bryce Harper when he reaches free agency in a few years. The numbers have been pretty incredible; $400 million? $500 million? Even $600 million? Then someone threw out an even larger number: $750 million.

The best thing to do is ignore these numbers because we are still a couple of years away from free agency and he just had a down year where he was “only” worth 3.5 WAR, which gave the team a value of $27.8 million. At some point the numbers don’t even make sense because the contract values are getting so inflated. But at the same time, good for him, maybe he’ll buy a baseball team once he retires, or a mega-yacht. But unfortunately we will need to wait until after the 2018 season before we find out the value of this contract. In the meantime, speculation will run rampant and the media will throw out inflated numbers for the amusement of the masses.

Now, the purpose of this article is not to predict the value of Bryce Harper’s future contract, but to examine a few scenarios as to the actual value in present-day dollars. To do this I will use the concept of Net Present Value (NPV) from Dave Cameron’s Chris Davis article and then use some of the numbers from his article predicting a contract for Bryce Harper. Let’s set a couple rules; (1) Match the length of contract given to Stanton — 13 years, (2) use nice round numbers and get as close to the total values as possible, (3) use a discount rate of 4%, (4) this is an exercise in futility and not to be taken too seriously and finally (5) to estimate NPV for a massive contract.

Here are the scenarios for a 13-year contract totaling in excess of $400M, $500M and $600M.

Over the life of this contract, the value of each in NPV is significantly less than the actual amount signed. That’s because $5 today won’t buy you as much five years down the road. To get a little more numerical, 13 years from now currency will lose ~40% of its value. Quoting the Chris Davis article again, the league and the MLBPA have agreed to use a 4% discount rate to calculate present-day values of long-term contracts. Since important people within the industry take this into account, that’s likely why we don’t see too many contracts with a significant amount of deferred money.

Since players are taking — and I use this term very lightly — a “hit” when they sign a long-term deal, I wondered what kind of contract structure would benefit a player the most. Again, I wanted to use nice round numbers, so I settled on a 10-year, $100M contract, looking at an equal payment structure, a front-loaded contract, and a back-loaded contract. Here’s what I came up with:

There’s not a huge difference, but a player would gain just over $5M by signing a front-loaded contract as compared to a back-loaded contract. It seems as though the agents and the MLBPA are more concerned about total dollars rather than NPV since they probably want to drive up total contracts.

And in case you’re wondering what those annual salaries would look like in NPV from the table above, I’ve created another table to show what those salaries actually look like in NPV over the life of our hypothetical 10-year contract.

What I was hoping to show you next was a cool interactive plot similar to the table above, but instead of showing you the annual salaries it will show cumulative earnings as the life of our 10-year/$100M contract as time progresses. Well unfortunately I am unable to get this plot to show up on this webpage; it has something to do with WordPress being unable to use Javascript. If you’ll bear with me, you can click the link below (it just opens a new window and shows the plot).

https://docs.google.com/spreadsheets/d/19qGcrwGmdZemmYG_LaP_Ay6_5g6hL3VKT8z-Q3-PWXI/pubchart?oid=422413074&format=interactive
Front-loaded contracts seem to have the most benefit to the players themselves since they actually get more value out of any long-term contracts they might sign. For a player to maximize their career earnings it looks like it would be way more beneficial to sign shorter-length contracts with higher AAV than those long-term contracts. Maybe that is why we are beginning to see more deals with opt-out clauses in them.

There are many things to harp on about traditional ERA, but one thing that has always bothered me is the inherited-runner portion of the base ERA calculation. Why do we treat it in such a binary fashion? Shouldn’t the pitcher who allowed the run shoulder some of the accountability?

As a Nationals fan, the seminal example of the fallacy of this calculation was Game 2 of the 2014 Division Series against the Giants. Jordan Zimmermann had completely dominated all day, and after a borderline ball-four call, Matt Williams replaced him with Drew Storen, who entered the game with a runner on first and two outs in the top of the 9^th and the Nats clinging to a one-run lead. Storen proceeded to give up a single to Buster Posey and a double to Pablo Sandoval to tie the game, but he escaped the inning when Posey was thrown out at the plate. So taking a look at the box score, Zimmermann, who allowed an innocent two-out walk, takes the ERA hit and is accountable for the run, while Storen, who was responsible for a lion’s share of the damage, gets completely off the hook. That doesn’t seem fair to me!

I’ve seen other statistics target other flawed elements of ERA (park factors, defense), but RE24 is the closest thing I’ve found to a more context-based approach to relief pitcher evaluation. RE24 calculates the change in run expectancy over the course of a single at-bat, so it’s applicable beyond relief pitchers and pitchers in general, and is an excellent way to determine how impactful a player is on the overall outcome of the game. But at the same time, it does not tackle the notion of assignment, but simply the change in probability based on a given situation.

wERA is an attempt to retain the positive components of ERA (assignment, interpretability), but do so in a fashion that better represents a pitcher’s true role in allowing the run.

The calculation works in the exact same way as traditional ERA, but assigns inherited runs based on the probability that run will score based on the position of the runner and the number of outs at the start of the at-bat when a relief pitcher enters the game. These probabilities were calculated using every outcome from the 2016 season where inherited runners were involved.

Concretely, here is a chart showing the probability, and thus the run responsibility, in each possible situation. So in the top example – if there’s a runner on 3^rd and no one out when the RP enters the game, the replaced pitcher is assigned 0.72 of the run, and the pitcher who inherits the situation is assigned 0.28 of the run. On the flip side, if the relief pitcher enters the game with two outs and a runner on first, they will be assigned 0.89 of the run, since it is primarily the relief pitcher’s fault the runner scored.

Let’s take a look at the 2016 season, and see which starting and relief pitchers would be least and most affected by this version of the ERA calculation (note: only showing starters with at least 100 IP, and relievers with over 30 IP).

The Diamondbacks starting pitchers had a rough year this year, but they were not helped out by their bullpen. Patrick Corbin would shave off almost 10 runs and over half a run in season-long ERA using the wERA calculation over the traditional ERA calculation.

On the relief-pitcher side the ERA figures shift much more severely.

Cam Bedrosian had by normal standards an amazing year with an ERA of just 1.12. Factoring inherited runs scored, his ERA jumps up over two runs to a still solid 3.18, but clearly he was the “beneficiary” of the traditional ERA calculation. So to be concrete about the wERA calculation – it is saying that Bedrosian was responsible for an additional 9.22 runs this season stemming directly from his “contribution” of the runners who he inherited that ultimately scored.

The below graph shows relief pitcher wERA vs. traditional ERA in scatter-plot form. The blue line shows the slope of the relationship of the Regular ERA vs wERA, and the black line shows a perfectly linear relationship. It’s clear that the result of this new ERA is an overall increase to RP ERA, albeit to varying degrees based on individual pitcher performance.

While I believe this represents an improvement over traditional ERA, there are two flaws in this approach:

• In complete opposite fashion compared to traditional ERA, wERA disproportionately “harms” relief pitcher ERA, because they enter games in situations that starters do not which are more likely to cause a run to be allocated against them.
• This does not factor in pitchers who allow runners to advance, but don’t allow that runner to reach base or score. Essentially a pitcher could leave a situation worse off than he started, but not be negatively impacted.

The possible solution to both of these would be to employ a similar calculation to RE24 and calculate both RP and SP expected vs. actual runs based on these calculations. This would lose the nature of run assignment to a degree, but would be a more unbiased way to evaluate how much better or worse a pitcher is compared to expectation. I will attempt to refactor this code to perform those calculations over the holidays this year.

All analysis was performed using the incredible pitchRx package within R, and the code can be found at the Github page below.

Baseball/wERA.R

Canadians! They walk among us, only revealing themselves when they say something like “out” or “sorry” or “I killed and field-dressed my first moose when I was six.” But we don’t get to hear baseball players talk that often, so how can we tell if a baseball player is Canadian? Generally there are three warning signs:

1. They have a vaguely French-sounding last name
2. They have been pursued by the Toronto Blue Jays¹
3. They bat left-handed and throw right-handed

¹ I honestly thought Travis d’Arnaud was Canadian until just now

Wait, hold on. What’s up with that third one? This merits a bit of investigation.
Read the rest of this entry »

“Make baseball fun again” is Bryce Harper‘s outcry against baseball fundamentalists who continue to police emotions and enforce baseball’s expressionless professionalism.  “Shut up and play the game right” might be something you’d hear uttered from the fundamentalist’s side — ideally through tobacco-glazed teeth — and maybe by Brian McCann.  The discourse is of course more involved than that, covering everything from retaliatory plunk balls to bat flips, and anytime something marginally inflammatory happens, it’s beaten so hard that we’re reminded how boring our lives are that we have to discuss the same things over and over and over.  I know you can picture the media package that accompanies the discourse: a young, brash, exquisitely coiffed, generational talent, who was hit in the ribs in his first ever plate appearance (then proceeded to steal home), is unabashedly passionate about a “fun” revolution in baseball.  His eye black is adorned like war paint; he has emojis on the bottom of his bats; his helmet never stays on his head when he runs the bases; and yes, he “pimps” his home runs.  Cut to Joey Bats‘ ALDS bat flip and the ensuing brawl and then connect it with Rougned Odor’s haymaker; cut to Brian McCann standing at home plate waiting for Jose Fernandez after his first career home run; then enter the commentator: “Is this wrong?”

While baseball’s moral code on gaining an edge is unpredictable, there’s always been the idea that individuals conform to the game, not the other way around.  Harper’s sermon won’t shatter the code of conduct, but it might move the needle, if it hasn’t already.  For example, I can’t think of a standout incident this season because of a bat flip.  That’s good! Because bat flips are really fun!  There’s really no need to overthink it.  There were plenty of memorable bat flips this year, and in an effort to make some fun out of baseball when there is no baseball being played, I’m breaking out my bat flip tracking equipment (a ruler, a stop watch, and a parabolic trajectory calculator) that I introduced last year, and booting up BatCast for a look back at the year’s most memorable wood-chucking moments.

A brief recap: arriving at these numbers is a sloppy and wildly imprecise affair.  I pull videos, gifs, and stills of a bat flip and start by measuring the height of the player as he appears on my screen.  I convert that measurement into the player’s real-life size and reference this ratio, as well as measurements on the baseball field, and rough estimates, to arrive at some of the data I present to you in meters and feet: initial height, apex, and distance.  Using a stopwatch or the time stamp on YouTube, I can declare a fairly accurate hang time of the bat.  Angles are roughly noted using the batter and the ground to form a 90-degree angle and are adjusted in the parabolic trajectory calculator.

Let’s kick this off:

Exhibit A – The one that’s probably at the forefront of your mind:

Le Flip

How about in slow motion?

Ejaculatory!

How many of his teammates do you think saw that flip?  They may have seen the tail end of it, but I’m willing to bet zero saw the flip in its entirety because everyone in the dugout was gazing at the ball in flight.  But this was a no-doubter.  Edubray Ramos resigned to the outcome likely before the ball had reached its apex.  The Phillies weren’t playing for anything at this point, but the Mets?  Before this pitch, the Mets were tied with the Giants and Cardinals for the top wild-card spot.  Before this pitch, in the 9th inning, Jose Reyes erased a two-run deficit with a home run of his own, only to see that lead given up again when Jeurys Familia and Jim Henderson allowed two runs to score in the top of the 11th.  After this pitch, this game ended and they had a half-game lead on any team in the National League for the first wild-card spot.  That bat flip is a team effort.  There’s some “I did it” in there, but the way he looks towards the dugout and offers his bat up towards his teammates makes this feel like “We did it!”

The numbers:

Cabrera is listed as 6′ tall.  On the freeze frame I measured, he’s 1.9″ tall.  So our key tells us that 1″ on the screen is 37.9″ in real life.  When he releases the bat, he does so from about shoulder height and we’ll call 5′ (1.52 m) in real life.  The acme is, it appears, not a great deal lower than the top of Asdrubal’s head, so we’ll tally that down at 5′-7″ (1.71 m).  To me, the launch angle looks to be right around 30 degrees, and we’ll refine this number once we get them in the parabolic trajectory calculator.  The duration of flight I’m using is the average number I’ve come up with through timing the video 10 times — 0.79 seconds.

Parabolic Trajectory Calculator:

Exhibit B – A Man Possessed:

If this picture was part of an emotional intelligence quiz, I’m sure the answers given as to what facial expression is being displayed would vary greatly.  To accurately assess the information in this picture it may behoove one to understand that, in baseball, home teams wear white and that the man in the background is most likely a fan of the home team and that his hands are held high in jubilation.  If you’re only looking at the horrifying ogre in the foreground who appears to be screaming at 67 Hz+, the pitch only a dog can hear, you’d be hard-pressed to say that is a happy man.  In fact, he may not be happy yet — he’s likely evoking a form of relief, having just exorcised the demons one faces when up to bat in the 16th inning of a tie baseball game; he looks like pure adrenaline.  Most of us don’t get to experience a moment like this in our lifetime so we don’t have a really strong reference point for what he’s feeling, but luckily you know what this article is about and there’s a gif:

PUMP! PUMP! PUMP IT UP!

That’s all lizard brain right there.  It’s a little undignified, but that’s the beauty of it.  Matt Adams is a dense, hulking man, and that makes it a little scarier and a little sillier.  Look:

Sassy.

The numbers:

This one is especially hard to measure because of Adams’ primitive (yet graceful) movements.  I extracted these numbers using the still image and the video:

Exhibit C – Into the Batosphere

Yoenis Cespedes made it into my BatCast segment last year with his nifty flip in the NLDS.  This flip follows a similar trajectory but he varies his look this time with a cross-bodied toss.  It’s rude:

“Hold my drink, bitch.”

While the lesson here is obvious, the mistake is not as easily avoided: get the fastball ball UP and in on Cespedes.

Because of the evidence we have, the numbers for this bat flip will be even more rough than the others — by the way, I hope you’re not a mathematician, and I apologize if you are.  The data we can gather is the launch angle and at what time stamp the bat reaches it’s highest point.  Here’s a better view of the angle:

Can we agree on shoulder height for the initial launch height to make things easier?   Let’s call it 5′ since Cespedes is 5′-10″.  We’ll say the bat was launched at a 70-degree angle and in the gif the bat appears to reach it’s apex at just before 0.4 seconds.

Exhibit D – The “I probably didn’t even need this bat to hit this home run” flip

After my long-winded intro it’s fitting to get to feature Bryce Harper in this piece.  He probably didn’t have as much fun this year as he did in 2015, but he appears to have gotten some enjoyment out of this shot.

Correct me if I’m wrong, but I believe that is what the kids call “Swagadoscious.”  I’ll just get right to the point this time.

Those are the ones that stuck out to me as the best flips of the year and I hope you were able to move past the rough estimates and get some enjoyment out of that as well.  I should note that Joc Pederson’s bat flip in the NLDS is omitted because I cannot find substantial evidence of an acme or distance.  And while a lefty going across his body like he did is pretty exotic, the uncertainty he exudes, combined with his panicked sashay, makes this effort pretty uncool.

(Scherzer looks super imposed here)

So what can we pretend to glean from this?  Based on WPA, it’s probably not surprising that Harper had the most disproportionate bat flip.  Looking at the Statcast data, Harper’s home run was also the “weakest” out of the group.  So I guess even if Bryce Harper says what he says just so he can get away with being a little douchey, he’s holding up his part of the deal.  Of course, bat flips aren’t what make baseball fun.  Baseball is fun because we can see so much of our own lives in the game — it’s the humanity.  It provokes endless curiosity and it will reward you if you know where to look.  It’s the only game that can end, not because of time, but with one swing, and flip, of the bat.

Don’t be afraid to clue me in to bat flips in the future — my Twitter handle is in my bio (below).

In the spirit of awards season, I decided to take a look at the BBWAA decisions of the past couple decades and, my goodness, I could not believe my eyes when seeing some of the down-ballot vote-getters. Middle relievers, players who didn’t even play long enough to make it out of arbitration, below-average corner outfielders, you name it. I could not help but put some of these names in writing to maybe strike a little nostalgia into some curious baseball fans.

Brad Hawpe, Colorado Rockies, 2007, 2009

Mr. Hawpe shows up on a ballot in TWO different years. I haven’t heard this name since 2012. Hawpe last played for the Angels in 2013 and posted a .185 slugging percentage in 32 plate appearances. He never received another contract. His two ‘MVP caliber’ years were eerily similar. Hawpe is the prototypical product of Coors Field. Although he didn’t have too different of numbers outside of Coors Field as a Rockie, he completely tanked once he got out of their organization. If you are from some other planet and don’t believe that Coors Field has any benefit for the hitter, then Hawpe’s offensive numbers were outstanding. He posted an on-base percentage above .380 in both years and hit over 20 home runs in both as well. He did all of that while still maintaining a solid batting average. The problem with Hawpe, and most likely a huge reason why he didn’t get more chances in the majors, was how god-awful his defense was. Sandwiched between his 2007 and 2009 seasons, he posted the worst defensive season in the league according to fWAR. If he would have been merely a below-average corner outfielder, or even first baseman, there is a chance Hawpe could’ve resurrected his career and maybe would still be playing today.

Scott Eyre, San Francisco Giants, 2005

Growing up a Giants fan, this name is familiar to me. Yet 99% of other baseball fans might need to do some thinking before they can figure out who he was, let alone realize that he actually received an MVP vote once. In 2005, Scott Eyre became the first-ever relief pitcher to receive an MVP vote without recording a save. He was outstanding. He posted a 2.63 ERA in 63.1 innings, appearing in 86 games. Now, 2.63 may not be too sexy for a middle relief pitcher nowadays, but 2005 was still feeling the effects of the steroid era. It is hard to believe a middle relief pitcher playing on the 2005 Giants got enough attention to receive a vote. The only thing bringing any attention to those 2005-2007 Giants teams were the controversies surrounding Barry Bonds. Trust me, I lived through it. Sadly, this was by far Eyre’s best year in the majors. He posted a couple semi-solid years before and after his 2005 season, but was all but out of the league by his 37th birthday.

Nate McLouth, Pittsburgh Pirates, 2008

Probably the most recognizable name on this list, Nate McLouth. McLouth had a weird career. He posted a couple stand-out season with the Pirates, toiled with an almost career-ending stint with the Braves, had a a solid comeback season with the Orioles in 2013, then was out of the league after the 2014 season. That 2008 season, though. If it weren’t for his almost-terrible defense he would’ve received several top-five votes. He went 26-23, had over 200 combined runs and RBIs, had a .356 OBP, and was one of the best baserunners in the league. It would’ve helped if he played for a better team too. Mr. McLouth is one of the few on this list I’d argue deserved a few more votes than he actually got.

Antonio Alfonseca, Florida Marlins, 2000

I’ll admit I had to look this one up. Alfonseca received one tenth-place vote way back when in 2000. If he had the same stat line in 2016, he might have a hard time keeping a job, but 2000 was a different time. He posted a 4.24 ERA in 70 innings, which was right in line with his 4.16 FIP. He only struck out six per nine but he tallied a whopping 45 saves, which I assume was the kicker for him nabbing that tenth-place vote. Alfonseca, surprisingly, was a perfectly viable middle reliever throughout the steroid era. Oddly enough, his 2000 season was probably his third or fourth-best season. Although he never came close to topping his 45-save number. Long gone are the days of average closing pitchers with high save totals receiving MVP votes.

Travis Fryman, Cleveland Indians, 2000

How I have never heard about this guy before this exercise is beyond me. He had a great career! Over 30 career WAR. Sadly for Travis he played through the steroid era and his skillset was completely overlooked, or else he may have seen a few more MVP votes. A slick-fielding third baseman with a solid walk rate was underappreciated in the years before Moneyball and modern defensive metrics. I’d describe Fryman as the very poor man’s Adrian Beltre. His 200o season was very Beltre-esque. He hit 22 bombs while sporting a .321 BA and a .392 OBP with solid defensive numbers, a type of season that gets overlooked when you think about the absurd numbers being put up around the league around the turn of the millennium. Unfortunately for Mr. Fryman, he was born 20 years too early, or else he would be heralded as one of the top three-baggers in the league and would’ve been in for one or two hefty paydays.

Bob Wickman, Cleveland Indians, 2005

Don’t get me wrong, while big Bobby Wickman is an easy player to overlook, he had an outstanding career. He recorded 13.7 WAR over his 15-year career, outstanding for a relief pitcher. He notched a career-high 45 saves in his 2005 season. What is so unbelievable about that particular season was that it was by far his worst season of his career. He was worth -0.3 WAR. And yet he received an MVP vote. You can make an argument he has had two or three different seasons where he warranted an MVP vote! But he never had the gaudy save total that he did in 2005. That along with the Indians’ solid 93-win season and Wickman takes some of the credit despite being worse than their best AAA pitcher. Maybe this was some kind of career achievement award for an underappreciated closing pitcher.

Who’s going to be 2016’s Antonio Alfonseca? My guess is Wilson Ramos, but that might be cheating with his season-ending injury already in the books. All in all, it is pretty amazing the types of names you can come up with just by looking at the historical results of baseball’s most prestigious award.

The best relief pitcher in baseball is not who you think he is. Most of you probably would not even include him in the top 10. If I were to take a poll on who is the best relief pitcher in baseball, the top voted would likely be Zach Britton, Dellin Betances, Aroldis Chapman, Kenley Jansen, and Andrew Miller. I will say that it is none of them. To illustrate my point, I will compare this mystery pitcher’s numbers to all of their numbers. Nothing too scary, just xFIP, K/9, and ERA. I also will not just tell you which pitcher produced which numbers. Where would be the fun in that? I will compare the numbers of all six pitchers and walk you, the reader, through determining which one is the best.

Pitcher A: 1.18 xFIP; 14.89 K/9; 1.45 ERA
Pitcher B: 1.92; 13.97; 1.55
Pitcher C: 1.17; 16.84; 1.16
Pitcher D: 1.75; 15.53; 3.08
Pitcher E: 2.41; 13.63; 1.83
Pitcher F: 2.09; 9.94; 0.54

At first glance, Pitcher F’s ERA of 0.54 is likely what stands out most. Alas, even calling him only by a letter cannot mask Britton. He has the lowest K/9 by far and the second-highest xFIP, so Britton is effectively taken out of consideration.

Pitcher D has an ERA over a run higher than any of the others. His K/9 and xFIP fit in the range but do not stand out. Thus, Dellin Betances is out as well.

Of the remaining four, Pitcher E rates the worst in each of the three categories. Goodbye, Kenley Jansen.

That leaves us with Pitcher A, Pitcher B, and Pitcher C. In this group, B is the worst across the board. Aroldis Chapman leaves the conversation.

Pitcher C is better than Pitcher A in all three statistics. Andrew Miller bows and exits.

Carter Capps stands victorious.

Yes, I know Capps did not pitch in 2016. I used his 2015 numbers. They stack up just as well against the elite relievers from that year as well. It is true that Capps pitched only 31 innings in 2015, but the stats I used are rates. Maybe a larger sample would have dragged him into mediocrity, but I doubt it. Capps was ahead of the field by such a large margin that even with regression in his 2017 return he would be #1.

I am crazy for saying Carter Capps is the best relief pitcher in baseball. Or am I, really? If Capps pitches as well in 2017 as he did in 2015, just over a larger sample, I believe many of you will agree with me. Some of you may even agree with me after reading this.

So, let me be the first to say it: Carter Capps is the real best relief pitcher in baseball.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

THE CURSE OF THE LCS DEFEAT

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

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

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

LOOKING FORWARD

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

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

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

A popular theme this preseason was parity.  Truth be told, it’s been quite popular since the 2014 preseason projections forecast the smallest disparity between the best and worst teams at least going back to 2005.  Since then, the term has been so worn out that BuzzFeed included it on their end-of-the-year list of “words that need to be stricken from the Saber community” (source needed).

While the AL was the main driver of parity-related conversation, it might be worth mentioning that the results show that the AL was more lopsided than it was in 2015 while the NL’s gap was more compressed compared to the previous season despite the existence of the Chicago Cubs World Champion Chicago Cubs.  It’s not that that’s incredible — projection systems are conservative and variables such as sequencing and luck are still unpredictable.  Reflections of these points can be seen in Texas’ record in one-run games, or the Phillies and Braves performing better than they expected, or the Twins performing more like the Phillies and Braves were expected to.

It’s possibly reasonable to think that, as front offices skew more towards advanced analytics, the trend of increased parity will continue.  Of course that’s too simple of a statement as revenue sharing and luxury-tax measures have played their part in balancing out the competitive environment as well.  But as front offices progress it’s more likely that the true-talent level at the major-league level will span a smaller range, fewer and fewer at-bats will go to poor players, and the top players should be more evenly distributed throughout the league, speaking in terms of true talent.

This article, however, is not really about anything based in analytics or reality and I don’t know how to segue from my intro into delivering to you what I set out to do any better than asking you to assume some truly ridiculous prerequisites:

1. MLB and the owners of all the teams only care about the viewer’s experience
2. Unpredictable variables have become somewhat predictable. This includes some luck, breakouts, injuries, and rapid declines or dips based on smaller injuries.  This does not mean, however, that Runs and RBIs are predictable; it just works out perfectly by FanGraphs WAR
3. The public is unaware of the predictability of baseball and there is an Illuminati-type presence in baseball headed by a board of trustees that includes, ironically, but obviously, Joe Morgan
4. Payrolls are dictated by the outcomes that MLB knows will happen and are strictly performance-based – by FanGraphs WAR
5. Rosters are reconstructed every single year
6. Reconstructing rosters has no effect on luck or sequencing or ballpark effects (maybe all ballparks have the same dimensions)
7. The DH is in both leagues but is only reserved for a portion of games throughout the year; teams are required to allocate at least 140 PA to pitchers
8. Dave Stewart somehow managed to mess up his last season as the Diamondbacks GM (They just happened to be the last team I constructed and there wasn’t enough WAR left to make them as good as the other teams — the Cubs got dinged by this, too.)

What I did was export all the data I felt was relevant from the leaderboards and build 30 rosters based on the average number of Plate Appearances, Games Started, Innings Pitched, and WAR.  The numbers for the league break down like this:

Offense

PA (Non Pitchers): 179,218 (5,974.93/team)

WAR (Non Pitchers): 572 (19.07/team)

PA (Pitchers): 5,366 (178.87/team)

WAR (Pitchers): -2.6 (-0.09/team)

Pitching

GS: 4856 (161.87/team)

IP: 43306.3 (1443.54/team)

WAR: 429.5 (14.32/team)

The only other things I wanted to be consistent with reality were the distribution of plate appearances by position and accounting for the IP by position players.  The first caveat doesn’t work out perfectly, but you’re not going to find a team that received 1,500 PA from their catchers and only 900 from all three outfield positions combined.  The second one, however, I believe I perfected.

After I had built the 30 rosters I realized they were only distinguished by a roster number, so in order to assign each roster a team, I simply took an alphabetical list of the team names and went down one by one with a random number generator and matched that team and random number to the roster with the corresponding number.

Here’s a link!

Who was on your favorite team?  Considering the public doesn’t know about the basically flawless projection systems, how did your team do compared to how you thought they would do? How much would this affect the way you watch the game?  How much would this affect your team loyalty?  Would you enjoy this?  Is this the dumbest exercise you’ve ever seen?  Is Joe Morgan a genius for complaining about the lack of dynasties while he secretly pulled strings to get all teams to be perfectly balanced, competitively, thereby creating a socialist baseball regime?

I’ll do this again when the 2017 rosters and projections are set so we can follow up on “equal” roster construction.

I’ve watched many a baseball game over my lifetime (that’s 50+ years), and I’ve cringed every time I see a National League manager send his starting pitcher up to bat any time prior to the seventh inning. Especially with runners on base! Doesn’t he know that pitchers can’t hit? Doesn’t he know that if he would just pinch-hit for the lame-batting starter he’d improve his team’s chances of winning?

So, after years of pondering this problem for five seconds at a time every couple of days, I decided to see if I could build a solid quantitative case for never letting a pitcher come to the plate for a National League team (obviously this is not an issue for the American League with their designated hitters). How would this change the look of the team’s pitching staff? And more importantly, how many more games would a team expect to win in a season if they adopted a “pitchers never bat” strategy?

The answer to the first question is pretty easy. The staff would “look” different. There were would be no more “starting pitchers.” A team’s pitching staff would consist only of “relievers.” Sure, one of the “relievers” would throw the first pitch of the game and could technically be called a “starter,” but given that he’ll be taken out of the game as soon as his spot in the batting line-up comes up, he’s effectively a “reliever,” just like the other 10 or 11 guys on the staff.

Now, the conventional wisdom would say that the current starting pitchers, especially the “aces,” get in a groove, and can give you six or seven solid innings. Why would anyone take them out the game in the second or third inning? Well, let’s do a “cost-benefit” analysis and see if we can make a case for “The Pitchers Never Bat” strategy.

Key Components of the Case:

The two primary components of the analysis are 1) how many more runs would a team expect to score in a season by pinch-hitting for every pitcher, and 2) how many more runs would a team expect to give up in a season because their starting pitchers are no longer going six, seven, or more innings in an outing? Or, maybe the team adopting such a strategy would actually give up FEWER runs per year by giving up on the century-old strategy of planning for the starting pitcher to pitch deep into the game.

A third component of the analysis could include the benefit of being able to choose from any of the team’s entire staff (probably 11 or 12 pitchers) and use only the ones that look like they’ve got their “stuff” while warming up before the game, instead of sticking with the “starter” who is scheduled to pitch today because it’s his turn in the “rotation.”

A fourth component of the analysis could include the benefit a team could achieve because the other team can no longer stack their starting batting order with a lot of lefties (to face a right-handed starter), or with lot of righties (to face a left-handed starter), because the team with no “starters” will pinch-hit for their first pitcher after one, two, or three innings. So, in total, the “handedness battle” tilts slightly more in favor of the team implementing the new strategy.

A fifth component could include the cost (or benefit) of reducing the size of the pitching staff by one or two, and adding one or two more everyday players, who would be needed to pinch-hit in the early innings.

A sixth component could be an added benefit that batters will not be able to get “used to” a pitcher by seeing them multiple times in a single game. Under the new strategy batters will see each pitcher once, or, at most, twice in a game.

I’m going to focus on the two primary components above, and let the lessor components alone for now. Perhaps others can weigh in on how to quantify the potential impacts of these changes.

Component #1: How much more offense will the “Pitchers Never Bat” strategy create?

This is the easiest of the components to quantify. I will use the wOBA (weighted On Base Average) statistic as defined and measured by FanGraphs to evaluate this component. Let’s start with some basic information and rules-of-thumb.

Using data from the National League for the 2015 season I find that pinch-hitters have a wOBA of .275 across the entire league, while pitchers, when batting, had a wOBA of just .148 across the entire league. The difference in wOBA between pinch-hitters and pitchers is .127 (that’s .275 minus .148.) Note that all position players in the NL combined for an average wOBA of .318 in 2015. I’m assuming that our new pinch-hitters won’t get anywhere near that figure, but will be comparable to the 2015 pinch-hitters, who came in way lower, at .275.

Now, let’s assume we can replace every pitcher’s plate appearance (PA) with a pinch-hitter. This improvement of .127 in wOBA needs to be applied 336 times per season, because that was the average number of times that a National League team sent their pitchers up to the plate in 2015. And lastly, we need to know two rules of thumb from FanGraphs that are needed to complete the analysis of the first component: 1) every additional 20 points in wOBA is expected to result in an additional 10 runs per 600 plate appearances, and 2) every 10 additional runs a team expects to score in season translates into one additional win per year. OK – so, let’s do the math:

If 20 additional points of wOBA translates into 10 runs per 600 PA, then our new pinch-hitters who are now batting for pitchers will provide the team with 63.5 incremental runs per 600 PA (which equals 127/20 * 10.) And since these pinch-hitters will be coming to the plate 336 times, not 600 times, we need to reduce the 63.5 incremental runs per season down to 35.6 incremental runs per season (which is 336 / 600 * 63.5).

Finally, the last step is to take our 35.6 incremental runs per season and translate that into incremental wins per year using the rule-of-thumb that ten runs equates to one win. Therefore, our 35.6 extra runs results in an expected 3.6 incremental wins per year. That’s a decent-sized pick-up in expected wins.

OK, so now, what about the pitching staff? Will replacing the conventional pitching staff with a staff consisting of no starters and all relievers cause the runs allowed to increase, and if so, by how much? Enough to offset our 3.6 extra wins that we just picked up on offense?

Component #2: How many more runs will pitchers give up using the “Pitchers Never Bat” strategy?

Imagine, for the moment, that a GM is to build his pitching staff from scratch. (We’ll worry about how to transition from a conventional staff to an all-reliever staff later.) And let’s just assume he’ll pick just 11 pitchers. (Most NL teams use 12-man staffs while some use 13, so that will give the team one or two additional position players.) Currently, starting pitchers typically throw 160-200 innings per season, and relievers tend to throw 50-80 innings per season. But with the new all-reliever strategy, and using only 11 pitchers, each of our new guys will need to average around 130 innings each, with perhaps some pitching as much as 160, and some as low as 100 innings per year. So, the GM is looking for 11 guys who can each contribute 100-160 innings per season. Each outing will be for about one to three innings for each pitcher. How will they fare?

Let’s look at the National League’s pitchers for 2015. Starting pitchers had an aggregate WHIP (Walks Plus Hits per Inning Pitched) of 1.299, while relievers, in total, recorded an identical WHIP of 1.299. So my takeaway from this is that the average starter was equally as good (or bad) as the average reliever. From this, I am going to take a leap of faith, and assume that a staff of 11 new-style relievers could be expected to perform equivalently. (And that doesn’t even factor in some of the lesser elements of the new strategy, as mentioned above, such as Components 3 and 4 of the analysis.)

From this, albeit simplified, evaluation of Component #2, I estimate that a team moving to an all-reliever pitching staff will have an expected change in Runs Allowed of zero, and therefore the change will neither offset, nor supplement, the offensive benefit evaluated in Component #1.

Conclusion and Final Thoughts

In summary, using the two primary components of my analysis, I estimate that adopting a “Pitchers Never Bat” strategy in the National League (a.k.a. an “All Reliever Pitching Staff” strategy) will improve a team’s offense by an expected 36 runs per year, which will increase the team’s expected win total by 3.6 games. I estimate that the impact on runs allowed will be near zero. Some lesser elements, Components #3 through #6, could also add some additional value to the strategy.

Implementing the strategy does not necessarily need to be a complete, 100% adoption of the “pitchers never bat” rule. Modifications can be made. Perhaps a pitcher is doing well through two innings and comes to bat with two out and no one on base. In this case the manager could let the pitcher bat, so that he can stay in and pitch another two or three innings. This would change the name of the strategy to something like the “Pitchers Very, Very Rarely Bat” strategy.

As far as transitioning to an all-reliever staff from a conventional staff, it could be done over time, or only in part, such that a team could maintain, say, its two top aces, and complement them with eight or nine relievers. This way, the aces could pitch as they do now, going six-plus innings, every fifth day, while limiting the “Pitchers Never Bat” strategy to the three out of the five days when the two starters are resting.

Finally, let’s try to put a dollar value on this new strategy. The guys at FanGraphs, and other places, have tried to estimate how much teams are willing to pay for each additional win. Without going into all the various estimates and approaches at trying to answer that question, let’s just go with a simple $8 million per win. I’m sure it could be argued to be more or less, but let’s just put $8 million out there as a base case. If that’s true, a 3.6-win strategy, such as the “Pitchers Never Bat” strategy, is worth about $29 million per year. Go ahead and implement the strategy now, and, if it takes, say, three years before any of the other NL teams catch on, you’ve just picked up a cool $87 million (3 * 29 million).

And if the other components of the analysis (#3 through #6) are quantified and it can be determined that they add another 0.5 wins per year, which I think is quite doable, then we can get the total up to 4.1 wins per year, for a value of $33 million per year, or just around a cool $100 million over the first three years. And that’s how you make $100 million without really trying!

Imagine for a second that MLB Commissioner Rob Manfred has gone senile. I know that’s a ridiculous premise, and this is sure to be a ridiculous post, but bear with me. Commissioner Manfred, perhaps after a long night of choice MLB-sponsored adult beverages, has placed the Minnesota Twins in the playoffs. Yes, the same Twins of the .364 win percentage and facial hair promotional days. What is the probability that they make or win the World Series? For simplicity, let’s say they take the place of both AL Wild Card teams and are just inserted into the divisional playoffs.

We are going to look at a bunch of ways of estimating the probability the Twins win a five-game series or a seven-game series, then multiply our results accordingly to find an estimate for the team reaching each round. We’ll start simply, and gradually progress to more complicated methods of estimation. Let’s start as simply as possible, then, and use the Twins’ .364 win percentage.  The probability of the Twins winning a five-game series (at least three out of five games) is 25.7%. The same process gives them a 22.4% chance of winning a seven-game series. Multiplying these out gives the Twins a 5.8% chance of reaching the World Series (roughly 1 in 17) and a 1.3% chance of winning it. For reference, those are nearly the same odds FanGraphs gave the Mets of reaching/winning the World Series on October 2nd. Of course, those Mets also had to get through the Wild Card round (and the greatest frat boy to ever pitch a playoff game), but failed to do so.

Okay, so maybe you didn’t like that method because we included the Twins’ entire regular season, instead of just including games against playoff teams. Noted, but just understand that the Twins had basically the same win percentage against playoff teams (.365) as their overall percentage. Just to note, I defined playoff teams as the six division winners plus the four wild card teams. Using the Twins’ percentage against playoff teams yields identical probabilities as above.

How else can we attack this problem? Well, the Twins played 162 games this year, which means they have 158 different five-game stretches and 156 seven-game stretches. Over all those five-game rolling “series”, the Twins won at least three games 24.1% of the time, and they won at least four games in 25% of their seven-game tilts. Multiplying those figures out gives them a 6% chance of reaching the World Series and a 1.5% chance of becoming world champs.

Again, those numbers are unsatisfying because they include all teams, not just the playoff teams. However, removing the non-playoff teams leaves us with a bit of a sample issue because they played 52 games against playoff teams. So, let’s change the problem slightly: what is the probability that a last-place team can reach, and win, the World Series? The teams I’ll be considering all finished in last in their respective divisions: Twins, Athletics, Rays, Braves, Reds, and Padres. Cumulatively, these teams had a win percentage of .412, won 37.4% of their games against playoff teams, won at least three games in 30.6% of their five-game stretches, and won at least four out of seven 29.9% of the time. You can multiply these percentages out and get some answers.

I’m still not satisfied, so there is one more tool I’m gonna break out: a bootstrap simulation. Bootstrapping basically means sampling with replacement, which means every time I randomly choose a game from the sample, that game is thrown back in and has the same exact chance of getting picked again. This resampling with replacement process gives the bootstrap some pretty useful properties that I won’t get into here, but you can check here for more info.

I’m going to put all the games the last-place teams played against playoff teams into a pile. I’m going to randomly sample five games from that pile, with replacement, and count how many games were wins. I’m going to do this 100,000 times. I will then divide the number of samples that included at least three wins by the total number of samples, giving me an estimated probability of these last-place teams winning a five-game series against a playoff team. I will repeat this process for a seven-game series.

The bootstrap probability of a last-place team winning a five-game series against a playoff team was 27%. The probability of them winning a seven-game series was 24%. They have a 6.5% chance of reaching the World Series and 1.6% chance of winning it.

Honestly, these probabilities are lower than I expected. I have believed in and learned to embrace the randomness of the MLB postseason. I went into this post expecting the outcome to highlight just how random the postseason really is, even absurdly so. However, the randomness of the postseason really depends on the extremely small differences between all the teams at the top, so inserting teams from the very bottom of the league introduces a level of certainty that would be new to the playoffs. However, imagine repeating a similar exercise for the NFL or NBA. The 27% or so chance I’d give the Twins of advancing seems much higher than the probability of, say, the Cleveland Browns winning a playoff game if inserted into the postseason.

My methodology was clearly very simple, but intentionally so. I gave no acknowledgement to a home-field advantage adjustment, and I looked only at the team’s W-L record. A more complex method could have taken into consideration Pythagorean Expectation or BaseRuns.

This was a ridiculous post and ultimately a meaningless exercise. The Twins probably couldn’t reach the World Series if they were placed in the playoffs, but I’ll point out that as of this writing (October 10th during Game 3 of Nationals-Dodgers) the Cubs also probably won’t reach the World Series. Baseball is a weird and wonderful sport, and the postseason is the weirdest and most wonderful time of the year. If the Twins could conceivably reach the World Series as currently constructed, don’t think too hard about what’s happening and just enjoy.