Evaluating the Myth of Postseason Experience

– The K Zone –

October 1st, 2018

Image result for walker buehler

Evaluating the Myth of Postseason Experience, by Ian Joffe

We’ve all heard MLB commentators, especially the old-school ones, complain about a team’s postseason chances because its players lack experience. The idea is that younger or less experienced players (I’ve heard both versions) are more anxious in the postseason, and are therefore more likely to choke on the grand stage. It’s undeniable that there are major psychological effects going on in the postseason. From Clemens to Kershaw (arguably), there are some players who just seem overwhelmed by the bright lights, and no matter how good their regular season was, they collapse when it matters. The questionable part, though, is if this correlates with age or experience. There may just be some players who, no matter their age, cannot figure out the playoffs, or players that may actually improve in October once given a chance to get used to it.

As a 17-year-old, I have always argued in favor of the youth. I don’t think there’s anything about being younger that makes one choke under pressure. A lot of that judgement is based on stereotypes that have little or no basis in hard evidence. Furthermore, one could just as easily put together an argument that youth should be better in the postseason because their energy can match the hype. That’s my problem with a lot of psychological arguments: it’s easy to make one up that will go either way, and they usually take the path more traveled by – that is, they tend to be prone to confirmation bias of stereotypes that most people already believe. Once again, there’s no denying that psychology is a science and that mastery of it can provide a tremendous advantage in sports. But, if it is a science, there must be scientifically gathered evidence for any conclusion to be valid. So that’s what I set out to do; this is my quest for evidence for the myth of postseason experience.

There are four buckets that I looked into to see if experience or age could impact postseason performance: experience for hitters, experience for pitchers, age for hitters, and age for pitchers. According to my numbers, all of which comes from the incredible Lahman Database and I spliced up using Python, 1470 batters have played in the postseason since 2000, which is far back as my data goes. That makes 42,369 plate appearances, or, a pretty good sample. Of those PA’s, 13,997 occurred during a player’s first postseason. The other 28,372 took place during some other nth postseason. The total wOBA (an explanation of which is linked here) of the batters playing in their first postseason was .316. The total wOBA of players in later series is .314. It appears solely based on these numbers that there are no advantages to having experience, and that batters are basically the same in their first and later postseason series. However, the average age (adjusted for their number of PA’s) of batters in their first postseason is 27.6 years old, while the average age of players in later series is 31.4. Here is the basic wOBA aging curve, which I got from fangraphs:

aging_curve_wrcp.jpgThe graphic actually splits up the curve by time period in modern baseball history, which is helpful, but my main point is that a 31-year-old is expected to have a wRC+ about 10 points lower than a 28-year-old. That translates to a 10% difference in wOBA, meaning if we adjust the 28-year-old to 31-year-old status, the 28-year-old actually has a wOBA around .284, significantly lower than that of the 31-year old. So, that would suggest that actually, experience plays a large role in postseason performance.

Let’s look at a progression now, rather than just a player’s first postseason vs. later postseason. For the sake of sample size, I grouped players by sets of two series of postseason experience, so there’s a group with 0-2 series, a group with 2-4 series, and groups all the way up to 9+ series. Here are all the wOBA numbers from each individual:figure_1-1.png

On the x-axis is years of experience, and on the y-axis is wOBA. Each dot is an individual in one series, and the red line is the average wOBA (weighted, of course for their number of PA’s). There are obviously a lot of outliers and a lot small samples there, so let’s zoom in on the red line.


Once again, it appears there is a strong possibility that experience helps players in the postseason. There are two potential competing forces that make up this almost parabolic progression. The aging curve (which we know is a factor) pushes a player’s wOBA down, while experience (which we don’t know is a factor – that’s what’s being tested) may push a player’s wOBA up. Based on the graph, it looks like the power of the aging curve starts out more influential than experience, but after a player’s 6th postseason series or so, they reach a threshold where that pattern reverses. Suddenly, it appears that experience matters so much that it reverses aging in the postseason, which is actually supposed to accelerate as one gets older. Don’t underestimate the gravity of that conclusion; according to it, the power of postseason experience can, at some point, reverse natural aging in the batter’s box. So, from the data we have collected on hitters, it seems that experience may actually be a notable factor in postseason performance.

Now let’s look at pitchers. My data holds 10,276.1 total innings from 652 pitchers. A whole 8160 of those occurred in that pitcher’s first postseason, while the other 2,116.1 innings took place in later postseasons. The FIP of pitchers in their first postseson is 3.91. That number goes down in later postseasons, to 3.82. Age, obviously, went in the other direction, up from 28.7 to 33.8. Here’s the pitcher aging curve, from . Focus on FIP, as that’s the stat that I will use.


FIP increases by about 10% between those two ages, adjusting the 3.82 all the way up to 4.30, which is not even close to 3.82. This analysis, like the one with hitters, suggests that pitchers do get better with postseason experience. We can look at it progressively, too. I didn’t make the graph with all the individual dots this time, as it didn’t really show us anything last time.


This looks similar to the graph for batters, but the threshold at which the benefits of experience take over the drawbacks of old age seems to be even earlier, around the pitcher’s 4th series. From this data, it seems like both hitters and pitchers are positively effected by postseason experience.

So, based on all that, it appears there is strong evidence that experience is a factor in the postseason. Now let’s look at age. Keeping in mind the regular season aging curves that have already been presented (treat those like a control group), this is how hitters and pitchers progressed as they aged in the postseason.


Despite the large samples, the postseason aging curves for batters and hurlers alike appear to be almost random, jolting up and down at unpredictable times. It doesn’t go directly down like it’s supposed to, but at the same time, it doesn’t go up, nor does it start going down and then go up. Every researcher hates to say it, but these charts are inconclusive. There’s no way of saying that age does or does not affect postseason play based on provided data.

Overall, my attempt to defend youth is probably not accurate as the data does suggest that players improve in the postseason with experience. It is possible that other factors contributed to those results. For example, strong young players can be brought up by any team, but good, older free agents are usually (see: Hosmer, Eric) only signed by teams that are already in postseason contention. However, if that were the sole factor in this correlation, there would be a pattern in the age chart too, which there is not. So, in conclusion, while it does not appear there is any correlation between older age and better postseason performance, I will no longer be calling foul when experience is cited as a factor in evaluating teams for a world series run. The evidence is here.

Be sure to follow us on twitter and be the first to know when we post new research!

Works Cited:
The Lahman Database

Images Attributed to:
USA Today
Hub Pages


The Sport of Revenge

– The K Zone –


The Sport of Revenge, by Ian Joffe

August 23rd, 2018

We all know the story: a player gets traded or DFA’ed by their team, and they can’t stand it. They join another team and, in their frustration with their old team or themselves or just the world in general, they start crushing the ball. Suddenly, the old unrosterable player is the player of the week and the month, until they eventually cool down. After that they may return to who they used to be, or be slightly better, until they come back to face their former team. As a Red Sox fan, I always remember David Ortiz punishing the Twins, especially in Minnesota. It seems like every team has players like that, but just because a few players crush their old teams it doesn’t mean everyone does. To answer that question, it’s going to require a look at a lot more players.

There are multiple reasons why a player would do better against their old team. The most simple reason is the psychological effect. I think that traditional sabermetrics (which would argue players do not do better against their former teams) often overlooks the science of psychology in places where data shows there could be a trend (for example, closers pitching poorly before the ninth). Players may feel angry or that they have something to prove to the club that abandoned them, and that could somehow affect their performance on their field. Additionally, they may have extra non-public knowledge about the pitchers on the team that they’re now facing. The same would apply in the inverse with the pitchers knowing more about the hitter in question, but because hitters tend to improve during in-game at bats against a pitcher as the innings pass by, it’s possible that hitters will have the advantage in this team-switch case as well. Third, there could be a park factor, where players are more comfortable in a ballpark that they used to play in, or fans motivate them by cheering or booing. That third reason is probably the least likely, as it only applies in the away ballpark and there’s little proof that fan interaction has a psychological effect, but it’s worth noting.

Since 2000, batters in MLB have had 107,790 plate appearances against teams they used to be on (I’m not sure, but I think that should be a large enough sample). Using a Python program to compile Fangraphs data, I compared how players did in those “Revenge PA’s” compared to normal plate appearances. Here are the results:

Average Player Players vs. Former Teams
AVG .260 .258
HR% 2.7% 2.8%
K% 18.5% 18.4%
BB% 8.4% 8.7%
wOBA .323 .324


In part due to the large sample, there is almost zero difference between the everyday player and players against their former teams. From this study, the simple, albeit disappointing conclusion is that the average player is no better against a club that they used to play on.

An interesting side note here from the study itself is the question of why I (and I assume many readers) had the misconception that many or most players will do better against their former teams. When players do well against their old teams, it creates drama which compels media to cover it, and the media covers it because they think people will be attracted to the apparent drama. And, they’re right. I wrote a whole article about this because it interested me. My point is that we have this misconception because the media over-covers stories that seem dramatic. They would be less likely to cover, say, Mookie Betts hitting .350 against the Giants because he’s never played for San Francisco, so there’s no drama there. We need to be aware of biases in what the media covers like this one and make sure that we realize that these stories about a few players that get covered for dramatic reasons do not necessarily reflect on everyone in the average population pool; whether that be baseball players or people in general. But I digress…

Having concluded that the average, aggregate player may not experience a change, I wanted to look into individual players. After all, everyone is affected differently by psychological factors and there may still be an abnormally high amount of players who perform well against their former teams. So, filtering out players with less than 20 plate appearances, I checked how many players hit .400 against their old teams – there were only 11. Then I looked at how many hit .350 – only 30. That’s a lot of players to reach those totals in a full season, but in the very small samples that I’m looking at, those numbers are very normal. They could be explained purely through luck in statistics, without psychology. Still, I wanted to see if there was any way I could show a real physiological difference for those few players.

Theoretically, if random streaks explain why these few players improved against their former teams, the increase in batting average would be independent of other statistical changes. A change in batting average would lead to a small change in other stats directly (for example, if BA goes up by .010, out rate will go down .010), but if there consistently are other effects than that, it can be shown that there is something unusual going on. In other words, if more than one statistic is drastically changing, this may not just be randomness and luck playing out. In order to look at a few different samples, I made three groups:

“The 30” are the 30 players who hit .350 in at least 20 PA’s

“The 11” are the 11 players who hit .400 in at least 20 PA’s

“The 5” are the 5 players who hit .350 in at least 60 PA’s

Here’s the data on a few stats from each group:

The 30 The 11 The 5
HR% 4.3% 4.6% 6.7%
K% 14.4% 14.2% 11.9%
BB% 8.6% 7.1% 10.1%


Comparing these numbers to the league averages you can see in the earlier table, there are clear changes. Even after factoring in the direct changes (the pure change in batting average for The 30 and The 11 should put K Rate around 16%, for example), players improved significantly in power and in avoiding strikeouts. The only place that we don’t see a consistent story is in walks, which is weird but can probably be chalked up to the small samples that I’m dealing with. Looking just at strikeouts and home runs though, we can see clear improvements. That tells me that there could be more than randomness going on in the BA improvements for these players. It looks like they actually are motivated to be better against their former teams. Furthermore, the decrease in strikeouts tells us that there is no conscious change in approach. None of the groups are more aggressive at the plate, which would lead to an increase in strikeouts and consistent decrease in walks. This leads me to believe that the change is less conscious and harder to explain in baseball language (such as “more aggressive” or “faster swings”) – it’s more purely psychological.

In the end, the results of this study are that revenge psychology factors little into the performance of major leaguers. Very few players are better against their former team than any other team. There are, however, a very small select group of players who seem to be legitimately better against their former teams, but it’s hard to explain exactly what they do differently. When asked about his performance against the Mets, Daniel Murphy, one of The Five and the subject of one of the first K Zone articles said “I don’t know. They’re a division rival.” There’s little information from other players. While some may not want to openly admit their feelings on the subject of their old team, for the most part it seems that these players are not particularly conscious of their performance at all, at least while it’s happening. This is more the work of deeper psychological effects in a small portion of the population. Perhaps one day we will have neurological information on draftees or free agents so that teams can predict if they will be an unconscious vengeance seeker. For now though, it’s safe to assume that a random player will not come back to bite.

Oh, and while I was writing this article this happened. We’ll see if Murphy goes on to terrorize the Nationals as a Cubbie.

The New York Post

Images Attributed to:
USA Today

If you liked this article, you may also be interested in this short piece about whether or not players hit any better on their birthdays. Or, you can follow The K Zone on Twitter and be the first to know when we publish research, interviews, or other great new content.

Birthday Bashing

– The K Zone –


Birthday Bashing, by Ian Joffe

July 4th, 2018

In honor of America’s 242nd birthday, we’re going to take a look at how players hit on their own birthdays. A strict “clutch hitting does not exist” model would argue that there should be no difference in batting outcomes, but a more psychologically based view could see significant changes in hitters’ approaches and results. It certainly seems as if some players consistently hit on their birthday, but that could be by random chance, or only apply to some. The question is whether a larger trend exists.

To test the two theories against each other, I compiled data from The Lahman Database and Retrosheet (two excellent sites and sources, by the way) using python. In total, there were 478 times in 2017 in which a player made a plate appearance on their birthday, which is not a huge sample size, but it should be enough for most stats. We will turn those 478 PA’s into a conglomerate player, who we’ll call the “birthday boy.” Here’s a comparison between The Birthday Boy’s numbers and the league averages from the 2017 season:

  League Average Birthday Boy
AVG .255 .263
HR% 3.3% 3.6%
K% 21.6% 22.2%
BB% 8.5% 9.4%

At least last season, there was no indication that players did better on their birthdays than any other day. The small differences in, say, BB% do not come close to holding significance when a t-test is applied. Not only do players not improve in power on contact by a statistically significant amount, but it seems their approach does not change either. Players are not more anxious and aggressive, nor are they more nervous and passive, according to strikeout and walk rates.

All in all, it looks like the robot within us has this one locked up. Don’t go picking up players in DFS just because it’s their birthday, and don’t count on your 25th man to come up clutch just because it’s his special day. There will be plenty of opportunities for clutch hitting down the stretch, when the team needs it most.

Who Got Figured Out?

– The K Zone –

June 16, 2018

Who Got Figured Out, by Ian Joffe

The hot start…and the extended slowdown. The rookie sensation…and the “sophomore slump.” Baseball is made of up and down streaks, and figuring out what causes them can be a secret to understanding the game. Sometimes they come from simple luck of defensive positioning and batted ball location. Usually those kinds of changes in offense will be associated with changes in BABIP. Other times, hitters can make legitimate changes to their swing or approach, although it’s smart to be suspicious of any supposed changes until there’s a large enough sample to confirm that the change made a real difference. In other cases, pitchers start to approach batters in different ways, and the batters become, at least temporarily, befuddled. A pitcher has two weapons at his disposal: Where to pitch the ball and how to pitch the ball. I’m going to focus mostly on the latter, in terms of pitch types, but I’ll also look at simple zone percent to see if a batter has stopped getting balls or strikes.

Often, pitchers will change their approach in response to a hitter becoming surprisingly hot, especially when there’s no clear indication of luck. To see which hitters received major adjustments from pitchers this season, one can look at the difference between the amount of pitches of a certain type they received in April vs. in May of this year. Here are all the qualified hitters whose opposing arsenal changed by at least 7% in at least one pitch, by Pitch f/x data (note that these are changes in percentage out of the total arsenal, not the individual pitch, for example +10% curveballs means the batter used to receive 15% curves and now gets 25%):

Mike Trout: -7.6% Sinkers
Rhys Hoskins: +9.3% Changeups (who Mike interviewed just about a year ago)
Jed Lowrie: -8.8% Sinkers
Freddie Freeman: +10.3% Sliders
Kris Bryant: +8.2% Sliders
J.D. Martinez: +7.8% Changeups, -10.3% Curveballs
Javier Baez: +12.3% Sliders
Odubel Herrera: +8.8% Sinkers
Lorenzo Cain: -10.1% Sinkers
Mike Moustakas: -7.8% Curveballs
Kevin Pillar: +8.7% Sinkers (who we also happened to have interviewed)
Jose Martinez: -7.1% Sinkers
Jose Ramirez: +7.8% Curveballs
Nick Ahmed: +7.6% Sliders
Mallex Smith: +12.5% Four-Seamers
C.J. Cron: +8.1% Sinkers
Nicholas Castellanos: -7.4% Sliders
Evan Longoria: +14.1% Four-Seamers, -10.7% Curveballs
Trea Turner: +7.3% Four-Seamers
Yonder Alonso: -8.9% Sliders, +7.3% Curveballs
Alex Bregman: +7.7% Cutters
Buster Posey: +8.0% Four-Seamers
Scooter Gennett: +8.6% Four-Seamers
Matt Joyce: +10.3% Changeups
Gary Sanchez: +11.4% Curveballs, -8.2% Four-seamers
Giancarlo Stanton: +9.8% Four-seamers
Tucker Barnhart: -8.3% Sinkers
Jose Peraza: +10.6% Four-seamers
Kyle Seager: +7.1% Sliders
Marwin Gonzalez: -8.9% Four-seamers
Michael Taylor: -8.2% Four-seamers
Victor Martinez: -8.2% Four-seamers
Justin Upton: -7.4% Sinkers
Miguel Rojas: +7.1% Changeups
Brett Gardner: +7.5% Curveballs
Adam Jones: +10.4% Changeups
Adam Duvall: -11.9% Sinkers, +7.7% Four-seamers
Edwin Encarnacion: -7.3% Four-seamers
Billy Hamilton: +10.3% Four-seamers, -9.6% Sinkers
Ian Desmond: -7.0% Four-seamers, +8.4% Sliders
Brandon Crawford: -8.2% Sliders, +8.2% Strikes
Lewis Brinson: +9.4% Four-seamers

Pitchers, catchers, and pitching coaches may choose to alter their selection against a hitter because his data shows a weakness on that specific pitch in the past, or a potential hole in sequencing is noticed that can be used to take advantage of the hitter. Surprisingly, however, very few of these changes were shown to have effects. Filtering out the batters whose offensive adjustments were based on BABIP luck, there were only a few hitters who lost at least 20 wRC+ (the best tell-all offensive metric that we have) between March and April:

Javier Baez was unable to keep up in sequences with sliders, losing 46 wRC+ but only 0.016 of BABIP when his rate of sliders increased 12%. Don’t look for too much from the potentially promising young Cub.

Lorenzo Cain‘s changes draw concern based on his recent league change. Pitchers in the NL took a little time to adjust, as Cain had a strong showing in April, but when they realized his weakness against the sinker, they capitalized, and his wRC+ dropped by 33 while his K-Rate rose by 4.3%. His specific case related to the league change is certainly worrying.

Mike Moustakas, who returned to the Royals on a one-year deal after a rough free agency, was quickly put in his place by opponents’ curves, whose increase cost him 37 wRC+ in May. If there’s one player here I’m less worried about, though, it’s Moustakas; I think we know, and pitchers already knew, who he is.

Jose Martinez was a surprise story in April, but a barrage of sliders have slowly started to chip away at his stats, and he, like Moose, lost 37 wRC+ but also experienced a 5.8% rise in strikeouts. Look for more regression as pitchers continue to figure out the young Cardinal.

Matt Joyce fell victim to the changeup this May, which isn’t surprising given his history and swing. Don’t hold your breath of Matt’s career year. In addition to the drop in wRC+, he struck out 10% more often in May than April.

Marwin Gonzalez showed plenty of signs of potential regression last year, especially on Statcast metrics, and as soon as pitchers stopped throwing him fastballs this year, his season collapsed in the form of 32 wRC+ and poor batted ball metrics.

Michael A. Taylor, the Nationals speedster who has stepped in and stepped up in place of several injured Nats looked strong at first, but couldn’t keep up the pace in response to an increase in fastballs and a decrease in offspeed stuff. If he wants to stay in the starting lineup, he’ll have to learn to keep up with velocity.

While there is some worry to be cast on the players above for the rest of the season, it’s even more surprising how resilient the vast majority of hitters were. Most seemed unfazed by the new way they were being treated after early success, and any changes were much more often associated with luck than real factors, like being pitched to differently. And, even for the seven who made the shortlist of concern, there is hope. Repeating this exercise on the 2017 and 2016 seasons showed that there is little reason to worry even for them.

Last season, in 2017, the shortlist was comprised of Michael Brantley, Mike Moustakas (again), Salvador Perez, Tim Beckham, Yasiel Puig, and Randal Grichuk. Inconveniently, only three of those six qualified in the second half: Moustakas, Beckham, and Puig – and none of them had problems. Puig had the second best half of his career, posting a 136 wRC+, and Moose put up a fairly solid 106 mark. Both maintained down-to-Earth strikeout rates too. Beckham also had a great half, with a 113 wRC+, although a higher K-rate and a .353 BABIP suggest that that may not be entirely natural. Either way, if the 2017 crop is any evidence, some guys may just take a while to get used to changes in how they’re pitched to, but will recover eventually.

Running the same program for 2016, eight names were churned out. That list included Anthony Rizzo, Neil Walker, Starling Marte, Chris Davis, Curtis Granderson, Corey Dickerson, and Billy Burns. Four of the eight: Rizzo, Davis, Granderson, and Dickerson, qualified in the second half, and once again, they had a strong showing. Other than Davis, who, to be blunt, isn’t that good anyways, the players each had a wRC+ over 108, topped off, of course, by Rizzo’s 121. Like in 2017, the 2016 slow-adjusters figured it out eventually. Based off what’s happened in the prior two years, I see little reason to worry about this season’s seven. They each may have individual concerns, but the simple fact that they are taking a while to learn how to hit after pitchers realized they were decent has not, historically, been telling of anything. They may be figured out for now, but just wait a month and see.

How to Win at Baseball

– The K Zone –

How to Win at Baseball, by Ian Joffe

April 17, 2018

With limited salaries and a finite number of high draft picks, teams are constantly forced to choose how, out of dozens of options, to build their team. Rosters can focus on hitting or pitching. They can look for power or on-base skills. They can make a core of speed and defense. A team might even try to build around leadership and personality traits. A roster with any kind of emphasis, or even a general well-roundedness, has the potential to be effective, but I want to figure out what teams are most effective. So, to do that, I turned to my Fangraphs spreadsheets and Python editor.

For data, I scraped information off all 480 teams from 2002-2017 (going back to 2002 because that’s when the pitching stats that I wanted became available). As the first step in seeing which skills are most effective to build around, I constructed a set of scatter plots that set each statistical category and team wins along the two axes. The categories I checked look at overall hitting (wRC+), on-base ability (OBP+), power (ISO+), speed (SB+), and two pitching metrics (xFIP+ and SIERA+), all of which, as you can see, have been normalized so that 100 is league average. In retrospect, I should have included at least one defensive statistic to look at, but I neglected to because given my process, it would have taken a long time to include that data, and now it’s too late. Here are the scatter plots for each stat, plus their Pearson correlation coefficients:


As we could have predicted, teams with good stats tended to win more games. Because they are only slight, the differences in P-Values doesn’t tell us much here given the fact that baseball wins are not highly controlled experiments, and everything is in the same ballpark. That is, every stat except one:


It turns out that steals had absolutely no correlation to wins, in fact, a set of 480 randomly dispersed points may have correlated even better. It’s possible that teams only run more because they have less power, but managers tend to keep the same strategy even when they move teams, so I would instead just say that in general, speed is not a key to winning at baseball. Sure, a steal now and then helps if there’s a high likelihood of reaching the base, but building a team around speed and hoping to win is a poor strategy, and historically has not worked.

To create a more telling story about which teams succeed and which teams fail, I looked at how teams that ended up in certain tiers were built. I defined a “playoff team” as a roster in the top 30%, a “Championship Series team” as one in the top 12%, and a “World Series champion” team as one in the top 3% (note that this has nothing to do with how the playoffs actually went, because the playoffs are essentially random). I then applied a label to teams based on whether they emphasized hitting or pitching by subtracting xFIP+ from wRC+. A team with a difference of 20+ has a “heavy hitting emphasis,” a team with a 10-20 differential has “some hitting emphasis,” a team with a value between 10 and -10 has “no significant emphasis,” a roster between -10 and -20 has “some pitching emphasis,” and finally a team with a difference under -20 is labeled with a “heavy pitching emphasis.” Here is the overall distribution of teams by emphasis:


As you can see, and potentially predict, most teams have no emphasis. More importantly, however, is that many more teams have some pitching emphasis than hitting. Keeping that in mind, let’s look at the distribution within each tier:


While the strength of the balanced team largely holds, we see an immediate dropoff in the number of teams who emphasize pitching, strongly or at all, and the number of teams who weight hitting is starting to grow.


As we move to the top 12% of teams, no rosters that emphasized pitching remain. And, nearly half of the teams emphasize batting.


And finally, as we reach the few elite teams, the vast majority have a hitting emphasis. Out of the 10 teams total that showed a heavy batting emphasis, all of them were playoff caliber and half of them were champion caliber. While teams with a hitting emphasis made up only 9% of total rosters, they comprise 42% of CS teams and 85% of championship teams. Meanwhile, not a single team who emphasized pitching made it to the top 12%, and despite being 31% of total teams, those who focused on pitching only made up 1% of the playoff teams overall. The lesson here seems clear: Build around hitting if you want success. When given the choice between two equally talented players in the free agent pool, or even more importantly the June draft, chose the hitter. There could be a few reasons for this. One reasonable theory may be the value of defense distracts and sets the value of the pitcher to, if you take an extreme stance, the point where pitchers become replaceable as long as the team retains a strong defensive cast. It’s also arguable that it’s easier to find good pitchers and more teams have been able to build pitching depth, as seen in the overall distribution. So, it would be harder to use pitching as a competitive advantage. Or, maybe because so many pitchers are used in today’s game, the value of each becomes diluted, therefore only when teams move to improve their hitting can they gain a competitive advantage. To be clear, I’m not saying that pitching doesn’t help a team; we saw from the correlation plots that it certainly does. However, given limited resources, ignoring hitting in pursuit of strong pitching – or even looking at the two in equal light – is not a recipe for success.

Now, let’s take a look at another potential difference in strategy: power vs. on-base skills. This one is a little harder to quantify because, while hitting and pitching make up almost all of the factors in a baseball game (minus defense), power and contact exist in a far less controlled experiment. But it’s worth a look anyways. I labeled the emphasis of teams in favor of power vs. on-base skills in a similar way I did with hitting and pitching (with the +20, -10, etc. differentials), except I used ISO+ and OBP+. Here is the initial distribution among all teams:


It’s pretty similar to the full distribution among hitting and pitching, with a heavy spike in the middle. Here’s the distribution among playoff teams:


It looks like power is winning out a little, although don’t read too much into the small sample of teams with heavy on-base emphasis. Still, the distribution doesn’t change too much.


As we continue through the postseason, we see a continued normal percent loss in each category, about equivalent to the percent lost overall. PowerContactChamp.png

And the trend continues, with “some power emphasis” remaining as about 20% of teams throughout the playoffs and categories with smaller amounts to start off with being eliminated as a whole. Unlike with pitching vs. hitting, there is no clear story here. I wouldn’t even say that a balance is necessarily the best option, because it started so heavily weighted.  So, teams can go either way. As long as the focus is on hitting, they can win through a power-heavy strategy, contact-heavy build, or a balance.

There was one last thing I wanted to check out: a comparison of playoff teams to trends. It’s possible that while since 2002, power and contact have been equal, in certain mini-eras one has been more valuable. This would be because of a league trend. Perhaps the winning team is the one that’s ahead of the trend and really exaggerates it. Or, the winning teams could be the ones who zig while everyone else zags, finding bargains along the way. So, over the 16-year period, I graphed the league trends in ISO versus the median ISO+ of a playoff team, and applied a polynomial regression:

power over time.png

There is no clear pattern between the power-emphasis of winning teams and the league trend. If anything, the playoff teams look to be behind the curve (imagine shifting the green line over about four years to the right). This further goes to show the original point, that teams can build both power, contact, or a mix, and will still have the same ability to win, no matter what the rest of the league is doing.

While these findings certainly apply to all methods of roster-building (such as free agency, trades, and Rule 5), it seems most important during the amateur draft, given the wide diversity of players available and the fact that there is usually little clarity on the future potential/reality of drafted players. That especially goes for systems that already lean hurler-heavy. Teams should seriously consider taking batters over pitchers, even if the pitchers appear to have slightly more raw ability. Because, simply, it works.




Image Attributed to:

Dodger Nation

2018 Season Projections

In just a few long days, the 2018 baseball season will finally be upon us. It is at this time that us baseball writers engage in the annual tradition of projections, so that come October, we can rejoice in where we were right, and be target of oh so many cruel, cruel jokes where we were wrong. So, without further ado, here is my projected playoff bracket for the coming season:


That’s right – led by a strong, deep pitching staff, the Cleveland Indians will defeat the Washington Nationals to win the 2018 World Series in five games. My bracket overall is pretty unexciting, mostly just teams from last year. I wouldn’t say that the baseball middle class is disappearing. Plenty of teams will finish with 79 wins or so, but that’s not good enough to make the playoffs. I’m not worried though. I’m sure I’ll be wrong about at least a couple surprise teams who will eek into contention.

Here are my awards picks for the coming season:

Most Valuable Player Cy Young Award Rookie of the Year Manager of the Year
American League Mike Trout (LAA) Chris Sale (BOS) Gleyber Torres (NYY) Scott Servais (SEA)
National League Freddie Freeman (ATL) Clayton Kershaw (LAD) Ronald Acuña (ATL) Dave Martinez (WSH)

downloadI went with the obvious picks twice, with Kershaw and Trout, and I don’t think there will be too much competition comparing to the stats that the two best players in baseball put up. For NL MVP, it was between Freeman and Votto. And, even though Votto will probably have the better season, I think Freeman will get far more attention because poor Votto never gets attention for anything. I could see Freeman putting up a .420 OBP with 40 home runs in the coming year. AL Cy Young was probably the hardest decision, and I nearly flipped a literal coin to chose between Sale and Kluber. In the end, however, I non-randomly chose to give the slight edge to Chris. For NL ROY, I went for a somewhat conventional pick with Acuña, even though he will spend the first 20 or so days of the season in AAA. And, in the AL, I picked Gleyber Torres, a.k.a.acuna not Shohei Ohtani. I was incredibly high on Torres before his surgery last season, and I don’t see to much reason to dock him now because of it. As for Ohtani, he won’t hit, and while I think he could do well as a pitcher after he gives up his bat, that should take some time. Injury is also a big risk for him. If Torres is upset by somebody, it will be a young pitcher who debuts surprisingly early, like a Whitley or a Kopech.  AL manager was easy because even though I don’t love or hate Servias, I’m picking the Mariners as the only surprise team to make the playoffs (and even that’s not even too surprising). In the NL, I didn’t see any breakout team winning enough games for their manager to muster an award, so I picked Martinez because the Nationals will win the top seed.

Now, finally, is my favorite part of projections: the bold predictions. Bold predictions are not things that I am betting will happen, but rather things that most people won’t consider, but I would see as somewhat likely.

  1. There is a team with 81 wins or less that makes the playoffs. I am not high on any non-elite roster this season.
  2. Luis Severino has the lowest ERA in the AL. He peripherals last year were completely legit, and even though he won’t win the Cy Young, he has a chance to shine in the New York spotlight.
  3. Aaron Nola finishes in the top two in NL Cy Young voting. He went on a crazy and largely unnoticed stretch in the last couple months of last season, and he is loved by the statcast metrics. My only regret in fantasy this year was not drafting Nola, because apparently someone else in the league liked him too, and he went for $27.download (1)
  4. Joey Votto puts up a .480 OBP with 170 wRC+ in a full sample size. My love for Votto’s skills is immeasurable, and I can’t wait for him to put up an even more insane season as a 34-year-old, only to lose the MVP again.
  5. Tyler Chatwood finishes with the lowest ERA in the Cubs rotation. This is as much about how good Chatwood has been on the road for Colorado as it is about how overrated the Cubs starting pitching has been.
  6. Trea Turner steals 80 bases. He is by far the best player on the roto fantasy leagues’ player raters, and deservedly so.
  7. MLB puts together an official plan for expansion into multiple cities, potentially including Montreal, Mexico City, or Vegas. Out of all my bold predictions, and considering all my fantasy investments, this is still the one I want the most. I know the teams wouldn’t be too good at first, but it would be so fun, and there seems to beknebel real momentum towards it.
  8. Corey Knebel saves 50 games. He’s a machine, and when the Brewers win games, which they will do often enough, the games will be close.
  9. Trevor Bauer finishes in the top two for AL strikeouts. Bauer has always had the potential to put together a monster year, and K’s finally lead him into it in 2018, helping lead the Indians to the World Series.
  10. After winning it all, the Indians celebrate by changing their name. It’s racist. It’s time, and society is ready.


Images Attributed to USA Today, The Associated Press, and NBC

Top 10 Players at Each Position

Over the past week or so, we’ve been compiling lists of the top 10 players at each position in baseball, predicted for the 2018 season based on where we expect them to play most of their games in the 2018 season. Here’s the master list:

Top 20 Baseball Players

Top 10 Starting Pitchers

Top 10 Closers

Top 10 Middle Relievers

Top 10 Right Fielders

Top 10 Center Fielders

Top 10 Left Fielders

Top 10 Third Basemen

Top 10 Shortstops

Top 10 Second Basemen

Top 10 First Basemen and Designated Hitters

Top 10 Catchers

We put up a new position every day, so be on the lookout until we close out with the top 20 overall baseball players!

You can also follow us on Twitter and be the first to know whenever a new list comes out.


Image attributed to Getty Images

Top 10 Catchers

Official K-Zone Ranking Mike’s Ranking Ian’s Ranking Mojo’s Ranking Guti’s Ranking
1. Gary Sanchez (NYY) Gary Sanchez (NYY) Gary Sanchez (NYY)  Gary Sanchez (NYY) Gary Sanchez (NYY)
2. Buster Posey (SF) Willson Contreras (CHC) Buster Posey (SF) Buster Posey (SF) Buster Poesy (SF)
3. Willson Contreras (CHC) Austin Barnes (LAD)  Willson Contreras (CHC) Austin Barnes (LAD) Willson Contreras (CHC)
4. Austin Barnes (LAD) Buster Posey (SF) Austin Barnes (LAD) Willson Contreras (CHC) Austin Barnes (LAD)
5. Yasmani Grandal (LAD) Yasmani Grandal (LAD) Yasmani Grandal (LAD) J.T. Realmuto (MIA) J.T. Realmuto (MIA)
6. J.T. Realmuto (MIA) Jorge Alfaro (PHI) Welington Castillo (CWS) Yasmani Grandal (LAD) Yasmani Grandal (LAD)
7. Tyler Flowers (ATL)  J. T. Realmuto (MIA) J.T. Realmuto (MIA) Mike Zunino (SEA) Mike Zunino (SEA)
8. Welington Castillo (CWS) Evan Gattis (HOU) Tyler Flowers (ATL) Tyler Flowers (ATL) Tyler Flowers (ATL)
9. Mike Zunino (SEA) Wilson Ramos (TB) Chris Iannetta (COL) Welington Castillo (CWS) Robinson Chirinos (TEX)
10. Jorge Alfaro (PHI) Salvador Perez (KC) Kurt Suzuki (ATL) Robinson Chirinos (TEX) Chris Iannetta (COL)
Sleeper Yadier Molina (STL) Wilson Ramos (TB) Kevin Plawecki (NYM)

Kurt Suzuki (ATL)

Alex Avila (ARI)


Catcher is by far the hardest position to rank. Not only , thanks to health-based platoons, did only four catchers play enough to qualify for the batting title last season, and not only does the position suffer from a general lack of depth at the top and down the middle, but one has to look at far more factors than usual, including framing, to evaluate the position accurate. Along with the usual Fangraphs and Baseball Reference, this article heavily cites Baseball Prospectus and Baseball Savant for their incredible, proprietary data and stats that have to do with pitch framing and “pop time,” respectively. This year, the 25-year-old Gary Sanchez unanimously captured the top spot on the list from the longtime incumbent Buster Posey. Sanchez followed up 20 home runs in 53 2016 games with 33 bombs in 122 2017 games. Sanchez is also known for his cannon of an arm; in fact, he threw the hardest of any catcher in baseball in 2017. Buster Posey remains fairly high on the weak list, number two, after slashing .320/.400/.462 with 128 wRC+. Posey ranked in the middle of the back for catcher defense last year. Buster is flanked by another youngster, Cubs’ backstop Willson Contreras. Contreras hit 21 home runs in 117 games last season while slashing a very respectable .276/.356/.499 and putting up solid defense. He looks to be part of the Cubs’ young core for years to come. The 28-year-old rising senior Austin Barnes led catchers with 142 wRC+ after putting up .408 OBP in 2017. He acted as a good, but not great, pitch framer for the Dodgers. Barnes’ biggest obstacle next year may be his fellow Dodger, Yasmani Grandal. That’s right, two Dodgers came in the top five catchers for the coming season – funny position, huh? Barnes’ platoon partner struggled to get on base but still showed strong power in 2017, belting 22 home runs in 482 PAs and going for a 17.7% HR/FB ratio. Pitch framing is perhaps Grandal’s strongest pursuit. BP tagged him for 22 runs saved as a framer, fourth best in the league. Marlins’ trade chip J.T. Realmuto makes the list as #6. One of the four qualifying catchers, Realmuto posted 3.6 WAR in 2017, and is one of the few catchers who can threaten to steal double digit bases in a season. Realmuto put up good pitch framing numbers (9th in the league) last season, and dominates the basepaths with his 1.90 pop time. Braves vet Tyler Flowers checks in at seventh overall, after hitting .281/.378/.445 in 370 plate appearances for Atlanta. Flowers has never been known for his arm, but recent metrics have shown that he’s actually quite a strong pitch framer – the second best in the league in 2017. White Sox signee Wellington Castillo ranks eighth because of his career year in Baltimore last season after being non-tendered by Arizona. Castillo hit 20 home runs in only 96 games while posting 2.7 WAR and contributing with both defensive tools. Castillo ranked eighth in pop time and seventh in pitch framing among catchers last season. Former Mariners prospect and current Mariners starting catcher Mike Zunino makes a close ninth, hitting for power and power only in 2017. Zunino smacked 25 homers in 435 PA’s last year while returning a 9.0% walk rate. Phillies’ current prospect Jorge Alfaro, who is expected to potentially start the year as their top catcher, closes out the top 10 list. While he has yet to prove himself, Alfaro certainly has the offensive (big power) and defensive (big arm) tools to justify his ranking through his play.

See the top 10 players at every other position here and follow us on Twitter to be the first to know whenever a new list comes out!


Baseball Reference
Baseball Prospectus
Baseball Savant

Images Attributed to:
Getty Images
Photo File
Steven Ryan

Top 10 Shortstops

Official K-Zone Ranking Mike’s Ranking Ian’s Ranking Mojo’s Ranking Guti’s Ranking
1. Carlos Correa (HOU) Trea Turner (WSH) Francisco Lindor (CLE) Corey Seager (LAD) Corey Seager (LAD)
2. Corey Seager (LAD) Carlos Correa (HOU) Carlos Correa (HOU) Carlos Correa (HOU) Carlos Correa (HOU)
3. Francisco Lindor (CLE) Francisco Lindor (CLE) Trea Turner (WSH) Francisco Lindor (CLE) Francisco Lindor (CLE)
4. Trea Turner (WSH) Corey Seager (LAD) Corey Seager (LAD) Manny Machado (BAL) Manny Machado (BAL)
5. Manny Machado (BAL) Manny Machado (BAL) Manny Machado (BAL) Trea Turner (WSH) Andrelton Simmons  (LAA)
6. Andrelton Simmons (LAA) Didi Gregorius (NYY) Andrelton Simmons (LAA)  Andrelton Simmons (LAA) Didi Gregorious (NYY)
7. Didi  Gregorius (NYY) Andrelton Simmons (LAA) Didi Gregorius (NYY) Didi Gregorious (NYY) Trea Turner (WSH)
8. Elvis Andrus (TEX) Xander Bogaerts (BOS) Elvis Andrus (TEX) Elvis Andrus (TEX) Elvis  Andrus (TEX)
9. Xander Bogaerts (BOS) Elvis Andrus (TEX) Xander Bogaerts (BOS) Xander Bogaerts (BOS) Jean Segura (SEA)
10. Jean Segura (SEA) Jean Segura (SEA) Jean Segura (SEA) Jean Segura (SEA) Xander Bogaerts (BOS)
Sleeper Chris Owings (ARI) Chris Owings (ARI) Paul DeJong (STL) Paul DeJong (STL)


The new wave of MLB shortstops is beginning to mature. The days of scraping the bottom of the barrel to find a 22nd rounder to fill the position on your fantasy team are long over. This wave is led by defending World Series champion Carlos Correa, who hit .315/.391/.550 in 109 games last season. There were MVP whispers before an injury cost of him the final third of the year. Corey Seager garnered the most 1st place votes but comes in second on the overall list after putting up a .375 OBP with 22 home runs and strong glovework in 2017. Cleveland shortstop Francisco Lindor finishes #3 on the list, after posting an .842 OPS despite a .275 BABIP. He was a member of the 30/15 club, and while he only put up 5 DRS last season, many expect a defensive resurgence to come this year. Like Correa, #4 SS Trea Turner missed about a third of the season on the DL, but was on pace for 76 steals as his .284 BA was going up every day. He lost about 100 points of SLG from his rookie season, which may be a realistic adjustment, but moderate power can still be expected out of the young bat. Impending free agent Manny Machado has been promised the Orioles’ starting shortstop gig in 2018, and he shouldn’t disappoint. Machado has averaged 13.5 DRS at third per season since his debut year, and expects a smooth transition to the position he had always played in the past. Manny had a rough start in 2017, mostly thanks to bad luck, but hit .290/.326/.500 in the second half with some speed. In 2016 Machado posted a 130 wRC+ and in 2015 he put up a 135 mark, so he will look to rebound to numbers similar to that. Andrelton Simmons had his “second breakout” with the Angels in 2017, hitting 14 home runs, stealing 19 bases, and hitting .278 with a .290 BABIP. Most notable, however, is his 32 DRS. From 2012-2017, the defensive wizard has saved 19, 41, 28, 25, 18, and 32 runs. Didi Gregorius comes in 6th after slashing .287/.318/.478 last year. The 3.9 fWAR player has improved every year, and looks to continue doing so under the eye of Aaron Boone. Elvis Andrus is the only veteran on this list, if you can call the 29-year-old that. Andrus stole more than 24 bases for the 8th out of 9 years last season, and, after buying into the “launch angle revolution” he set a career high in home runs (20) and near-career high in batting average (.297). Didi’s rival Xander Bogaerts experienced a down year 2017 but still managed to post 3.2 WAR, as his improved on-base skills saved him. The “X-man” will look to rebound in the power department for 2018. Last but not least (well, least out of the players already mentioned) is the Mariners’ Jean Segura. Segura has sustained a high BABIP 2 years in a row, which led him to a .300/.349/.427 line with 22 steals last year. Should he continue to defy his luck, Segura could continue to threaten as a multi-tool talent.

See the top 10 players at every other position here and follow us on Twitter to be the first to know whenever a new list comes out!



Images Attributed to:
The Houston Chronicle
The Associated Press