Bill Shakespeare had it right over 400 years ago. A baseball game in its essence is a sequence of plays, always starting at the same point and then tracing out a unique road that leads to the final outcome. And all the best games have a long and winding road. Having previously established that Power WPS is the ULTIMATE measure of how exciting a baseball game is, we’re ready for the real fun to begin. The fun of course being the compiling of various lists which can be the basis for pointless arguments. Since we rate the games based on the plays, we should establish a framework by first talking about the plays. When we remember a game, what we really remember is a few plays. So what is a typical play, what is the distribution of plays, what is a ‘big’ play?

Through 2017 there have been 305 playoff series, comprised of 1535 games and 119,712 plays, or about 78 plays per game. The average play moves the probability by 3.4%; The median play moves the probability by 2 percent. The table below shows the distribution of all those plays.

Only about 6.27 percent of all plays move the needle by as much as 10%, so a typical game will have about 5 of those.

Only about 1.14 percent of all plays move the needle by as much as 20%. A typical game will have about 1 of those. That’s the play they’ll show on the evening news.

Only about 0.42 percent of all plays move the needle by as much as 30%. About 1 in 3 games would feature a play like that.

Only about 0.13 percent of all plays move the needle by as much as 40%. About 1 in 10 games would feature a play like that.

Only about 0.03 percent of all plays move the needle by as much as 50%. A team might have about 4 of them in a season.

Caveat: BBRef has updated the percentages over time, my results reflect the values I captured over the years which may differ slightly from the current values.

Choosing one great game, we can see what at all of those levels look like. Game 6 of the 2011 World Series (Cards 10 Rangers 9, in 11 innings) has them all.

In the top of the 2nd, Ian Kinsler hit a 2-out double with a man on second tying the game at 2 (11%)

In the top of the 7th, Adrian Beltre hit a leadoff homer, breaking a 4-4 tie (21%)

In the bottom of the 11th, David Freese led off with a walk-off homer (37%)

In the top of the 10th, Josh Hamilton hit a 1-out 2-run homer to break a 7-7 tie (43%)

In the bottom of the 9th, David Freese hit a 2-out 2-run triple to tie the game at 7 (54%)

Obviously, the biggest plays will tend to come at the end of the game when there are fewer (or no) opportunities to come back.

But what are the highest rated early plays in a game?

1st play 12%: A Pete Rose homer off Catfish Hunter in G5 of the 1972 WS

2nd play 16%: Most recently a Stephen Piscotty 2-run homer off Jason Hammel in game 4 of the 2015 NLDS

3rd play 21%: A 1-out 2-run homer by Frank Robinson off Don Drysdale in game 1 of the 1966 WS

4th play 23%: A 1-out 3-run homer by Willie Stargell off Doug Rau in Game 3 of the 1974 NLCS

5th play 24%: A 2-out 3-run homer by Garrett Anderson off Randy Johnson in Game 3 of the 2005 ALDS

6th play 26%: A 2-out 3-run homer by Lucas Duda off Jason Hammel (AGAIN) in Game 4 of the 2015 NLCS

7th play 23%: A 2-out 3-run double by Moises Alou off Greg Maddux in Game 1 of the 1997 NLCS

8th play 25%: A 2-out 3-run homer by Bob Watson off Jerry Reuss in Game 1 of the 1981 WS (bottom of 1st)

9th play 26%: A 2-out 3-run homer by Ron Cey off Dave Righetti in Game 3 of the 1981 WS (AGAIN) (bottom of 1st)

10th play 26%: A 2-out 3-run double by Alex Gordon off CJ Wilson in Game 3 of the 2014 ALDS (bottom of 1st)

11th play 33%: A 2-out Grand Slam by Ryan Roberts off Randy Wolf in Game 4 of the 2011 NLDS (bottom of 1st)

12th play 26% yada

13th play 28% yada

14th play 22% yada

15th play 24%

16th play 33%: A 1-out 3-run homer by Gene Tenace off Jim McGlothlin in Game 5 of the 1972 WS (bottom of 2nd)

17th play 25%

18th play 39%: A 2-out Grand Slam by Jose Canseco off Tim Belcher in Game 1 of the 1988 WS (Top of 2nd, turned into a footnote 7 innings later)

19th play 30%

20th play 30%

The earliest we get a 40% play is

41st play 42%: A 2-out grand slam by James Loney off Ryan Dempster in Game 1 of the 2008 NLDS (top 5th). Gives team 2 run lead

The Earliest we get a 50% play is

56th play 55%: A 2-out 2-run Pete Rose homer off John Candeleria in Game 3 of the 1975 NLCS (top 8th). Gives team 1 run lead

Speaking of those over 50% plays, how many of those biggest 41 plays that changed the odds by at least 50% can you think of? No credit for naming the Pete Rose play…

Part of the fun of a list like that is the memories of players I have not thought of in many moons (apologies to the Cleveland Indians for that cultural appropriation) and other players that I’d never heard of in the first place. Of course while these plays turned an individual game around, some had a greater impact on the outcome of a series, those plays being the ones that happened in the closing games of a series. We can make that list too…

Here are the 24 plays that changed the odds of victory for a playoff series by at least 35%. NINE of them occurred in just four games (they are marked with funny symbols).

Yep, of the top 24 decisive plays EVER affecting a series outcome, Three happened in Game 5 of the 1980 NLCS.

You can also factor in the series level (WS =100%, CS=50% DS=25%). As this focuses down to just a few games, repeats become more common

A list dominated by the 42 World Series that went a least seven games.

Now don’t get accustomed to seeing these kind of results. One would need to watch nearly a decade of games to see this many impactful plays.

The holiday season has come and gone, but fear not — the offseason, the most wonderful time of the year is still here! Though the “hot” stove has been anything but, it’s still a great time to discuss one of the more popular tools for evaluating free agent contracts sabermetrics: $/WAR. Love it or hate it, $/WAR is a useful tool for evaluating free agent contracts if used properly. $/WAR can reveal quite a bit about the state of the free agent market, as well as where the market might be headed. So, let’s jump in like a Bartolo Colon doing a cannonball.

On the Calculation of $/WAR

The concept of $/WAR, or as it is otherwise known, “The Cost of a Win,” is simple enough to grasp: MLB teams treat players as bundles of WAR to be had in exchange for money. The unit price of 1 WAR is the cost of a win, or $/WAR.

That’s $/WAR in simplest terms, but the strict calculation of $/WAR is actually a little trickier, largely due to disagreements in the way people feel that it should be calculated. For example, Dave Cameron used a simple projection of true-talent WAR of free agents to calculate $/WAR in his series on Win Values, but Matt Swartz (who has written a wealth of articles on the topic of $/WAR that I highly recommend) prefers to use retrospective WAR values to determine the cost of a win. In other words, Cameron’s method for $/WAR measures how much production that teams thought that they were paying for, but Swartz’s looks at how much teams actually paid.

So which method to use? I personally prefer Cameron’s method, largely because I think teams are only paying for production that they assume they will get without 100% certainty.

For this article, I used the Marcel projection system to generate predictions for free agents’ fWAR over the course of their contract for all MLB free agents who signed contracts from 2006 through New Years Eve 2017, with a modified aging curve based on the one used by the FanGraphs Contract Estimation Tool. From these projections, I then divided the total projected fWAR by the total monetary value of the contract to get $/WAR. These projections are hardly precise or representative of what teams think a free agent will produce, but they’re good enough that I can get a rough idea of a players’ production over a contract.

On the Linearity of $/WAR

For those unfamiliar with the metric, $/WAR might seem flawed in that it assumes a linear value of $/WAR. It seems unintuitive that a 6 WAR player will cost only twice as much as a 3 WAR player on the free agent market — after all, since 6 WAR players are more scarce than 3 WAR players, it would seem logical that teams would have to pay more for 6 WAR players. Practically, however, this hasn’t been the case.

This is the roughest implementation of a $/WAR scatterplot, but even then, a strictly linear plot emerges. Teams giving out contracts above the line are overpaying based on $/WAR, and teams below are getting a good deal.

But this $/WAR plot is missing a couple of things — for one, inflation. The purchasing power of a dollar in 2006 is not the same as it is in 2017, so we need to adjust our calculation to take that into account (after all, under the $/WAR model, teams are essentially purchasing a good just as an average American might purchase bread at the grocery store). These values will be put in terms of the value of the dollar in 2017.

We also need to take a look at the fact that $/WAR is dramatically different for relief pitchers as opposed to starting pitchers or position players. Since 2006, the cost of a win for starting pitchers is $4.2 million and $5.7 million for position players, but for relief pitchers, the price is $10.9 million. Since WAR accumulation for pitchers is based largely on IP accumulation, and RPs typically only pitch 50-70 IP on a year if healthy, it might be inappropriate to include RPs in our calculation for $/WAR since there clearly exists a wide gap between how teams pay for production from RPs compared to how they pay for SPs and position players.

With this in mind, we can now examine the linearity of $/WAR from 2006-2017, with separate charts for SPs/hitters…

… and for RPs.

It’s blindingly obvious why I can’t lump in RPs with the rest of the FA population — RPs have a dramatically different range of projected WAR values and contract sizes, and their $/WAR slope is much steeper than that of the general population.

But in both instances, $/WAR is generally linear. When we reach the “elite player” end of the curve — the players who are being paid more for more production — there exists quite a lot of variance, but on average, these players still are paid the same rate for a win as players in other parts of the curve. Why is this? Perhaps it is a matter of teams not being pressed for roster space — MLB players have 25 roster spots and 9 starting players, so having a single 6 WAR player gives teams only a small efficiency advantage over having two 3 WAR players. Given how few elite players are on the market at any given time, it would be difficult to quantify that advantage and how much teams pay for it, and thus, the linear model works well.

If we shrunk the MLB’s roster size and starting player size, perhaps then we would see scarcity manifest itself, where it becomes significantly more advantageous to use roster space efficiently. We can look to the NBA, which has a maximum roster size of 15 and only five players take the court at any given time. Here is the $/VORP chart for NBA free agents from 2015-2017 (VORP stands for “Value Over Replacement Player,” and if the name alone doesn’t make it obvious enough, it’s similar to WAR but for NBA players).

This chart is different from either of the MLB $/WAR charts that I’ve discussed thus far — notice how a majority of replacement to low-level players (0-5 VORP) fall below the $/VORP line, and a majority of middle-tier to elite players (5+ VORP) fall above the line. NBA teams are forced to overpay their best players since roster-space efficiency is more important in the NBA. But since MLB teams have an abundance of roster spaces, the consideration of roster space efficiency doesn’t affect the linear model.

On The Luxury Tax Threshold

The linear model that we’re oh-so-in-love-with might start breaking down soon. As the Cespedes Family BBQ twitter account pointed out, very few top-tier free agents have signed thus far this offseason compared to other offseasons. Only two free agents this offseason have signed for contracts of $50 million+, and only Carlos Santana has landed a $20 million+ AAV.

Teams are far more reluctant to sign huge free agent contracts that teams have done in years, partly because of an increasing prevalence of analytics, and partly because of the luxury tax threshold, as Bob Nightengale noted in a column Tuesday, which has led to the slow-down. Teams are waiting longer and longer for big-ticket FAs to lower their prices, and as a result, we’ve had a relatively slow FA market for elite players.

As a result, we might see the linearity of $/WAR begin to fail for elite level players. Simply put, if teams collectively are unable to pay what players feel that they are owed for their production thanks to the luxury tax, players must lower their asking price and accept deals that fall below the $/WAR line, meaning that the slope of $/WAR will decrease at lower levels. While we will need to see what deals players like J.D. Martinez and Yu Darvish accept to verify this effect, it appears as though we may see $/WAR fall at the very least in 2017.

On The Efficiency of FA Contracts

$/WAR also provides us with the ability to judge teams on their ability to make shrewd deals — get the most bang for their buck, if you will. There exists a market price for $/WAR across the MLB, so teams that consistently pay less than the market price are optimizing their payroll cash. Conversely, teams who consistently pay above the $/WAR market price are making significantly less efficient use of their payroll. I’ll exclude relievers from this analysis on the basis that their contracts don’t fit well into our $/WAR model.

I’ve highlighted the five best teams at making efficient deals since 2006 in green and the five worst in red. Surprisingly, the Padres, who are rumored to be offering Eric Hosmer a seven-year contract that would make him the highest-paid-player in team history, have the best history of making efficient deals based on the Marcel projection model. What is hardly surprising is that the historically-sabermetrically-minded Athletics make the top five, in addition to small-market teams like the Padres, Pirates, Rays, and Twins.

On the other end of the spectrum, the teams that have been paying the most $/WAR include the Mets, Diamondbacks, White Sox, Angels, and the Rockies. On average, since 2006, the Rockies have paid almost twice as much for a win on the free agent market as the Padres. Ouch.

I’m very careful to avoid making a blanket statement like “The Padres are the shrewdest investors in baseball,” because the Padres aren’t paying for production on the basis of my model. Instead, they’re using their own tools to determine intelligent investments, like every other front office in baseball. Every front office has their own perspective on the future production of players — but using a highly generalized model, the Padres appear to be doing a good job of investing what little money that they have in free agency.

Unfortunately, smart investing can only take you so far. Baseball is inherently random, and players can suffer career-ending injuries, fall into slumps, or end up like Pablo Sandoval (Sandoval was projected for about 12.2 fWAR over the course of his contract with the Red Sox, but has instead posted -2.9 fWAR during his first three seasons). And only 98 players signed MLB free agent contracts last season, meaning that the other 652 available MLB roster slots had to be filled by other means. Still, it’s wise to play the FA market and play it efficiently — it’s tough to find wins so easily available elsewhere.

The shift has taken the MLB by storm in recent years.  Broadcasters love to criticize the shift, despite its numerous advantages.  One potential problem that the shift may cause is an increase in fielding errors.  This may be a direct result of fielders playing out of their normal position.  Using the shift data provided to FanGraphs courtesy of Baseball Info Solutions, as well as batted ball data courtesy of Baseball Savant, I ran a logistic regression to find the likelihood of a batted ball resulting in a fielding error.

The approach I used to find the probability of a batted ball being a fielding error was to run a logistic regression.  The variables included in the regression were release speed, hitter-pitcher matchup (dummy variable with a value of 1 if the pitcher and hitter were both righties or lefties), runners on base dummy, launch speed (exit velocity), effective speed, launch angle, and dummy variables for both traditional and non-traditional shifts.  The model only included batted balls that were hit in the infield, as the majority of shifts occur in the infield.

Above are the results of the logistic regression used to determine the probability of a batted ball being an error.  The dependent variable is whether or not the error occurred.  Two results that logically make sense are Exit Velocity (Launch Speed) having a positive coefficient and Launch Angle having a negative coefficient.  Both of these variables are significant on the 1% level.  Exit Velocity having a positive coefficient shows that the harder the ball is hit, the harder the ball is to field.  Launch Angle has a negative coefficient, meaning that the lower the angle (meaning a ground ball over a fly ball) the more likely the fielder is to commit an error.  Both of these results are logical, and are consistent with research that has been conducted in the past. The most interesting results from the model are both traditional and non-traditional shifts leading to an increased likelihood of an error occurring.  Both variables were statistically significant on the 5% level, and prove that players struggle more in the field when playing out of their normal position.

While teams are unlikely to change their shifting patterns (more good comes out of the shift than bad), they must take into account which fielders are worse when playing out of position.

Despite the increased probability of an error occurring, I still believe that the positives out weigh the negatives when it comes to shifting.  In future research, it would be interesting to look at this data on a minor league level, as well as seeing if fielders who shifted more in the minors are more prepared to field out of position in the majors.

Over the past few seasons there has been a definite trend toward harder-throwing pitchers in the big leagues. The league average fastball velocity has gone up every year for the past few years, led by hard-throwing reliever Aroldis Chapman. Whether this increase in velocity is leading to a harder time for hitters at the plate would seem to be a topic of big concern for many of these teams who are investing in these hard-throwing players. Currently we see strikeout rates increasing at a rapid pace, but at the same time a home-run surge is happening. Are hitters just swinging hard and hoping to make good contact with these faster speeds? What kinds of effects are these higher velocities having on offensive performance?

Taking data from AB results in 2015-17 we can see how batting average changes for hitters with respect to velocity. Here we can see that average of hitters goes down from close to .300 at pitch velocities down around 90 mph to around .200 at pitch velocities of above 100 mph. Clearly intuitive preconceptions, that faster pitches are harder to hit, seem to be justified by the data. Average, however, is not the be all end all of hitting metrics; we can look at the batting average on balls in play (BABIP) to get an idea of how hitters do when they do make contact with the faster pitches.

Here we can see the opposite effect compared to AVG. BABIP tends to slightly increase as velocity of the pitches go up. This tells us that the higher speeds of these pitches aren’t causing batters to make less solid contact, but they are causing the hitters to miss the ball completely. In addition, the rise in BABIP at the higher end of pitch velocity suggests that when contact is made at that speed it comes off of the bat faster and therefore is more likely to go for a hit. This seems to keep in line with what I was taught growing up: the faster a ball gets to the plate, the faster it leaves. That would suggest, however, that a higher percentage of hits are going to go for home runs when hit off of Aroldis Chapman rather than Bronson Arroyo, but does that happen?

A look at isolated power (ISO) says that the assumption does not hold true. While the physics may appear correct in repeated lab tests, the conditions are not so predictable in the real world. Clearly the decrease in solid contact at higher velocities is having a major effect on power numbers. It seems that even among the balls that go for hits, more of them are ending up as singles than hits from pitches at lower velocities. This is another great sign for teams with hard-throwing pitchers that the money spent is worth it over a conventional pitcher.

The numbers presented in this article help to statistically show what was already intuitively known. Harder-throwing pitchers are harder to hit, and when they are hit, the hits are less damaging. Perhaps the one surprising conclusion was that faster pitches do not tend to result in more extra-base hits and home runs. In fact they lead to quite a bit fewer, even when looking at just balls that fall for hits. This all translates into good news for teams such as the Yankees who have invested a good amount of money in hard-throwing pitchers. Overall, while most likely detrimental to long-term health of the arms of many of the pitchers, I predict that with data like this coming out we will continue to see a trend of arms going the way of Chapman. Hard throwers that can put up a few seasons of good numbers and can be replaced by another hard thrower when they get injured or lose velocity. Speed is an easy trick to pick up and to use, and data here shows its effectiveness. All of that combined should lead to front offices targeting these types of hurlers for years to come.

(All data comes from Statcast and Pitch F/X via Baseball Savant)

For those willing to reinvest, Byron Buxton dangled a carrot of hope on life’s treadmill at the end of the 2016 season. Many lunged for the carrot, others did not.

Those who resisted the temptation found a renewed confidence in their visceral opinions, for about 70 games. Those who bought in, questioned why they continued to buy in, for about 70 games.

Elite speed was always present. Elite defense was always present. But often times relevance to baseball’s general population is contingent upon offensive output, and that’s what drove the division around Buxton at the end of 2016. Buxton rode a .370 BABIP to a productive 165 wRC+ in the Twins’ final 29 games of the season. To start 2017, Buxton put together a 70-game stretch that quickly made us forgot about his final productive month of 2016. (Qualifying because of a small sample will be a recurring theme in this column.)

While I often enjoy looking for mechanical tweaks that fall in line with production changes, the final month of 2016 didn’t bring with it substantial alteration for the righty. The results still manifested for a variety of fleeting reasons, but there wasn’t that “ah ha!” moment — from my digging — that caused some Buxton-doubters to change their affiliations. As we can reluctantly concede in this fickle sport, Buxton was just better in that month.

This point doesn’t hold when you break down, in video, how Buxton progressed as 2017 aged. He adjusted throughout the season and improved production-wise according to various metrics.

Here is a Tweet I sent out a few days ago.

Mentioned within those 280 characters is my interest in Buxton’s elimination of his leg kick, and positive results not coming immediately afterwards. The approach took further tweaking, most notable when you compare Buxton’s upper-body mannerisms in the earliest forms of his tweaked stride (5/27) and in the last frame of the gif above (9/26). But with the final stages of Buxton’s tweaking came another product that I find particularly interesting.

When we look at two of Buxton’s swings from the above gif side-by-side, where I’m going with this point becomes more apparent.

The result of these two at-bats from Buxton are small representations of the trend that will fulfill the title of this column: Buxton and the opposite field. The gif directly above is filled with selection bias and a plethora of other qualifiers, I know. Yet fundamental difference in Buxton’s approach gives an encouraging look at what could come in Buxton’s future. With Buxton’s swing on the left, keep an eye on his hips and lower body as his upper body lunges at this breaking ball on the outside part of the plate. His lower body flies toward the third-base dugout because his initial intention was to take this ball to his pull side.

While this swing actually results in a hit, the contact quality isn’t encouraging. Fooled by the breaking ball, Buxton’s athletic ability allows him to adjust, and put bat on ball, but there is very little opportunity for him to take that pitch — or one that is more hittable — and drive it the other way given the position of his lower body. As with most hitters, Buxton isn’t particularly productive versus breaking balls, slugging only .324 versus a pitch he saw around 20% of the time in 2017.

With Buxton’s closed-off stance, he’s quieted nearly everything from his hands to lower body. His toe tap and nearly invisible stride allow him, even if he is fooled, to stay inside of a pitch on the outer half of the plate. This can hopefully help Buxton’s ability to hit balls on the outer half of plate, a spot most pitchers are going to target regardless of prospect status or lack thereof. Buxton’s lack of production against pitches in this location of the plate, which isn’t uncommon, contributes to the 30 percent strikeout rate Buxton holds against right-handed pitchers.

This minor opposite-field trend is shown in FanGraphs’ rolling average of Buxton’s opposite-field percentage below. From just after the 80th game of Buxton’s 2017 through the end of the season, his tendency to go the other way started to tick upward.

This opposite-field tendency isn’t earth shaking because once again, we’re looking at a relatively small sample of data. But it’s fascinating to look at how far Buxton has come in such a short amount of time. Even though this spray change doesn’t correlate with endless positives — Buxton’s strikeout rate went up compared to his former, pull-happy self — the intentions are correct. The results have yet to manifest in a large sample that I so desire to see.

A productive Buxton can emerge if this approach continues. If he ever evolves into the above-average power tool some speculated he could become is another story. If you were to ask me whether power comes, I would remain doubtful, with Buxton’s current skill set, that it occurs in the next two or three years.

Are these changes a step along the path to power? They very well could be. But is this the end of the road? Unless your ceiling for Buxton is the 20 home runs I think he can edge towards in 2018, the answer is clearly no.

I see no doubt Buxton’s refined stance is better for his long-term value. Stubbornness to change is something I’m convinced Buxton has no conception of, given the multiple variations we’ve seen from the center fielder in his last 140 games.

We’re left with a 24-year-old who has been considered associated with the term “bust.” All the while possessing two plus-plus abilities other prospects would dream to have one of. Whether his other tools venture into merely average or plus territory remains to be seen.

This, among other subplots, is something I’m thoroughly interested to see the progress of in four months.

A version of this post can be found on my site, BigThreeSports.com.

Thanks to Richard Birfer (@RichardBirfs) for help gathering and organizing my thoughts.

When Shohei Ohtani, AKA the Japanese Babe Ruth, was deciding which MLB team he wanted to join, the bet was that it would be an AL team, because of the possibility of batting as a DH when he wasn’t pitching. While he would get some guaranteed PA as a pitcher in the NL, plus probably some more as a pinch hitter, the total would likely have been no more than about 200 in a season. Hitting as a DH, he could theoretically bat every game except the ones he was pitching (because if he were removed from the mound, his replacement would have to hit or be taken out for a pinch hitter). While in practice it’s expected he won’t play every day between pitching starts, he has the possibility of getting, say, 300-400 PA as a DH.

Sure enough, Ohtani chose the AL Angels. No one knows exactly how much he will bat this year, but Steamer projections give him 65 games and 259 PA. Since he’s projected to start 24 games as a pitcher, his batting projections represent about half of the games he’s expected to have available when he isn’t starting (162 – 24 = 138). The hope, obviously, is that Ohtani can provide value with his bat as well as his arm.

But based on Steamer, the bat will not be nearly as productive. While he’s projected to produce 3.1 WAR as a pitcher, as a hitter the expectation is only .5 WAR. After all the talk about how good a hitter Ohtani may potentially be, this seems disappointing. Of course, 259 PA is less than a full season’s worth of hitting, but even if he were able to hit for about a full season — say, 650 PA — and maintained the same rate stats, he would be worth only about 1.3 WAR. That would actually make him a below average player.

Even allowing for the fact that he is a pitcher, his projected WAR value still doesn’t seem that impressive. To put it in perspective, Madison Bumgarner, widely recognized as one of the best hitting pitchers currently playing, had 0.5 WAR last year, despite missing about half the season with an injury. In fact, he produced that 0.5 WAR with just 36 PA, less than 15% of Ohtani’s projected total. And last year was not even Bumgarner’s best as a hitter. His wRC+ was 86, excellent for a pitcher, but in 2014 his wRC+ was 114, and he produced 1.3 WAR in just 78 PA. As we’ve just seen, that’s as much WAR as Ohtani would be projected to achieve in a full season of 650 PA.

Pitcher Hitting is Far More Valuable than DH Hitting

Why is Ohtani’s projected WAR as a hitter so low? It’s not because he’s expected to perform poorly with the bat. His projected wRC+ is 113, just about the same as Bumgarner’s best, and historically good. Since WWII, only 32 pitchers have exceeded that value for a season (minimum 70 PA). And those were career years, whereas Ohtani if anything would be expected to improve his hitting as he matures. In fact, since the live ball era began, only one pitcher has reached a career wRC+ of 100 (minimum 1000 PA): Wes Ferrell, who hit that number exactly. Since WWII, the highest career wRC+ by a pitcher is 81 by Bob Lemon, or 87 by Don Newcombe, who barely misses the 1000 PA minimum; only one other pitcher has even reached 60. Among active pitchers (minimum PA: 300), only Zack Greinke (54) and Bumgarner (51) are > 50, though Bumgarner has been a little over 90 for the past four years.

So Ohtani is projected to be an exceptionally good-hitting pitcher. His WAR problem is the result of playing the DH position. WAR, of course, measures a player’s production relative to other players at that position. The DH generally is one of the best hitters on the team, since any player who is a good hitter can fill that role; it doesn’t matter if he’s a disaster at any defensive position. Pitchers, in contrast, are almost always by far the worst hitters on the team.

Calculating WAR involves summing four values: batting runs + positional runs + replacement runs + league runs. The total is then divided by runs/win, which is currently very close to 10.0. Different amounts of positional runs are assigned to different positions, with pitchers getting by far the largest benefit, and DHs the worst. As of 2017, the positional run value for pitchers was about .119 R/PA*. This is about the same as the league average R/PA, reflecting the view that a replacement level pitcher will produce essentially no runs at all.

Thanks to the positional runs, Bumgarner got a big boost this past season, despite being a below league average hitter with just 36 PA:

36 PA x -.017 = – 0.7 batting runs

36 PA x .119 = 4.3 positional runs

36 PA x .0305 = 1.1 replacement runs

36 PA x. 0015 = 0.1 league runs

Total = 4.8 runs (.5 WAR)

The value of about – .017 R/PA for batting runs is based on a league average R/PA value of .122, and Bumgarner’s wRC+ of 86, or 14% less than average: .122 x – .14 = – .017. The other values can be determined by dividing total PA by total replacement runs or league runs for any hitter with a large number of PA (the larger the PA, the more accurate the calculation). Though Bumgarner was a below-average hitter, his hitting was far above average for a pitcher, and that produces value that is recognized in the very large positional run adjustment.

In contrast, the DH has a very large negative positional run value; as of 2017, it was about – .029 R/PA. Ohtani’s projected WAR for 2018 can thus be calculated as follows:

259 PA x .016 = 4.1 batting runs

259 PA x -.029 = – 7.6 positional runs

259 PA x .0305 = 7.9 replacement runs

259 PA x. 0035 = 0.9 league runs

Total = 5.3 runs (.5 WAR)

The value of .016 RAA/PA for batting runs is based on a 113 wRC+ and a league R/PA value of .122: .122 x .13 = .016.

How Much WAR Would Ohtani’s Hitting be Worth as a National League Pitcher?

So from a WAR point of view, Ohtani is at a considerable disadvantage hitting as a DH, rather than as a pitcher. In fact, the positional disadvantage is so great that it considerably outweighs the fact that he will get many more PA as a DH in the AL than he would as a pitcher in the NL. Assuming his wRC+ is 113, how much value would he produce as a hitting pitcher in the NL? Steamer projects him to throw 148 innings. Assuming he pitched the same total in the NL, and that he batted fairly high in the order (at least, say, fifth or sixth; based on his AL hitting projections of 259 PA/65 games, this should indeed be the case), he might come to the plate as often as 65 times.

65 PA x .016 = 1.04 batting runs

65 PA x .119 = 7.74 positional runs

65 PA x .0305 = 1.98 replacement runs

65 PA x. 0015 = 0.1 league runs (note league runs/PA are less in the NL)

Total = 10.86 runs (1.1 WAR)

So Ohtani, assuming he was the same hitter, would be worth more than twice as much WAR as a hitting pitcher in the NL than as a DH in the AL, though he would come to the plate only about 25% as often (and we haven’t even considered the possibility that he could add further value in the NL as a pinch hitter). This begs the question, actually two closely related questions: 1) how many more PA would Ohtani have to have as a DH to produce the same 1.1 WAR he would produce as a NL pitcher? 2) how high a wRC+ would he have to have as a DH with 259 projected PA to match that 1.1 WAR?

In both cases, Ohtani would need to produce about 5.6 more runs above replacement. To do that while maintaining his projected 113 wRC+, he would need about 266 more PA, or a total of about 525. To do that while maintaining his projected 259 PA, he would have to elevate his wRC+ to 131.

Of course, if were able to produce a 131 wRC+ in the AL, he could presumably do it in the NL, too, which would increase his value there. It would not increase it as much, though, because of his much fewer PA. So a better question to ask would be: how high does his wRC+ have to be to match his NL WAR, given the projected PA of 259 as a DH, vs. 65 as a NL pitcher? It turns out his wRC+ would need to be about 137. Above this value, he would produce more WAR as a DH, while below it he would produce more WAR as a pitcher. This value is close to what is usually considered the mark of an elite hitter, 140.

However, the projected PA values that we’re working with may be low if we want to consider Ohtani’s potential in years beyond his rookie MLB season. On the one hand, if he proves to be a good hitter, he may get more PA. We might project a maximum of 400 PA. To get this many, he would have to play as a DH in about 100 games. Of the remaining 62 games, he would pitch in 24 and rest in 38. In order to rest both on the day before and the day after he pitches, he would need a total of 48 rest days, but the remaining ten might come on the team’s day offs.

With regard to pitching, if Ohtani were in the NL, and becomes an ace, let’s assume he would start a little more often, and log a total of 90 PA as a pitcher. This is pretty close to a maximum value in the current environment; in the past decade, only seven pitchers have had more PA in a season. In addition, let’s assume he appears as a pinch-hitter 110 times, giving him a total of 200 PA. In the 90 PA as a pitcher, his positional run value would be .119 R/PA, as explained before. In his 110 PA as a PH, we assume his positional run value is – .029 R/PA, the same as for a DH in the AL.

Using these values, we can estimate the number of runs above replacement Ohtani would be worth in the NL, compared to the AL, for various values of wRC+:

1 – Assumes 90 PA as a pitcher + 110 PA as a pinch-hitter

2 – Assumes 400 PA as a DH

Because of the larger number of PA as a pitcher, plus the additional PA as a PH, Ohtani now produces more run value in the NL up to wRC+ values > 140. He would have to have a wRC+ of nearly 150 before he would produce more value as a DH.

Has Ohtani’s Decision Eliminated Some of His Potential Value?

Will Ohtani be as valuable a hitter in the AL that he could he have been in the NL? Probably not. If we start with his projected stats for 2018, he will produce only about .5 WAR as a DH. Assuming the same wRC+ of 113, and the 65 PA likely to accompany his projected 148 IP, he would produce more than twice that total, about 1.1 WAR, as a pitcher. This is because the positional advantage for a pitcher is huge, while there is a large positional disadvantage for the DH.

Some of this value gap may be reduced if Ohtani becomes a much better hitter than is projected for 2018, because the much greater number of PA available as a DH allows him to take greater advantage of better hitting. But he would have to hit considerably better. Still assuming 259 PA as a DH vs. 65 PA as a pitcher, his wRC+ would have to nearly 140, an elite level, for his WAR as a DH to match that of a pitcher in the NL. That wRC+ value could be lowered to as much as 120 if Ohtani were to log as many as 400 PA, which seems close to the maximum compatible with his pitching program. But we might also argue that were he in the NL, he would perhaps pitch a little more often, and thus receive more PA as a pitcher, plus appear as a PH in most games in which he didn’t pitch. Making what I think are some reasonable assumptions about total PA under these conditions, Ohtani would have to produce at nearly a 150 wRC+ clip to produce as much value as a DH. Only seven qualified hitters managed that this past season.

Considering the reputation as a hitter that accompanies Ohtani as he comes to the U.S., this seems a little deflating. Even if he managed to produce a 150 wRC+, which seems quite unlikely, his total hitting WAR would be about 2.6. That would just about equal Wes Ferrell’s mark in 1935, the highest single season WAR for a pitcher in the live ball era, which certainly would be a major accomplishment. But it would not be that much more than a good hitting pitcher like Bumgarner manages even without pinch-hitting, nor would it add so much to Ohtani’s total WAR as a two-way player that his combined value would likely reach historic levels. If he is to finish anywhere near the top of the WAR leaderboard, it will have to be mostly through his arm, not his bat.

But even if Ohtani produces relatively little WAR as a hitter, this should serve as a reminder that there are different ways to understand value. The prospect of a pitcher who can hit well enough to DH even just part of the time has another kind of value to a team. Ohtani’s presence as an option at DH may open up a roster spot for another player, much as Ben Zobrist has had value beyond his WAR because of his ability to play multiple defensive positions. Surely the Angels are aware of this, and won’t be put off by his actual WAR totals.

*Though pitchers are not usually included in discussions of positional runs, this value can be calculated from the values table for the batting data of any pitcher. It corresponds roughly to 80 total runs for a whole season, batting every game, though of course pitchers never even approach this.

Tyson Ross is going back to San Diego on a minor league deal! This is realistically concerning a veteran released by the Rangers, coming back for his second go-round on the rebuilding Padre pitching carousel. However that much is obvious, and thus one may wonder where things went awry for the him during the 2017 campaign. After all, Ross produced 4.3 WAR in 2015 for the club. He has proven to be a valuable and talented pitcher in previous seasons, so looking at the ways in which he may rediscover what made him prosperous during the aforementioned years is an intriguing investigation.

To grasp the essence of the struggles Ross has experienced, one must examine the specific aspects of his 2017 decreases in performance. Ross was the Padres’ 2016 opening day starter, yet was shelled in the start and spent the rest of the season on the DL with shoulder inflammation. He had thoracic outlet syndrome surgery on his shoulder between the 2016 to 2017 seasons, which seems to have taken away from the quality of his stuff. He spent the 2017 season with the Rangers, yet was relegated to a bullpen role in September after a dismal ten starts. The Rangers released him on September 12th, 2017.

Ross has always battled injuries throughout his career, which is the primary concern above all else. That much is obvious. However that doesn’t mean there aren’t other parts of his profile to consider. Comparing his 2015 career year to his 2017 season statistically, one notices many red flags. His BB/9 rose drastically from 3.86 in 2015, to an alarming 6.80 BB/9 last season. Walks were always a somewhat prevalent part of his profile as is, yet this high of a walk rate certainly raises eyebrows. His 61.5% ground ball rate from 2015 decreased to 46.8% in 2017. His heat maps of pitches in 2015, compared with 2017, illustrates why his ground ball rate decreased so drastically, and shows a part of why his ERA ballooned to 7.71 last season:

2015:

2017:

Ross threw more pitches towards the low outside corner to right-handed hitters in 2015, that he did less of in 2017. In the lower part of the zone, he more often caught the middle part of the plate, which explains a part of why he didn’t induce as many ground balls, and was hit so hard last season. One can identify, based on the data presented above, that the primary culprit of Ross’ struggles was pitch location; he threw more pitches over the heart of the plate, and fewer toward the outside corners. What is puzzling is why Ross threw so many pitches up and in to right-handed hitters, as one can see in the top left hand corner of the chart, in 2017. After all, he didn’t get a single whiff on pitches in that location all year! See the chart below:

2017:

Ross likely didn’t mean to throw pitches in that upper-left hand location on the chart, yet the fact that he did so often is indicative of his struggles with command. In video analysis, there were subtle differences in how Ross finished his delivery in San Diego compared with in Texas. He wasn’t finishing his delivery as well as he could have at times in 2017 with the Rangers, which was a likely cause for his missing up and in to hitters with noticeable frequency.

Another significant issue for Ross was the deterioration of his stuff. His average fastball velocity, for example, dropped from 93.9 mph in 2015 to 91.6 mph this past season. His slider also lost velocity, which dropped from 87.2 mph in 2015 to 84.7 mph in 2017. He has lost about two ticks in velocity since he had thoracic outlet syndrome surgery on his shoulder. Perhaps he won’t get his old velocity back, in which case he’ll have a smaller margin of error.

Ross threw a cutter more often in 2017, which was an interesting development, however the implementation of the pitch did not have a significant effect on his performance. Ross very rarely throws a changeup, and primarily relies upon his fastball / sinker and slider combination. Given that he really only uses a single secondary pitch, one would expect that it is more affected by his drop in velocity. One of the ideas of throwing a slider is to make it look like a fastball that breaks away late, fooling a hitter who is hopefully out in front of the pitch. Obviously Ross is not throwing as hard as he used to, so there’s more time for the hitters to see all of his pitches, comparatively with the velocity of his stuff in 2015.

Ross’ slider had a 12.2% whiff rate in 2017, whereas in 2015 it drew a 23.4% whiff rate. His slider has always been his wipeout pitch, and the fact that it has not been as effective is a reflection of his loss in velocity and ability to command pitches this past season. The movement on his slider was not significantly different this season compared to 2015. Thus, the decrease in velocity, along with Ross’ command issues, can be most logically blamed for his rough 2017 campaign. Check out his slider location in 2015, followed by where he threw it last year:

2015:

2017:

Above it’s clear that Ross didn’t locate his sliders particularly well last season. In 2015, he did a much better job placing it on the outside corner away from right-handed hitters, and burying it in to left-handed hitters with consistency. The intention looks similar on both charts, though the execution of putting the ball where he wanted to was superior in 2015, compared with his efforts to do so in 2017.

He should be throwing his pitches low and away more often, as he did in 2015 with the Padres. If he can do more of that, and retain his old velocity, he could end up being a steal for the Padres in 2018. That will help him induce more ground balls, whiffs, and weak contact. Being able to throw his fastball and sinker down and away will go a long way in terms of generating the ground balls he was so previously talented at generating.

Hopefully Ross will regain his velocity, and have better command of his pitches next season. In San Diego he flourished with Padres pitching coach Darren Balsley, and he’ll have every chance to win a job in the rotation this spring. It should be good for him to be working with Balsley again, and to return to an organization where he is likely fairly comfortable. Given the home run spike and juiced ball, it makes sense to root for guys like this to get back to being the impressive pitchers people hope they can be once again.

All the data used in this article is from Brooks Baseball.

Pitching performance can often be pretty volatile and difficult to predict. Look at Rick Porcello’s 2017 season, for example. After turning in a Cy Young-winning season in 2016, he regressed to have a below-average ERA. His ERA ballooned from 3.15 in 2016 to 4.65 in 2017.

This is where predictive pitching metrics come in. By just looking at Porcello’s ERA from 2016 it may have been hard to predict his 2017 ERA. Thus, we should use different metrics to better predict his performance.

One popular statistic for more accurately quantifying and predicting pitching performance is FIP (Fielding Independent Pitching). FIP attempts to approximate a pitcher’s performance independent of factors which the pitcher cannot directly control himself, such as his defense’s performance. For example, a good pitcher with a weak defense can induce lots of weak contact but still give up lots of runs due to his defense’s inability to successfully field a lot of balls. Additionally, luck may play a significant factor in how many runs a pitcher concedes. A pitcher may be unlucky and give up lots of bloop hits, or weakly hit balls that land away from fielders. Thus, FIP focuses on the factors that pitchers can directly control, such as strikeouts, walks, hit batsmen, and home runs.

The formula for FIP is:

FIP = (13*HR + 3*(BB + HBP) – 2*K) / IP   +   FIP constant

where HR is home runs allowed, BB is walks allowed, HBP is hit batsmen, K is strikeouts, and IP is innings pitched. FIP is scaled to ERA (Earned Run Average) by the FIP constant, and can be read the same way as ERA (i.e., lower FIP corresponds to better performance).

FIP’s formula may look complicated, but all it does is weight certain pitching statistics per inning pitched. Because a favorable FIP is one that is lower, strikeouts are weighted negatively since they contribute to favorable pitching performance, and home runs, walks, and hit batsmen are weighted positively since they contribute to unfavorable pitching performance. Home runs are weighted the most positively (at a coefficient of 13) because they are most detrimental to pitching performance and cause the most runs to be allowed.

Variability Between FIP and ERA

FIP provides an estimate of pitching performance independent of defensive performance and luck. If it is compared to ERA, the variance between the two statistics can provide an estimate of how much defensive performance or luck affects the number of runs allowed by a pitcher. FIP and ERA can be compared by creating a distribution of FIP – ERA for yearly pitching performance. In Figure 1, a distribution of FIP – ERA for all single-season starting  pitching performances (minimum 162 innings) from 2011 to 2015 is created using FanGraphs’ databases. The spread of this distribution is fairly symmetrical. The average FIP – ERA is 0.058 runs, meaning that qualified starting pitchers tend to have slightly higher FIPs than ERAs. The standard deviation is 0.498 runs, signifying that on average starting pitchers’ ERAs tend to differ from the average FIP – ERA of 0.058 by 0.498 runs. Thus, defensive performance and luck cause a starting pitcher’s ERA to differ from what it would be based off fielding-independent factors by about a half run.

Figure 2 shows a distribution of FIP – ERA for all single-season relief pitching performances (minimum 50 innings) from 2011 to 2015. Like the distribution for starting pitchers, the spread of FIP – ERA for relief pitchers is fairly symmetrical. However, the average FIP – ERA is 0.253 runs, meaning that on average qualified relief pitchers have significantly higher FIPs than ERAs. A possible reason for this could be that relief pitchers often throw harder than starters and can induce weaker contact from hitters, thus allowing the defense to convert more outs off balls in play than they would normally. Additionally, the standard deviation is 0.734 runs, meaning that on average relief pitchers’ ERAs tend to differ from the average FIP – ERA of 0.253 by 0.734 runs. Thus, defensive performance and luck cause a relief pitcher’s ERA to differ from what it would be based off fielding-independent factors by close to one run.

Predicting Future Pitching Performance

FIP is also useful in that it can help predict future pitching performance. Since the fielding-independent statistics that FIP uses in its formula (strikeouts, home runs, walks, hit batsmen) tend to stay more constant year to year than ERA, FIP tends to be consistent than ERA year to year. Thus, due to its lack of variability, it can be a better estimator for future pitching performance.

To determine how well ERA and FIP predict future pitching performance, the pitching statistics for the 50 pitchers that pitched at least 162 innings in both 2014 and 2015 are obtained. 2014 ERA and FIP are tested to see how well they predict 2015 ERA by looking at their correlation with 2015 ERA. This is demonstrated by Figure 3, which tests how well 2014 ERA predicts 2015 ERA. There is a moderate, positive, linear relationship with a correlation  coefficient of 0.382. Thus, it can be said that 2014 ERA is a moderately accurate predictor of 2015 ERA. Figure 4 demonstrates how well 2014 FIP predicts 2015 ERA. There is also a moderate, positive, linear relationship, but the correlation coefficient is higher at 0.462. Thus, there is a stronger relationship between 2014 FIP and 2015 ERA, and it can be said that 2014 FIP is a better predictor of 2015 ERA.

However, FIP is not the only fielding-independent statistic that is commonly used. xFIP is a variant of FIP that uses a pitcher’s fly ball rate instead of home runs in its formula. The logic behind this is that fly balls a pitcher gives up are a strong indicator of how many home runs a pitcher will give up in the future — an even better indicator than home runs themselves. The formula for xFIP is:

FIP = (13*(Fly Balls*League Home Run per Fly Ball Rate) + 3*(BB + HBP) – 2*K) / IP   +   FIP constant

Figure 5 demonstrates the relationship between 2014 xFIP and 2015 ERA. Similar to the aforementioned relationships, there is a moderate, positive, linear relationship, but with an even higher correlation coefficient at 0.520. Thus, in comparison to ERA and FIP, xFIP is the strongest predictor for pitcher success.

Skill-Interactive ERA, abbreviated as SIERA, is another fielding-independent statistic. It is a variant of xFIP, but it accounts for various factors that make xFIP less accurate. For example, each walk given up by a pitcher is less detrimental if he generally walks few batters, whereas each walk given up by a pitcher is more detrimental if he generally walks more batters. Thus, SIERA takes this into account. The complete formula of SIERA can be viewed here. Figure 6 shows the relationship between 2014 SIERA and 2015 ERA. There is a moderate, positive, linear relationship with a correlation coefficient of 0.517. This is almost the same as xFIP’s correlation coefficient with 2015 ERA, which was 0.520. Overall, there is likely not a very significant difference in predicting ERA using SIERA or xFIP, but this assertion can be better tested through obtaining more data.

Conclusion

What can be concluded from this piece is how much defensive performance and luck can alter a pitcher’s ERA, and what statistics should be used to predict future performance for pitchers. On average defensive performance and luck account provide about half a run in variation of a starting pitcher’s ERA, and about one run in variation of a relief pitcher’s ERA. Additionally, the statistics that are most effective in predicting future pitching performance are xFIP and SIERA.

Acknowledgments

I want to thank my AP Statistics teacher, Ms. Rachel Congress, for teaching me a lot of the material about statistics that I applied in this paper.

Bibliography

DuPaul, Glenn. “Occam’s Razor and Pitching Statistics.” The Hardball Times. FanGraphs, 26 Sept. 2012. Web. 24 May 2016.

“Fielding Independent Pitching (FIP) Added to Baseball-Reference.com » Sports Reference.”
Sports Reference RSS. Sports Reference, 17 Apr. 2014. Web. 24 May 2016.

A Guide to Sabermetric Research.” Society for American Baseball Research. Society for American Baseball Research, n.d. Web. 24 May 2016.

McCracken, Voros. “Baseball Prospectus | Pitching and Defense.” Baseball Prospectus. N.p., 23 Jan. 2001. Web. 24 May 2016.

Petti, Bill. “How Teams Can Get the Most Out of Analytics.” The Hardball Times. FanGraphs, 27 Jan. 2015. Web. 24 May 2016.

Sawchik, Travis. Big Data Baseball: Math, Miracles, and the End of a 20-year Losing Streak. New York: Flatiron, 2015. Print.

Swartz, Matt. “New SIERA, Part Three (of Five): Differences Between XFIPs and SIERAs.”
Baseball Statistics and Analysis. N.p., 20 July 2011. Web. 24 May 2016.

Swartz, Matt. “New SIERA, Part Two (of Five): Unlocking Underrated Pitching Skills.” Baseball Statistics and Analysis. N.p., 19 July 2011. Web. 24 May 2016.

Over the last year I made a series of studies on Statcast and I thought it would be interesting to write a little overview article to summarize my findings.

In June I looked at the launch angle profile of the league. The average went up of course, but it accelerated faster at the top than at the bottom, so we have not reached a stage of consolidation yet where the league is moving closer together in launch angle, which ultimately should be expected (the LA is increasing at the bottom but less than at the top.

That means there still is room for more growth in elevating but mostly in the bottom half of launch angle.

In the above I found that there are limits to elevating. I found the top guys usually average 11-16 degrees of launch angle. Below that players definitely can benefit from elevating more.

Then I was looking at the cost of too much elevation. A common theory is that swinging up more leads to more Ks because you are not really matching the plane of the pitch. I found a small effect there but nothing really big.

However I did find that there is a BABIP cost, especially if it comes with pulling the ball, and confirmed that with more research and found out that elevating more without a BABIP cost is possible if you get off the ground while limiting pop-ups and high outfield FBs above 30 degrees like Daniel Murphy does very well, while the 50+% FB guys with 20+ degrees of average LA tend to have low BABIPs, especially when coupled with pulling a lot to sell out for power.

I also looked at the relationship of EV and LA and unsurprisingly found out that between like 8 and 20 degrees, exit velo doesn’t matter much, while above 20 degrees almost all production comes from homers. Balls above 20 degrees and below 95 MPH are basically worthless so you need a certain minimum power to make elevation work. Off the ground is always good, but for some it might make sense to stay between 5 and 20 degrees.

Not quite related to that topic, I also created a formula for the relationship between power, patience, and K rate. An old argument between sabermetric and traditional writers was whether Ks matter. We know that Ks are not worse than other outs and high-K hitters do not perform worse, but that is also because there is a selection bias against high-K, low-power guys. Everything being equal, low Ks is better, and I found a pretty linear relationship between K, BB, and ISO.

If production is equal, Ks obviously don’t matter, of course.

If you could pick any catcher to have on your team for the next, say, five years, who would you pick?

The first name that probably comes to most baseball fans’ minds is Buster Posey, who has been the undisputed best all-around catcher in the game for the past several years. Additionally one might think of rising stars such as Gary Sanchez, Willson Contreras and J.T. Realmuto. One is not wrong for doing so, as the four names I mentioned are all fantastic players and certainly deserve credit for being some of the top catchers in the game. But if I had to choose just one catcher to have on my team for the next five years, I would not pick any of those guys.

I would pick Austin Barnes.

Some might think I’d be crazy for picking a guy who was a backup catcher for almost all of last year. But Barnes was, in my opinion, the Dodgers’ deadliest secret weapon. Everyone knew about the sudden emergence of Chris Taylor and Cody Bellinger, but Barnes often got lost in that conversation. While he didn’t receive as much playing time as the average starting catcher, he was one of, if not the best catcher in baseball in the playing time that he did receive. Among catchers with at least 250 PA (Barnes had 262), he ranked first in wRC+ with 142, ahead of Sanchez’s 130, Kurt Suzuki’s 129 and Posey’s 128. Relatively small sample size aside, Barnes was the best hitting catcher in baseball last year.

So if Barnes was the best hitting catcher in baseball, why did he spend almost the entire year as a backup? Well, we can’t blame Dave Roberts too much for that one, considering that they also had Yasmani Grandal, a guy who has established himself as one of the best catchers in baseball with his elite framing skills and power. However, while being a switch hitter, Grandal has always been worse as a right-handed batter than as a left-handed batter, (106 wRC+ vs. 117) and had considerably less power against lefties (.138 ISO vs. 211), so Barnes was used mostly against lefties while Grandal played most games where the Dodgers faced a right-hander. And while one could argue that Barnes’s success was a product of playing against more favorable matchups, he actually had a reverse platoon split, hitting worse against lefties than he did against righties (136 wRC+ vs. 147).

In the middle of the season while Barnes was posting better numbers than Grandal and the Dodgers were in the division race, I do think that it was actually smart of the Dodgers to continue playing Grandal over Barnes the majority of the time, since Grandal was an established player and there was understandable skepticism that Barnes would maintain these numbers. It’s not uncommon for mediocre players to ride an insane BABIP-fueled hot streak for a month or two before regressing back into mediocrity. Just look at Sandy Leon’s 2016. But as Barnes started to get more at-bats and Grandal started to regress in the second half, it became clear that Barnes was not just a fluke, but a legitimately really good player.

First, the offensive side of things. As mentioned earlier, Barnes had the best wRC+ among catchers with at least 250 plate appearances. He hit .289 with a .329 BABIP, which is a little high but certainly not unsustainable considering his above-average batted ball profile. His quality of contact percentages were all roughly or slightly below league average, but what sticks out is that he hit line drives 6% above league average, and instead of strictly pulling the ball, he went up the middle and used the opposite field a lot. Barnes maintaining a .289 average in the future is a completely reasonable proposition.

Perhaps the most undervalued part of Barnes’s game was his above average power. He had a .197 ISO, so he wasn’t just some singles-only slap hitter. To put that into context, Barnes had more power than Corey Seager, Hanley Ramirez and Joc Pederson. So while he hit line drives to all fields and was no slouch in terms of power, probably the most impressive part of his offensive profile is his plate discipline. Barnes walked 14.9% of the time while striking out only 16.4% of the time. His BB/K of 0.91 was second among catchers behind only Posey’s 0.92, and 11th in all of baseball. This was due to his tremendous plate discipline and selectivity. He swung at only 17.4% pitches outside of the strike zone, whereas the average MLB batter swung at 29.9%. And when he did swing at pitches outside of the zone, he made contact 7.8% more often than the average batter. While he did swing at pitches in the zone at a below average rate, due to his selectiveness, he made contact with the pitches he did swing at in the zone 7.3% above league average at 92.8%. This is the sign of a batter with a truly great eye, swinging at the pitches he was confident he could hit while laying off the ones he couldn’t. As a result, he swung and missed only 4.7% of the time.

But let’s get back to the original question that I’m trying to answer: why I would pick Barnes over any other catcher to have for the next five years. A lot of people might pick Posey due to his track record, but I would pick Barnes because, as I’ve explained, I believe he can sustain the numbers he put up this year. Plus, Posey has been declining in the last few years, specifically in his isolated power, which has been worse than Barnes’ ISO in every year of Posey’s career except in 2012 when he won MVP. One could technically argue that Posey is still better than Barnes due to the tiny edge in BB/K, but while Posey has similar overall plate discipline, he also walks 4.2% less than Barnes and has been experiencing a power decline as he’s gotten older. His ISO last year was .142, .055 lower than Barnes. Plus, he’s three years older and on the wrong side of 30. Plate discipline is a skill that ages well, but power is not, and the fact that Posey has about equal plate discipline and significantly worse and declining power easily puts Barnes over the edge for me. I’m a believer in the notion that strikeouts don’t matter that much as long as you walk a lot, so I’ll gladly take Barnes’ extra walks over Posey’s lower strikeout rate, meaning I prefer Barnes’s plate discipline and power over Posey’s. Posey’s career accomplishments can’t be denied, and I’m sure he’ll still be great in the next few years, but I would much rather take my chances with a less-proven Barnes.

Defense (which is also extremely important for catchers) is a whole other story I’ll get to after I wrap up my analysis of his offense. But as far as offense is concerned, Barnes simply has a much more impressive and well-rounded offensive profile than any other catcher in the game today. Does he do everything better than everyone? No. Sanchez has more power and Realmuto is a better baserunner. But Barnes is the best overall hitter among them, walks more than all of them except for Alex Avila and Andrew Knapp (who are clearly worse catchers than Barnes for a myriad of other reasons), strikes out less than most of them, has above average power, and has speed. Using Bill James’s Speed rating, or “Spd,” Barnes gets a 4.9, which puts him 4th among catchers, a tick behind Realmuto and Chris Hermann (5.0) and Christian Vazquez (5.1). Catchers have always been notoriously slow, so to have a serviceable runner who can steal bases and take an extra base from the catcher position is extremely valuable, especially considering how awful most other catchers are at running. His baserunning is admittedly far from perfect, as evidenced by his -1.6 BsR, but he definitely has the speed and athleticism to steal more than the four bases he stole this year, and really anything you can get out of the catcher position in terms of baserunning is valuable, considering that there are MLB catchers who go multiple seasons without even attempting to steal a base. Barnes’s combination of contact, plate discipline, power and speed are the most well-rounded of any catcher in baseball, and it’s a coach’s dream to have a player with the amount of tools that he has.

Of course, these offensive tools would be valuable at any position. But what really makes Barnes special is that he’s additionally a fantastic fielding catcher. In Baseball Prospectus’s Fielding Runs Above Average, which combines framing runs, blocking runs, throwing runs, and basic defensive components such as fielding ground balls, Barnes ranked 9th among all catchers and 8th in FRAA_ADJ, which takes out the “normal” FRAA components that are included in all players’ FRAA and instead focuses just on a catcher’s framing runs, blocking runs and throwing runs. If Barnes had played the same amount of innings at catcher that Grandal played, while defending at the same level, he would have had 23.7 FRAA, which would have been 2nd among catchers behind only Austin Hedges, and 25.5 FRAA_ADJ, which would have led baseball. Barnes is an elite defensive catcher. To say exactly how good he is would be tough due to the imperfectness of these fairly new defensive statistics and the relatively small sample size. But another argument one could make for starting Grandal over Barnes could be that Grandal is a great defensive catcher, which he undoubtedly is, but Barnes is just as good if not better. Additionally, Grandal had an alarming 16 passed balls in 2017, his second straight year leading the league in passed balls, while Barnes had just three. While Barnes is a fantastic defensive catcher, he’s also shown that he can play a serviceable second base as well due to his agility and athleticism that few catchers have.

Overall, there really just isn’t anything Barnes can’t do. He hits for average, gets on base, has great plate discipline, can hit for power, plays a great defensive catcher, and can even play second base. He’s a little old for a rising star, but still relatively young as he’ll be playing his age-28 season in 2018, and I would prefer to have him on my team than a younger catcher like Sanchez, Realmuto or Contreras. Posey’s entering a power and age decline, and while Sanchez and Contreras may be “flashier” with their towering home runs, I believe Barnes has a more well-rounded toolset that will age well and provide value even if he does happen to struggle with the bat, which I don’t think he will due to the reasons explained earlier. Realmuto is basically Barnes with slightly less power and far worse plate discipline, but is more well known by most fans, mostly due to him having already established a starting role. A case could certainly be made for any of these guys over Barnes, but after looking at the strengths, weaknesses, and tools of each player, I would be extremely confident to pick Barnes over star catchers such as Posey, Sanchez, Contreras, Realmuto, Grandal, Mike Zunino, and any other active catcher. Am I overreacting to 262 plate appearances? Maybe. But after looking closely at the stats and watching Barnes develop as a player, I am fully confident that he will blossom into one of the best if not the best catcher in the game over the next five years.