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.

I wondered how the power revolution changes the impact of power on winning. Does the abundance of HR mean that HRs are less valuable? Or are they even more necessary?

For that I compared 2017 and 2008. 2008 is kind of an arbitrary cutoff; I used it because it was 10 seasons ago and not a completely different game.

In 2008 the top-10 HR-hitting teams averaged 86 wins, and in 2017 just 82 wins. Also in the top 10 in HRs in 2008, three teams had losing seasons, and in 2017 it was a whopping five teams. So it seems being a top-HR team helps less.

However, when looking at the bottom 10 HR-hitting teams, it is 74 wins for both years. Three teams of the bottom 10 in HRs had winning seasons in 2008 versus just two in 2017. So it didn’t become easier to succeed as a no-power team.

The league also got closer together in HRs. In 2008 the bottom-10 average was 127, and it as 1.6 times as much for the top 10 (197). In 2017 it was 172 for the bottom and just 1.3 times as much for the top (230).

Of course park factors and year-to-year variations play a role, but last season Colorado wasn’t even in the top 10 for example.

So it seems power is at least as much needed to win as it used to be, but it isn’t really much of a difference maker anymore, it is more a baseline needed to win. But teams like the Rays and A’s who hit tons of homers in a pitcher’s park show that you can’t really build around power as a main skill; you need to make sure you don’t suck at power, but since you can’t really separate anymore with power, you need other primary skills.

I would probably say make sure to be in the top third in power, but once you are there, don’t sacrifice other stuff to get even more power.

That is especially true for defense. The A’s led the league in average launch angle and were fourth in HRs. Since they were only seven HR behind the Yankees and four behind the Astros in a vastly less hitter-friendly park, we can probably say they were the top HR-hitting team.

They tried to sell out for power and it clearly wasn’t enough to make up for historically bad defense and other flaws.

So teams definitely shouldn’t sacrifice in other regards; there is enough power around to not put bad defenders or super low OBPs in the field to get more power.

Power is as important as it ever, was but it is not possible to dominate with it anymore like the 1927 Yankees did. Now it is now one necessary skill of many and well-roundedness is the name of the game in 2017. Same can be said for contact-hitting. People said after 2015 that contact was the future. However, low-power slap hitting didn’t prove to be successful, but with power now available so easy, teams now might be able to cut back on the Ks a little without sacrificing power like the Astros did, because super high Ks can suppress on-base percentage when it doesn’t come with Adam Dunn-like walks.

Last month, the sabermetrics community descended into complete and utter anarchy over the latest and greatest debate on WAR. Industry heavyweights like Bill James, Tom Tango, and our own Dave Cameron all weighed in on the merits of baseball’s premier metric. After the dust settled, Sam Miller published an article on ESPN igniting a different sort of debate on WAR.

Miller’s piece noted that aside from the possible flaws behind WAR itself, each corner of the internet is calculating it a different way. For pitching specifically, FanGraphs (fWAR), Baseball Reference (rWAR), and Baseball Prospectus (WARP) all publish measures of WAR that oftentimes have significant disagreements. But that’s by design.

These three metrics were brilliantly characterized by Miller as so:

• rWAR – “What Happened WAR”
• fWAR – “What Should Have Happened WAR”
• WARP – “What Should Have Should Have Happened WAR”

The rest of the piece is outstanding, and comes highly recommended by this author. In the aftermath, though, Tom Tango of MLB Advanced Media responded with the following challenge:

For you aspiring saberists: take the 10 pitchers that fWAR and rWAR most disagree with in year T, and tell us how they did in year T+1. Do that for say the last 5 years. Repeat the exercise with fWAR, WARP, then with rWAR, WARP. Report your findings. https://t.co/BjtbBRYhMd

— Tangotiger 🍁 (@tangotiger) November 28, 2017

Given that I humbly consider myself to be an aspiring saberist, I took that challenge. Well, I first took the challenge of college final exams, but then the pitching WAR challenge!

The dataset from which I worked off included 1165 qualified individual pitching seasons spanning from 2000-2016. For each season, I collected the player’s fWAR, rWAR, WARP, RA9-WAR, and RA9-WAR in the subsequent year. As Tango suggested, using RA9-WAR to look retrospectively at our 3 competing pitching metrics will be the most effective way to measure the differences amongst the metrics themselves.

For those interested in the raw data, feel free to check it out here, and make a copy if you’d like to play around with it yourself.

Given the nature of the dataset, a logical first place to start was with a straightforward correlation table and go from there. That correlation table is displayed below.

As expected, small differences do exist between the various metrics in their abilities to predict future performance. In the sample, fWAR leads both WARP and rWAR by slight margins. For all you statheads out there, a linear regression on the data returns statistically significant p-values for fWAR and WARP, but not rWAR.

So that was fun, wasn’t it? With all of the nitty gritty math out of the way, let’s dive into a few examples. Miller already highlighted Teheran’s strange 2017 season, but as it turns out, there are far more extreme instances of metric disagreement.

Take Felix Hernandez’s 2006 season for example. His first full season in the bigs culminated in an underwhelming 4.52 ERA, but a 3.91 FIP and a 3.37 xFIP were promising signs of future success. Similarly, the WAR metrics were unable to come to any sort of consensus.

By WARP, the 20-year-old Hernandez was the 14th best pitcher in 2006. He was surrounded on the leaderboard by names like Roy Halladay, Randy Johnson, and Greg Maddux. By rWAR, his 2006 season ranked 135th alongside Jose Mesa, Cory Lidle, and interestingly enough, Greg Maddux.

fWAR, on the other hand, seems to have found a happy medium between the other two metrics. Sure enough, it was also the most accurate predictor of Hernandez’s RA9-WAR in 2007.

Taking a step back, I now wanted to determine which of the three metrics was the most accurate predictor of a pitcher’s future RA9-WAR. Just as Tango does, we’ll call the current season”Year T” and the next “Year T+1.” The results of this exercise are displayed below.
Yet again, we see a slight victory for the FanGraphs WAR metric. However, with over 1100 seasons in our sample, no single metric stands apart from the others. After all, they are designed with the same goal in mind: measure pitcher value. As you’ll see below, each metric usually ends up with a similar result to the others. (Click to view a larger version)

What happens, though, in instances like Teheran’s? When the metrics have stark disagreements with each other, which metric remains most reliable? To answer this question, I dug up the 10 most significant head-to-head disagreements among each of the metrics, and again looked at which version of WAR best predicted the RA9-WAR in Year T+1. Those results are listed below.

What stands out to me here is not only that fWAR still appears to be the best forward-looking metric, but also that in nine of its ten most significant disagreements with rWAR, the DIPS approach to WAR won out.

Just as in “The Great WAR Debate of 2017,” this discussion too is entirely dependent on what one intends to use WAR for. Here, we’ve established fWAR as an excellent forward-looking metric. Depending on who you ask, rWAR likely serves its best purpose illustrating, as Miller put it, what did happen. WARP may either be many years ahead of its time, or could still use a fair amount of tweaking. Or both. No matter, each version of pitching WAR comes with its own purpose, and each purpose has its own theoretical use.

Jake Arrieta turning himself from a Baltimore castoff to a Chicago Cy Young Award winner was a fascinating thing to watch, especially considering how it happened. This wasn’t just a guy who benefited from a change of scenery. When Arrieta adopted a new look, it was much more than his jersey color that changed.

The alterations were covered in a great 2014 Jeff Sullivan article titled Building Jake Arrieta. Among the things noted in that piece was his new release point that was primarily the result of pitching from the third-base side of the rubber.

Sullivan noted changes in Arrieta’s delivery yet again this May, pointing out an even more extreme horizontal release point in a piece titled Jake Arrieta Has Not Been Good. How extreme? Well, he’s throwing like a giant. No, not the kind that play in San Francisco. Arrieta has achieved nearly the exact same release point as Minnesota Twins pitcher Aaron Slegers, who at 6-foot-10 is one of the tallest hurlers to ever grace the mound.

Among the 562 right-handed pitchers Baseball Savant has data on from 2017, only three of them averaged a release point of at least 6.2 feet vertically and 3.3 feet horizontally: Arrieta, Slegers, and Brewers reliever Taylor Jungmann. Jungmann only thew 0.2 innings for Milwaukee last season, so there’s not much to unpack there. Below is the release point chart for Arrieta, courtesy of Baseball Savant:

And here is the chart for Slegers:

And finally, below is a graph showing how Arrieta’s horizontal release point has evolved over his career. You can see the dramatic dip to his first full season with Chicago in 2014. Things leveled out somewhat from there to 2016, but then there’s another noticeable dive last season.

Arrieta’s horizontal release point was farther toward third base than 98.6 percent of right-handed pitchers last year. It’s easy to see why a pitcher would want to create a unique look, as hitters aren’t accustomed to picking up a ball from that point, but how much does that really matter? Well, by the sound of this Francisco Cervelli quote from an MLB.com article in October 2015, I’m guessing it matters a lot.

“What makes him so tough is he throws the ball from the shortstop,” Cervelli said. “He’s supposed to throw straight. It should be illegal.”

Given Arrieta’s struggles, however, you can’t help but wonder if maybe he has taken this too far. He hit a career-high 10 batters and led the league in wild pitches for the second-straight season. Coming into 2017, Arrieta had averaged up just 6.2 H/9 and 0.5 HR/9 as a Cub. Last year, those numbers ballooned to 8.0 H/9 and 1.2 HR/9. His quality of pitch average also dipped from a score of 5.31 over his first three seasons with the Cubs to 4.98 last year.

The free agent market has been slow to get moving, but you’d have to figure things will start to pick up once the calendar turns over to 2018. It’ll be interesting to see if Arrieta’s new team tries to tweak some things with his mechanics. If nothing else, he’s shown a great openness to experiment.

Arrieta used his feet to get his arm into an angle that only a much taller pitcher should be able to achieve. Is it possible another set of eyes could get him pointed back in the right direction in 2018?

Tom Froemming is a contributor at Twins Daily and co-author of the 2018 Minnesota Twins Prospect Handbook.

About 12 hours apart, news of two very similar moves broke out of Chicago and Minnesota, as the Cubs agreed to terms with Drew Smyly while the Twins signed Michael Pineda. Both pitchers inked two-year deals with $10-million guarantees and additional incentives based on innings pitched, but the two deals shared an even more important similarity: both pitchers underwent Tommy John surgery this summer and seem unlikely to contribute significantly during the 2017 campaign. Both clubs are clearly betting on a return to health and productivity in 2019 for the two still relatively young pitchers, as evidenced by the financial distribution of the contracts. Pineda is only owed $2 million for the upcoming season but will receive $8 million in 2019, while Smyly will be paid $3 million next year but will pull in $7 million the following year. Since both pitchers underwent surgery around the same time, during the middle of the summer, it seems unlikely that either will throw pitch in the coming season.

While uncommon, these types of deals certainly aren’t entirely unprecedented. The Kansas City Royals have inked three pitchers with similar situations over the past few years, with varying degrees of success. These contracts, given to Luke Hochevar and Kris Medlen in 2015 and Mike Minor the following season, seem to represent the most relevant examples of such a deal. While Minor was non-tendered by the Braves following repeated shoulder issues, both Medlen and Hochevar underwent Tommy John surgery the previous year. All three pitchers would appear for the Royals in the major leagues over the life of their deals, albeit with differing results. Hochevar would appear in 89 games for the Royals, and accumulate only marginal value, as he posted a FIP around 4.00 and tallied only 0.3 WAR combined before succumbing to thoracic outlet syndrome surgery. Kansas City declined their option over Hochevar last winter, who became a free agent and sat out 2017 recovering.

Medlen would also return to pitch in 2015, making eight starts and seven relief appearances for Kansas City. He saw an uptick in walks and a downturn in strikeouts compared to his previous work, but overall pitched his way to a 4.01 ERA with similar peripherals and rang up half a win of value. 2016, however, would not be so kind to Medlen, as he was shelled to the tune of a 7.77 ERA while walking more batters than he struck out and battling a shoulder injury. He would sign a minor-league deal with the Braves after the season, but would not return to the majors. Although he did not appear with the Royals in 2016 after struggling in AAA, Minor marks the largest success story of the three. Over 65 relief appearances, Minor registered a 2.62 FIP and was worth 2.1 WAR out of the bullpen. He recently signed a three-year contract with the Rangers to return to a starting role.

In total, the Royals invested $25.75 million in the three pitchers and saw them accumulate a grand total of 2.9 WAR, with most it coming from Minor. This works out to a $/WAR figure of $8.88 million per win, which is slightly higher than the $8 million per win value assumed of the free-agent market. Based on these three deals, it would appear that this type of signing is not a bargain, but rather an overpay on average. However, it isn’t fair to make such an assumption without looking at a larger sample of data. If we classify a similar deal as one in which a team signed a pitcher that was injured at the time of the signing and expected to miss at least part of the following season and either signed a major-league deal or a two-year minor-league pact, that leaves us with 18 similar signings since 2007. One of these signings, Nate Eovaldi, has yet to return from his injury but should in 2018, so we won’t include him in the sample.

These 17 signings correlate to 25 player seasons following injury, with 24 of those representing guaranteed contract years, as well as one option year (Joakim Soria, 2015). The breakdown of these player seasons by games, innings pitched, strikeouts, walks, earned runs, and WAR are presented in the table below:

Altogether, when on a big-league mound, the group pitched to a 4.30 ERA to go along with a 7.52 K/9 and a 3.05 BB/9, numbers not entirely dissimilar from, say, Dustin McGowan or Sal Romano in 2017. So even the healthy group put together fairly middling results, but it’s also important to remember that eight of these player seasons wouldn’t see the player throw a single big-league pitch, and therefore provided no value to the club. Let’s plot the distribution of value produced by WAR:

That 2.1 WAR recorded by Minor last season was the highest figure of any player season in the sample, and besides Mike Pelfrey’s 2013 season, no other player season really comes close. Of the 10 player seasons recorded by primarily starting pitchers, only Pelfrey’s season even came close to average production, as every other starter either wasn’t durable or good enough to rack up any significant value. On the relief side, Minor and 2014 Joakim Soria both excelled, but no other relief season (out of the 15 in the sample) even crossed the 0.5 win threshold. As with the Royals pitchers earlier, it is important to look at these deals from a value standpoint. We can do this by calculating $ per WAR for the whole sample to find a mean, and for each deal to find a median, and visually represent the distribution. Overall, teams invested a total of $78 million in these 25 player seasons, with $71 coming in guaranteed money and $7 million in Joakim Soria’s club option. All minor-league deals to MLB veterans were assigned a dollar value of $333,333 for ease of calculation. Bonuses and incentives were ignored from this figure, as it is very difficult to find these details of the player contracts and few of these seasons would reach such incentives. As we saw above, the sample produced a total WAR of 6.9. This means that on average, teams paid $11.3 million per win when committing money to injured pitchers in hopes of a bounceback, well above the market rate of $8 million per win in free agency. Based on some quick calculations, teams paid that $78 million for production worth $55.2 million, for a net loss of $22.8 million. Let’s now look at the value gained/lost for each contract (in millions of $):

As you can see, only five such contracts actually generated positive (above market value of $8 million per win), while the remaining 12 contracts provided their team with below-market value. The mean loss per contract is $1.34 million, while the median is represented by the Phillies’ $700k loss on Chad Billingsley. While neither number is outrageously high, both figures only serve to reinforce the fact that teams have generally lost more often than they have benefited from inking an injured pitcher.

None of this is necessarily to say that the Pineda, Smyly, and Eovaldi contracts are doomed or that no team should ever make this type of investment, but simply to look at how similar deals have worked out in the past. Admittedly, the sample is hardly big enough to make any sort of definitive conclusion, but the overall trend on these “bargain” signings isn’t pretty. Both Smyly and Pineda are better pitchers than most in the sample, so it is entirely possible that they (along with Eovaldi) could significantly shift the outlook on these types of deals in the future. Whether this trio of pitchers can buck the trend or will follow in the footsteps of their predecessors will certainly be an interesting, if minor (pun intended) storyline to watch over the next few seasons.

FanGraphs.com leaderboards, Baseball-Reference transaction data, and MLBReports Tommy John surgery database were all used extensively for this research.

Aaron Judge and Jose Altuve were seemingly neck and neck in MVP voting this year (even if they are neck and belly button when standing next to each other). Judge had the edge in FanGraphs WAR, while Altuve held an edge according to Baseball Reference. Altuve had gaudy batting average and stolen-base totals, while Judge reached the coveted 50-home-run plateau to go along with his jaw-dropping Statcast numbers. Heading into awards season, the American League MVP was hyped as a two-man race that could go either way.

But, then the voting happened, and Jose Altuve got 27 first-place votes to Aaron Judge’s two. There were a lot of reasons for this from storyline, to traditional numbers, to team record. One of the most prominent among them for sabermetric voters was Aaron Judge’s clutch performance. According to the Clutch metric found on this site, he was the least clutch player in baseball this year. Actually, he was the least clutch player in baseball this entire millennium. Wait no, actually, he had the single least clutch season in the history of the metric (since 1972).

Up until now, Clutch has not been shown to have predictive value, even if it is important in deciding things like MVP races which are based on things that have already happened. But, as you may have guessed from the article title, I think there may be evidence to suggest otherwise. Here a list of the least clutch players in history for their entire career according to the clutch metric.

This list is populated by a certain type of player: good ones. The difference in WAR between Jermaine Dye and Lance Parrish in 9th place would be a fantastic career for almost anyone. But, more importantly, it is populated by high-strikeout, power-hitting sluggers. Every single player on this list has a double-digit strikeout rate and everyone but Chet Lemon has at least 300 career home runs. The list of the most clutch players in history, on the other hand, is not made up of power hitters.

This list is made up of a very different kind of hitter. Tony Gwynn, Pete Rose and Ichiro are perhaps the three most well-known contact hitters of all time. Only three players on this list have double-digit strikeout rates, and only one has 300 career home runs. Chet Lemon, dead last in home runs on the other list, would rank second on this one.

Aaron Judge fits right into the pattern of these lists, as one of four qualified players with a 30% strikeout rate or higher. If you sum the clutch score of the top 10 players in strikeout rate this year, you get -9.05, or nearly one win per player lost due to clutch performance. If you remove Aaron Judge, the sum is a still gaudy total of -5.41.

I charted Strikeout rate against clutch score for all players qualified in 2017, and there is a small but definite trend. Below the chart, you can see the regression equation along with the P value for the coefficient and the R^2.

CLUTCH = 0.593 – 3.736*KPCT
R^2=.045
P=.0101 pic.twitter.com/msrDUblKtJ

— Arron Kruse (@arron_kruse) December 22, 2017

Ultimately I don’t have the tools or the time to fully explore this idea, but it would appear that there is an actual relationship here. The effect may be minuscule as the R^2 indicates, but the general trend seems to indicate that clutch players are more contact-oriented. This makes sense, because the most clutch situations in a game happen with men in scoring position, where the difference between a strikeout and a fly out or ground out can be an entire run. Further work needs to be done, but I would not be surprised to find that batted-ball type or walk rate also has an impact. For example, hitters with higher fly-ball rates may be more clutch because, with runners on base, a fly ball avoids a double play with a man on first, and may drive in a run with a man on third. With nobody on base and nobody out, the way a batter gets out does not make a difference. But in clutch situations, all outs are not created equal.

I’ve always wondered if certain players are aware of the comparisons floated with their names.

For one, it could be valuable to observe and learn from a player with similar mechanics. Struggle can be an unexpected teacher, and if their look-alike possesses a career with peaks and valleys, those turning points make invaluable late-night research material for a baseball nut. On the other hand, comparing can create unrealistic expectations.

Because I have not had the pleasure of speaking to Brewers pitcher Josh Hader, knowing whether he sees value in comparisons eludes me. What I do know is the most frequent comparison attached to Hader immediately creates those lofty expectations: Chris Sale.

Not as lanky, or elite, Hader’s sidearm-lefty slot causes Sale-like deception.

David Laurila of FanGraphs spoke with Hader about mechanics, and a few points resonated with me.

Hader is cognizant of the value biomechanical analysis can have, disclosing his run-in with motion-capture cotton balls affixing themselves to his body as he pops a glove with 95-mph heat. His max-effort delivery may cause worry for some, but reading about Hader’s confidence in his concoction of a motion is settling, even if it’s coming from the horse’s mouth. If you subscribe to the theory that past injury predicts future injury, Hader eclipsing 100 innings every year since 2013 should ease your concerns. (Thanks to Laurila for getting Hader’s thoughts in the column linked above.)

Hader also confirmed his awareness of the deception he creates when talking with Laurila. The less time a hitter has to pick up the ball out of his hand, the better. Left-handed hitters, in particular, have been decimated by Hader’s fastball-slider combo.

Lefties combined for a .158 slugging percentage against Hader last season. That was second in baseball, behind Pittsburgh Pirates closer Felipe Rivero (minimum 70+ total batters faced). Firmly inside the 99th percentile; when you drill down to how effective Hader’s slider was, I fear for any lefty who had to deal with this release point and horizontal bite (see gif above). Hader threw his slider 77 times last year to left-handed hitters and the resulting slugging percentage was .071. When they swung at this slider, 44% of the time they missed. Both metrics sit comfortably above average in relation to average slugging percentages and whiff rates for hitters, adding statistical backing to Hader’s dominance.

Unique about Hader is not only this slider, his hair, and his effectiveness, but his role heading into the offseason.

Since his move to Milwaukee from the Houston Astros in 2015’s Carlos Gomez swap, Hader was a starting pitcher for every one of his minor-league appearances. Craig Counsell & Co. entertained the reliever role for Hader only upon his promotion to the major leagues on June 10. Culprits for the switch could be situational — the Brewers were contending, and needed bullpen arms — but you could also convince me they were performance-based. A 13.6% walk rate over 52 Triple-A innings doesn’t inspire confidence.

This isn’t breaking news to Brewers fans.

Control issues have always been a problem for Hader, but as a reliever, the Wayne’s World look-alike had a good enough fastball to utilize it 75 percent of the time to lefties, upwards of 85 percent to righties, and net himself a shiny 36 percent strikeout rate (47 2/3 innings). In the process, Hader cut his walk rate to 11.7 percent in the majors, from north of 13 percent at Triple-A.

Unfortunately for Hader, even that improvement shouldn’t inspire confidence. We haven’t had a qualified pitcher at the major-league level, with a walk rate greater than 11.6%, since Francisco Liriano in 2014. I wouldn’t fault Hader for making a deal with the devil and taking Liriano’s 1,500-inning career, but my intentions are to consider a pitch vital to determining Hader’s 2018 role.

***

“Considering everything” headlines an MLB.com column from Brewers beat writer Adam McCalvy just over a week ago.

The vocalist of that quote was Craig Counsell, and the topic was our very own Josh Hader.

Indifference exists because Hader pitched so well in his 35 relief appearances and because of the smattering of question marks. The biggest of which is emerging ace Jimmy Nelson’s shoulder health. One depth chart has Hader as Corey Knebel’s set-up man. With an individual named “B. Suter” in the Brewers 2018 rotation. (Not “Bruce” Suter, just to confirm. Sorry, Brent.)

One question mark Hader can control is the development of his changeup. Stop me if you’ve heard this before, but a developed third pitch — so often the changeup — is how many minor-league arms get a chance to work for five-plus innings in the upper levels.

One of my favorite finds from 2017 has been the scout Chris Kusiolek (@CaliKusiolek on Twitter). In regards to changeups, Kusiolek mentioned on the Fantrax Baseball Show how much of a feel pitch it truly is. He detailed how he looks not at the present state of a pitcher’s changeup when determining the viability of the pitch’s future, but the athleticism of the pitcher, his arm action, fastball, and other aesthetics, to make that call. I’m nowhere near as seasoned of a scout as Kusiolek, but Hader hits a few of those points.

Even Hader will admit changeups are a feel pitch, and found in that same McCalvy column, the Brewers beat writer tweeted out the grip Hader was working on back in March of 2017.

“Messed up” can often prime one to think inconsistent, but that may apply to the resulting action Hader achieved on the pitch, rather than the results.

FanGraphs has Hader’s changeup just below 86 mph. This average velocity was the more common action on the pitch I observed watching tape of Hader. Other times, however, I’ve seen Hader’s change kick up to 88 mph. From my crude observation, the harder changeup only came spontaneously and later in counts. You’re about to see an 88-mph changeup on a two-strike pitch to Adam Duvall.

Harry Pavlidis has conducted extensive research on why some changeups are effective, noting those who generate elevated levels of ground balls and swinging strikes with the pitch are ideal (Stephen Strasburg is the poster-child).

Hader’s changeup hits one of those two criteria. Among starters and relievers with 50 or more changeups thrown, when Hader’s is put in play, it generates grounders at a 75-percent clip, sixth-highest in all of baseball (320 total starters and relievers). I understand it’s a pipe dream to ask Hader to replicate the arm action or grip that leads to the harder offering — if it is spontaneous — but if the structure of his general changeup leads to an elevated level of ground balls, this harder changeup might push him further into worm-killer territory.

Given Hader’s changeup has a sub-par whiff-per-swing rate in the bottom quarter of the league, playing to his strengths and embracing the harder version could make an interesting case for change.

You could argue Hader needs to continue mixing the two, but if the hittable, 86-mph changeup is thrown more as an early-count offering to righties, exploiting Hader’s attempt to pitch backwards could become an game plan. Or, in a perfect world, Hader can refine the swinging-strike rate on the slightly softer offering and turn into a two-changeup lefty. (A boy can dream, right?)

***

Considering Hader for a rotation spot is not a spontaneous decision, especially with Hader’s talent and polished, 23-year-old arm.

Both of his raw pitch count season-highs throwing his changeup came in consecutive appearances during late September. His usage with the pitch crept towards 19 percent, and both outings lasted north of two innings.

Hader can survive as a starting pitcher if his changeup becomes a legitimate weapon to right-handed hitters, especially if opposing managers understand Hader’s dominance against lefties and stack against his natural platoon split.

While Hader’s changeup is often knocked for being inconsistent, I counter that sentiment by saying he has a substantially better feel for the pitch than most, especially given the tendency of hitters to pound it into the ground, regardless of the velocity.

My gut tells me Hader will be utilized as a multi-inning reliever, and dominate both sides of the plate in 2018. My heart tells me to give Hader starts to further refine his feel for a pitch he’ll have to use effectively the second and third time through major-league lineups in order to survive.

In Craig Counsell and Derek Johnson I trust.

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

Statistics all from BrooksBaseball, BaseballSavant, Baseball Prospectus, and FanGraphs, unless otherwise noted.

Ichiro is one of the most bizarre players of the past 20 seasons. While many hitters have come over from Japan to the MLB, Ichiro has stuck in North America like no one else. The NPB is famous for its ground-ball-heavy approach — per DeltaGraphs, the NPB ran a GB% of 48% compared to 44% for the MLB last season — but that approach usually doesn’t work that well across the pond. That wasn’t the case for Ichiro. He made it work, and he made it work all the way to capturing the single-season hit record. And he did it in a really, really weird way.

How to Hit In Japan

To explain why it was so weird that Ichiro did what he did, we have to go all the way back to the beginning, back to Ichiro’s home country of Japan. Nippon Pro Baseball is the highest level of professional competition in Japan, and it’s where MLB superstars (and future superstars) like Ichiro, Shohei Ohtani, and Hideki Matsui started their careers.

The NPB is traditionally referred to as a ‘AAAA league’ — its level of competition is below that of the MLB, but above that of typical AAA team, which is why players who could mash in AAA but couldn’t hang on in the majors usually end up in the land of the rising sun (guys like Álex Guerrero and Casey McGehee were among the best hitters in the NPB in 2017).

The NPB’s style of baseball, however, is unique. It exists as some strange mesh of dead-ball play and modern baseball, where ground ball machines can thrive.

Earlier this year, Ben Lindbergh took a look at the biggest ground-ball-machine in the world, Nippon-Ham Fighter Takuya Nakashima, who ran an astonishing 74.4% GB% in 2016. Nakashima’s batted-ball profile looks like something of a caricature of the rest of the league, a gross exaggeration of the way the rest of the league plays.

League-wide, the NPB GB% year to year falls between 47% and 48%, which is quite a bit more than the 44%-45% that the MLB posts every season. Japanese players also traditionally reach base more frequently on grounders too, posting a BABIP of .245 on ground balls in 2017 compared to the MLB’s .241 figure.

But the biggest difference between MLB and NPB grounders? Ground balls are generally worth 30% more in Japan as they are in North America. MLB batters posted a 29 wRC+ on grounders, but NPB grounders were worth 42 wRC+. That’s a huge difference, especially for a league-wide figure. While it’s still not technically beneficial to hit ground balls, in Japan, hitters are rewarded for doing so more frequently than their North American counterparts.

How does such a huge difference exist between NPB and the MLB? Lindbergh, in the above article, suggests that the spongy Japanese turf is to blame, causing ground balls to have more life on them. In addition, Lindbergh suggests that the NPB, which has been slow to adopt many sabermetric and modern ideas, is shift averse, meaning many pull-happy hitters can run higher BABIPs. It’s also possible that since NPB has a lower skill level than the MLB, NPB infield defense could allow more hits than MLB infields.

Whatever the reason, hitters who came to the MLB from the NPB while relying on the ground ball as a means of production generally saw their production suffer. Tsuyoshi Nishioka, for example, hit .346/.423/.482 the season before coming to the MLB, but managed only a paltry .215/.267/.236 with the Twins in two seasons. Nishioka relied heavily upon the ground ball in both leagues but was punished more heavily for doing so in the MLB than in the NPB, and that, coupled with the difficulty of facing MLB pitchers, doomed him to mediocrity.

Ichiro was much the same — a ground-ball production machine. When he came over from Japan, perhaps in hindsight, he should have flopped for the same reasons that Nishioka, Kensuke Tanaka, Munenori Kawasaki, and Akinori Iwamura flopped. He fit the profile — speedy, high-contact ground-ball hitter coming over from Japan. Hell, Ichiro’s best-case scenario should have been what Nori Aoki turned out to be.

Instead, he thrived.

Ichiro Breaks the Mold

When Ichiro arrived in America, he was nothing short of a revelation, and a key factor in the Seattle Mariners posting the best record of the modern era in 2001 — and he was arguably the face of the franchise for close to a decade.

Ichiro’s high-contact, low walk/strikeout approach shouldn’t have worked. I ran Ichiro’s 2003 season through my similarity tool, and the best comps I generated were Jose Vizcaino’s 2004 season, Warren Morris’ 2003 season, and Brad Ausmus’ 2004 season (yes, that Brad Ausmus). None of these guys posted a wRC+ over 90 in those years, but Ichiro was at 112. How did Ichiro get by using a strategy that had failed so many hitters before him?

On paper, the answer is BABIP. For his first four seasons, Ichiro never posted a BABIP below .333. While the league average for BABIP is around .300, elite players generally have a BABIP skill above .300 as a result of making elite contact. If we make a rough and naive assumption that a high SLG means that a player made good contact, we see that the among the top 15 career BABIP leaders (with 10000 PA), most of them made good contact, except for Lou Brock … and Ichiro.

It gets weirder. Remember all that talk about ground balls? Ichiro hit a lot of them — since 2002, the earliest season for which we have batted-ball data, Ichiro has hit the most ground balls in the majors, almost 800 more than 2nd place (Derek Jeter). Here is a scatterplot of GB% versus BABIP for qualified single seasons since 2002.

There exists a weak, but roughly positive correlation between BABIP and GB%. Most everyone is hanging out somewhere around the 35%-50% GB% and .250-.350 range, but then there’s Ichiro, who consistently posts BABIPs well above what he should be getting. Ready? It gets even weirder.

Here’s that same chart, but I’ve thrown in the ages of each hitter in a gradient color scale. There’s a good spread around here, but I’ve highlighted Ichiro’s 2004 season, and it should stand out in three big ways. First, he posted one of the highest GB% since 2002 (63.1%). Second, he posted the second highest single-season BABIP since 2002 (.399). And third, he was 30 when he did this! Many of the light blue values in the upper right of the column belong to Ichiro. Which is really unusual, since many of them are when he’s older than the median MLB player (29 years old).

In this chart, the red dots represent hitters 29 years old or younger, and the blue dots represent hitters 30 years old or older. Notice how there’s a roughly even mix in the middle, but older hitters tend towards the bottom left, and younger hitters tend towards the upper right (though there are exceptions to each).

Here’s that same chart, but I’ve removed Ichiro’s seasons — look at the far upper right. See the difference?

Ichiro’s specialty is defying all aging curves and all logic by consistently posting these ridiculous BABIPs while acting like a ground-ball machine, and making contact that most hitters would be ashamed of.

Legs Don’t Fail Me Now

We’ve already identified that Ichiro makes sub-par contact, hits a lot of ground balls (not exactly a recipe for production), and doesn’t strike out or walk much. No, the biggest tool for Ichiro, as anyone who watched him play could tell you, was his speed.

August Fagerstrom previously found that Ichiro had elite speed in his younger days, estimating his time-to-first in his prime as just under 3.75 seconds, which would blow Billy Hamilton (3.95 seconds) out of the water. It’s no exaggeration to say that Ichiro could be one of the fastest men in MLB history.

So many hitters came over from Japan with profiles similar to Ichiro — speedy ground-ball hitters who make a lot of contact. But none of them had Ichiro’s generational speed, and so, none of them found the type of sustained success that he did.

One cannot help but feel a sense of wonder in looking at Ichiro’s career. Because his production relies almost solely on his ability to make contact and his speed, tools that decay slowly with age (I’m aware that speed tends to decrease with age, but exceptionally speedy runners such as Chase Utley and Rajai Davis can retain their prowess on the basepaths well into their late 30s), he was able to defy what we might expect from someone of his age and with his batted-ball profile.

Ichiro was shooting the moon with his approach the plate, in a way. Sabermetric wisdom tells hitters to elevate, draw walks, don’t be afraid to strike out, make solid contact, and don’t worry about speed. Ichiro did the exact opposite and was rewarded handsomely rewarded for it. I can think of no more unique player with such a storied career and legacy. Here’s hoping 2017 won’t be Ichiro’s last hurrah.