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.

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.