The White Sox’ rotation this year can charitably be described as “rocky”. They began the year projected to have the worst rotation in the majors by WAR and thus far they’ve ranked 28th, between the Jeter-decimated Marlins and the aging Rangers. That’s not terribly surprising considering they’ve given out the most walks by far at 4.61 BB/9; besides them, only the Cubs’ rotation is over 4 at 4.21. The White Sox’ rotation also has the lowest strikeout rate in the majors this year at 6.20 K/9. The only thing preventing them from having the worst FIP of any team’s starters is middle-of-pack home run prevention, but their home field is a launching pad come summer.

As I stated before, they weren’t expected to have a good roster of starters, but being a rebuilding club filled with young and therefore volatile players, there was at least theoretically the chance that they made the jump to competence and beyond earlier than expected and surprise people like the Braves have this year. That obviously has not happened, but back in February, when everything is possible, Rian Watt took a look at the surprisingly large error bars in the projections for Chicago’s starters. The backstories of their projected starters agreed with what those large error bars said about a wide range of outcomes.

Lucas Giolito, a former No. 1 global prospect traded to the Sox last year from the Nationals, looked very sharp in spring training, having apparently rediscovered the massive 12-6 curve and some of the fastball velocity that had made him such a vaunted prospect and pairing it with newly found command and an improving, fading changeup. Reynaldo Lopez, fellow right-hander and former top-100 prospect who came over from the Nationals, had disappointing strikeout numbers despite big stuff, between a fastball that averaged 95 MPH, above-average curve and average slider and change– perhaps an improvement in sequencing or location would tap into the strikeouts he clearly had the talent to produce. Carson Fulmer, former No. 7; overall draft pick, has a lively arsenal in which everything moves in unpredictable ways that hitters dislike, albeit unpredictable to him too; perhaps he could make a mechanical adjustment and find the control and therefore success he had in college. Carlos Rodon, former No. 3 overall pick, was out with minor shoulder surgery (bursitis) until June but can flash complete dominance with his overpowering fastball/slider combo from the left side. Everyone knows about the world-class talent of Michael Kopech, who is currently stuck vaporizing poor saps in Triple-A (12.13 K/9!) until he limits his walks to acceptable levels. Bringing up the rear were Miguel Gonzalez, Hector Santiago, and James Shields, three veterans for whom the reasonable hopes were “eat innings better than cannon fodder”.

This article is not about any of the eight pitchers above, or their struggles with control (Giolito, Fulmer), relative successes (Shields), or weirdness (Lopez, who is having some success despite still not getting many strikeouts). Instead, it’s… Dylan Covey?

Yes, the Dylan Covey who ran both an ERA and FIP over seven last year in seventy innings as a rookie, good for -1.1 WAR. Pitching like, well, cannon fodder is not exactly an auspicious start to one’s major league career. Brief background of Covey: He was considered an elite high school arm, the riskiest category of draft picks, thought of high enough to be selected fourteenth overall in 2010 by Milwaukee– one pick after the White Sox selected a certain stick-figure lefty at a little-known Florida college whom Covey out-dueled earlier this June. During his pre-signing medicals, though, Covey was diagnosed with Type 1 diabetes, and he decided not to sign in order to learn how to deal with the disease before the stresses of pro ball. He chose to attend San Diego State and three years later was selected in the fourth round by Oakland.

After another three years of middling results hampered by injuries, Oakland left him off the 40-man roster despite an encouraging AFL and Chicago pounced in the Rule V draft. It was a bit of an unusual choice in that Covey was quite raw, almost akin to the Padres’ Rule V hijacking of prospects straight from A-ball, because Covey had thrown all of six starts at his highest level (Double-A). After hearing that, it probably makes a lot more sense why A) he got rocked the way he did last year and B) there was and is still hope for him. Although he was 25, the rawness showed, but the White Sox were entirely alright with absorbing the losses, as they would only help them pick higher in the 2018 Draft anyways (Nick Madrigal says hello).

Ironically, when he was drafted fourteenth overall in 2010, he was considered as safe as any high school arm could possibly be, on the basis of a low to mid-nineties sinker, above-average curve, ideal workhorse frame (currently listed at 6-2/195), and remarkably clean mechanics for his age. Ground balls, control, good health, and a reasonable number of strikeouts sounds like the perfect profile of a high-floor starter prospect. Of course, it didn’t work out that way in 2010, nor did he really come around while with Oakland. Thus, one might reasonably conclude, this article is being written because he appears to be finally delivering on his talent in his second year with the White Sox.

And so he has. Of course, the disclaimer of “small-sample size” applies here, as Covey has seven starts, and 35.1 innings total in those starts this year, but still, those 35.1 innings have been a complete reversal from his performance in 2017. He’s gotten a shot only because two rotation spots needed filling before Kopech was ready (i.e. past his Super Two deadline). First, Gonzalez went down with a shoulder injury in mid-April; that spot was filled by Santiago sliding from the bullpen into the rotation as he was signed to do. By mid-May, Fulmer’s wildness became too much to bear, and he was sent down to Triple-A to work on that, and Covey was called up to Chicago to get his second shot in the bigs. He’s taken that chance and run with it.

Thus far this year, Covey is the proud owner of a 2.29 ERA, 2.17 FIP, 3.31 xFIP, and 3.48 SIERA, good for a 1.3 fWAR (!) that currently leads all White Sox pitchers. No, I don’t think Covey is suddenly the third-best pitcher in baseball, and yes, that SIERA is a over a run higher than the FIP, and that’s because Covey has yet to give up a home run. That SIERA is still really good, though: among starters this year with at least 30 IP, the highest bar Covey clears, that would be good for 29th, slotting between Blake Snell and Alex Wood. Other pitcher evaluation metrics mostly agree: Baseball Savant’s xwOBA-against judges him at .293, 21st-best among starters. Baseball Prospectus’ DRA, how ever, does not like what he’s done, as his DRA this year is 5.38. There have been 4 unearned runs against him this year, so BBRef’s RA/9 dings him for that but still evaluates him well at 3.31 (Note: two of those unearned runs scored as inherited runners off a reliever). I cannot say why DRA hates him, but when a black-box statistic is in complete disagreement with literally every other ERA estimator, I have to ignore it.

Of course, the instinct of any saber-savvy fan is dismiss this as a fluke, small sample, etc. Anything can happen in small samples– once upon a time, Philip Humber threw a perfect game! That’s what I said, so when I trawled through Covey’s peripherals just to make sure this was a fluke, I kept expecting to find something or another that screamed regression. If there is a statistical red flag for harsh regression beyond his steadfast refusal to give up a home run, it remains as elusive to me as the average Bigfoot. His K% is a bit above average at 22.2% (starters’ average this year is 21.7%), his walk rate is a little better than average at 7.4% (avg is 8.2%), for a just above average K-BB% of 14.8% (avg of 13.6%). His LOB% is a bit low at 71.1% (avg 73.0%), and his BABIP-against is maybe a touch unlucky at .333 (avg .288). His WHIP is a smidge worse than average at 1.30 (avg 1.28). There is, in sum, absolutely nothing out of the ordinary there; by those measures he looks like a league average or slightly above starter. Which isn’t bad, as it suggests that his floor is that of a perfectly cromulent major-league starter, which is already a great outcome for a Rule V pick and vast improvement over last year.

Where Covey starts getting real interesting is when you start looking at the ways in which he might be suppressing home runs. I already told you that Covey’s primary pitch as a high schooler was a heavy sinker, and he’s gone back to his roots with it this year. In 2017, he threw fastballs about 60% of the time, splitting usage about evenly between his sinker and a four-seam. This year, he’s throwing even more fastballs, up to 68.3%, but he’s ditched the four-seam almost entirely; those are nearly exclusively sinkers he’s thrown. The point of a sinker is to get ground balls, and boy oh boy has his sinker done so.

Put simply, Covey’s been a ground ball machine. Among all starters with at least 30 IP this year, he’s tops in ground ball rate at 61.0%. The sinker has done most of that work; when batters put it in play, they beat it into the ground 68.1% of the time, 8th among starters. As one would expect, he’s also not allowed many fly balls; his FB% is a tiny 23.5%, seventh-lowest among his peers. Also unsurprisingly, he’s got the fourth-highest GB/FB, at 2.56, of starters. If his FIP is low because he’s not allowed a home run, well, it’s at least in part because it’s rather difficult to get a home run out of a grounder. When examined more closely, the metrics on his sinker back up its excellent results.

First of all, he’s added some velocity to it. This year his sinker is averaging 94.4 MPH, compared to last year’s 92.9 MPH. The addition of 1.5 to 2 MPH this year versus last is found in all his other pitches, too. Throwing harder across the board: always a good sign! It’s more than just respectably hard. Although Statcast classifies it as a 2-seamer, the pitch has the 29th-lowest average spin rate among either sinkers or 2-seamers this year.

While that and the velocity of the pitch (26th-fastest in the same mix of starters’ 2-seams & sinkers) are both good-not-great numbers, the combination of the two is actually pretty unusual– fastball velocity and spin rate usually have a positive correlation. Less spin is good in this case; the spin is mostly backspin, and the less backspin on a sinker, the more it sinks and (probably) the better it is. Of the 25 starters that throw their 2-seamers/sinkers harder than Covey does, only two– Erick Fedde and Fernando Romero, both rookies with small sample sizes themselves, also have lower spin rates. Stephen Strasburg and Sal Romano also throw harder and barely missed the spin rate cutoff. For comparison, the 2018 preview on Fedde’s FG page describes his sinker as “potentially premium”, Romano and Romero both have their fastballs graded by the FG prospect experts as 70s (plus-plus), and Strasburg rarely throws his 2-seamer.

In short, his sinker is elite for the sum of its parts. It’s generated an exactly league-average 6.8% whiff rate, which doesn’t sound special, but when it’s put in play, hitters can’t help but beat it into the ground. Its grounder/ball in play rate is an incredible 68.1%, 4th among starters and 10th among all pitchers this year. As would be expected, hitters haven’t done too well against it, with a xwOBA against of 0.324, checking in at 13th of all starters’ sinkers/2-seamers.

The three guys ahead of him on the starter list– Trevor Cahill, J.A. Happ, and Marcus Stroman— are interesting for comps, too. None strike out a ton of guys– all have career K/9s under eight– and none walk too many either, like Covey. Unsurprisingly, Stroman and Cahill, sinker/slider righties like Covey, are No. 2 and  No. 3 in starter GB% after Covey. Cahill’s having his best year yet in the A’s rotation, having upped his strikeouts to almost 9 K/9, cut his walks to 2 BB/9, and limiting home runs enough that ERA & ERA estimators are all around 3. Stroman, though he’s been hurt and not pitched well this year, has a track record of four years of being a solid No. 2 starter, especially according to SIERA.

Covey’s secondary pitches– slider (15.6% usage), curve (8.2%) and split-finger changeup (8.7%)– are all about average or better. The slider’s whiff rate is 13.5%, not spectacular but solidly above the league-average slider whiff of 9.0%. It’s not been murdered when it gets hit, either; Statcast’s xwOBA against the pitch is a pitiful .209, good for 16th among starters’ sliders. The change is an effective swing-and-miss pitch too, also with an above-average whiff rate at 15.6%. Hitters haven’t hit the change well either, with a xwOBA against of just .220, 16th among starters’ changeups. The curve hasn’t generated many swings-and-misses (just 2 out of 44 thrown, 4.5%) but hasn’t killed him at an xwOBA of .273, about middle of the pack for starters.

Baseball Savant sure doesn’t think that Covey’s just been extremely lucky in home-run suppression, but just to be sure, I went to go see what xStats.org thought of him. It thinks he should have given up 1.5 homers so far. Ignoring for a moment the fact that one cannot in fact hit half a home run, although a ground rule double seems close to it, that works out to a deserved rate of 0.382 HR/9. Which, in case you’re wondering, would still be good for fourth-lowest HR/9 of starters— Covey of course currently has the lowest of all at 0. Not perfect, then, but damn close to it. The other names in the top 10 lowest HR/9 are unsurprisingly for the most part really good to great pitchers: Arrieta, Nola, Severino, Bauer, Chatwood (???), deGrom, Buehler, Cueto, and Carlos Martinez, in ascending (towards lowest) order.

So that’s Dylan Covey in 2018: a pitcher with an excellent bread-and-butter sinker, two very good secondaries, and a passable fourth pitch. He’s not walking many, striking out close to a batter per inning, getting ground balls like they’re going out of fashion, and bucking the home run trend. I’m particularly reminded of Stroman in overall profile, but Covey has the advantages of size, a bit of youth, a home field with dirt instead of turf (grounders come off turf faster, meaning more hits), and a considerably younger and rangier infield behind him. He’s also got Don Cooper and Herm Schnieder on his coaching staff, which makes it less likely that he’ll be derailed by either mechanical or health issues. I for one didn’t see this coming, but the White Sox’ patience has already been rewarded with an unexpected breakout by Matt Davidson, so why couldn’t they have found another post-prospect gem? It’s at least interesting to note that Dallas Keuchel and Jake Arrieta, probably the best examples of guys who became great pitchers out of more or less nowhere after given time to reinvent themselves on rebuilding squads, are both in the top 20 in ground ball rate for starters– the category, of course, wherein Covey currently reigns supreme. I don’t really know what more to say. Small sample size notwithstanding, how about Dylan Covey, No. 2 starter?

Notes on process: with a small sample size of just seven starts at time of writing, the minimum cutoffs I employed to compare Covey to other pitchers were usually the minimum that he himself cleared– 30 IP with his 35.1 IP, 10 PA for his xwOBA against his curveball that has 13 PAs, etc. As he gets more starts, the exact numbers and rankings will of course change; the rankings are there not to be exact but rather to give some context for the raw numbers, most of which are obscure enough that the average reader likely cannot evaluate how “good” it is. Everyone knows a 2.29 ERA & 2.16 FIP are great, but I doubt many readers can instantly discern how good, say, a xwOBA of .220 against a certain pitcher’s changeup is. I also made the decision to evaluate almost exclusively against other starters’ 2018 years, as the baseball is again different this year and relievers are increasingly a different, turbo-powered breed of pitcher that cannot fairly be compared to starters.

In order for a team to win the World Series, it needs to win a whole bunch of games. In order to win games, a team needs to score more runs than its opponents. Over time, we’ve come to accept that a team’s winning percentage, while very important, is an imperfect predictor of how likely a team is to win and lose games in the future.

Take the year-to-year performances of teams over the last three seasons (the time frame of focus for this post). The correlation between a team’s year one and year two winning percentages (Win%) is limited (R² of 0.19). Extending the sample back to 1995 improves the correlation, but only slightly (R² of 0.25).

Replace a team’s year one winning percentage with a team’s year one run differential per game (RD/G) and you’re left with a slightly stronger correlation (R² of 0.21). Again, a slightly stronger correlation exists if we extend the sample back to 1995 (R² of 0.26).

Now, a lot of different things go into scoring more runs than your opponent does—namely hitting, pitching, running and fielding well. In the Statcast era (2015-17, hence why I limited myself to the small sample above), we have new ways of examining hitting and pitching. Instead of being limited to what actually happened, we can observe what was expected to happen, given the combinations of exit velocity and launch angle associated with a batted ball.

That led me to wonder if there was any use in creating a version of RD/G that was regressed from components taken (in part) from this Statcast data. Intuitively, there should be, as RD/G suffers from two big issues—batted ball luck and cluster luck—which could introduce statistical noise and drown out the metric’s signal.

Batted ball luck comes from the fact that a team may run a high RD/G not because they have been hitting and/or pitching well, but because they have been getting lucky results relative to the underlying contact. Vice versa for artificially low RD/G caused by unlucky results.

Cluster luck comes from the fact that a team can score an unsustainably high number of runs if hits are clustering in a small number of innings—the classic example compares two teams that produced nine hits in a game. The one that gets one hit per inning may end up with zero runs scored, while the one that gets nine hits in one inning may score a handful of runs. A team that is scoring a lot of runs because it is generating lots of hits is likely experiencing more sustainable success.

If we produce a RD/G based on xwOBA (alongside some other metrics), we may be able to overcome both issues. This expected run differential per game (xRD/G) would avoid a great deal of the batted ball luck problem, as it rewards teams for generating lots of good contact and limiting good contact from the pitching side of the equation. It would also overcome a great deal of the cluster luck problem, as xwOBA is unaffected by the order or clustering of batted ball events. The xRD/G that I will elaborate on in this post certainly seems like a useful contribution to the discourse. For example, a team’s year one xRD/G is much more strongly correlated to its year two winning percentage (R² of 0.37) than either RD/G or Win%.

[Statistical note: I’m going to be using R² frequently in this post. R² measures how well two variables are correlated to one another. It ranges from 0 to 1. Interpreting an R² requires context, as 0.37 may be considered high in one context and low in another. In this context, comparing different variables from different time frames, a lower R² would be expected. The key then is comparing the R² of different relationships, as I’ve done above.]

How is xRD/G calculated?

xRD/G seeks to estimate the run differential per game that a team would have been expected to produce given the team’s batting xwOBA, starting pitching xwOBA, relief pitching xwOBA, baserunning runs (BsR per 600 PA) and defensive runs saved (DRS per 150 games, when possible given data split limitations). Given the recent conversation around what “expected” means in these new x-stats, let me make clear that I agree with Craig Edwards’ take: “I have always interpreted the ‘expected’ to mean ‘what might have been expected to happen given neutral park and defense.'” That said, as we have already seen, xRD/G has predictive value as well.

I started working on xRD/G by regressing the RD/G produced by a team in a given season (from 2015-17) against that team’s batting xwOBA, SP xwOBA, RP xwOBA, BsR/600 and DRS/150. [I used DRS given that it accounts for pitcher and catcher defence, unlike UZR.] These five stats explain about 79% of the variation in a team’s full-season run differential per game and were each highly significant. I opted against using a constant term as it was not statistically significant, nor did it increase adjusted R².

Then, I incorporated interaction terms, particularly between 1) batting xwOBA and BsR/600, 2) SP xwOBA and DRS/150 and 3) RP xwOBA and DRS/150. The eight terms explained 81% of the variation in a team’s full-season RD/G. However, I found that the latter two were statistically insignificant. After removing them, I was left with six highly significant variables that still explained 81% of the variation in full-season RD/G. These six variables comprise the full xRD/G equation that I chose to settle on:

xRD/G = 23.31*(Batting xwOBA) – 2.52*(BsR/600) + 8.34*(Batting xwOBA)*(BsR/600) – 13.16*(SP xwOBA) – 10.19*(RP xwOBA) + 0.004*(DRS/150)

The coefficients are mostly straightforward. A higher RD/G is correlated with a higher batting xwOBA and lower SP/RP xwOBA. Better defence leads to a better run difference. However, the correlation between base running and run difference is a little tricky to read because of the interaction term. In a nutshell, base running is good, but it’s more useful for teams that have more base runners (higher batting xwOBA). [Let me explain via an example. The average team batting xwOBA in the sample is .317. For a team with an average batting xwOBA, a one-unit increase in BsR/600 is associated with a 0.13 run increase in its RD/G. For teams with a low batting xwOBA (around .300), base running isn’t associated with a change in RD/G. For teams with a high batting xwOBA (around .350), a one-unit increase in BsR/600 is associated with a 0.39 run increase in RD/G.]

There is a great deal of wiggle room in producing other versions of xRD/G. I opted to build the equation by regressing teams’ full-season RD/G against six variables taken from the same time frame. Alternate versions of xRD/G could be built by regressing teams’ RD/G over smaller periods (month, half-season, etc.) against variables taken from the same time frame. Alternate versions could also put more emphasis on predictiveness, by regressing teams’ RD/G over some period against variables from a previous period of time.

Similarly, I opted to go with xRD/G because it seemed most fruitful after a brief analysis of potential alternatives. I also played around with expected runs scored and allowed per game (xRS/G and xRA/G) and expected win percentage (xWin%). While not as initially fruitful as xRD/G, these are ideas worth coming back to. As such, consider the analysis in this post to only be a jumping off point in building an all-in-one team performance statistic based (in part) on Statcast variables.

Testing the reflectiveness, predictiveness and consistency of xRD/G

When examining a new metric, there are three key questions to answer.

1) How well does the metric reflect what has happened?

Pretty well. A team’s xRD/G explains 75% of the variation in same year winning percentage. For context, RD/G explains 85% of this variation. That RD/G is better than xRD/G at telling us what happened is not surprising. After all, wins require teams to score runs and limit runs against. A team’s full-season xRD/G is also highly correlated to RD/G, explaining about 83% of its variation. The slope of the trendline is roughly one.

2) How well does the metric predict what will happen?

Predictive power is the true strength of xRD/G, which is interesting because it wasn’t specifically built to predict. As mentioned earlier, a team’s full-season xRD/G explains 37% of the variation in next season’s winning percentage, compared to only 21% for the team’s first-year RD/G and 19% for the team’s first-year Win%.

Similarly, a team’s full-season xRD/G is a better predictor of next-season RD/G than RD/G itself. While a team’s first-year RD/G explains only 20% of the variation in second-year RD/G, a team’s first-year xRD/G explains 36% of this variation.

xRD/G is also useful for in-season prediction. Let’s split the three seasons of data into halves, demarcated by each season’s all-star break. For this purpose, I’ve had to create a modified xRD/G, as DRS splits are unavailable. For this purpose, I used the following equation to build xRD/G:

xRD/G = 26.05*(Batting xwOBA) – 2.91*(BsR/600) + 9.73*(Batting xwOBA)*(BsR/600) – 13.49*(SP xwOBA) – 12.69*(RP xwOBA)

Let’s start with some context: a team’s first-half Win% explains 27% of the variation in its second-half Win%, while a team’s first-half RD/G explains 34% of this variation. However, a team’s first-half xRD/G explains 39% of the variation in its second-half Win%, despite the forced exclusion of DRS/150. Theoretically, including DRS/150 would make a team’s first-half xRD/G even more predictive of its second-half record.

The predictive power of xRD/G is even more evident when explaining a team’s second-half RD/G. While first-half RD/G explains only 24% of the variation in its second-half cousin, first-half xRD/G explains 33% of this variation.

3) How consistent is this metric over time?

Beyond its predictiveness, xRD/G is a relatively consistent metric. Again, let’s first look at the other two stats for some context. A team’s Win% is the least consistent of the bunch. As observed earlier, a team’s full-season Win% explains only 19% of the variation in its next-season Win%, while a team’s first-half Win% explains only 27% of the variation in its second-half Win%.

RD/G is about as consistent as Win%. As observed earlier, a team’s full-season RD/G explains about 20% of the variation in its next-season RD/G, while a team’s first-half RD/G explains about 24% of the variation in its second-half RD/G. In contrast, a team’s xRD/G is much more consistent both from year-to-year (R² of 0.35) and from half-to-half (R² of 0.47).

xwOBA vs. wOBA

Given my intention of using Statcast data to create xRD/G, incorporating batter, starting pitcher and relief pitcher xwOBA into xRD/G was an obvious choice. However, it is fair to ask whether xRD/G would be an even better metric if wOBA was used in place of xwOBA.

In order to test this, I built two versions of xRD/G based on wOBA. For the sake of consistency, I used the same variables as above, but with wOBA instead of xwOBA.

The full-season version, which includes DRS/150:

wOBA-xRD/G = 28.42*(Batting wOBA) – 0.49*(BsR/600) + 1.67*(Batting wOBA)*(BsR/600) – 13.63*(SP wOBA) – 15.03*(RP wOBA) + 0.0006*(DRS/150)

And the half-season version, which excludes DRS/150:

wOBA-xRD/G = 29*(Batting wOBA) – 0.45*(BsR/600) + 1.55*(Batting wOBA)*(BsR/600) – 13.69*(SP wOBA) – 15.56*(RP wOBA)

Unsurprisingly, the base running and fielding variables are not statistically significant, likely because wOBA already accounts for those aspects of the game—good base running helps batters get extra bases (leading to a higher batter xwOBA), good fielding helps limit the number/quality of base-hits that a pitcher allows (leading to a lower SP/RP xwOBA).

It would appear that xwOBA makes a more useful foundation for xRD/G than does wOBA. The xwOBA-based xRD/G is more reflective of what happened in a given season, in terms of both RD/G (R² of 0.83 vs. 0.73 for the wOBA-based version) and Win% (R² of 0.75 vs. 0.69). It can better predict a team’s future RD/G in both a season-to-season (R² of 0.36 vs. 0.29) and half-to-half time frame (R² of 0.33 vs. 0.30). Similarly, it is more predictive of a team’s future Win% in both a season-to-season (R² of 0.37 vs. 0.31) and half-to-half time frame (R² of 0.39 vs. 0.36). It also has the edge in terms of half-to-half consistency (R² of 0.47 vs. 0.33). The one edge that the wOBA-based xRD/G has is in season-to-season consistency (R² of 0.35 vs. 0.42).

xRD/G vs. FanGraphs’ Projections

A much bigger test of xRD/G’s predictive power is the FanGraphs Playoff Odds projections: “FanGraphs Projections Mode…uses a combination of Steamer and ZiPS projections and the FanGraphs Depth Charts to calculate the winning percentage of each remaining game in the MLB season.” Conveniently, one can find a rest-of-season Win% projection for any date since the start of the 2016 season (so this section will focus only on the 2016-17 seasons).

FanGraphs’ preseason Win% projections for each team explains 43% of the variation in a team’s full-season Win%. As noted earlier, a team’s previous-season xRD/G explains about 37% of the variation in a team’s Win%. So, while it’s more predictive of Win% than a team’s previous-season RD/G and Win%, xRD/G comes up a little short when matched up with the FanGraphs preseason projection.

We can repeat the test using FanGraphs rest-of-season Win% projections at the 2016 and 2017 all-star breaks. In this case, the FanGraphs projected Win% is less correlated with future Win% than its preseason version. This midseason projected Win% explains 40% of the variation in a team’s second-half Win%, more than either first-half Win% (27%) or RD/G (34%).

However, first-half xRD/G has even more predictive power. Earlier, we saw that (from 2015-17) a team’s first-half xRD/G explains 39% of the variation in its second-half Win%. However, over the last two seasons, a team’s first-half xRD/G explains 46% of the variation in its second-half Win%.

The idea that first-half xRD/G was less predictive of second-half Win% in 2015 than in 2016-17 makes a lot of sense. As has been well-documented by FanGraphs, FiveThirtyEight and countless others, the 2015 all-star break represented a turning point in the MLB. There is very strong evidence that baseballs were altered at that point to help them travel farther, leading to a power surge across the majors.

This change is also reflected in a key component of xRD/G: xwOBA. The largest half-to-half xwOBA gap in the last three seasons occurred in 2015—MLB batters produced a combined .302 xwOBA before the all-star break and a .315 mark afterwards. In fact, from 2015 to 2016 to 2017, two trends emerged: the absolute half-to-half gap in xwOBA shrunk—from 0.013 to 0.007 to 0.003—while the ability of first-half xRD/G to predict second-half Win% improved—from 32% to 38% to 52%.

Similarly, the ability of FanGraphs’ midseason Win% projection to predict a team’s second-half Win% improved from 2016 to 2017. In 2016, it explained 34% of the variation in second-half Win%, while in 2017 it was able to explain 45% of this variation. However, both of these single-season marks fall short of xRD/G. Going forward, if MLB’s run-scoring environment continues to be stable over the course of the season (as it was in 2017), the midseason predictive power of xRD/G may continue to be quite strong.

An important test

A big issue with building xRD/G is the limited sample size. Not only is the Statcast era limited to three full seasons but, since xRD/G is a team-level stat, I only have 30 observations per season. One of my concerns is that I’m using 2015-17 data to build the xRD/G equation, then going back to the same data and testing the metric’s predictive power, which could lead to artificially positive results.

In order to test for this, I decided to rebuild xRD/G (temporarily, for this purpose only) using only data from 2015-16. Then, I’d only use 2017 data to test this new metric’s predictiveness.

The full-season version, which includes DRS/150:

xRD/G = 22.24*(Batting xwOBA) – 1.68*(BsR/600) + 5.65*(Batting xwOBA)*(BsR/600) – 11.51*(SP xwOBA) – 10.85*(RP xwOBA) + 0.005*(DRS/150)

And the half-season version, which excludes DRS/150:

xRD/G = 25.70*(Batting xwOBA) – 2.28*(BsR/600) + 7.71*(Batting xwOBA)*(BsR/600) – 14.13*(SP xwOBA) – 11.64*(RP xwOBA)

The results suggest that this particular concern is nothing to worry about. This version of 2016 xRD/G was able to account for 34% of the variation in a team’s 2017 Win%, implying that its predictive power was much stronger than that of a team’s 2016 record (12%) or RD/G (16%) and roughly equal to that of FanGraphs’ 2017 preseason Win% projections (35%). Moreover, this version of a team’s 2017 first-half xRD/G explained 50% of the variation in second-half Win%, a better mark than a team’s first-half record (31%), RD/G (39%) and midseason FanGraphs projected rest-of-season record (45%).

“Predicting” the Postseason

Finally, let’s examine how well xRD/G predicts the outcome of playoff series relative to RD/G, Win% and FanGraphs’ playoff odds. This section is mainly for fun, as we are working with a very small sample of series. Moreover, I will assume that all predictions are equal—whether a team has a one run edge in xRD/G or a 0.01 run edge, they will be viewed as the predicted winner.

From 2015-17, xRD/G was superior to both Win% and RD/G at predicting series winners, correctly predicting the winner in 19 of 27 series (vs. 16 correct predictions for the other two metrics). This is impressive, as the team that Win% predicts to win a series is always also the home team (except for the 2016 World Series), which is sort of an unfair advantage.

Focusing on the last two seasons allows us to include FanGraphs’ playoff odds in our comparison. Over the 2016-17 seasons, xRD/G, RD/G and FanGraphs’ playoff odds made correct predictions 13 out of 18 series. Win% made 12 correct predictions. Again, the fact that xRD/G is at least as predictive as Win% and FanGraphs’ playoff odds is impressive (for xRD/G), as the latter two account for home-field advantage as well as quality.

Let’s have a look at the individual series predictions. In the 2015 postseason, xRD/G was correct six times, erring exclusively in series that the Royals won. xRD/G was the only metric of the bunch to pick the Mets to win a series, let alone make the World Series. In 2016, xRD/G almost ran the table, erring only when it gave the Red Sox a slight edge against Cleveland in the ALDS. Also, as a Jays fan, I can’t help but note that only one team that made the LDS over the last three seasons had a negative xRD/G: the 2016 Texas Rangers. The 2015 Rangers are the second-worst LDS team in the bunch, by xRD/G. xRD/G had its worst postseason in 2017. It joined the other predictors by whiffing on Cleveland over the Yankees and the Nationals over the Cubs. xRD/G was sort of low on the Astros last season, at least relatively speaking, figuring that the Yankees and Dodgers would edge them in the ALCS and WS. For what it’s worth, those were both seven-game series.

Concluding Thoughts

xRD/G seems like an idea with a great deal of potential. Over our relatively small sample, xRD/G has been more strongly correlated with a team’s future record (whether next season or next half-season) than simple metrics like a team’s record or actual run differential per game. There is also evidence that it can be a better predictor of future Win% than FanGraphs’ team projections, particularly at the all star break. It even holds its own in postseason series predictions, despite it not accounting for home-field advantage.

xRD/G is also relatively consistent, another key strength. It is not improved by replacing xwOBA with wOBA, implying that the Statcast data is key to its usefulness. Finally, the ability of first-half xRD/G to predict a team’s second-half record has improved each year of the Statcast era—likely because, due to unrelated reasons, the MLB run-scoring environment has been more stable with each passing year. This implies that continued stability might allow xRD/G to explain around half of the variation in a team’s second-half record.

There’s also a great deal of potential in the fact that there is a lot to play around with here and improve upon. An xRD/G built specifically to predict future records may, logically, be better at predicting future records than the version built in this post. Other foundational stats could be brought in which may enhance xRD/G’s reflectiveness, predictiveness and consistency. There are a lot of different threads to explore from here.

Finally, since I presented a number of different equations, let me end with what I consider to be the “proper” equation for xRD/G, which I use for posts on Jays from the Couch:

xRD/G = 23.31*(Batting xwOBA) – 2.52*(BsR/600) + 8.34*(Batting xwOBA)*(BsR/600) – 13.16*(SP xwOBA) – 10.19*(RP xwOBA) + 0.004*(DRS/150)

On June 24, 2017, Joe Mauer went 2-for-2 with two walks against Corey Kluber. It was, perhaps, one of his most impressive performances of all last season; Kluber allowed only one other base runner in seven innings, and struck out 13. For this performance, and many others, he got his second Cy Young last year.

But on that particular day – June 24 – the numbers say (my numbers say) Corey Kluber had reached his peak. In the third inning, when he faced Joe Mauer, he was more difficult to reach base against than any other pitcher, at any time, in 2017.

I present… relOBP! (Also its cousins, relAVG and relSLG, but they can wait.)

relOBP attempts to quantify how good a batter is at reaching base, and how good the opposing pitcher is at preventing that, for each play in the season. relOBP therefore requires two numbers, a pitcher-score and a batter-score. Let’s jump in

[Editor’s note

Defining relOBP

Math follows, but not particularly hard math.

Suppose that, in a given play, the probability of a batter getting on base is given as P(reach) = o*c, where o = the effective number of opportunities the pitcher gives per plate appearance (average 1.000) and c = the batter’s rate of capitalizing on those opportunities.

If every pitcher always has o-score = 1.000, then the c-score is simply a batter’s on base percentage (or, for my purposes, his on base percentage in the surrounding +-30 plate appearances).

But once we have an initial estimate for each batter’s c-score by plate appearance, we can use it to estimate the opposing pitcher’s o-score at the appearance as well. How? Well, let’s suppose that a pitcher has a constant o-score o over an interval of plate appearances 1…n. Then the expected number of batters to reach base is E[Total_Reach] = o*∑c_i, where c_i is the c-score of the ith batter (at this point, his on-base percentage over his recent and future plate appearances). Thus, the pitcher’s o-score in the center of the interval can be estimated as the number of batters that do reach base, divided by the sum of their c-scores.

We now have first estimates of o- and c-scores; but with better o-scores, we can calculate better c-scores, and vice versa. Thus, we can just iterate this process until o- and c-scores converge (which they do, rather rapidly).

I did 20 iterations of this process on all plays from the regular season 2017, calculating c-scores for on-base percentage, slugging percentage, and batting average, though as I said I’ll focus on on-base percentage in this article. For purely arbitrary reasons, my intervals were a batter’s previous and next 30 plate appearances, and a pitcher’s previous and next 50 plate appearances (when available); however, the final numbers are not especially sensitive to interval sizes.

/end math

relOBP Leaders

Let’s check out some leaderboards! Consider the following table, showing the Top 10 batters by relOBP in 2017, as well as the actual Top 10.

The following table shows a player’s relOBP as his average c-score, weighted by how many adjacent plate appearances were available to calculate it (e.g. early and late season plate appearances are weighted less heavily).

And 2017’s actual season leaders:

While the order changes a bit, the top-10 are mostly the same (Freddie Freeman falls from 10th in OBP to 16th in relOBP, however, and is replaced by Eric Hosmer, who was 11th in OBP).

Note, however, that not everyone has faced the same quality of competition. Justin Turner faced weaker pitchers, on average; he had effectively had 1.017 as many opportunities to get on base as a hitter facing neutral pitching (~9 more PA over the course of the season), while Jose Altuve faced tougher competition, effectively losing 32 plate appearances.

relOBP and Luck

relOBP lets us see (approximately) how good the opposing pitcher is in each plate appearance (of course, we’re not accounting for handedness in our simple model).

For example, here’s a season of plate appearances from Mike Trout.

When the o-score (orange) dips low, that’s a tough matchup; when it spikes, it’s an easy one. In gray, you can see Mike Trout’s actual rolling OBP, and in blue, his c-score. When the c-score is higher than the OBP, Trout was hitting better than he appeared (given the matchup); and vice-versa. You might wonder what the cumulative difference in those scores is; did he gain or lose expected times on base? On net, he was unlucky; he lost almost 6 times on base because he faced harder pitching.

Trout was not the hardest hit, however. That would be Miguel Cabrera (and indeed, much of the Detroit Tigers’ lineup):

The Tigers were facing some tough pitching, apparently. One wonders if some of the other fine hitters here (Altuve; Machado; Abreu) were particularly prone to face difficult relievers, and if this would explain their presence on the list. On the flip side, the luckiest player of 2017 in this respect was Ozzie Albies, with 9.7 bases.

I have a lot more graphs, but that’s really not the point of the article: I only wish to introduce relOBP to you all, and now you’ve met it and can be friends.

I would love to hear your reaction to relOBP, to the methodology behind it, and any suggestions you might have for improving it. Also, if you would like to see my code or play with some of the data, let me know in the comments!

The Diamondbacks entered this year with some legitimate excitement for their starting rotation – led by Robbie Ray, Taijuan Walker, a resurgent Patrick Corbin, and the breakout Zack Godley, they emerged as a rising force in the NL West. Since a nuclear April, almost all of that excitement has slipped away as one by one their big 4 have succumbed to injury: Taijuan Walker is down with Tommy John, Robbie Ray has yet to come back from a lat strain, and Zack Godley seems to have given back all of the gains he made last year, and then some.

Godley, led by a wipeout curveball and a sharp cutter, was universally anointed as a rising star both in fantasy circles and in real life. Two-plus months into the season, and he’s universally disappointed. His command has fallen apart, he’s getting hit hard, and even his xFIP isn’t saying he’s much better than he’s performed (5.12 ERA, 4.19 xFIP). I’m going to go ahead and bury the lede, as well as spoil my findings: I think this is going to get much worse before it gets better for Godley.

Graph dump!

Zack Godley Slugging Against

So what I’m noticing right off the bat is his cutter and sinker suddenly started getting hit this year. Like, legitimately getting smoked. He’s had spikes like that at other times in his career, but something like that definitely puts up some red flags.

2017 Cutter Usage vs LHH

2018 Cutter Usage vs LHH

2017 Cutter Swing% vs LHH

2018 Cutter Swing% vs LHH

2017 Cutter Whiff per Swing vs LHH

2018 Cutter Whiff per Swing vs LHH

Ouch. Godley’s almost completely lost his ability to get chases outside the zone to lefties on his cutter. He’s also leaving the pitch in the zone much more than in 2017, and it’s getting hit when he does. This feels like lefties are seeing the pitch better, or have adjusted. Let’s keep going to the sinker:

2017 Sinker Usage vs RHH

2018 Sinker Usage vs RHH

2017 Sinker Whiff per Swing vs RHH

2018 Sinker Whiff per Swing vs RHH

This is…bad. Godley’s lost the entire bottom of the zone, and completely abandoned his strategy to ride the pitch down and in on right handers. He can’t buy a whiff right now, and guys still aren’t biting when it’s away. He still has decent effectiveness on the pitch over the inside part of the plate, so to me that isn’t suggestive that the pitch lost its ability to tie guys up. He just can’t get it in there.

All of this brings me to this penultimate chart:

Godley Career Horizontal Release Point

Hooooooo boy. Godley’s seen an aggressive change in his release point outwards since the beginning of 2017. His splits in 2017 get worse as he floats more towards the sidearm – BABIP went from .236 to .316; BB% 7.8% in the first half to 8.9% second; HR/9 from .52 to 1.16; only his K% got better – 24.3% to 27.9%. Those rates have continued to move in the wrong direction to start 2018 too: K% – 21%, HR/9 – 1.76, BB% – 11.2%, BABIP – .316.

I’m not sure what this means. The data suggests his pitches are flattening out a little, and he’s having issues locating side-to-side. That tells me his arm is being dragged along and getting off to the side of his pitches, rather than being on time and getting on top of them. His velocity is down across the board 1-2 mph as well, which paired with his release point issues is a major red flag. A side effect of being farther out to the side is off-hand batters get a better look at what you’re throwing (side-armers tend to be on-hand specialists), which explains why lefties have effectively stopped swinging at cutters inside. It also explains why he can’t drive his sinker in on the hands of righties – if his arm is late he’s going to miss either off the plate outside or in the middle of the zone.

To me, and I’m assuming this release point change isn’t an intentional change, there’s likely an injury that he’s pitching through. I dug through news feeds and didn’t see anything mentioning injury or soreness, but I can almost guarantee something is wrong physically. I can’t tell you what’s wrong, but I can tell you this: unless he gets his arm on-time and back in line, his struggles will continue.

What a boring headline in any other season. “One of game’s top players does good thing at plate” isn’t intriguing in nearly any context. But there’s so much to consider here. 

The D-backs started the 2018 season off gangbusters, winning nine series in a row and going 20-8 through April. They held at least a five and a half game lead on the rest of their division. And that was without Jake Lamb for most of the time, or Steven Souza, and Paul Goldschmidt off to an utterly pedestrian start. But the team was winning.

Then May happened.The injuries kept coming: Robbie Ray, Taijuan Walker, AJ Pollock, Randall Delgado, and Steven Souza (again) all hit the DL. Goldschmidt went from pedestrian to abysmal; his batting average was flirting with the Mendoza line, he was piling up Ks, and he couldn’t make contact in the zone. Arizona finished the month at 28-27, a game and a half back of the Rockies.

And that’s just the team. The humidor installed at Chase Field in the offseason has been another beast all on its own. The data that’s available on its impact to this point isn’t necessarily reliable yet because the sample size is still relatively small. It sure seems to have made a pronounced — if not definitive — impression, though. Offense is down in the desert by about 20% across the board. Add that into the mix with a team that was probably playing over its head, and then sinking, and suddenly the waters are much choppier.

Some wondered if the humidor’s presence had snuck into the back of Goldschmidt’s mind and taken up residence. Every additional out he made seemed to sell the idea. He was pushing a 200 strikeout pace. There were at-bats where he simply looked lost, and it was fair to wonder whether he’d been occupied by a Pod Person.

But he started showing signs of hope: a couple multi-hit games, a couple extra base knocks. Even if those things happened on the road, every little bit helps for a player struggling as badly as Goldschmidt was. And then, on May 30, he did something he hadn’t done even once in 2018. He homered at home. He hit a long, humpback line drive down the right field line off Sal Romano that cleared the fence. Take a look.

Do you notice Goldschmidt’s face? It’s almost like he couldn’t believe the ball finally went out. The relief was palpable.

He’s never taken more than six games to homer at Chase Field in any season. This year it took him 27. I’m not always a fan of referencing exit velo, but it’s relevant here. Just last year, pre-humidor, he hit a homer on April 23 at Chase that came off the bat at 97.3 mph and went 390 feet. His cue shot down the line for his first homer at home this season came off the bat at 102.1 mph and traveled 349 feet. 

And then just three days and three more games after that, Goldschmidt homered again on June 2. This time, it was a towering shot down the left field line that went 431 feet and left the bat at 109.9 mph. I’ve told you both of his dingers at home this year were down the line, making them extreme. Peeking at his career home run spray chart at Chase give us a sense of just how severe they really were.

The black boxes indicate Goldschmidt’s two homers at home so far entering last week. You’ll note that the one down the left field line doesn’t actually surround a red dot indicating a home run — that’s because Baseball Savant hasn’t yet updated Goldschmidt’s most recent shot. For reference, the dot immediately above the empty box traveled 438 feet. 

You may also note that both shots this year easily push the bounds of literally every other home run Goldschmidt has ever hit at Chase Field in his entire career. Maybe, just maybe, the humidor had taken residence in the back of Goldschmidt’s mind. Just look at that green circle in left-center, the one he hit at barely 97 mph. Imagine being able to flick that pitch nearly 400 feet on a regular basis, then clobber one this season at 102 and have it barely clear the shortest fence in the yard, and only after enduring 26 dinger-less games. 

Goldschmidt can go to any part of the field on any pitch. You probably wouldn’t expect him to get spooked by what essentially amounts to air conditioned baseballs. But if the humidor did punch a hole in his game, he may have found a way to patch it by opting to work the foul lines to get the fairest results.

—

Game data from FanGraphs. Home run exit velocity and spray chart from Baseball Savant. Gif made with Giphy. 

Lewis Brinson has underwhelmed during his time in the majors. Despite dominating AAA competition (wRC+’s of 163 and 146 across two stints), he has struggled to find his stroke against MLB pitching. As a highly-rated prospect, Brinson provided power, speed and defense in a unique combination in the minors – a desirable trilogy of skills. He has played closer to his floor than his ceiling, though, since debuting in the majors. Using both FanGraphs and Statcast data, obtained the morning of June 8th, as well as video, I sought out to try to find a fix for Lewis Brinson.

In Brinson’s initial cup of coffee, in 2017, he ran a rough 30 wRC+ and .225 wOBA in 55 PA with the Brewers, according to Fangraphs. After being traded to the Marlins, Brinson rode a solid spring training performance (.328/.365/.586) into a starting center field role, having appeared to turn a corner after 2017. Since the season began, though, Lewis Brinson began to perform like it was 2017 again. Through June 7th, he’s batting .168/.214/.313 with a 32.1% K-rate and a measly 4.1% BB-rate, good for a 41 wRC+, second-worst among qualified batters. His Fangraphs prospect tools, seen below, suggest he is much better than his current performance indicates.

Lewis Brinson was a top prospect in the minor leagues, peaking at 13th on MLB.com and FanGraph’s prospect rankings lists as some point within the last year. Brinson’s tools are promising. Essentially, he was seen as a power hitting speedster with a strong arm, average to above hands and fielding instincts, and a below to average contact ability. In the majors, Brinson has displayed above-average fielding and great to excellent speed – 29.5 ft/sec sprint speed, 8th at his position and 44th in the majors – but has yet to flash his game power and has mightily struggled with contact, to the point where it may be masking his power. When he makes contact, like in AAA, he displays uncanny offensive abilities: .343/.392/.575 with 17 home runs and 15 stolen bases in 433 PA, with a 19.2% K-rate and 7.9% BB-rate.

One of the major areas of concern for Lewis Brinson is his ability to make consistent contact. Specifically, he has had a hard time getting under the ball, too frequently hitting the top instead. As seen below, his Statcast data suggests he has a flat swing, as opposed to the slight uppercut many pros pursue. Brinson hits too many ground balls and topped balls, resulting in a low launch angle. Higher launch angles could help him utilize his natural power.

I felt Dexter Fowler was a decent comp to use for Lewis Brinson because of his similar body type and skill set. Brinson is 6’3″, 195lbs. and Fowler is 6’5″, 195 lbs. During the 2016-2017 seasons, Fowler displayed similar power and speed numbers to Brinson’s AAA performance. Given that similar power and speed profile, I chose to compare Fowler’s 2017 season’s launch angle distribution to Brinson’s. Below are both of those distributions – on the left, Lewis Brinson’s 2018 season and on the right, Fowler’s 2017.

Clearly, Dexter Fowler capitalized on productive launch angle zones. Ideal launch angles are between 10 and 20 for line drives and 20-30 for fly balls (wide estimates, but they paint the right picture). Lewis Brinson, however, struggles to find those optimal launch angles. His launch angle distribution reflects that – the majority of his batted balls are hit into the ground, at launch angles at which balls rarely becomes hits.

Given his 6’3″ 195 lb. frame, Brinson struggles to make contact with pitches low in the zone. Below is a Fangraphs heat map of contact rate per area of the strike zone. On the left is Lewis Brinson’s 2018 contact rate. On the right is Dexter Fowler’s right handed contact rate from 2016 and 2017.

Lewis Brinson clearly struggles with low pitches, especially on the corners. Compare that to a 2016-2017 right-handed -batting Dexter Fowler (who ran a 120 wRC+ with a .355 wOBA), and you see the difficulties Brinson has had with making contact. Part of this surely is because Brinson is a rookie and needs to acclimate to MLB pitching. The likely cause, though, is his swing, which we can break down over time.

Through browsing the video archives (translation: Google Search Engine), I came across three separate swings Lewis Brinson has deployed in recent years, with varying success. The first swing was from his 2016 AA stint with the Frisco Roughriders, while the other two are both from 2018 – April 21st and May 4th. The three clips I chose are of home runs, with two of them (AA and May 4th) having the pitch in the same location. For convenience, here are gifs of each swing.

2016 AA:

A few key details to notice. Brinson starts his leg kick before the pitch is released, . He also has a slight drop in his hands, a timing or loading mechanism for his swing. Also, from this perspective, we can’t see Brinson’s back knee until the moment he makes contact. We can see quite a bit of his jersey number, implying a strong turn and load.

2018 April 21st:

Here, Brinson is using a different leg kick. He lifts it prior to release, but earlier than in AA, and holds his leg in the air a bit. He’s removed the depth of his hand movement in loading, as well. Even though it is a different camera angle, it’s clear that Brinson’s back leg is exposing itself prior to contact. Not as much of his jersey number is exposed on his turn, though some of that could be camera angle differences.

2018 May 4th:

In his most recent swing, only a few weeks after the April 21st swing, Brinson has reduced the magnitude of his leg kick. He starts his kick as the ball is being released, but uses a few leg movements prior to the release as a timing mechanism. Similarly to the previous swing, Brinson has reduced the magnitude of his hand drop and exposes his back leg prior to impact. Despite an aiding camera angle, not much of his jersey number can be seen.

Upon first view, I felt like the 2018 swings lacked athleticism which, for such an athlete as Brinson, is suboptimal. It appears that his upper and lower bodies aren’t working as one – exposing his back leg prior to contact implies he is opening up too early, even if his hips don’t appear to do so. These swings can be viewed as a one-two swing, where his lower body fires and then upper body, in a one-two sequence. By removing his hand drop, Brinson may have thrown off his load timing. Whether this is affecting the timing of his leg kick, or if the timing of his kick is conscious, is unknown, but his leg kicks in 2018 also appear suboptimal. Neither the larger, hanging leg kick nor the on-release leg kick appear to help his timing. Brinson appears to lack a deep load – even with an off-center camera angle, not much of his back can be seen, implying his shoulders aren’t in a powerful location during his load.

Compare his 2018 swings to a 2017 Dexter Fowler home run swing. Despite it being a left-handed swing, differences are immediately apparent.

While Lewis Brinson is starting his current leg kick upon pitch release, Dexter Fowler is almost finishing his leg kick then. This allows Fowler to load into an athletic position, with his shoulders and hips turned, exposing most of his jersey number despite us having a camera angle that would hide his back. Fowler drops his hands upon loading, moving them back which supports his athletic load and turn. Despite starting in a slightly open position, Fowler doesn’t expose his back leg until impact or even slightly after. His upper and lower body work together, in sync as opposed to sequentially. Fowler’s swing here is explosive.

We can identify a few swing fixes we can suggest to Brinson, based on our swing breakdowns. The first would be his load mechanism – previously, he used his hand movements to load his swing into a turned, athletic position, while timing his swing with a small but effective leg kick. By trying to remove his hand motion, Brinson lost his deep load. Changing his leg kick led to a loss of timing, breaking the athletic chain and resistance his swing needs for coverage and power. These two changes broke the synchronization between his upper and lower body, which makes both contact and power more difficult to find. Brinson is very athletic – he likely has been relying on his athleticism more than his swing as of late.

How could these changes help Brinson? They could help put his swing in better positions to cover the lower part of the plate, and to cover the entire plate with greater efficiency. The quality of his contact could increase, as he gets his entire body working as a single, power-transferring unit. With better quality contact, he could get under the ball and square it up more, driving it along ideal launch angles and utilizing his natural power. Or, these changes could hurt him. As with many sports, fixes that may help some may not help others.Whatever Brinson is trying, though, doesn’t seem to be working.

– tb

This and posts like it can be found at my personal blog,

First Pitch Swinging

Heading into the season, the NL Central was expected to be a one horse race. The Chicago Cubs were projected to win 96 games, nine games better than the second-place-projected Cardinals. The Cardinals, for their part, were projected seven games better than the Brewers (79 wins) and eleven better than the Pirates (76 wins).

Fast forward to this writing and the NL Central mix is much cloudier. The Brewers sit atop the division at 37-24. If we only knew about the projections, they’d be the biggest surprise in the division. However, we do know more about the Brewers than the projections, such as the fact that they won 86 games in 2017 before adding very good players in Christian Yelich and Lorenzo Cain. The projection algorithms didn’t buy the Brewers as a threat, but I’d bet most people did.

The Pirates, on the other hand, won only 75 games last year. Then they got rid of staff ace Gerrit Cole and best-player Andrew McCutchen. They didn’t sign a single major league free agent. The only established major league player they acquired was Corey Dickerson after he was DFA’ed by the Rays following a dismal second half of 2017. Frankly, the Pirates were supposed to suck. Instead, their playoff odds have thus far peaked at 30% and currently sit at 11%. The Pirates were  the NL Central’s biggest early surprise before a recent cold spell.

During their 2013 to 2015 run as one of the NL’s best teams, the Pirates ranked fourth in the majors by ERA- while giving up the third fewest total runs. This time around, the Pirates staff is basically OK, with a slightly better-than-average FIP- and a below average ERA-.

Instead, the Pirates are riding an offense (excluding pitchers) that ranks eighth in the MLB by wRC+. Not unrelated, here is the Pirates ground ball rate by year since 2013:

That’s a drop-off-a-cliff drop. In 2017, the Pirates had the fifth-highest ground ball rate in the Majors. In 2018, they have the second-lowest. The Pirates have a chance to hit more fly balls than ground balls, which they’ve never done this century. It was a trend that Alex Stumpf noted a month ago for the Point of Pittsburgh and revisited again last month. For a team whose manager told his players that their OPS is in the air, the Pirates were late to the fly ball revolution. And yet, here they are.

At risk of oversimplifying Alex’s findings, nearly everyone on the Pirates is hitting less grounders, and nearly everyone on the Pirates is putting more of their hard contact in the air. Hard hit balls in the air are good. Trying to lift the ball more often is a tradeoff that can lead to more strikeouts, but the Pirates are doing it without striking out more than before. The Pirates have a recipe for success.

The change in approach hasn’t benefitted anyone more than Francisco Cervelli. Looking at the 240 players with at least 100 plate appearances in both 2017 and 2018, Cervelli has the second largest decrease in ground ball rate, down to 31.3% from 52.3%, and he’s also decreased his strikeout rate by several points. The Pittsburgh catcher owns a 152 wRC+, which represents a 59 point increase over last year, the eighth largest gain, and more than doubled his isolated slugging. And he’s doing it with a .308 BABIP, which is both perfectly normal and below his .333 career BABIP.

Francisco Cervelli is driving the ball in the air, and he’s doing it without making less contact. There isn’t one right way to accomplish that goal, and Cervelli’s success is probably a combination of several factors. Alex suggested to me that Cervelli appears to have lowered his hands, and it does look like he starts them lower in 2018 than he did in 2017. Lower hands often puts a hitter in a better position to drive the ball in the air, and Cervelli has gained 3.2 mph of exit velocity on line drives and fly balls – the 11^th biggest gain in baseball (min. 50 LD/FB in 2017 and 2018).

Cervelli has improved his plate discipline, too. According to Pitch Info, Cervelli’s chase rate of 24.9% last season was his highest since 2013. This year, he’s lowered it to a career low 19.2% while continuing to swing at strikes at approximately his career rate.

While it’s good to know Cervelli is swinging at the same rate of strikes, we also know all strikes aren’t created equal. It’s just as important, and perhaps more so, to know what kind of strikes a player is swinging at. Here, we see significant change through late May:

Last year, Cervelli’s swing core is toward the low-outside corner. This year, he’s swinging at pitches up and over the heart of the plate. According to Statcast, the vertical pitch location when Cervelli swings is up from 2.20 feet to 2.40 feet – that’s the largest height increase among 226 players with 200+ swings in 2017 and 2018. Jason Heyward is second at an increase of 0.19 feet, and Alex Bregman is third at 0.13 feet. Cervelli is identifying better pitches to hit and he’s now driving them with authority.

Francisco Cervelli spent most of his career behind the dish as an unspectacular, solid hitting catcher. By reinventing himself in his age-32 season, he’s become a force at the dish for the surprise Pirates.

A big thank you to Fangraphs, Baseball Reference, and Baseball Savant for the data used in this post.

We are living in a world where TTO is the new normal. MLB teams are willing to overlook high strikeout numbers if you can hit 30 HRs and draw a few walks. High school, college, and minor baseball coaches are looking at analytics like launch angle and are not getting upset if a guy strikes out a few times a game.

Here’s why I don’t think that trend is one that teams should follow. Simplistically, TTO and launch angle were bred from the analytics world. Pitchers have started pitching up a lot more this season and having an uppercut swing on a pitch up in the zone will lead to more pop-ups. There’s data to back me up on this, I’m sure. Problem is, I am new here kids, so bear with the rookie. TTO rates are trending way up, but outcomes are trending in the SO direction, not HR.

Here’s a link to TTO leaders in 2017 (thank you, @srlauer): https://www.fangraphs.com/community/will-we-see-a-record-number-of-three-true-outcomes-specialists-in-2018/

This is a list that, for the most part, is kind of blah. Joey Gallo has an OPS+ this year of 96. Ryan Schimpf has barely played this year and had an OPS+ last year of 86. I’m embarrassed to say what Chris Davis’s is this year. Aside from Aaron Judge, who homers on freaking pop ups, no one in the top 10 in TTO last season has an OPS+ above 100 this year.

Per baseball-reference, the leaders in Offensive WAR this season are as follows:

(Take a bow, Scooter Gennett, you made it.)

Trout’s TTO is 43.9%.
Betts’ TTO is 31.0%%
Ramirez’s TTO is 31.1%
Machado is 31.9%
Lindor is 32.5%
Martinez is 40.7%
Gennett is 29.8%
Arenado is 38.3%
Simmons is 15.2%(!)
Freeman is 33.2%

Why am I bringing up this stat? It’s because with the league average TTO over 34% for the first time ever, it is becoming more and more apparent that the best hitters in baseball are actually cutting down on this stuff more than ever. If you look at Mike Trout’s numbers, he is a high TTO guy, but he has walked 51 times this year and his K% has actually been dropping ever since his first MVP season. His teammate, Andrelton Simmons, has struck out 10 times all freaking season. Do you mean to tell me you would take a Joey Gallo type of hitter over Simmons?

I seem to find it weird that TTO is rising to all-time highs this season, when 7 of the best 10 offensive players this season are below the league average. Maybe I’m missing something. I don’t know. But as a guy from Toronto, when I see Vlad Guerrero Jr. with a TTO under 25% in AA, all I keep thinking is that there is something to making solid contact and letting the BABIP gods do their work.

Let me preface this: I’m biased. I absolutely LOVE Luis Castillo. His ceiling is near-unmatched in the MLB. He’s got 4 pitches that have plus-to-plus plus upside, elite velo, and age on his side. That being said, he’s been truly atrocious so far in 2018. Allow me to dive into the numbers and graphs and play a little bit of doctor!

You may know me as the guy that does the dScore evaluations of players. While I haven’t done any so far this year, I’ve kept up with the analysis on my Google Doc and I’ll probably release one closer to the All-Star Break. One thing that I’ve noticed is, despite the putrid surface-level numbers (5.64 ERA, 1.45 WHIP) Castillo has consistently scored well on my metric. Not as well as last year when he was a certified stud, but he’s floated around the lower end of the #2 breakpoint (20+ points). This tells me that, purely based on his stuff, he’s getting pretty royally unlucky – or that something else is wonky. There’s been documented evidence that his velo is down from last year and that he had an issue getting his arm slot dialed in. His last month has been measurably better, showing regression towards last year’s outcomes (K% up from 18% to 25%, BB% down from 10% to 8%, BABIP normalized from .330 to .290). The two things that haven’t regressed are pretty key to this analysis: his hard contact has stayed abnormally high (38%) and he’s continuing to not generate ground balls at near the rate he was last year (45% vs 58%).

Here’s where the fun begins.

Last year, if you remember, part of the fun of Castillo was the fact that he learned two new pitches midseason that made his stock and performance explode like it did: the sinker and the slider. His sinker, in particular, was near miraculous due to how quickly it became a vital piece in his arsenal; and the slider was a groudball inducing, line drive avoiding monster. Guess what two pitches are thorns in his side so far this year? Now I’m not here to argue that these two pitches all of a sudden are bollocks and he should consider scrapping them. I’m here to argue that he’s simply struggling to harness two new, difficult pitches to locate consistently, and that simple issue is causing a snowball effect.

I took a look at Brooks, and outside of the noticeable early-season change in his arm angle, I didn’t anything that’s super out of the ordinary there. What was weird, though, was his sinker and slider have virtually stopped generating ground balls. His pitch mix is similar to last year as is his whiff percentages (actually his sinker’s gotten somewhat more whiffy), so it’s not just simply a change in pitch profile.

2018 GB per BIP

Here’s some relevant pitch-specific zone profile graphs:

2017 Sliders vs LHH

2018 Sliders vs LHH

2017 Sinkers vs RHH

2018 Sinkers vs RHH

I chose those profiles specifically. In English, Castillo has had serious problems leaving meatball sliders to lefties and consistently hitting the backdoor sinker to righties. Addressing the sinker, what this has done is allowed right handed hitters to forget about covering the outside part of the plate on fastballs and target anything in. He’s also running it into the barrel, and not really giving the sinker a chance to get pounded into the ground. That’s borne out in the ISO profiles for the pitch:

2017 Sinker ISO

2018 Sinker ISO

In terms of the slider, he’s eliminated its ability to tunnel off anything vs righties, as he’s been quite good at getting that slider down and away from them. Meatballing anything is bad — especially offspeed that needs to be buried down and in vs lefties. He’s consistently been dropping it right into lefties’ nitro zone, and because he’s somewhat lost confidence in his ability to execute a good slider vs offhand, he’s been using it less as the year goes on.

In general his pitch mix and locations haven’t really seen a large change from last year. His velocity being down across the board probably hasn’t helped much at all – although it’s slowly coming back as the season goes on, and I think Castillo is going to see ups and downs the rest of the year. He’s already shown the ability to consistently locate with those pitches last year so I’d take the bet that he’ll find it again. His swinging strikes, contact, and in-zone contact rates are all in the top 10 in the MLB among starting pitchers, which tells me when he hits his spots his stuff is absolutely still intact.

My take on him for fantasy is he’s a hold/buy. I don’t believe this is mechanical, injury-related, or his stuff backing up. This is all about him basically not having the feel for two specific locations of two specific pitches. I wonder how much of this is rooted in him missing most of spring training to the birth of his kid. He maybe never got a chance to iron out his mechanics, causing the arm slot issue. Maybe his arm slot issue caused him to lose feel/command of the sinker and slider, or they didn’t get the reps needed preseason. Whatever the reason he doesn’t have feel I’m confident he’ll figure it out. I’m also confident in his value 2019 and onward, so especially in a dynasty format I’d be looking to buy.

Introduction:

In Major League Baseball, there are countless bad contracts. There are also many contracts that are unjustifiably lucrative. This is because of baseball’s economic system and the rules that govern service time. In most cases, a player can’t get a fair market contract until after the player has 6 years of MLB experience. This causes young players to be underpaid, and older players to often times become overpaid.

The ability to find players in true free agency (after 6 years of service time) who will continue to produce and will sign reasonable contracts is extremely important; in fact it was essentially what the movie “Moneyball” was centered around.

I set out to identify the 25 most valuable, or team friendly contracts, so far this season. In order to look at this fairly, I decided to only examine players out of their 6 years of service time, so their contracts were not a result of baseball’s economic advantages to the team.

To calculate which contacts were most valuable, I reviewed each player’s Wins Above Replacement (WAR) so far this season and projected it out over the full season. I then researched each player’s current salary and divided it by their Projected WAR to find their Salary per Win. This number is key, as it represents how much their respective team pays for each win the player earns for them. The lower the Salary per Win, the better for the team.

The Top Three:

1. Daniel Descalso, Arizona Diamondbacks

Projected WAR: 3.8, Salary per Win: $530,864.20

Descalso is an interesting candidate, especially for the top of this list. He is easily the least known of all the players in the top 5. It makes sense that the number one player wouldn’t be well known, because he is paid like a below replacement level player ($2,000,000/ year), even though he’s contributing quite well.

His slash line of .250/.345/484 doesn’t stand out to the average fan, but if you look deeper into the numbers he is having a very productive season. His wOBA is over .350 for the first time ever in his big league career, and his wRC+ is a career best as well, sitting 23 points above average.

Descalso does not have a track record of this performance in the past, as his previous highest full season WAR is .6. His career is definitely on the back 9, at 31 years old, and he is still probably not an all-star, but if he continues to perform this way, he will be looking to get paid more than double when his contract is up at the end of the year.

2. Jed Lowrie, Oakland Athletics

Projected WAR: 7.7, Salary per Win: $776,014.11

This kind of a ranking wouldn’t be complete without an A’s player on it. Billy Beane, who was the center of “Moneyball”, has always operated under a small budget with the main goal of the organization being to find undervalued assets that can be signed for below market value.

Jed Lowrie is the perfect example of Moneyball. The A’s middle infielder is hitting a slash line of .314/.382/.545, all of which are the best of his career. HIs  wOBA is sitting at .393, which is just points off of his career high, and his wRC+ of 151 is a career high. Along with his greatly improved hitting, his WAR is also on track to be over 4 wins above his previous career best.

There is no question that Jed is off to an incredible start, in fact if this continues, he will almost surely be an all-star at mid-season. Unfortunately for him, this season comes at age 34, so his $6,000,000 salary probably won’t be upped much during contract negotiations at the end of the season.

3. Mike Moustakas, Kansas City Royals

Projected WAR: 4.9, Salary per Win: $1,122,981.96

Mike Moustakas was the result of a poor free agent market this past offseason. He was looking to be handsomely paid after hitting 38 home runs last year but there just was no market for 1st basemen. As a result, Moustakas was forced to sign a one year $5,500,000 deal with an option for next year and hope he will have better luck in next year’s market.

Moustakas has a solid slash line of .289/.333/.513, along with being well on his way to another 35+ homerun season. His wOBA sits at .356 and his wRC+ is currently a 123, both of which are career highs. Moustakas is also on track to have a career year in WAR.

His negotiations over the offseason were unfortunate; he deserved to be paid a lot long term considering his age and previous productivity. If he continues this stretch of good play for the rest of the year he will not be a steal for the Royals much longer.

Rank 4 – 25:

4. Asdrubal Cabrera, New York Mets

    Projected WAR: 7.1, Salary per Win: $1,168,300.65

5. Bartolo Colon, Texas Rangers

    Projected WAR: 1.4, Salary per Win: $1,215,277.78

6. Howie Kendrick, Washington Nationals

    Projected WAR: 2.3, Salary per Win: $1,296,296.30

7. Clayton Richard, San Diego Padres

    Projected WAR: 2.2, Salary per Win: $1,388,888.89

8. Francisco Cervelli, Pittsburgh Pirates

    Projected WAR: 7.2, Salary per Win: $1,466,861.60

9. Carlos Carrasco, Cleveland Indians

    Projected WAR: 4.6, Salary per Win: $1,728,395.06

10. Nick Markakis, Atlanta Braves

    Projected WAR: 6.2, Salary per Win: $1,782,407.41

11. Chris Sale, Boston Red Sox

    Projected WAR: 7.0, Salary per Win: $1,786,874.59

12. Charlie Morton, Houston Astros

    Projected WAR: 3.6, Salary per Win: $1,944,444.44

13. Jon Jay, Kansas City Royals

    Projected WAR: 1.5, Salary per Win: $1,990,740.74

14. Justin Verlander, Houston Astros

    Projected WAR: 9.7, Salary per Win: $2,057,613.17

15. Todd Frazier, New York Mets

    Projected WAR: 3.7, Salary per Win: $2,139,917.70

16. Max Scherzer, Washington Nationals

    Projected WAR: 10.0, Salary per Win: $2,207,977.19

17. Brandon Belt, San Francisco Giants

    Projected WAR: 7.6, Salary per Win: $2,275,132.28

18. Lorenzo Cain, Milwaukee Brewers

    Projected WAR: 5.5, Salary per Win: $2,353,909.47

19. Gio Gonzalez, Washington Nationals

    Projected WAR: 4.6, Salary per Win: $2,592,592.59

20. Freddie Freeman, Atlanta Braves

    Projected WAR: 7.7, Salary per Win: $2,768,807.87

21. Mike Trout, Los Angeles Angels

    Projected WAR: 10.3, Salary per Win: $3,225,308.64

22. Rick Porcello, Boston Red Sox

    Projected WAR: 6.3, Salary per Win: $3,375,090.78

23. Francisco Liriano, Detroit Tigers

    Projected WAR: 1.1, Salary per Win: $3,539,094.65

24. Brett Gardner, New York Yankees

    Projected WAR: 3.2, Salary per Win: $3,549,382.72

25. Justin Smoak, Toronto Blue Jays

    Projected WAR: 1.1, Salary per Win: $3,734,567.90

Statistics from Fangrpahs.com, Salary Information from spotrac.com, Projected WAR calculated 5/18/2018.

You can read more by me at cjwhittemorebaseballanalytics.com