We are reaching the end of the 2016 fantasy-baseball season, which means two things: 1) It’s time to look ahead to next season a bit, and 2) the sample sizes on many metrics are either stabilized or right around the corner from stabilizing.

With that in mind, let’s take a look at a few of players who are sure to be trendy draft picks in 2017, and see what their potential downfalls might be. This is not to say to avoid these players, but rather to spot a potential weakness so that if you do draft this player, and they start to struggle you can maybe know why, and see if it is a recurring issue, instead of just a bit of bad luck. Let’s meet our contestants:

Rougned Odor

In many ways Rougned Odor has had his coming-out party this season. He has hit 31 home runs through 135 games, adding in 12 steals, to go along with a .282 batting average, and strong production (85 runs and RBI each). He’s been the fifth-best second baseman by the ESPN player rater, and that’s with two players (Daniel Murphy and Jean Segura) with multiple positions above him in the ranks. He even got famous on the national scene with his punch heard round the hot-take tables in May.

However, when looking at his plate discipline, it has somehow got even worse than it was last year. His walk rate has dropped to 3.0 percent (17 walks all season!), while his strikeout rate has climbed above 20 percent (20.9, to be exact). His swing rate on pitches outside the zone is seventh in all of baseball, while his contact on those pitches outside the zone isn’t even in the top 80. That’s a dangerous duo. Talented players who hit the ball as hard as Odor can sustain success while flailing that much for a little while, but in the long haul, it almost always burns you. Odor could certainly make strides to improve his discipline, but coming off the season he is having, why is he going to try to change anything? He may well need a rough season, or at least rough couple of months, to admit he needs to fix the holes in his swing, and you don’t want to deal with that when he does.

Jon Lester

With a record of 16-4 and a 2.51 ERA, Lester is having arguably the best season of his career. He’s a candidate for the Cy Young, and currently trails only Noah Syndergaard and teammate, Kyle Hendricks, in the race for the lowest ERA in all of baseball. There are plenty of signs for regression, though.

For starters, Lester is 32, and while left-handed pitchers seem to age like a fine wine sometimes, that’s only because we forget about the guys who crash and burn and are out of the league by 34. Now Lester is showing no real signs of aging, but he also has signs of regression elsewhere.

His left-on-base rate is currently leading all qualified pitchers, at 85.5 percent. That’s more than 10 percent higher than his career rate, and by far the highest percent of his career. That’s especially amazing considering Lester can’t even throw to first base, meaning runners should be moving around the bases faster on him if anything. He is also one of the FIP-ERA leaders, thanks to a well below-average opponent BABIP (.257).

There’s also the fact that, while not by a huge margin, Lester’s strikeout rate has also dropped this season, while the rest of the league is striking out more batters than ever. If you’re already going to get some regression in terms of ERA and wins, you best not be losing strikeouts, as well.

I don’t think Lester is going to fall off a cliff, but I also don’t think he’ll be repeating his 2016 performance next year.

Michael Fulmer

We’ll start with the most obvious candidate for overdrafting. Fulmer will be a 23-year-old, (likely) coming off an American League Rookie of the Year Award, and pitching for a strong Tigers team. He may well win the AL ERA crown, and will easily have a winning record. Heck, he’s got an outside shot at a Cy Young in what has been a weak year for AL pitchers.

That being said, there are some definite weak spots in his profile, the most obvious being his FIP-ERA. If one were to simply go to the FIP-ERA leaderboard — a good spot to at least start to find potential regression candidates — Fulmer’s name is sitting there in fourth, trailing only Kyle Hendricks (borderline historic ERA), Brandon Finnegan (a guy who would have certainly made this list if he were famous enough), and Ian Kennedy (a professor at Hogwarts in the baseball offseason).

It’s more than just the FIP with Fulmer, though. His left-on-base rate is over 80 percent (81.3, to be exact), and his opponent BABIP is just .251. He allows over 30 percent hard-hit rate and his line-drive rate allowed is nearly 20 percent, which while not terrible, do not portend a Cy Young winner.

With Fulmer, it’s more an accumulation of slights rather than one big one, combined with the fact that he will be an extremely trendy pick. There’s no reason to believe he won’t finish the year 13-8 with a 3.35 ERA and 140 strikeouts, but you’ll have to pay for much better stats than that to land him.

Ryan Braun

Like all of the players on this list, Braun is having one of his best seasons in 2016, which is saying something for the six-time All-Star. Braun is hitting .310 with 27 home runs, both of which are his highest since 2012. He has also stolen 14 bases, and only been caught three times, impressive for a 32-year-old.

But it’s not Braun’s age that is troubling (although it is obviously worth remembering come draft day); it’s his ground-ball rate. Braun is seventh in all of baseball in ground-ball rate, surrounded by names like Jonathan Villar and Cesar Hernandez. He has hit ground balls on 55.6 percent of the balls he has put in play in 2016, and has only hit fly balls on 25.4 percent. Because of that, it’s hard to imagine his home-run totals staying as high as they are right now in 2017.

Braun is currently sporting a HR/FB rate of 28.4 percent, highest in the major leagues. Ask Jose Abreu owners what it is like to own the reigning HR/FB rate champion. Here’s how the last four HR/FB rate champions fared the next season:

2012 Adam Dunn – 41 HRs; 2013 Adam Dunn – 34 HRs

2013 Chris Davis – 53 HRs; 2014 Chris Davis – 26 HRs

2014 Jose Abreu – 36 HRs; 2015 Jose Abreu – 30 HRs

2015 Nelson Cruz 44 HRs; 2016 Nelson Cruz 35 HRs (with 18 games to go)

Only Chris Davis fell off the earth, but none of the four went up. If you’re buying on Braun’s power, you’re basically buying at its highest point, which is never a good idea. Especially with a 32-year-old.

Jose Fernandez

Yes, we are getting a little bit into “snake eating his own tail,” by turning advanced metrics against Jose Fernandez, after spending all of the first couple months using the advanced numbers to show a turnaround was imminent, but it was foolish to ignore Fernandez’s biggest weakness: his line-drive rate allowed.

Opponents are hitting line drives off Fernandez 29.3 percent of the time in 2016, by far the highest percent among qualified pitchers, and a rate that tops even Freddie Freeman, the 2016 league-leader in line-drive rate.

Part of that can be explained by the fact that Fernandez throws so hard, that of course the ball is going to come out faster — that’s just physics. It also isn’t as big a deal to allow such a high line-drive rate, when you also have, by far, the highest strikeout rate in the big leagues this season (34.9 percent). It doesn’t matter how hard you hit it, if you simply can’t hit it.

However, that line-drive rate certainly helps to explain the fact that opponents have a BABIP of .341 off Fernandez this year, a figure that would seem due for some regression in favor of Fernandez if we missed looking at the full picture. Some 2017 drafters may see Fernandez’s .341 BABIP and his 2.27 FIP and assume that his 2017 ERA will drop into the low 2.00s. If Fernandez keeps allowing line drives at the rate he has this season, there’s no reason to think his ERA will drop at all. Now drafting a player with an ERA of 3.00 and the best strikeout rate in baseball is still never a bad idea, but if batters start to elevate their swings against Fernandez, while maintaining that same hard contact, Jose could see his home-run rate jump up quite a bit, even when pitching in pitcher-friendly Marlins Park.

Fernandez allowed just as high a line-drive rate in his 11 starts in 2015, and while some of it may still be noise, it is something to keep an eye on. Especially if you have Fernandez in a long-term keeper league, and he eventually makes a move to somewhere like Fenway where the stadium might be a lot less forgiving than in Miami.

I watch a lot of baseball. I get to see a lot of players. Some of them will go on to have productive major-league careers, but most will not. The point of this article is to look at some of those who may, at the the very least, reach the show.

This report comes after observing two NY-Penn League (low-A) series in late August/early Sept. and includes players from the Oakland Athletics, Boston Red Sox, and Houston Astros organizations.

I will introduce each player as follows:

Name, Position, Organization, Organizational Prospect Rank, Age

Dakota Chalmers, RHP, Oakland Athletics, Rank: 9, Age: 19

Chalmers was drafted out of a Georgia high school in 2015. He’s a four-pitch pitcher– fastball, changeup, curveball, slider. Though, there’s only a 2-3 mph difference between his slider and curveball and not much of a visible difference. His fastball sat 91-93 when I saw him last week; I’ve seen him as high as 93-95. He has a high-effort delivery and control remains his biggest issue, which I’d say is a pretty good place to be as a 19-year-old. His fastball and curveball/slider look above-average, while his changeup shows potential but still is inconsistent in terms of location. I imagine he didn’t have to throw it that often in high-school competition last year.

Logan Shore, RHP, Oakland Athletics, Rank: 12, Age: 21

Shore’s strength is his command. His fastball sits 90-92 and he also throws a changeup (his best pitch) and slider. He pounds the zone and shows the ability to throw any pitch for a strike in any count. He made one (big) mistake during his last outing – an opposite-field three-run home run– but otherwise was solid. His slider remains his weakest pitch, but when it’s on (and it mostly is) he sees a lot of quick and easy outs. I would imagine he won’t add much velocity in the future as he’s already filled out, but can still see him being an effective pitcher nonetheless.

C.J. Chatham, SS, Boston Red Sox, Rank: 15, Age: 21

Interestingly, Chatham is a tall (6’4) shortstop whose biggest strength is his defense. Many at his size project better as third basemen, but it looks like Chatham has the ability to stay at short. He uses his long frame well to cover ground and also shows good arm strength. At the plate, the first thing that stood out was his aggressiveness as he swung at seven of nine first pitches. He also showed some line-drive power, hitting two doubles (one over the center fielder’s head and one down the left-field line) in the two games I saw.

Bobby Dalbec, 3B, Boston Red Sox, Rank: 21, Age: 21

This guy hits the ball really really hard. I saw him in eight at-bats – three strikeouts and five very well-hit balls. Even his outs were hit hard. He looks like an all-or-nothing type hitter. Lots of doubles and home runs but a lot of strikeouts. A former pitcher in college, Dalbec definitely has the arm to remain at third base. His range looked good too — he made one nice play to his right, a charging backhand near third base while having to throw across his body to get the out.

Ronnie Dawson, OF, Houston Astros, Rank: 18, Age: 21

Another all-or-nothing-type hitter, Dawson was drafted in the second round of the 2016 draft out of Ohio State. He looks like he could have been a running back at OSU too — standing 6’2 and 225 lbs. His power and bat speed definitely show – he smoked a line-drive double down the right-field line when I saw him. But so do the swings and misses – he struck out in his other three at-bats. Defensively, Dawson projects more as left fielder as his arm and speed aren’t two of his better tools. From the eye test, Dawson reminds me of the Indians’ Carlos Santana, except Santana strikes out a lot less (14% compared to Dawson’s 24%).

[This analysis is also featured in our emerging blog www.theimperfectgame.com]

With less than one month to go, the American League MVP race is very close. While usually nothing is set on stone in early September, during the last few years the AL MVP has been a two-man race (Mike Trout with either Josh Donaldson or Miguel Cabrera). This year, however, features five remarkable candidates: Mookie Betts, David Ortiz, Jose Altuve, Mike Trout and Josh Donaldson. Yes, I expect a few other to grab a few top-five votes (e.g. Cano, Cabrera, Lindor and Machado) but I don’t anticipate the award to fall outside those five players.

Let’s look at the classic, old-school numbers first, which not only are sometimes referenced in casual conversations at local bars and pubs but also frequently (and occasionally unfortunately) followed by voters. I’ve plotted R, RBI, HR, OBP, SLG and SB as percentiles of the entire population. Let’s take a quick look.

If you like well-rounded players, probably this year you’re excited with Altuve, Trout and Betts, who dominate across the board. In an era where stolen bases keep declining, 20+ SB will get you to the 90th percentile. On the other hand, if you’re into true sluggers, then the show Ortiz has put this season should be one to remember. However, then again, these metrics paint only part of the picture — they don’t take into account when or where each event happened nor they include defense or base running on its most complete form.

Let’s take a deeper look at WAR and a quick indicator for each batting, fielding and base-running performance.

Obviously when we move away from batting, David Ortiz loses ground — he only contributes in one aspect of the game, and while he has been outstanding in the batter’s box, likely it will not be enough for him to win. When we adjust by park and league, we realize the Trout – Betts race for the best OF is not as close as I initially thought. Trout has quietly put a(nother) great season on an awful team (again) — he’s already at 8.1 WAR and a 175 wRC+, with both easily leading the league. His defense is slightly below average at best but he compensates by running extremely well. Altuve and Donaldson have had similar seasons offensively. However, Altuve is having a down season in both defense and base-running (remarkably low on Ultimate Base Running (UBR), which measures how frequently and effectively a runner takes an extra base via running). Betts drives his value largely from his defense, where he’s settled in nicely as one of the best OF this year.

One of the metrics I tend to assess when I look at awards is how performance was spread the entire season. I want an MVP to be someone that I rely throughout the year, not only during a hot stretch. Additionally, having a big month can really uplift the numbers and build up a misleading argument in favor of someone. Let’s understand how wRC+ is split by month.

This picture to me is interesting for a couple of reasons. First, part of the argument on Betts’ candidacy is that he’s getting better, and delivering when it matters the most — in the middle of a pennant race. After a below-average March/April, Betts has been a beast since July, when Ortiz cooled off a bit. Now, then again, Mike Trout has also followed an upward-trending curve — peaking at 206 in August — and his lowest point is at 144, which is the highest of all lowest points in the sample. From my perspective, if everything else is equal, I’d rather have a Trout-esque curve than Donaldson’s one, who has the highest single-month wRC+ (213 in June) but also with the largest swing (118 difference between May and June). And then you have remarkably constant Altuve — with the narrowest gap between highest and lowest points throughout the season and at least 140 wRC+ in any given month.

Now, most of what we have shown up to now is context-neutral. An argument could be made that every single game is worth the same, regardless of whether it’s in April or July — what’s really important is to deliver in key, high-leverage situations. There is where true MVPs show their full potential to influence a team and define its fate. As they say, a home run against a non-contender team when you are losing by five runs is not as valuable as a game-winning double against our wild-card-rival’s closer in the 9th inning. I’ll admit neither OPS in high-leverage situation or Win Probability Added (WPA) is the perfect metric to evaluate this, but they provide a very good proxy to how well they have fared in tough, game-changing situations. If you are not familiar with WPA, please click here.

Again we see the usual suspect — Mike ‘King’ Trout — leading not only this graph but the MLB with his 5.66 WPA, closely followed by Josh Donaldson, and they’re the only two players from this sample to have a higher OPS in high-leverage situations than in low-leverage ones. Interestingly, Boston’s Betts and Ortiz’s OPS go down 9% and 15% respectively when the stakes are high. I definitely don’t want to say that Altuve’s 0.841 OPS in high leverage is bad, but I certainly want to recognize Donaldson’s and Trout’s clutchier performance.

Another way of looking at the MVP is to ask yourself: Where would that team be if that player wouldn’t have been part of it? While in essence it is impossible to know for sure the answer, a nice proxy is to measure what percentage of position-player WAR is that player responsible for, i.e. what percentage share does this player represent.

Well, this is another way to see Mike Trout’s leadership on the field. Almost half of the Angels’ WAR have Trout’s name attached to it, which is amazing. (For reference, the leaders in this table are Khris Davis and Marcus Semien with 122% (2.2 WAR each out of 1.6 Athletics total WAR). Now, Donaldson and Altuve have, too, a remarkable 33% and 35% of their total, but probably Betts falls short again with his 23%.

At the end, when all is said and done, it looks like numbers indicate it should go down to a Donaldson vs. Trout race, just as it was in 2015. Ortiz has had an amazing season but his base-running and defense (or lack thereof) limit his overall impact on his team. Betts is definitely an exciting, five-tool player, but his performance hasn’t been as good as Donaldson’s or as consistent as Trout’s. Additionally, Boston’s talent-loaded team reduces his value (this is the opposite of the Trout-Angels argument – how valuable can you be when your team would perform well, even if you’re not there?). His future is extremely bright though. Finally you have Altuve, who may have a legitimate case but falls (a bit) short on overall performance to Donaldson and Trout. Houston has under-performed and arguably that’s a worse outcome than Trout’s, because we knew the Angels were going to be bad, but we thought the Astros would be better.

Last year, Donaldson built his case with a magnificent August, when he posted a 1.132 OPS and Toronto got to first place in the AL East. This year it was Trout who had a torrid August, but the Angels are not in the wild card race. It surely seems to me as if we are measuring the MVP as a team award. Though I understand the rationale of having an MVP on a winning team, there is more to it. If I had a vote, and still being a few games away from the end of the season, I’d support Trout in his quest for his second MVP (as of today), but it looks like momentum and narrative are gaining traction around Donaldson — who has posted much better numbers than in his MVP season — Altuve — who brings new blood to the MVP discussion and might get an extra push if Houston makes it to the playoffs — and Betts — who is clearly the face of Boston’s extremely talented young generation. They, though, despite great Septembers, will post worse numbers than Trout. Yes, the Angels are a bad team — but to what extend is that Trout’s fault? What else could he have done? When did ‘valuable’ translate into ‘winning by himself beyond reasonable expectations’? When did we change this award to ‘best player on the best team’? In 2012 it was Cabrera’s Triple Crown and in 2015 it was Donaldson’s ‘ability’ to get Toronto to the postseason for the first time in many years. In 2016, Trout has been comprehensively better, avoided any deep slumps during the season, and performed very well under pressure and shown that you can put counting stats up on a bad team. We are running out of excuses this year.

I’m working under the hypothesis that you can use launch angle on balls hit to the outfield to determine an outfielder’s relative strength.

The more I look at the data, the more convinced I’m becoming.

So I downloaded the 2015 and 2016 KC Royals Statcast data to see if I could compare their major outfielders’ performance year to year and see a couple things. What I’ve done is bucket hits to the OF by launch angle (in two-degree increments) and calculate a percentage of that contact resulting in a HIT or an OUT. Simple as that. So what I’m comparing between years is:

1) Are the hit likelihood percentages for each angle by OF reasonably projectable year to year
2) Does improvement in my angle metric result in improvement in other defense metrics

First let’s look at Jarrod Dyson. He’s one of the best outfielders in MLB. He recorded, per FanGraphs, 11 DRS in 2015 and to date has 18 DRS in 2016. His 2015 UZR/150 was 18.4 and in 2016 to date it’s 28.7. So both of the “new-traditional” type defense stats are saying, he’s not only good but he’s getting better in 2016 versus 2015. What does my angular stat suggest?

The red points are for ’16 Dyson while the blue is ’15. The left linear regression equation (with the .837 R2) is 2015 while the right (R2 .7796) is 2016. This shows Dyson as a similar player year to year, but likely a bit better. On the higher-angle fly balls, it does appear that Dyson has done a better job this year tracking them down; however, it also appears that in 2015 he did a bit better catching some of the lower-angled fly balls. So it’s not entirely clear, from this graph, why Dyson is per DRS and UZR having such a better defensive year. To have something like this happen, it could indicate that maybe Dyson is starting to play deeper than before. This would limit the likelihood of him catching the low-angled line drives to the OF, but help track down more true fly balls. I’d certainly be interested to see if Dyson is actually doing that very thing this year.

When it comes to projecting year to year, the R2 for Dyson’s ’15 to ’16 hit likelihood % was: 0.532. In real life this is a pretty strong correlation, so I’d say it’s a reasonable estimator.

How about we look at KC OF defensive darling Alex Gordon:

Again the red points are for ’16 Gordon while the blue is ’15. The left linear regression equation (with the .939R2) is 2015 while the right (R2 .8424) is 2016. It jumps right out to you how much smoother Gordon’s regressions are than Dyson’s. Maybe experience leads to that, who knows. So the 2016 regression line (the dashed one) shows that contact to him in the OF is a bit more likely to land for a hit now in 2016 than it was in 2015. This would suggest that Alex Gordon is having a worse year defensively in ’16 than ’15.

How do DRS and UZR/150 compare? Well, Alex has a DRS of 3 in 2016 and had a DRS of 7 in 2015. So he does seem to be trending a bit lower, though not too much. And he has a UZR/150 in 2016 of 9.9 whereas that was 10.5 in 2015. So in this case it all sort of agrees. Gordon seems to be a step or two slower (age and injuries easily could account for that) and as a result his defense has stepped backward a bit. Interestingly he’s still doing about the same job on balls that are high-likelihood hits — the more difficult plays. It’s really at the end of the spectrum where the balls are unlikely to be hits anyway that Alex seems to be struggling. So maybe the “skills” are still there, but the athleticism has just faded a bit and he can’t run down those long fly balls anymore. This is sort of the opposite of Dyson. Maybe Gordon is in fact playing too shallow, cheating to ensure his reputation for robbing sure hits stays intact while losing a bit of overall range, creating a situation where some balls land that probably should have been outs.

When it comes to projecting year to year, the R2 for Gordon’15 to ’16 hit likelihood % was: 0.778. This is excellent and I think it is clearly visible from the chart just how projectable year to year this would be.

What about All-Star and defensive stalwart Lorenzo Cain?

Again the red points are for ’16 Cain while the blue is ’15. The left linear regression equation (with the .8876 R2) is 2015 while the right (R2 .9073) is 2016. Well this is interesting — it’s just as though you shifted the line up ever so slightly. A 2016 higher trendline would indicate that contact to the outfield around Lorenzo would be more likely than last year to result in a base hit. This would indicate he too has backslid some from his 2015 self. So what do UZR and DRS say? DRS in 2016 is 11 whereas it was 18 in 2015. But UZR/150 is currently 15.4 in 2016 and it was only 14.1 in 2015. So there is a bit of confusion as to Cain’s 2016 performance, relative to ’15. Clearly he is still an excellent outfielder by all measures, but I would lean toward him trending in the negative direction in ’16 and moving forward.

Given the two linear regressions and data sets, you’d have to believe you could use this data to project very accurately the future year. And you’d be right. Cain’s year-to-year R2 checks in at 0.955.

Well what about newcomer Paulo Orlando? he already seems to be living up to the newfound tradition of excellent KC outfield defense:

Paulo Orlando is sort of the exact reverse of Cain. His trend has basically just taken an entire step down. This means balls are less likely to be hits now than before. So do UZR and DRS agree with Orlando taking what appears to be a reasonable step forward? Surprisingly no. DRS from ’15 to ’16 has jumped from 8 to 12, but Orlando has played a lot more innings which more or less would explain that growth. And his UZR/150 went from 14.0 in 2015 to 8.7 now in 2016. So these metrics both seem to think Orlando is the same if not a little worse than in ’15.

Projecting using Orlando’s earlier year is, like with Cain, excellent. There is an R2 of .90 between the two data sets.

So for my questions:

1) Are the hit-likelihood percentages projectable year to year? This seems to be a resounding yes, at least in the case of KC Royals. The R2 was always greater than 0.5 with two instances of the four being over 0.9! I’m starting to believe this really could mean something in regards to defense evaluation.
2) How does my angle measure compare to UZR/DRS? There do seem to be some differences; however, this is basically the norm in the “new” defense evaluations. No universal system has been developed and there are plenty of cases where UZR and DRS themselves have disagreements.

I do think in the end this has some merit and I will be looking further into it. I also think similar work can be done with regards to hit speed, as I already alluded to in my earlier article:

http://www.fangraphs.com/community/using-statcast-to-substitute-the-kc-outfield-for-detroits/

I think it’s important to view both the angle and hit speed as two pieces and going forward that’s something I’m hoping to include for these players.

It didn’t take long for Ken Giles to make a name for himself, despite coming up as “just” a set-up man on a relatively bad Philadelphia Phillies team. In his debut season in 2014, Giles struck out 64 batters in 45.2 innings and posted a minuscule 1.18 ERA and 1.34 FIP as a 23-year old. Last year, Giles followed up his stellar freshman season with an equally impressive sophomore campaign, fanning 87 batters in 70.0 innings of work, notching a 1.80 ERA and 2.13 FIP. After a trade-deadline deal sent incumbent closer Jonathan Papelbon to the Washington Nationals, Giles officially took over the closer role in Philadelphia and finished the year with 15 saves, all coming after July 28.

After those two fantastic seasons in the back end of Philadelphia’s bullpen, the rebuilding Phillies decided that their young relief ace was more valuable to them as a trade chip than a current player, and on December 12, 2015 Giles was traded (along with a low-level prospect) to the Houston Astros for a quintet of players, including former No. 1 overall draft pick Mark Appel and young right-hander Vince Velasquez.

Giles’ role with his new club was not immediately obvious, but many speculated he would be the team’s closer heading into spring training. The club, however, kept quiet on the matter, further fueling public debate over Giles’ best fit on the team. On April 4, Astros manager AJ Hinch announced that club veteran Luke Gregerson would begin the season as the team’s closer. This displeased some — despite Gregerson’s success within the role in 2015 — due to the seemingly steep price the club paid to acquire Giles.

Hinch’s decision was validated almost immediately, as Giles’ season began about as poorly as one could’ve imagined. In 115.2 innings pitched between 2014 and 2015 in Philadelphia, Giles allowed just three home runs — a number he matched in less than four innings with the Astros, as he allowed longballs in three of his first four outings with Houston. Through April, Giles had allowed 10 earned runs in 10 innings. However, his peripheral numbers were not awful, as he struck out 14 batters and walked just four, giving him an xFIP of 3.23 despite the 6.75 FIP and 9.00 ERA. Giles’ HR/FB rate was an astounding 40 percent, compared to the league average of 11.8 percent over the season’s first month.

May was a slight improvement for Giles, as he went the entire month without allowing a home run and continued to strike out batters at a good rate. Over 11.1 innings, he fanned 14 batters and walked five while allowing five earned runs. For the month, he accumulated a 3.97 ERA, 2.00 FIP, and 3.93 xFIP. At the end of May, Giles had pitched 21.1 innings with a 28:9 K:BB ratio, and had a 6.33 ERA, 4.23 FIP, and 3.60 xFIP. Perhaps the most troubling statistic, however, was Giles’ ground-ball rate, which sat at just 31.1 percent after his first two months. Over his first two seasons, Giles’ ground ball rate was much higher, at 44.6 percent. Giles’ strand rate was also nearly 78 percent in his time with Philadelphia, but stood at just 66.9 percent over April and May of 2016.

While May was an improvement over April, Giles was still not nearly as effective as he was in his stint with the Phillies. June was a bigger step in the right direction, though, and Giles once again brought down his monthly ERA to 2.31. He allowed three earned runs in 11.2 innings of work, striking out 14 and walking just two. That month, his FIP and xFIP were both around 2.50 and his strand rate and ground-ball rate increased to 88.2 and 38.7 percent respectively.

July went even better than Giles could’ve hoped, as he allowed just three hits and no runs over 8.2 innings, striking out an astounding 18 batters while walking just two. Once again, his strand rate and ground-ball rate increased, posting 100 percent and 45.5 percent marks, respectively. For the month, his FIP was actually negative, at -0.31. August has gone well for Giles, too, as he’s struck out 21 batters against two walks in 10.2 innings (through 8/30). The long ball has hurt him a bit — solo homers accounting for two of the three earned runs allowed in the month — but his ERA for the month sits at 2.53 ERA, and he owns a 2.49 FIP. His strand rate in August sits at 88.2 percent, and his ground-ball rate has gone up again to 52.4 percent. On August 7, Giles even had a game in which he struck out six batters in just 1.2 innings.

Since the beginning of June, Giles’ numbers are eye-popping, especially when compared to his numbers through May:

Giles is a pitcher who relies heavily on his “stuff” to get outs. He throws just two pitches — fastball and slider — so getting hitters to guess on a wide variety of pitches isn’t his game. However, both his fastball and slider are excellent offerings, which gives him the ability to succeed despite a limited arsenal. When working with just two pitches, location is important to keep hitters off-balance. In the first two months of the season, Giles’ location was his issue — the velocity and movement on both pitches has been comparable throughout the season — as you can see from the heat maps of his pitches through May:

Fastball:

Slider:

The fastball seemed to be erratic, with no one area particularly heavily-worked compared to others. The highest-concentrated area was inside to right-handers, which is an area that allows batters to hit to the pull field, where the most damage is done. His slider was also left close to the zone most of the time, which limited his ability to generate swings and misses on the pitch. Since the beginning of June, however, Giles has improved his command considerably, as evidenced by the second set of heat maps from June-August:

Fastball:

Slider:

Giles’ fastball is more consistently located closer to the middle of the zone and towards the lower half, which not only allows him to force batters to swing at the pitch but keeps them from turning on balls and doing damage to the pull field. His slider heat map is almost identical to the one from April and May, but shifted lower by almost a foot. Instead of working from the middle of the zone to the bottom, he’s now working the slider from the bottom of the zone to down below the knees. This has given Giles the ability to not only get more whiffs on balls out of the zone, but to generate more ground balls with the pitch. The fact that the fastball and slider locations are more similar also likely gives Giles an advantage, as he can play the slider off of the fastball or vise versa.

Giles has also — perhaps even more importantly — changed his usage patterns since the end of May. He’s not only used the slider much more, and more effectively, but he’s also changed when he uses the slider, particularly to right-handed hitters. Compare Giles’ usage charts from the first and second “halves” of his season:

April-May:

June-August:

As you can see, Giles has begun to pitch to right-handed batters the same way he has pitched to left-handers this year. All season, Giles has used his fastball heavily to begin at-bats against lefties, and even more so when behind in the count. However, to righties — the majority of the batters he’s faced — he’d more or less mixed the two pitches equally in all situations. Yet, since the start of June, Giles has leaned more towards using the slider when ahead of righties and with two strikes. The adjustment has worked to perfection, as Giles has allowed just a .083 batting average and .167 slugging percentage to righties on the slider since June 1.

Thanks to both of the major adjustments he’s made, Ken Giles has been able to reclaim what looked in May to be a down year. Due to other struggles in the Houston bullpen, he’s even taken over the closer role, recording saves in four of his last five appearances. With the Astros desperately needing to make a push for the playoffs in the season’s final month — they enter play on August 30 two games behind Baltimore for the second American League Wild Card spot — Giles is the type of power reliever that could help the team’s playoff chances immensely down the stretch. If Giles could be the difference between winning and losing just a game or two in September, he could be the difference between the Astros making and missing the playoffs. With the way he’s been performing lately, there’s no reason to doubt that he will be a dominant closer down the stretch for Houston.

As a former prospect struggling to find his footing in the majors, Dan Straily wasn’t given an extended look in a big-league rotation after 2013. He bounced around from the A’s to the Cubs to the Astros. Now he’s on the rebuilding Reds. With the Reds, he has finally gotten another shot. The Reds were looking for someone with any kind of upside to fill the hole in their rotation. Straily fit the bill. 154 innings later, Straily is running an insanely low .239 BABIP, the third-lowest among qualified starting pitchers. That has helped him to a solid 3.92 ERA, which was at 3.50 before a recent blowup against the Angels. Before then, however, he had managed 10 starts in a row without allowing more than three runs. Can Straily keep running a BABIP this low? Let’s find out.

The first thing that sticks out to me about Straily is that he’s an extreme fly-ball pitcher. He has the third-lowest groundball percentage and the eighth-highest fly-ball percentage among qualified starters. He also has allowed the 11th-highest average launch angle on batted balls out of the 92 pitchers who have thrown at least 2000 pitches this year. Ground balls go for hits far more often than do fly balls (although fly balls go for extra-base hits far more often), so that explains part of why Straily has such a low BABIP.

If you’re like me, you would have thought that since Straily gets a lot of fly balls, maybe he gets a lot of popups. That would certainly help him keep a low BABIP, as popups almost never go for hits. Although Straily’s fastball has good rise (he’s tied for 27th out of the 78 qualified starters who throw four-seamers), he doesn’t actually generate many popups. In fact, his IFFB% of 7.9% this year puts him firmly below the league average of 9.7%. While his career IFFB% is at 11.8%, that doesn’t help explain why he’s run such a low BABIP this year specifically. Let’s look elsewhere.

Does he do a good job of limiting quality contact? He has allowed the 39th-highest exit velocity out of the 92 pitchers who have thrown at least 2000 pitches this year. That’s below average. He’s also below average in terms of hard-hit rate: he has the 30th-highest out of the 81 qualified pitchers. Worse, he’s tied for the seventh-lowest soft-hit rate. His line-drive rate is worse than average, the 32nd-worst out of 81. These are some troubling signs.

On the other hand, there is some good news. Straily has a nasty changeup. Observe:

Of the 73 qualified pitchers who throw a changeup, Straily’s is tied for the sixth-most drop. That’s not surprising, especially when you consider this: there are 133 pitchers who have thrown at least 150 changeups this year, and Straily’s has the fifth-lowest average spin rate. A low spin rate allows gravity to do its job and make that sucker drop right off the table.

Straily has a nice slider, too. It’s a frisbee, with solid horizontal movement and decent drop, without sacrificing too much velocity. Observe:

I don’t think that Straily will maintain a .239 BABIP. Although his extreme fly-ball tendencies seemingly make it easier for him to maintain a lower BABIP, he doesn’t do enough things right otherwise. He allows too much quality contact. On the other hand, he has three solid pitches, which are also his three most-used pitches (his sinker and curve aren’t great, and he uses them accordingly). His four-seamer has good rise, and, despite mediocre velocity, that can work. Just look at what Marco Estrada is doing with a four-seamer that has good rise and averages a mere 88 MPH.

Straily’s change and slider have above-average swinging-strike rates, at 15.8% and 14.5%, respectively. The change and slider even have average groundball rates (44.9% and 46.7%). They both have lofty O-Swing percentages as well, which leads me to believe their swinging-strike rates are for real (45.5% and 38.8%). My advice for Straily would be to stop pitching to contact. He seems to be pitching to contact because his Zone% this year is at 46.9%, the highest of his career. That mark ties him for 20th-highest among the 81 qualified starters. It is far above the league average of 44.8%. So, he should stop pitching to contact because 1) he has strikeout potential and it would be worth trying to tap into it and 2) his luck with BABIP will probably run out soon.

Data from FanGraphs and Baseball Savant. Gifs courtesy of Bleacher Report and MLB.com.

Thanks for reading!

When the Arizona Diamondbacks signed Yasmany Tomas to a $60-million deal, many thought the Cuban “third baseman” would be an instant star. Little is known about Cuban players when they come over; their skills are often exaggerated and their numbers in the Cuban National Series inflated. While some players, such as Yoenis Cespedes and Jose Abreu, do come over and become instant stars, others, such as Hector Olivera, simply don’t have what it takes to make it in the majors. For the better part of last year, Tomas seemed a lot closer to the bust category than major-league stardom. That assessment seems destined to change soon.

Too quick and binary is our collective assessment of players. They’re either good or bad and we know within the first weeks of April. We care little about their story, or struggles to adapt. It’s the Twitter era; context and nuance is dead.

That is the story of Yasmany Tomas. The Diamondbacks miscast Tomas as a third baseman and the metrics hated him there. They probably knew he was not a third baseman, but there he was. Unable to help the team defensively, and struggling a bit in his first offensive season at the major-league level, Tomas got a label. He was a bust, just another of the missteps in a reign of terror  for a Diamondbacks front office that doesn’t even know the rules.

But that label loses all context. Craig Edwards reminded us about context with regards to Byron Buxton’s struggles. To paraphrase Edwards: Buxton has been really bad, but he’s also young and has plenty of time to figure it out. With Tomas, the story is similar. Yes, Tomas was a -1.3 bWAR and -1.4 fWAR player in 2015, but reducing a player to a single number does him an injustice. He was actually a positive contributor on offense. As a starter (non-pinch-hitter) he had a 103 OPS+. That’s not bad. He was also just 24. Joc Pederson and Jorge Soler are just 24 this year and we think of them as young players. Why are we so unforgiving with Tomas?

Fast-forward to this year and Tomas is still not very well regarded in baseball circles. He’s at 0.2 bWAR, -1.1 wins above average, and 0.6 fWAR. He still grades out as a very average player, but is now around 10% better than league average offensively according to the advanced stats. He’s got 26 homers (24th in the league) and a .519 slugging (30th in the league), both better than Paul Goldschmidt, Carlos Beltran and Giancarlo Stanton.

Those raw numbers suggest that Tomas is already among the top-30 or so sluggers in the league.  He even gets on base at a non-Trumbonian clip. But my early season introduction to xSLGBB said that Tomas was due for some improvement in his slugging percentage based on his batted-ball profile. Andrew Perpetua’s set of stats based on batted-ball information (the Google doc at the bottom of the post, also available on xstats.org) appears to show that Tomas has leveled. Perpetua’s xSLG stat shows Tomas’ expected slugging percentage at .549. Such a mark would tie him with Josh Donaldson.

I appreciate Perpetua’s stats, but I made up my own (xSLGBB), just for these types of analysis. I ran the numbers and by my xSLGBB, based on the league-wide expected set of outcomes from when I ran this the first time in May, Tomas is expected to improve by a grand total of .005 points of slugging.

Still, even if he has already normalized to the stats that we would expect based on his batted-ball profile, a 25-year-old with 26 home runs and a top-30 slugging percentage is pretty darn good. Yes, he has deficiencies in his game, but Tomas still has room for improvement. He’s never going to be Kris Bryant, Nolan Arenado, or one of the other MVP-type of young stars in the game, but he’s quietly hitting himself out of the bad label that we too quickly stuck him with.

Perspective
How we rank players in our own minds can tell us a lot about what we value in a ballplayer. For decades the statistics that mattered to sportswriters and the public at large were those that were simple, easily understood, and still relevant to the game. Stats like batting average (AVG), runs batted in (RBI), and home runs (HR) were regularly quoted when writing articles or voting for MVP awards. Each of these numbers tells a piece of the story of what a ballplayer is. AVG shows a players ability to put a ball in play and reach base, RBI is a representation of run creation and hitting while men are on base in front of you, and HR show your power in hitting.

These numbers still hold great significance today. That said, they are not flawless expressions of player prowess with the bat. A player could have a high average and still struggle to get on base often due to strikeouts or weak contact. RBI is often a product of opportunity as much as hitting success. After all, you can still receive RBI when creating an out. HR meanwhile can be a very one-sided affair if your average is low, leading to an all-or-nothing scenario for a hitter.

I’m not trying to disparage anyone from using AVG, RBI, and HR in a debate of great players, but when you use them keep in mind that they make up only a fraction of what a ballplayer can be.

Modern statisticians have begun using much more advanced numbers like WAR or OPS+ to determine a players quality. These numbers take into account positional skill differences, park factors, and many other aspects of the game. Much like the traditional stats mentioned before, these stats have both positive and negative aspects to them. No one stat can give you a complete picture of a player’s skillset and value.

Whenever an article comes out discussing the quality of a player’s career or season we often get quotes like these:

“Since Trout debuted in 2011, he leads all players with 37.9 WAR. Further, that 37.9 WAR through Trout’s age-23 season are the most by a player in the modern era.” — ESPN Stats & Information

OR…

“Harper finally displayed his prodigious tools last season, as he led the National League in runs (118) and home runs (42) while leading MLB in OBP (.460) and slugging percentage (.649).” — ESPN Stats & Information

While all of the numbers in these quotes are valuable, and even more so impressive, they come with very little context with respect to the league as a whole. It’s great that Trout has 37.9 WAR since 2011, but who is second? And by how much is he second? So Harper led the league in OBP, but what was the league average? Or how many plate appearances did he have? Did he miss any time with injury?

Each of these questions would further add to our understanding of the value and quality of the players mentioned, but that information is never going to be answered in this context. Additionally, this practice of “cherry picking” the best stats to fit our argument negates the whole and presents the players out of context. For example, these numbers neglect the fact that Harper struck out about 25% of the time that season. Even by today’s standards that is a lot of strikeouts. I understand of course that a lawyer is never going to give out unnecessary information about a client’s failings, but in the context of ranking players it is paramount that we take into account as much of the information as we can. Ultimately, we find ourselves back where we started.

If all stats are flawed, then how are we to determine an adequate ranking for players? I propose that we use more stats. That’s right. More stats, not less.

When you fixate a ranking on a single stat, then that stat accounts for 100% of your result every time. It doesn’t matter if the stat is meant to incorporate a host of stats together. Your results are the result of a singular point of reference. If you use three stats, then each is equivalent to one-third of your conclusion.

What would happen if we used 20 different stats to determine a ranking? While each individual stat is devalued, the whole average together will give us a better understanding of the whole spectrum of a player’s ability in the game. Be warned…results may incite head-scratching.

There is a great axiom in the world of baseball stats that goes something like this: “Just because a stat has Babe Ruth at the top and Mario Mendoza at the bottom does not mean it is a good stat.” Like all statistical analysis, take this one with a grain of salt.

Methodology
My process here is rather simple. Take a group of player data, a single year or all-time, across 20 stats. Rank each player individually against the others in the set from 1 to the total number of players across all the data. Finally, average each player’s rankings across the 20 stats. Our result…rAVG (Rank Average).

For ease in data gathering and processing, I’ve decided to use the 19 dashboard stats from FanGraphs plus hits to make 20 total stats. For all-time stats, the pool of players has been limited to players with a minimum of 5,000 plate appearances.

Notes:
• Each position has t50/b50: how many times a player ranks in the
  top 50 or bottom 50 across all categories.
• * denotes active player.

All-Time • Position Players (895 total)

Name - Pos

rAVG

t50

b50

1

Willie Mays - OF

93.2

17

0

2

Barry Bonds - OF

95.3

16

0

3

Tris Speaker - OF

105.3

15

0

4

Rogers Hornsby - 2B

110.7

16

0

5

Stan Musial - 1B/OF

113.6

17

0

6

Ty Cobb - OF

118.2

16

0

7

Alex Rodriguez* - SS/3B

118.9

15

1

8

Honus Wagner - SS

133.1

14

0

9

Mel Ott - OF

136.2

15

0

10

Eddie Collins - 2B

136.6

16

0

11

Babe Ruth - OF

137.2

16

1

12

Hank Aaron - OF

143.6

14

0

13

Mickey Mantle - OF

147.7

15

1

14

Ted Williams - OF

150.2

16

2

15

Lou Gehrig - 1B

156.1

15

1

16

Charlie Gehringer - 2B

158.5

13

0

17

Larry Walker - OF

159.7

13

0

18

Chipper Jones - 3B

162.4

15

0

19

Frank Robinson - OF

163.2

14

1

20

Jimmie Foxx - 1B

167.8

16

1

102

Mike Piazza - C

272.7

9

2

Thoughts

1. Larry Walker. At first glance this list appears to contain all the requisite names for a best-of-all-time list… that is until you reach #17 Larry Walker. I can assure you that I have not fudged the data in anyway. I, like you, are equally as shocked to find Mr. Walker parading alongside greats like Ruth, Mays, and Gehrig. Maybe we all should re-evaluate our opinions on Larry Walker.
2. Mike Piazza. I have included him at the bottom of the chart, because he is the highest-ranking catcher of the 73 that met the 5,000 plate appearance requirement. While ranking #102 would appear to be a slight to him, when viewed in the context of the total list of 895 players…Piazza ranks in the top 12% of all players in history.
3. Babe Ruth. Many of you, me included, probably feel that there is no way that the Great Bambino could rank outside of the top 10 all-time. I will remind you that this list is a ranking of statistics. It cannot evaluate impact on the game, cultural relevance, or popularity. It simply counts each stat as 5% of the whole and spits out a result. A closer look at Babe’s numbers and you will find that he was a terrible baserunner (SB & BsR) and his defense left much to be desired as well. Out of 421 outfielders he ranks 229 in SB, 411 in BsR, and 110 in Def. All this serves to remind me that no player, however great they might be, is without deficiencies.

Conclusion
As part of my research into this topic I ran numbers for each of the nine positions all-time and the cumulative all-time list seen above. In order to keep this article from becoming a novel, I’ve chosen to only include the top 20 of all-time here. The rest of this information will be available for viewing some time in the near future either on here or on my website.

While I may not agree entirely with the outcomes of this exercise in rankings, I do feel that it has caused me to better consider the totality of a player’s stat line rather than a few simple metrics. No one stat can give you a well-rounded, complete view of a player’s value and skill.

I await your fevered comments below.

As I write this post the KC outfield defense is ranked No. 1 in Defensive Runs Saved (DRS) with 43, and is No. 2 in UZR at 28.6 (first is the Cubs with 29.0).  KC sports one of the best, if not the best defensive outfield in the majors this season.

Detroit on the other hand has a fairly poor one.  They rank last in DRS, with -44, and last in UZR at -31.8.  Though Baltimore gives them a good run for their money, Detroit is probably the worst defensive outfield in the majors so far this season.

So I wondered if we could do an analysis to show what would happen if we substituted them entirely for one another?  How would that work?  Well, one simple approach would be to just use the DRS metrics for each team and basically say that DET would go from -44 to +43, so that’s a swing of +77 runs. Using the 10 runs per win thumb-rule, that’d be a pretty big swing, nearly eight games. Detroit is a whole lot better.  But I’m not sure this method is really the best we can do.  After all, we have all this Statcast data now.  Could we use that?

I set out to try to do just that.  So my first step was to hypothesize that the likelihood of a ball hit to the outfield actually dropping for a base hit could be correlated to the launch angle provided by Statcast and then that this likelihood would change depending on the team.  So to test this theory out I went to Baseball Savant and grabbed all the Statcast data for balls hit to the outfield for KC and for Detroit.

The KC data consisted of 1722 balls hit to the OF (when removing the few points that had NULL data for launch angle).  I took these 1722 points and bucketed them by launch angle in buckets that were 2 degrees each.  I then calculated the percentage of hits to total (hits + outs) for each bucket.  This percentage was the likelihood that a ball hit to the outfield at a certain launch angle would end up being a base hit.  This led me to my first realization, which was that anything that was basically < 8 degrees on launch angle (so including all negative angles), and made it to the OF, was a guaranteed hit.

The results of this analysis for the 1722 KC points made a lot of sense intuitively.  As the launch angle increased, so did the likelihood that it was an out, so my hit percentage trend went down.  Using a simple linear regression projecting the likelihood of a hit by angle had a 92.5% R^2.  This equation was going to work nicely.

It’s no secret that Jason Heyward is having an epic, epic bad season. Heyward is not just last in wRC+ for right fielders, but last by a wide margin. He has the seventh-worst ISO in all the land, worse than Billy Hamilton. Worse than Cesar Hernandez. Worse than Alexei Ramirez. Alexei Ramirez, for God’s sake. Finally acknowledging the soul-crushing reality, Cubs manager Joe Maddon benched Heyward last Friday.

This is historically bad power from a right fielder. In the wild-card era, Heyward’s ISO constitutes the 11th-worst power season for a right fielder. Of the ten other seasons, Ichiro! owns five of them, and Nick Markakis two more. So only four actual guys have managed a worse ISO in right than Heyward since 1994.

Heyward’s power has declined against all pitches, but not evenly. (I’m actually using ISO x 1000 to eliminate those pesky decimal points):

Pitch               2016 ISO                Career ISO                  Diff

4-seamer             143                           154                           -11

2-seamer             087                           177                          -90

changeup            029                          160                         -131

slider                   083                           157                           -74

curve                    000                          122                         -122

In battling the 4-seamer, 2016 Heyward looks pretty much like the factory model. Against the other pitches, 2016 Heyward looks like Enzo Hernandez. Back in May, Jeff Sullivan wondered why Heyward was swinging disproportionately often at high pitches when historically he had been a better low-ball hitter. The above chart may provide an answer. Four-seamers tend to live upstairs, while the other pitches like to drink Milwaukee’s Best down in the basement den. Heyward may have made a rational adjustment, swinging more often at the pitch he can hit (or rather, pitches that look like the pitch he can hit) and less often at the others.

Even if accurate, this simply answers one riddle with another. What could have made a historically good low-ball hitter suddenly lose the lower half of the strike zone? And the power disappearance was indeed sudden. In 2015, Heyward actually hit with more power in the second half, though his ISO did drop off in September.

Heyward may have begun hearing the spine-tingling incidental music back in 2014. That year his power against lefties, seldom menacing, completely winked out.

Year                   ISO vs. L

Career                   .119

2013                      .191

2014                      .056

2015                      .093

2016                      .096

This may have been foreshadowing, or not. There is no clear pitch-type pattern evident in Heyward’s disappearing power against lefties. He collapsed against all offerings, doing somewhat less badly only against the slider. Indeed, one of the main criticisms of his eight-year contract was that Heyward had become a platoon player.

In 2016 the platoon split has disappeared, but not in a good way. Heyward has actually hit lefties with more power than righties this year (.096 vs. .083). But let’s face it, for hitters, almost any number that begins with “.0” is a wrong number.

The most likely explanation is some form of injury. Heyward had wrist problems earlier this year, and wrist injuries notoriously sap power. But .088 is whole lotta sappage. When Derrek Lee hurt his wrist in 2006 his ISO plummeted to … .189. Certainly one can imagine any number of nagging injuries that slow bat speed or reduce plate coverage. But it seems peculiar that Heyward would struggle least against the pitch that is usually the most overpowering. Perhaps Heyward is selling out to get to the 4-seamer because the injury has slowed his bat enough that he simply has to get started early.

Another possibility, perhaps, is a vision problem, as very briefly suggested in the comments to Sullivan’s post in May. Perhaps Heyward is able to pick out the 4-seamer, but unable to differentiate reliably among the other pitches, thus approaching them all with punchless caution. A vision problem could also be causing Heyward to sell out as discussed above. In either case, selling out would seem to cut against Heyward’s grain as a (sometimes maddeningly) patient hitter.

There is nevertheless some evidence Heyward is trying to start the bat earlier, not because of ocular or muscular problems, but because of a complex, misaligned swing. There have been a number of stories concerning Heyward’s poor mechanics, but most of them were written this season, when the poor results became manifest. Outright criticism of his swing, at least in public, was relatively uncommon before this year.

But there were signs, perhaps (as signs are wont to be) obvious only in retrospect. In 2014, David Lee wrote an excellent piece scouting Heyward’s rapidly evolving stances — the pictures alone are worth a look. Two years earlier, Terence Moore wrote about Heyward’s swing coach praising Heyward for having Plans A, B, and C at the plate. Both of these pieces are hopeful, treating Heyward’s willingness to tinker as a sign of dedication — a player relentlessly seeking continuous improvement.

But relentlessness doesn’t solve every problem, and improvement is very rarely continuous. Hitters can be comically addicted to routine, fearing that the slightest change will plunge their careers into Oylerian Darkness. But there is some virtue to having a baseline from which to work. In music, it’s literally a bass line. In oral presentations, it’s a theme. In cooking, it’s a recipe. In none of these cases does the baseline translate directly into real results, but it provides critical direction so that the (or at least an) end result actually results.

It’s possible that Heyward has lost his anchor. He wouldn’t be the first player to do so. Roy Halladay famously had to reconstruct his pitching motion in the purgatory of Dunedin. But Halladay had become an arsonist, spraying the field with a 10.64 ERA. Until this year, Heyward hadn’t ever truly pancaked. It’s possible that Heyward is tinkering his career into oblivion. I’m not sure I buy this, but at this point there is even less evidence for the competing theories. A serious bone, muscle, or vision problem probably would have landed him on the DL.

Heyward may be treating his swing like jazz, but baseball is the blues. At least he plays in the right city to learn that lesson.