The Giants made a big splash by acquiring Evan Longoria, owner of three All-Star nominations, three Gold Gloves, a Silver Slugger, and the 2008 AL Rookie of Year. I will come right out and admit that I have hardly spent any time thinking about Longoria at all through his 10-year career. As a fan of an NL West team, the Rays are about as far away from my realm of focus as you can get. Throw in the fact that they are a small-market team dwarfed by the Yankees and Red Sox, and Longoria simply hasn’t made a huge impression on me.
After reacquainting myself with his player page, I realized how much I have been missing. Longoria has amassed almost 50 WAR in his career so far, placing him on the bubble of many Hall of Fame stats despite being only 32 years old. He has avoided any disastrous seasons, as his lowest WAR total was 2.2, and that came in a 2012 season when he only played in 74 games. Almost as impressive as his WAR totals – that 2012 season has been the only season in 10 years that he missed significant time due to injury. In the past five years, he has played in more games (798) than anyone in the MLB. He has been the epitome of health and consistency for a decade.
Longoria has earned his value by being very well-rounded. He provides significant value with his bat, as his career wRC+ mark of 123 matches up with the likes of Yoenis Cespedes, Jose Altuve, and Mookie Betts, all extremely accomplished hitters that have yet to enter their late-career decline phases. As the three Gold Gloves imply, Longoria is also an impressive fielder, with career marks of 75 DRS and 89.1 UZR. While not a massive base-stealing threat, he has shown enough speed and baserunning intelligence to provide slightly above-average baserunning value. Simply put: the dude is good at playing baseball, and he’s been proving it for an entire decade now.
As impressive as that resume is, the Giants don’t get to enjoy any of his past accomplishments. They didn’t trade for 2008-2017 Evan Longoria, they traded for 2018-2022 Evan Longoria. So now the question becomes: Is Evan Longoria still good? Jeff briefly touched on this immediately after the trade, but I wanted to take a deeper look.
At 32 years old, he is past the typical peak years for most baseball players, and in Longoria’s case, he already sustained a pretty clear peak over his first six seasons (ages 22-27). As Jeff noted, he put up a wRC+ of 135 during this time; compare that to his four seasons since then (ages 28-31), when his wRC+ has dropped to 108. Don’t get me wrong – 108 is still good! It’s just not the elite All-Star player we saw at the front of his career. His defense has followed the same trajectory, as he put up +79 DRS and +78.4 UZR over his first six seasons, then dropped to -4 DRS and +10.7 UZR over his last four seasons.
This is a familiar story: good baseball player gets older, becomes worse baseball player. But it’s so familiar that it can also be a trap – Longoria might end up following the Adrian Beltre career path, who posted a 6-WAR season at 37 years old. Looking at the numbers, though, I just can’t make myself believe that Longoria is anything more than a useful starter right now, and one that will shortly become a below-average player.
Longoria’s strikeout rate immediately jumped out to me, as he only struck out 16.1% of the time last year, setting a new career low, almost 4% below his career average. This is promising! In an era of increasing strikeouts, Longoria is figuring out how to put more balls in play, giving him more chances of getting on base. Of course, this line of thinking requires that he is trading strikeouts for quality batted balls, and considering his ISO last year sat 50 points below his career average, it didn’t look like this was the case. After digging deeper into some plate discipline numbers, it became very obvious to me what was happening.
2013 was Longoria’s last star-caliber season. The following year, his wRC+ dropped from 132 to 105, with a corresponding spike in Swing%. All of a sudden, Longoria was much more aggressive, swinging at more pitches both inside and outside of the zone. And especially in 2017, he seemed to be focusing intently on putting the ball in play, with a large spike in Contact% despite seeing the 2nd lowest Zone% in his career. Some people are able to cut strikeouts by controlling the strike zone better, but it looks like Longoria was cutting strikeouts by swinging more often and making poor contact on bad pitches. Consider his batted-ball distribution:
The first big red flag here is the red line along the bottom. Once again, starting in 2014, we start seeing a worrisome trend as he began hitting more and more infield flies. All his improvements in strikeout rate are erased here, as infield flies are essentially automatic outs and are just as bad. The other interesting tidbit in this graphic is the interplay between his GB% and FB% the past two years. Longoria had a mini-offensive resurgence in 2016, and it looks like that can be attributed to him lifting the ball more often. In 2017, he lost all of his FB% gains and then some, driving more balls into the ground than ever before.
Jeff also touched on the relevant Statcast data. Longoria’s exit velocity dropped significantly last year, as did his rate of barrels and xwOBA. There was nothing fluky going on for Longo in 2017 – he was swinging more often but making worse contact, and more of his batted balls were either going into the ground or popped up in the infield.
Is a turnaround completely out of the question? Of course not, nothing is out of the question. Perhaps a change of scenery will provide a spark for the 32-year-old. Perhaps he will be motivated to prove to the baseball world that the Giants made a good trade, and he will work harder than ever to make it back to All-Star levels. Even if he simply sustains his current production, he is still a 2-3 win player right now. But the Giants need more than that, and we’re already four years into a significant decline for Longoria. Both his bat and his glove are on the wrong side of the age curve, and it looks like the Giants just added another expensive, aging veteran to throw onto the pile.
Charlie Blackmon is an atypical human being.
For one thing, he is a professional baseball player, meaning he is in the extreme upper echelon of athletic ability. But he is atypical even in his personal life, and his recent success has only highlighted his eccentric personality. He still drives a 2004 Jeep Grand Cherokee that he got in high school. He once had to be rescued on the side of the highway by DJ LeMahieu when he ran out of gas. He buys his clothes from Amazon. And of course, he is easily recognized by his impressive beard-and-mullet combo (the latter of which is pronounced “mu-lay” according to Blackmon).
Based on all his quirks, it should be no surprise just how unique his major-league career has been. He didn’t see regular playing time until his age-28 season, an age when some guys are already entering free agency. Despite this late start, he has steadily grown into an MVP candidate. In 2014, his first full season, Blackmon posted 2.0 fWAR, the exact threshold for a starting caliber player. In the three subsequent seasons, he posted an fWAR of 2.3, 4.1, and 6.5. I thought it seemed rather strange to have back-to-back seasons with ~2 WAR improvement, so I went to the leaderboards.
I searched for all batters with a minimum of 400 PAs in each of the past three seasons, producing a sample of 111 players. Then, I calculated the difference between each player’s 2015 WAR and 2016 WAR, and did the same for 2016 to 2017. This gave me two year-to-year improvements for each player, and I threw both values onto the scatter plot below, with Blackmon highlighted in purple.
Players generally don’t see improvements like this in back-to-back seasons; Blackmon is about as far to the top-right as you can get in this plot. Of course, value can come from many different places, and a player might make large defensive improvements one year and large offensive improvements the next. While Blackmon did see some improvement in his defensive metrics this season, the bulk of his improvements have come while batting. To get the following plot, I followed the same method as above, this time for wRC+.
Again, we see Blackmon floating towards the top right. Baseball is a game of adjustments, and if a batter enjoys a period of success, pitchers will generally approach him differently to gain an advantage. This is why players generally go through cycles, following the push and pull of the game. The past few years, Blackmon seems to be part of a small group of players who have been immune to this tug-of-war effect. He has stayed one step ahead of the pitchers, not only maintaining his gains but improving upon them as time goes on.
How has he found these improvements? Between 2015 and 2016, his walk rate and strikeout rate remained fairly constant, so he must have been getting much better results on balls in play. Sure enough, his batting average increased by 37 points and his ISO increased by 65 points, giving him 49 extra points of wOBA overall. At the time, Jeff Sullivan looked under the hood and found that Blackmon’s GB% was trending downward, and he had been attacking the low strike more so than ever before. Presumably, he realized that his swing path was conducive to driving low pitches into the air, and that balls in the air are more valuable, so he made the adjustment and enjoyed a power spike.
That all makes sense, but it begs the question: how did he improve even more in 2017? If he doubled down on the fly-ball revolution, he risked becoming Ryan Schimpf or Trevor Story.
Much to my surprise, the opposite happened – his GB% actually returned back to his career average. He increased his rate of ground balls, but he still managed raised his ISO by another 42 points. Before you cry BABIP or Coors Field, I’ll briefly note that in both years, his wOBA and xwOBA increased by approximately the same amount, so something real is going on here. In this case, I think he was finding more success on batted balls based on the pitches he didn’t put in play. Stay with me here.
In 2017, Blackmon’s strikeout rate rose by about 2.5%. This is what people in the industry call “not good,” but hold on, his walk rate also rose by…about 2.5%. This isn’t a player who suddenly developed a swing-and-miss problem to sell out for power, this is a player who is intentionally going deeper into counts. When a batter is more selective about the pitches he goes after, he is putting fewer balls in play in early counts, which leads to an increase in both walks and strikeouts simultaneously.
Let’s look at it a different way: Z-swing% measures the percentage of pitches inside the zone that a player swings at, and O-swing% measures the percentage of pitches outside the zone that a player swings at. Generally speaking, you want to swing at strikes and take balls, so you want your Z-swing% to be higher than your O-swing%; the larger the difference, the better your plate discipline.
In 2016, the difference between Blackmon’s Z-swing% and O-swing% ranked in the 9th percentile – he’s always been a bit of a free-swinging leadoff hitter. But in 2017, that difference increased by 4.7%, pushing him into the 26th percentile. While he’s still more aggressive than average, he has become decidedly less so, being more selective about the pitches he attacks and remaining comfortable in deep counts. By swinging at the right pitches, he’s able to avoid the easy outs that result from poor contact on pitches outside of the zone.
We have every reason to believe that Charlie Blackmon just had a career year, and he will never sniff an MVP race again in his career. But then again, we had every reason to believe the same thing last year. When it comes to Charlie, I have some advice: if you expect him to do something, he’s probably getting ready to do the exact opposite. It’s about time we stop trying to figure him out.
Eddie Butler has been a different pitcher in 2017. Through four starts with the Iowa Cubs, he is sporting a shiny 1.46 ERA, a far cry from his career 6.50 ERA after years of turmoil in the Rockies organization. The story here is an easy one to latch on to: top-shelf pitching prospect gets devoured by an offense-heavy ballpark, gets traded to the Cubs, becomes a bona fide ace. It’s the same backstory as Jake Arrieta’s, and the early returns are pointing to the same conclusion.
I am here to disagree. The further I looked into his underlying stats, the more I realized that Eddie Butler still needs to make a serious change if he wants to have any big-league success. In 2017, the “new” Eddie Butler is really the same old Eddie Butler masked by some small-sample good fortune.
Butler has never been a big strikeout guy, so he has to limit walks to survive. While his 2017 walk rate is below his career average, his strikeout rate has dipped as well, leading to a K-BB% of 3.0%. That is not good, and unfortunately, it is right in line with his career mark of 3.3%. The most recent qualified starting pitcher to post a lower K-BB% was 2012 Ricky Romero who had a K-BB% of 2.3%…which was accompanied by a 5.77 ERA. Pitchers with this profile don’t accrue very many innings, and when they do, the results show why.
If you are walking guys without striking them out, you better keep the ball in the ballpark, and Eddie Butler has definitely done that this season. He has yet to give up a single home run in his time with Iowa. David Laurila recently chatted with the right-hander about his new approach, which involves driving the ball downhill and getting back to his two-seamer, while the Rockies always prioritized his four-seamer. This new approach sounds like a recipe for a successful ground-ball pitcher, and it would explain the new ability to limit home runs. But, hold on, Butler’s career FB% is 28%, and his 2017 rate has skyrocketed to 41%, which doesn’t fit the rest of the narrative. Allowing this many fly balls is not a good sign for Butler, who has always had a bit of a home-run problem, as his career HR/FB rate sits at 18% (and no, that’s not just Coors Field – since the installation of the humidor, Rockies pitchers have posted a HR/FB rate of 11.6%). When I look at Butler, I don’t see a guy who found success with a new approach; I see a guy who is prone to home runs with a massive spike in fly-ball rate.
Butler’s success at limiting home runs thus far has allowed him to strand runners at a rate that is screaming for regression: his LOB% of 86.7% would be the highest mark for a qualified starter since Dwight Gooden in 1985. Opposing teams haven’t been able to string hits together against Butler, but that tends to even out over time, especially considering the fact that Butler’s career LOB% is below league average. When you don’t strike guys out, the ground balls eventually find holes, the fly balls eventually leave the yard, and the walks start to come around to score.
To be fair, I have not seen footage of Eddie Butler pitching this year. He might be making guys look foolish, and he might be on the path to becoming the next king of soft contact; if you have watched him pitch first-hand this year, please feel free to share your observations. He does still have the pedigree and he seems to have the work ethic to go with it, and it would make an incredible story if he could put it all together.
After all, Eddie Butler is a professional baseball player, and I’m just a guy with some spreadsheets. But right now, the spreadsheets are telling me to seriously pump the brakes on the Arrieta comps – underneath the ERA, this still looks like the same Eddie Butler.
“The Coors Effect.”
These three words evoke a strong reaction from most people and are impossible to ignore when discussing the offensive production of a Rockies player. Ask anyone who was around for the Rockies of the ‘90s and they will tell horror stories of games with final scores of 16-14. Ask anyone at FanGraphs and they will laugh and point at the Rockies’ 2015 Park Factor of 118. Heck, ask Dan Haren and see what he has to say:
I would count out the days about a month in advance to see if I was gonna pitch in Coors field.
— dan haren (@ithrow88) January 4, 2016
I would count out the days about a month in advance to see if I was gonna pitch in Coors field.
— dan haren (@ithrow88) January 4, 2016
Suffice it to say that Coors is a hitter’s park. Nobody will argue that. But there have been murmurs recently about another effect of playing 81 games at altitude, an effect that actually decreases offensive production. These murmurs have evolved into a full-blown theory, which has been labeled the “Coors Hangover.”
This theory supposes that a hitter gets used to seeing pitches move (or, more accurately, not move) a certain way while in Denver. When they go on the road, the pitches suddenly have drastically different movement, making it difficult to adjust and find success at lower elevations. In other words, Coors not only boosts offensive numbers at home, it actively suppresses offensive numbers on the road, which can take relatively large home/road splits for Rockies players and make them absolutely obscene.
The concept seems believable, but thus far we have no conclusive evidence of its merit. FanGraphs’ Jeff Sullivan recently tested this theory, as did Matt Gross from Purple Row. Although neither article revealed anything promising, Jeff is still a believer, as he recently shared his personal opinion that the Coors Hangover might simply last longer than any 10-day road trip. With this is mind, I decided to approach the problem by examining the park factors themselves.
If you haven’t read the article about how FanGraphs calculates its park factors, I highly recommend you do so before continuing. The basic approach detailed in that article is the same approach that I use here. As a quick example, the park factor for the Rockies is calculated by taking the number of runs scored in Rockies games at Coors (both by the Rockies and the opposing team) and comparing that to the number of runs scored in Rockies games away from Coors. Add in some regression and a few other tricks, and we have our final park factors.
This method makes a number of assumptions, most of which are perfectly reasonable, but I was interested in taking a closer look at one critical assumption. By combining the runs scored by the Rockies with the runs scored by their opponents, we are assuming that any park effect is having an equal (or at least, an indistinguishable) impact on both teams. This seems like an obvious assumption, but it becomes invalid when the Rockies play on the road. According to the Coors Hangover, Rockies hitters experience a lingering negative park effect after leaving Coors which the opposing team is not experiencing.
In other words, we expect a gap to exist between a hitter’s performance at Coors and his performance at an average park. If the Coors Hangover is true, this gap would be larger for Rockies hitters than anyone else.
Let’s start by taking a look at the park factors we have now. The following tables only contain data from NL teams for simplicity sake.
No surprises. Teams score a ton of runs at Coors and hardly ever score at AT&T Park in San Francisco. Now let’s split up those middle columns to get a closer look at who is scoring these runs.
These are the two pieces of run differential — runs scored and runs allowed — and we generally see agreement between the home and away stats. If a team out-scores their opponents at home, they can be expected to do the same on the road. Good teams are better than bad teams, regardless of where they play. Although, if you subtract a team’s run differential on the road from their run differential at home, the difference will actually be around 100 runs due to home-field advantage. Doing this for all 30 teams yields a mean difference of 83 runs with a standard deviation of 122.
Where do the Rockies fall in this data set? Not only have they scored over 400 more runs at home than the next-best NL team — they have also scored almost 200 runs less on the road than the next-worst NL team. Comparing their home and road run differentials, we see a difference of 483 runs (+44 at home, -439 on the road), or 3.3 standard deviations above the mean. To put it plainly: that’s massive. This is a discrepancy in run differentials that cannot be explained by simple home-field advantage.
Furthermore, I followed the same process of calculating park factors for each team explained above, but I split up the data to calculate a park factor once using the runs scored by each team (tPF), and again using the runs scored by each team’s opponents (oPF). Generally, these new park factors are closely aligned with the park factors from before…except for, of course, the Rockies.
On average, a team’s tPF is about two points higher than its oPF — again, this can be attributed to home-field advantage. The Rockies, however, are in an entirely different zip code with a discrepancy of 17 points. We aren’t talking about home-field advantage anymore. We are talking about something deeper, something that should make us stop and think before averaging the two values to get a park factor that we apply to the most important offensive statistics.
We have no reason to believe that any team should have a 17-point difference between their tPF and oPF; the fact that the Rockies are in this situation either means that they are enjoying hidden advantages at home, or they are suffering hidden disadvantages on the road. To date, we don’t have a theory supporting the former, but we do have one supporting the latter. This is the Coors Hangover.
Does this mean that the Rockies’ Park Factor should actually be their oPF of 110? Should it be some weighted average of different values? I don’t know. But I do know these numbers can’t be ignored. Something is going on here, and we need to talk about it.