The Only* Division Race in Baseball
With the start of the regular season just four days away, we find ourselves in the thick of preview season. No matter where you look, it all boils down to the question: what’s 2016 going to look like? At FanGraphs, we’ve just wrapped up our yearly Positional Power Rankings that assess the season through the lens of each position.
As you might have noticed, each team is made up of the sum of these positional projections and they will all start playing together as 30 units in nine-inning contests next week. If you’re into that sort of thing, we offer Playoff Odds that estimates each club’s shot at postseason baseball (explained here).
It’s important to remember, for all the reasons cited in the previous link, that these projected standings are incapable of total precision. In reality, even with a perfect model for individual player projections, you still wouldn’t hit on every team. And we don’t have anything close to a perfect model for individual players. Yet these projections do offer an objective reading of where the teams stand relative to one another based on what we know. They might wind up being wrong, but they’ll be wrong because they’re flawed not because they’re trying to write an interesting narrative.
Despite clear signs of parity, especially in the American League, our projections think only one division is going to be particularly close: the National League East.
Let’s pull it together visually for clarity. This graph shows the difference in the odds of winning the division for the top two teams in each division. The only two teams within 27% of each other are the Mets and Nationals at 52.8% and 42.1%, respectively. Keep in mind these projections change as we learn about playing time, so if you’re reading this a day or two after publication, it might be a bit different:
Of course, this is the estimate of one system. Baseball Prospectus’ PECOTA, for example, thinks the AL East will be very competitive. Setting aside the fact that the projections aren’t perfectly accurate and that our model likely has flaws, let’s focus on the only interesting division race, according to the FanGraphs Depth Charts.
If you look at the race in the aggregate, the Mets’ projected WAR of 47.3 lines up favorably with the Nationals’ mark of 42.6. Mets position players stand at 24.1 WAR compared to the Nationals’ 21.1. On the pitching side, Mets starters lead 19.7 to 18.1 and the relief split is 3.6 to 3.1 in favor of the Mets.
If baseball were solely the result of independent, context neutral events, we’d probably have the Mets favored by a lot more than 10% in the NL East. But when you turn to the simulations, which take into account the construction of the team and the schedule they will play, the Nats close the gap to roughly one win.
In each facet of the game, the Mets are slightly ahead of the Nationals, but if we break into the individual positions, the story looks a little different. The Mets project to be better than the Nationals in seven of nine spots. Anthony Rendon leads David Wright at third base, but the huge difference is Bryce Harper’s lead over Curtis Granderson in right. Granderson is a solid player, but Harper is probably one of the five best players in the game. Observe:
The starting pitching battle is a little less funky. Scherzer projects to be a step ahead of deGrom/Harvey, but Noah Syndergaard and Steven Matz make up the difference when they’re lined up against Gio Gonzalez and Tanner Roark/Joe Ross.
#1 Mets
| Name | IP | K/9 | BB/9 | HR/9 | BABIP | LOB% | ERA | FIP | WAR |
|---|---|---|---|---|---|---|---|---|---|
| Jacob deGrom | 202.0 | 9.3 | 2.3 | 0.8 | .302 | 75.6 % | 3.00 | 3.00 | 4.9 |
Matt Harvey  |
209.0 | 8.8 | 2.1 | 0.8 | .297 | 74.8 % | 3.00 | 3.05 | 4.9 |
| Noah Syndergaard | 182.0 | 10.0 | 2.3 | 0.9 | .306 | 75.5 % | 3.12 | 3.04 | 4.3 |
| Steven Matz | 159.0 | 8.8 | 2.9 | 0.8 | .306 | 73.9 % | 3.48 | 3.49 | 2.8 |
| Bartolo Colon | 113.0 | 6.1 | 1.3 | 1.1 | .306 | 70.8 % | 3.98 | 3.87 | 1.5 |
| Zack Wheeler |
65.0 | 8.7 | 3.5 | 0.8 | .308 | 72.9 % | 3.72 | 3.72 | 1.0 |
| Rafael Montero | 28.0 | 8.3 | 3.1 | 1.0 | .304 | 72.8 % | 3.87 | 3.87 | 0.4 |
| Gabriel Ynoa | 9.0 | 5.3 | 2.1 | 1.4 | .305 | 67.1 % | 5.03 | 4.86 | 0.0 |
| Seth Lugo | 9.0 | 7.7 | 3.1 | 1.3 | .307 | 70.0 % | 4.59 | 4.47 | 0.1 |
| Total | 976.0 | 8.8 | 2.3 | 0.9 | .304 | 74.1 % | 3.32 | 3.30 | 19.7 |
#4 Nationals
| Name | IP | K/9 | BB/9 | HR/9 | BABIP | LOB% | ERA | FIP | WAR |
|---|---|---|---|---|---|---|---|---|---|
| Max Scherzer | 211.0 | 10.6 | 2.1 | 0.8 | .304 | 78.2 % | 2.70 | 2.71 | 5.8 |
| Stephen Strasburg | 182.0 | 10.2 | 2.0 | 0.8 | .310 | 76.2 % | 2.92 | 2.83 | 4.7 |
| Gio Gonzalez | 170.0 | 8.6 | 3.4 | 0.7 | .311 | 72.9 % | 3.58 | 3.43 | 3.1 |
| Tanner Roark | 132.0 | 6.2 | 2.2 | 1.0 | .301 | 71.7 % | 3.91 | 4.01 | 1.4 |
| Joe Ross |
121.0 | 7.5 | 2.7 | 1.0 | .304 | 71.7 % | 3.88 | 3.86 | 1.5 |
| Lucas Giolito | 94.0 | 8.3 | 3.3 | 1.0 | .306 | 72.1 % | 3.97 | 3.94 | 1.1 |
| A.J. Cole | 19.0 | 7.0 | 2.5 | 1.2 | .301 | 72.6 % | 4.07 | 4.25 | 0.2 |
| Yusmeiro Petit | 19.0 | 8.0 | 1.9 | 1.2 | .304 | 72.9 % | 3.86 | 3.82 | 0.2 |
| Taylor Jordan | 9.0 | 6.0 | 2.4 | 0.9 | .309 | 69.8 % | 4.11 | 3.98 | 0.1 |
| Total | 957.0 | 8.8 | 2.5 | 0.9 | .306 | 74.0 % | 3.40 | 3.37 | 18.1 |
In the bullpen, the teams look similar at the back end, but the projections favor the Mets’ middle relief.
#9 Mets
| Name | IP | K/9 | BB/9 | HR/9 | BABIP | LOB% | ERA | FIP | WAR |
|---|---|---|---|---|---|---|---|---|---|
| Jeurys Familia | 65.0 | 9.8 | 2.7 | 0.7 | .301 | 76.8 % | 2.83 | 2.98 | 1.4 |
| Addison Reed | 65.0 | 9.0 | 2.9 | 0.9 | .305 | 74.6 % | 3.48 | 3.51 | 0.7 |
| Antonio Bastardo | 55.0 | 10.6 | 4.1 | 0.8 | .297 | 76.6 % | 3.28 | 3.45 | 0.5 |
| Hansel Robles |
55.0 | 10.0 | 3.1 | 1.0 | .297 | 75.9 % | 3.31 | 3.47 | 0.4 |
| Sean Gilmartin | 45.0 | 8.4 | 3.0 | 0.8 | .304 | 74.1 % | 3.42 | 3.51 | 0.3 |
| Erik Goeddel |
40.0 | 8.6 | 3.4 | 0.9 | .303 | 72.1 % | 3.78 | 3.72 | 0.1 |
| Logan Verrett | 35.0 | 7.6 | 2.1 | 1.1 | .302 | 71.9 % | 3.87 | 3.86 | 0.0 |
| Bartolo Colon | 30.0 | 6.1 | 1.3 | 1.1 | .306 | 70.8 % | 3.98 | 3.87 | 0.0 |
| Jim Henderson | 25.0 | 9.5 | 3.7 | 1.1 | .304 | 73.0 % | 4.05 | 3.98 | 0.0 |
| Jerry Blevins | 20.0 | 9.3 | 3.0 | 0.9 | .298 | 75.7 % | 3.28 | 3.49 | 0.0 |
| Dario Alvarez | 15.0 | 10.0 | 3.9 | 0.9 | .306 | 75.0 % | 3.55 | 3.73 | 0.0 |
| Josh Smoker | 10.0 | 9.4 | 4.0 | 0.9 | .307 | 73.1 % | 3.75 | 3.74 | 0.0 |
| Akeel Morris | 10.0 | 10.7 | 5.0 | 0.9 | .306 | 74.4 % | 3.76 | 3.84 | 0.0 |
| Josh Edgin |
10.0 | 8.7 | 3.6 | 0.9 | .300 | 74.5 % | 3.57 | 3.78 | 0.0 |
| Jeff Walters | 10.0 | 8.5 | 2.8 | 0.8 | .303 | 72.8 % | 3.54 | 3.51 | 0.0 |
| Total | 482.0 | 9.1 | 3.1 | 0.9 | .302 | 74.5 % | 3.45 | 3.52 | 3.6 |
#11 Nationals
| Name | IP | K/9 | BB/9 | HR/9 | BABIP | LOB% | ERA | FIP | WAR |
|---|---|---|---|---|---|---|---|---|---|
| Jonathan Papelbon | 65.0 | 7.8 | 2.1 | 0.9 | .299 | 74.8 % | 3.32 | 3.55 | 0.6 |
| Shawn Kelley | 65.0 | 10.5 | 3.1 | 0.8 | .309 | 76.8 % | 3.04 | 2.99 | 1.2 |
| Blake Treinen | 55.0 | 8.4 | 3.1 | 0.6 | .311 | 74.3 % | 3.23 | 3.25 | 0.7 |
| Oliver Perez | 55.0 | 10.4 | 3.4 | 0.8 | .313 | 76.1 % | 3.26 | 3.34 | 0.5 |
| Yusmeiro Petit | 45.0 | 8.0 | 1.9 | 1.2 | .304 | 72.9 % | 3.86 | 3.82 | 0.1 |
| Matt Belisle | 40.0 | 6.9 | 2.9 | 0.7 | .312 | 71.9 % | 3.80 | 3.71 | 0.1 |
| Trevor Gott | 35.0 | 7.3 | 3.6 | 0.7 | .305 | 72.0 % | 3.81 | 3.87 | 0.0 |
| Felipe Rivero | 30.0 | 8.3 | 3.0 | 0.7 | .298 | 74.5 % | 3.32 | 3.43 | 0.1 |
| Rafael Martin | 25.0 | 10.3 | 3.0 | 0.8 | .309 | 76.3 % | 3.16 | 3.18 | 0.1 |
| Abel De Los Santos | 20.0 | 8.1 | 3.0 | 1.0 | .305 | 73.0 % | 3.89 | 4.02 | 0.0 |
| Burke Badenhop | 15.0 | 5.6 | 2.5 | 0.7 | .307 | 71.0 % | 3.86 | 3.84 | 0.0 |
| Sammy Solis | 10.0 | 7.2 | 2.9 | 0.7 | .306 | 72.0 % | 3.74 | 3.70 | 0.0 |
| Matt Grace | 10.0 | 6.7 | 3.2 | 0.7 | .309 | 71.7 % | 3.83 | 3.84 | 0.0 |
| Nick Lee | 10.0 | 8.8 | 6.1 | 0.9 | .305 | 72.5 % | 4.44 | 4.62 | 0.0 |
| The Others | 21.0 | 8.2 | 4.0 | 1.2 | .322 | 69.5 % | 4.73 | 4.50 | 0.0 |
| Total | 501.0 | 8.5 | 3.0 | 0.8 | .307 | 73.9 % | 3.52 | 3.55 | 3.4 |
Clearly, the story is that the Nationals have a lot of their eggs in the Bryce Harper and Max Scherzer basket. It’s a wonderful basket filled with flowing hair and multi-colored eyes, but that doesn’t mean it isn’t susceptible to injuries just like the less talented baskets. Without Harper or Scherzer, the Nationals would be in serious trouble. The Mets would obviously prefer not to lose deGrom or Harvey, but it would be a much easier hit to take.
This is probably an under-appreciated aspect of roster construction. Generally, we don’t think of two 3 WAR players being much better or worse than one 6 WAR player from a performance standpoint, but when you’re forecasting health it gets a little more complicated. In order for the Mets to lose 6-7 WAR, they need to suffer two major injuries. The Nationals just need to suffer one to either of their stars.
Teams almost certainly take this into account when working up contract offers and offseason strategies, but it’s not something we talk about very much when discussing projections. The uncertainty surrounding the projections for teams that have a lot of value concentrated in just a few players is higher than the uncertainty for a team where the talent is distributed more evenly throughout their roster. Phrased another way: while the projections represent median forecasts for wins, the distribution of wins likely isn’t uniform around those midpoints.
If the odds of a season-ending injury for any given player is 5% (number illustrative only), then there is something like a 9% chance that at least one of two players goes down, but only a 1% chance that both go down at the same time. Obviously things get more complicated as you increase the number of players in your population, but there is definitely a difference between assessing the odds of a serious injury and assessing the odds of typical under-performance. There’s upside to stacking talent in a few spots, such as roster flexibility and an easier ability to upgrade at the deadline, but it’s a higher risk strategy for sure.
Our projections think the Mets and Nationals will be locked in the closest division race in the game this year. They think the Cubs will run away with the NL Central and that the Dodgers, Astros, Indians, and Red Sox are in comfortable positions ahead of Opening Day. Even the youngest reader has been around long enough to know things won’t work out the way the projections expect because the projections just don’t have enough information to accurately predict a baseball season.
But right now, the projections are telling us that, on paper, the Mets and Nationals are closer in talent level than any other set of division rivals. Players will get hurt, breakouts will happen, and some will have down years. These projections aren’t a road map to the end of the season, they’re a quick assessment of what we know at this moment. For now, they’re telling us that the tightest race is between the Mets and Nationals and that Bryce Harper is the only thing* that stands between us and six months of inconsequential baseball.
Neil Weinberg is the Site Educator at FanGraphs and can be found writing enthusiastically about the Detroit Tigers at New English D. Follow and interact with him on Twitter @NeilWeinberg44.
I know the Cubs are good, but the NL central had three teams that won at least 97 games this year. I don’t think it will be the most lopsided divisional race in 2016.
Agreed. The Pirates’ odds look unreasonably low to me. I can easily see them pushing Chicago and even winning that division.
Their 3-5 starters are uninspiring. Glasnow needs to replace a.j Burnett if they want to be close to last year. Kang out to start the year, cervelli not likely to repeat at that level, missing Alvarez power. Wouldn’t shock me if they miss the playoffs.
How in the world will the Pirates ever replace Pedro Alvarez. The guy have been worth .2 WAR over the the last two year. That’s a full 20 wins over what I would have done out there. That guys is irreplaceable.
Melancon WPA probably won’t be 5 this year either. Though to be fair, Bryant and Rizzo probably won’t be in the top two position player spots.
Yeah losing Alvarez is addition by subtraction. And while Cervelli may regress a bit, there’s little reason to believe he’ll fall off a cliff. If puts up a wRC+ over 100 (it’s at 108 for his career albeit in a smallish sample) combined with his defense and framing, he’ll still be a well above average catcher. And other than a handful of teams, everybody has meh starters in the backend of the rotation.
That’s fair, but I’m buying in on Nicasio and the back of their rotation had problems last season too. Despite presumed regression from Cervelli, I expect their overall offensive production to tick upwards while their defense should improve dramatically. Meanwhile I’d be pretty surprised if the Cubs’ top three starters were as good as their projections.
I think just looking at war doesn’t emphasize what Alvarez power added to the lineup. Sure jaso will be worth 1 war in the 80 games he is on the field but I think the offense will be substantially worse. I have a hard time believing cervellis .341 career babip is sustainable now that he is a catcher on the wrong side of 30. That infield is bad until kang gets back, weak bullpen, weak starting pitching after the top 2, is the outfield enough to carry the other glaring holes against the best team in baseball and the cardinals. I don’t see how anyone can see them getting to 90 wins without a huge jump from polonco or Glasnow coming up and being dominant day 1.
What about the cubs starters is off? 13 war from arrietta, Lester, and lackey is all they are projected for. Arrieta has actually been a 7 win pitcher the last 2 seasons if you prorate 2014 to a whole year. Lester is pretty clearly a 3-4 win pitcher, lackey as a 2-3 win pitcher is all it takes for 13 war.
I’m not saying the Pirates are better than the Cubs (they aren’t) but please stop saying the loss of Pedro has anything to do with that. His power won them what? Two games last year maybe? But his defense absolutely cost them the same way. The power is nice but not only is the rest of the offense lacking for him, the defense was an absolute disaster. He used to be a somewhat decent 3b but ever since he developed his throwing problems, he has lost all defensive instincts. He’s a terrible receiver at first and don’t ever ask him to turn a DP. He will not be missed.
Scott Boras thanks you.
Expect a fruit basket by the end of the week.
Wait a sec – which AL East team does Pecota have with a 27% better chance of winning than the 2nd place team?
oh it’s not pecota, it’s fangraphs that thinks the red sox have a 27% better chance than the 2nd place team, despite having major question marks at all but 3 or 4 spots on the team. figures.
blah, blah, blah… find something new to whine about
I’m just curious what will he say if the Sox do win the division.
Maybe I’ll try denial. Seems to have worked for the guys who thought the Sox’d be contenders the last couple of years, at least.
It worked for my baby boy.
Out of curiosity – why do you always attack Fangraphs but never the steamer or zips systems which are actually responsible for the projections? Szymborski has a chat here and he responds to people on twitter.
I’ve mentioned before that steamer in particular seems to be off and suggested a review of it. I do wonder if most of the projection systems are working under largescale assumptions that were very accurate 5-10yrs ago but which may be missing some changes in player development curves nowadays. (I could be wrong of course).
But individual player projection systems can only be blamed so much.
Fangraphs makes a number of questionable assumptions on how to put together those individual player projections, and how to accurately reflect the strength of depth. They choose to treat all projections as risk-equal, even though we know that the error bars can be wildly different depending on the player. They also choose to rely entirely on fipwar for pitching (which isn’t a choice made by the projection systems), which is becoming increasingly dubious imo.
There is nothing about Fangraphs which says they have to slave themselves to projections, imo. They should be able to question projections which seem overly reliant on older data, or which seem to ignore relevant recent physical changes, or which are based on tiny track records, or which depend far too much on inconsistent defensive data and theoretical positional adjustments.
For example, fangraphs’ depth charts last year indicated that the red sox had the best outfield in baseball…despite the fact that they didn’t have ONE SINGLE PLAYER who had been a regular starting mlb OF in recent years. That kind of disconnect is too jarring to ignore, imo.
Damaso, do you have an example of where the Fangraphs projections were off that doesn’t involve the Red Sox or Blue Jays?
why are you posting about the Red Sox in an article that has nothing to do with them…?
Zips projected the red sox to win the division last year, it wasn’t just Steamer.
Damaso, error bars don’t really help here. They are 50% projections, so each player has as much chance of overperforming as underperforming, no matter their variance. Maybe no player should be projected for 550 PA or 180 IP, but that’s the only depth issue I can see.
I don’t think FIP WAR has a significant effect on the standings. Most of the difference between ERA and FIP for pitchers is defense and park effects, so you don’t want to double-count those.
Trying to beat the projections is more likely to be wrong than trusting them blindly. The Fan Projections on here basically expect teams to average 5 runs a game on offense and 3.5 runs a game allowed, way too optimistic. Yes, Hanley’s defense was projected way too high last year, but it was also actually worse than most anyone expected. There is enough of a track record of infielders moving to the outfield and doing well to warrant non-negative ratings.
There is no way that BOS was projected with the best OF last year. Trout alone was equal to Mookie, Rusney, and Hanley. The other funny thing about last year’s Boston OF is that Betts and JBJ actually outperformed their projections. The Diamondbacks had the best OF, for which none of the main starters had an MLB season with at least 500 PA.
If you go through and find true systematic bias in the projections, Dan and Jared would be more than happy to take the advice and adjust their systems. Until you do that, anecdotal evidence won’t get you very far.
I said earlier that the special coating needs to be free and comment on it. I wonder if the majority of the system has been implemented to a large extent is a very accurate means of 5 to 10 years, but it lacks some of the changes in the player on the development curve these days. (I could be wrong, of course).
But the system only compares the error of each player.
Fangraphs some questionable assumptions about how to build a prediction of the various actors and to accurately reflect the strength and depth. They decided to control all passports as an equal joint venture, even if we know the track errors can vary depending on the players. And they have chosen to rely on the help of fipvar (which is not an option made the movie system), it has become more important than ANYTHING ELSE.
Nothing on Fangraphs, who say they were robbed at a premiere MMO. They should require that looks too depends on big data, recently seemed to ignore the physical changes in online or from a small or professional experience that too many people are relying on data protection are inconsistent theories and customize your site.
For example, the depth chart last year Fangraphs red Sox noted that there is a better baseball team. … Although no single player has regularly from MAJOR LEAGUE BASEBALL that year. This type of separation is too shocking to ignore, MMO.
The raw projections might be objective, but the projections and playoff odds that FG uses are not objective. They are entirely dependent on the subjectively assigned depth charts of the FG staff.
“Entirely” is an exaggeration. Largely dependent on the depth charts.
Tthan poor playing time estimates. There just aren’t many instances of he subjectivity isn’t all that impactful. I bet if you added up depth chart projected WAR across the league, 80+% would be by clear starters in normal starter playing time. Back-end relief roles, platoon battles, etc. just don’t matter a ton, and if they do end up mattering, it’s more because of players performing drastically above/below their projections guys who are expected to get bench playing time but objectively project as impact performers. That includes prospects – a lot of those guys aren’t necessarily projected to be huge upgrades in year 1 anyway. ZiPs had Correa at 1.3 WAR (430 PA), Lindor at 1.9 WAR (550 PA), Matt Duffy at 1.6 WAR (500 PA), etc. Obviously there are exceptions, anomalies, etc. (Kris Bryant), but on the whole the playing time allocations just don’t seem all that meaningful, considering how few instances that they’re actually subjective.
downvote for first sentence…
University bad weather is estimated to be in the game. No, this is not the case, he did not hear anything subjective. I bet they add strategic depth chart awaiting the tournament, 80 +% will have clear results for beginners starting in a normal game. The role of the rescue team, etc. Just a small note, and if you like to end, plus the players perform significantly above / below the prediction of the expected time meta crying, but the project has an effective impact. This includes the opinion of many that can not be targeted as a significant improvement in year 1. 1.3 Correa Tractors rat (dad 430), 1.9 rat Lindor (dad 550), Mat Dafi rat 1.6 (500 pA), so obviously seem unusual exceptions, naive (Kris Briant), but all in all to play at a certain time does not seem all that important, given how few cases actually subjective.
Is that really what the projections say? I don’t think so. All three of the projected American League division winners have about the same odds as the Mets. What they don’t have is a bad team and two terrible teams in their divisions. I would guess that these projections, if you did the math (which I am not smart enough to do) would say that there is a greater chance of a close pennant race in at least every division in the AL, and possibly 1-2 of the other divisions in the NL. Unless the Mets and Nationals play similarly to or above their projections, one will likely run away with the division. The other divisions have more bullets in the chamber, more scenarios that could play out with two or more teams being competitive down the stretch. It’s just that we have less of an idea of what teams might be competing.
Was going to make basically the same comment. We pretty much know the NL East is going to be the Mets or the Nats, so while they might be the most evenly matched of the division leaders, it’s all the other divisions that have more parity that will likely have interesting division races.
You’d have to have a heart of stone to read this article without weeping a little for the sad plight of Fangraphs writers, trying to drag themselves through the last stretch of the offseason, when every imaginable topic has already been agonized over, and they are reduced to taking the projected seasonal outcomes seriously. Hold on, Mr. Weinberg! Baseball is coming!
Sure, there’s more risk in having one 6 WAR player than in having two 3 WAR players, but there’s also a significant benefit, because there can only be nine players on the field at a given time.
The team with a star and an average starter has possible WAR outcomes of 8, 6, 2 and 0. The team with two 3 WAR players has outcomes of 6, 3 and 0.
Agree to some extent, the 6 win player means that possibly no other player at that position can help your team more.
That’s not how WAR works. Whether you’ve got the (6,0) pair or the (3,3) pair you have a range of expected outcomes all over the spectrum, from maybe 12 wins (+/-, just for illustrative purposes) to maybe -2 wins on the low side.
The distribution of outcomes may be different. But it’s some sort of curve, not a handful of discrete points.
And of course the expected distribution of WAR is different even comparing a (3,3) pair to another (3,3) pair. Your distribution for someone who has put up 2.5 WAR to 3.5 WAR in each of the last five years will look markedly different from someone who has ranged from, say, 0.5 to 5.5.
Cool Lester was not talking the range of possible outcomes. He simplified the situation to be either zero WAR if the player gets injured, or the projected WAR if they do not get injured.
Yeah, just looking at it from a simplified, roster-building standpoint.
Gotcha! It is an interesting point/counterpoint. In the “assigning risk points” exercise FG readers generally assigned greater risk to the “all eggs in one basket” teams (Angels with Trout, Nats’ offense with Harper) but conversely those 0-1 WAR guys filling out the rest of the roster should be easier to upgrade or replace versus a team full of 2-3 WAR types with few or no holes.
“It’s a wonderful basket filled with flowing hair and multi-colored eyes.”
That… doesn’t… sound wonderful. That sounds more like… forensic evidence.
Great info! Be absolutely sure to let us know where and when the Projected Champion Ring Ceremony will occur, please. I completely missed Bryce Harper getting his ring last year. Let’s not let that happen again!
Seriously, though. If these projections and subsequent Base Runs calculations misread the Royals by 11 wins again this year, let’s all please admit the formulas we’re using need some comprehensive revision.
Great information! Absolutely sure to tell where and where the championship ring ceremony is expected to occur, please. I fully Brice Harper last year, all belts. We will not allow this to happen again.
Seriously. If the sequence of bases and computer fraud Royals Edge 11 wins this year, could be facing, we use the formula for verification.
That’s why the Royals are projected for .500: computer fraud!!
At least Bryce Harper won’t ever have to worry about his pants falling down
Only if you promise to admit you have no idea ho projections work, if the Royals come back to earth like the Orioles did after their “magic” wore off?
Fair deal? Or did i mistake you for someone interested in rational interaction?
See my comments posted at the bottom of the chain.
The Royals excel in defense, relief pitching, and clutch, possibly the three most fickle areas of the game. Since those two things are heavily regressed and clutch is completely ignored, teams that perform like the Royals do get underprojected.
That being said, there’s a reason why defense and relievers are heavily regressed. Looking at the top 6 UZRs from 2014, three of them (Headley, Lagares, Pedroia) rated average or worse last year. The 2009 Mariners had one of the best defenses of the UZR era, yet with mostly the same guys, were a bit above average in 2010.
Relievers come and go, especially outside of the elite ones, so relying on another 2.75 bullpen ERA is foolish. There is a slight correlation between clutch hitting and low K rates, but it’s not enough to explain the extra 5 wins of clutch offense for KC.
KC is easily the most polarizing team. Their clutch overperformance boggles saber-minded people, yet their defense and baserunning help legitimize the “weight” those categories should have in WAR.
These projection systems are updated each year with new translations, so any consistent beating of the system is likely superior player development in that organization, not misreading of stats.
It’s not really a matter of regression when last year’s Base Runs calculations say the Royals were extremely lucky to produce their scoring margins. However, their true talent seems to show through even more when they can win two post-season series in 2014 (and nearly a third), and then 3 more in 2015. You can say it’s ‘luck’ or ‘clutch’ or ‘expected performance variability’, and maybe I’ll concede a little of it might be attributable to that, but let’s look at this in October and see if two years of good luck becomes three.
Prediction: The difference in the odds of winning the division for the top two teams in each division will finish the season at 100% for all divisions, therefore, by the odds of winning the division, there will in fact be zero division races in baseball in 2016.
It’s entirely possible the Royals are like the full team version of the pitcher who consistently outperforms his FIP. It doesn’t mean the metric or projection system is useless. It’s just that, you know, it’s a complex game.
Should’ve been a reply to the post above yours.
Well the 2014 royals did have every pitcher with at least 50 innings pitched beat their FIP. Last year it was only like 75% but Chris young counts for beating it twice I think, then great baserunning and they did finish the season with 6 hitters above league average in their lineup. The projections take in to account 2014 when hosmer and moose were below average hitters. And projects the e.r.a closer to the fip
Nope. Not possible. Steamer and Zips were created years ago specifically to produce projections that would undervalue and tarnish the 2014-2015 Royals.
I contend that these people can’t predict next season, but they did know that the Royals would be good in 2014-2015 at the time they started their projections, and created their systems to be biased against MY Royals.
-a totally rational Royals fan
Oh, wait, I figured out the answer to my own questions. These projections aren’t predicting future team records; they’re projecting end of season Base Runs calculations. That has limited correlation to actual team records. At one time, we all thought the correlation was stronger, then 2014 and 2015 occurred, and now we know we didn’t really know.
2015 was the only anomalous season on the high end so far, as 2014 was right around the average. 2009 was the previous high in standard deviation, followed by 2010, which was easily the lowest deviation since 2002. There’s also little evidence that certain teams are able to consistently beat the projections, so I really don’t know how else you could make a projection that isn’t a blind guess.
Yes, I do remember reading about how much of an outlier 2015 was compared to any other year since Base Runs computations have been made.
It seems to me the biggest changes in strategy over the past couple of seasons are the nearly universal shifting against lefties and the power bullpens. In theory, these could be eliminating crooked numbers on the scoreboard more than we’ve seen in decades, and thusly, a greater number of innings played in MLB today are occurring after the outcome of the game is essentially settled.
To me, it seems obvious the batted ball velocities and defensive range measurements are going to completely flip performance metrics on their heads. One terrific article on FG discussed the 94 MPH batted ball threshold and how BABIP seemed to plummet to nearly nothing below that launch velocity. Once the data sets of Statcast mature, I think we might finally see MLB talent evaluations using the naked eyeball become the equivalent of tea leaves and palm reading.