Will Won’t Myers Live up to Lofty Expectations or Will he Won’t?

Earlier this year I asked Rays fans to take a simple survey on how they thought the near-term future would go for rookie sensation William Bradford Myers. The dynamo was coming off a season when he put up a slash of .293/.354/.478 good for a wOBA/wRC+ of .357/131. Tantalizing doesn’t even begin to describe the season put up by the 2013 Rookie of the Year. While folks have heard of regression, it would appear their powers to understand this phenomenon rarely go past lip service.

The above link shows the basic form, in which, I wanted to ascertain what fans thought Wil was likely to put up from 2014 – 16 in such basic statistical categories as K%, BB%, BABIP, and ISO. Using these four inputs it is possible to construct a passible version of a batter’s line and this method helps to remove some of the bias that fans are going to have when they’re used to seeing things in slash form. In order to insert some serious anchor bias I let the respondent know the figures that Myers put up in each category in his fantastic 2013:

8.8 BB%, 24.4 K%, .362 BABIP, and .185 ISO

Here’s the results from the 28 people that took the few minutes that this survey required:

We start off with the actual line that he put up where he struck out a quarter of the time, which was a little more than people expected this year. He also walked slightly more than people though he would, but his BABIP and ISO were far removed from the high bar that was projected. His Iso was basically half of what people thought it would be, while his BABIP was closer to league average. You can also look ahead to how people think his game would evolve over the years as he became a hitter that walked more, struck out less, had better success on balls in play and hit for more power. Let’s extrapolate these out to get an idea of what this looks like:

Note that I have used formulas to derive each of these lines based off of the previously shown inputs. There will be subtle differences between his actual line and what I’ve shown here because I’m not including minutiae like sac flies, and because it can get tricky to perfectly peg batting average from BABIP due to home runs being included in at bats, but not BABIP. This version is very close to actual and that same formula is used throughout so you should have some confidence in this stuff. Additionally, it can be difficult when distributing hits so you may notice slight differences there. I hope that doesn’t detract from your appreciation of this read. Also, here’s a chart for those that prefer that sort of thing:

Readily apparent is how close folks were to pegging his K/BB numbers this year. Unfortunately, this also showcases how much went wrong. Respondents saw marginal improvements across the board with Myers approaching the end of the MVP discussion by 2016. It’s still possible that 2015 and ’16 end up looking a lot more like this, and for that we will rejoice, afterall, 2014 is one data point, but it’s proximity to the present means we should be looking to temper our expectations a nudge compared to where we were close to a year ago.

Wil Myers still has a chance to be a very good offensive player for the Rays despite how poorly 2014 went. So here’s your chance to vote again on this. Please take the barely triple-digit seconds you will need to complete this survey and I hope to present the results before the beginning of this next season.

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Joel Peralta is a Boss at Giving up Dingers

Somebody call the fire department, this one’s out of control. Joel Peralta gave up another home run in a high leverage moment yesterday afternoon bringing his HR/9 up to a robust 1.47 for the season. When it comes down to guys that you want on the mound when you just absolutely need to surrender a tater there’s just few better choices. Peralta now ranks 21st out of the 229 pitchers with 20+ IP this year in HR/9, but the best part, arguably, is that he’s being used as if he was some relief ace that can come in and not give up runs.

Of the 20 guys better than Joey Pinetar at giving up home runs only three of them (Romo 1.87, Logan 1.81, Reed 1.77) have a higher pLI than the man nobody calls not homer prone. Here’s a chart:

Peralta really garbage

Bottom right is where you do not want to be if you want your relievers to give up home runs when it really matters. Fortunately, we find Peralta squarely in the best box for guys that absolutely do a great job of ruining your chances of winning a game. His HR/9 is well above the average meaning when you need to squander a lead or put a close game out of reach he’s your man. There are better pitchers at committing the worst atrocity in baseball for a pitcher by doing it either more often or in higher leverage spots, but only a handful of guys can match Joel Peralta’s ability to drop a deuce on the mound.

Seriously, Joe Maddon, fuck that guy.

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Introducing Weighted Runs Above Opponent

Many folks are probably aware of the offensive statistic weighted Runs Above Average:

Weighted Runs Above Average (wRAA) measures the number of offensive runs a player contributes to their team compared to the average player. How much offensive value did Evan Longoria contribute to his team in 2009? With wRAA, we can answer that question: 28.3 runs above average. A wRAA of zero is league-average, so a positive wRAA value denotes above-average performance and a negative wRAA denotes below-average performance. This is also a counting statistic (like RBIs), so players accrue more (or fewer) runs as they play.

By the way, I wrote that entry. We can take this concept of using wOBA to estimate offense relative to something else by tweaking the original formula:

wRAA = ((wOBA – league wOBA) / wOBA scale) × PA

Instead of comparing a season’s worth of a player’s wOBA to league average we can use each team’s wOBA from a game. Take, for instance, last night’s game against the Rangers. Using my calculations I get a wOBA of .396 for the Rays hitters while our pitchers yielded a wOBA of .210. Well we can plug that in and get ((.396-.210)/1.15) * 43 which if you crunch the numbers gives us a wRAO of 7. The actual run differential? 7. This one worked out pretty well, but if you do this for the entire season you get an r value of 0.87 which indicates a very strong correlation between wRAO and the actual run differential on a game-to-game basis. Here’s what that looks like:

Anything above the origin shows where the Rays should have won a game and vice-versa for below. Gaps between wRAO and RD show where performance differed from the actual game score. You can think of this as the luck component of winning. The Rays had a very long, sustained stretch where they were vastly outplaying the other team, but that was not manifesting itself in the score. Alas, just more evidence that this 2014 Rays team has played much better than their score throughout this season, with the exception of a 30 game stretch in the second quarter of the season. Pretty crazy that the red line takes until the half way point of the season before we see it get back topside, despite the fact that the wRAO line lives north of the border about as often as a comfortable snowbird.

Keep putting up a wRAO of around 2.0 the rest of the season and good things are going to happen. Buckle your seatbelts for this last quarter of the season, shit’s about to get bumpy.

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What Coulda Been?

Over at Grantland, Jonah Keri has done an admirable job of running a weekly power ranking, of sorts. He’ll typically create some different tiers and breakdown a team from each level to give the casual fan a better understanding of what is affecting each franchise. This is great for those that typically only follow one team, because it gives that person something to talk about when they end up chatting with fans of other teams. Well, Jonah focused on the Rays in the most recent article. He got around to mentioning something that he has gone over before that many fans can easily agree with because it’s just so intuitive. That idea is the notion of “clusterluck”.

Jonah isn’t the first, of course, but he’s a vocal and respected leader with great connections so he’s probably the initiator for many folks’ first foray into this concept. The link above does a pretty good job of giving the gist and here’s a few more from Dave Cameron whom wrote a couple of really good posts earlier this year looking into this concept. They’re all pretty good and build upon one another so if you have some time you should check those out.

The basic concept is that, in the aggregate, using runs scored and allowed works pretty well, but due to the timing of events smaller samples can be deceiving. An offense estimator like wOBA does a better job of portraying what should have happened due to the timing of events. Sometimes a double drives in three runs, sometimes that guy ends up stranded on 2nd. There is a wealth of lost information in between so it makes sense to use the average number of runs scored to give a better idea of what should have happened. To that end I’m using wOBA values from The Book for each event:

BB: .72, HBP: .75, 1B: .90, ROE: .92, 2B: 1.24, 3B: 1.56, HR: 1.95

I do not differentiate for double plays or fielder’s choice. An out is an out and carries a value of 0.00. I’m also creating a baseline using the matchup tool created by myself and Ian Malinowski to give an idea of where the Rays were facing better competition. You’ll notice gaps where, on paper, you’d expect the Rays to be much better or worse than they’re opponent, so it’s always interesting to compare reality to the expectations. Let’s start by looking at the Rays expected and actual wOBA. All following data through July 29th:

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Taking a Look at Swing and Contact Aging Curves

Aside from pedigree, why do we think batters will perform better as they move from their fledgling opportunity into and through their prime? Well the easiest way to think of this is the following chart:

Over time players become smarter as they pick up on more and more of the nuances of the game. What you can get away with and what you can’t, but Father Time is still undefeated. As a player gets older they lose their physical superiority to younger players. The boxed area represents the idea of prime where a player has enough mental and physical ability to be one of the best players in the game. Prior to this period they can get by on physical gifts and post they can get by on savvy veteran guile, but within the box is when they’re best positioned to be all that they can be. This isn’t just a baseball thing, but all sports or contests which require physicality or even dating follow this same principle.

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Statistically Scouting the Minors

I generally don’t like to talk about minor league guys that I haven’t seen, but couching this post with a blanket caveat that this is strictly based on this year’s numbers in the minors should keep the critics off my back, I think. This is going to be a heavy numbers post so let me give an idea as to how I arrived at these rankings. All statistics current through the All-Star Break.

First off, I used the truly excellent Minor League Central to grab virtually every play in the minors this year. I removed a few players that played for two different teams within a league, but that’s minor, and I set some age limits to keep the oldest of the older players out of our pool:

International League <= 30 YO
Southern League <= 28 YO
Florida State League <= 26 YO
Midwest League <= 25 YO
NYP <= 24 YO

You’ll also notice that I’ve only drawn players from leagues that the Rays play in so sorry PCL/CAL/Texas/Mars leagues.

After establishing the pool of players I then used Baseball America’s Park Factors to adjust wOBA (wOBA*) for batters and ERA/FIP (ERA*/FIP*) for pitchers. These are the only park adjusted statistics so keep that in mind, but I think it helps put everyone on an even keel.

The next step for each league was to create weighted averages (including age) and standard deviations for the following statistics for each league:

Batter:

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