Good Manager, Better Man

It should be no secret that the Rays have come a long way, baby. I commend those that have been here since day one, but I attended my first game in 2000 and didn’t pay much attention until 2003 when the Rays made, arguably, their highest profile trade to that point. You see, they were coming off a year where they won 55 games. Double nickel. It was a stupendously terrible season where Hal McRae was never really given a chance after inheriting another pretty terrible team 14 games into 2002.

Shallow Hal’s predecessor could probably also make a great case for why he never had a chance since he was the first manager in team history, and despite flirting with the mythical figure of 70 wins in his second and third years the aforementioned slow start in ’02 cost him his job before cashing in on the fruits of his third offseason.

Little did former GM Chuck Lamar know, but it would take the recipient of our fated trade, Sweet Lou Piniella, until his second season to whip this lackluster bunch into a 70-win team on the upswing. Of course, it might have helped if the team hadn’t traded their best player at the time, Randy Winn for a man that ended up being better known for tantrums than tactics.

It wasn’t always like this. Once upon a time Piniella took over a mostly terrible team in the Mariners and in his ninth season at the helm, and after years of ups and downs with some real talent, he guided the 2001 Mariners to 116 wins. The 1954 Indians can probably stake the claim to having the best record in the modern era when they went 111-43, but these M’s won more games so I wouldn’t fight you if you wanted to say they take the crown.

While, he would ultimately get three years here, no where near long enough to turn a truly terrible team into a contender, he does give us a very interesting parallel for our current outgoing manager, Joe Maddon. I’ve avoided talking about the man, the myth, the legend to this point, but it can be put off no longer. Everyone that got to watch this guy on a daily basis for the last nine years should have a hole in their soul that wasn’t there before. Call that love. Call that spirit. Call that friendship. He was an unflappable symbol for perseverance and patience.

Years ago there was a website called Maddon’s Mission, in which, the site’s prodigy/editor R.J. Anderson did a credible job of running the numbers on key decisions made in real time by the namesake Joe Maddon. It took about six weeks before the site had ran it’s course as folks got sick of questioning Maddon. He was a maestro. The guy could do no wrong. Of course it helps that he was inheriting a team that had never played .500 ball. Heck, even after Maddon’s first two years it was still Piniella as the only manager to lead his ruffians to even that laughable total of 70 wins.

Then 2008 happened. The prior season saw a team take a big step forward, but a historically bad bullpen, and it’s intrinsically linked, equally terrible defense sunk a ship that was just waiting for the right wind to come. In 2008 the Rays caught their breeze, fixing their bullpen and putting a focus on defense, while supplementing and supporting what was a very good offense to begin with.

Since then, Maddon has been the one constant as names and faces leave in seemingly the blink of an eye. Truly, the wheel in the sky keeps on turning, and for the Rays and their fans that meant learning to live with disappointment. Don’t get too accustomed to your favorite player. He probably won’t be here much longer. Maybe get a shirsey with the Rays logo on the front, but no name on the back. Get more mileage out of that puppy. The thing is, it’s not hard to see how the Rays were adding talent and getting better and better even with payroll fluctuations. Here’s a look at annual WAR for both pitchers (P) and hitters (H):

Readily evident is just how absolutely shitty the Rays were in their formative years. Rothschild peaked in his first year at 26 WAR close to evenly split between his pitchers and hitters. McRae one-downed even that modest bar by receiving 16 WAR from his players in 2002, while Piniella saw year-over-year improvement ending his final season on a personal high note with the Rays at just north of 26 WAR.

Unto the breach steps Maddon. His first year was a disaster as the team was in heavy transition, but you can see how things took off from there. It’s hard to believe, all these years later, but 2008 ended up being the best team, by WAR, that Maddon ever managed. While 2009 was disappointing that the team did not make the playoffs it was still an incredible lineup from 1 – 25. You can see some dips and rises in there, but every single one of his teams between 2008 and 2013 put up at least 43 WAR, just trouncing any other manager in history.

Then we get to 2014. The highest payroll in team history. The highest expectations in team history. What should have been a tremendous team fell flat from the start and never picked itself up off the mat leading to the biggest disappointment in team history. It’s this mention of expectations that should act as a solid segue into what I want to focus on next. Each of these managers was handed various ingredients. It can’t really be argued that Maddon was the only one of our managers to be handed a team even capable of contending, but how do we go about quantifying that?

Probably the most basic projection system is something created by Tom Tango(tiger) called Marcel that attempts to use a player’s past performance, weighted to more heavily accentuate the more recent, to give an idea of how a player should perform. More advanced versions regress to league average in order to help control or offset small samples. It can get a lot more sophisticated, but the basic version gets you a good portion of the way there so let’s run with it.

For every player that has thrown a pitch or taken an at bat I have applied the following formula to reach an idea of what they should have put up in a given year:

= (((Year 1 WAR/Year 1 PA) * 1) + ((Year 2 WAR/Year 2 PA) * 3) + ((Year 3 WAR/Year 3 PA) * 5) + ((Year 4 WAR/Year 4 PA) * 7) +((Year 5 WAR/Year 5 PA) * 9)/25) * Year 6 PA

So we ultimately end up with something like this for each player for each year:

In this way we’re able to come up with an estimate of a player’s WAR/PA and then use his actual PA for our given year to get an idea of what he should have put up. The bigger the sample, the better the results we’ll see, and note that true rookies will be projected at 0.0 WAR. You can quibble with this, but I think if a manager is having an impact anywhere it’s with rookies getting their first taste of the Show. They’re the most in need of adjustment and should be the most receptive of correction. We’re not going for perfection here, though obviously more right is better than less right. How close did we get:

The dotted, 45 degree line shows what a perfect projection would look like. This basic Marcel shows around 34% of WAR being explained by xWAR. Not a super strong link between the two, but not nothing, either. You can see some outliers in there and the vast majority of players slotting between the origin and 2.0 in either direction. It’s cool if you don’t think this is a suitable projection, but I think it suits are needs and allows us to look back at years long ago and in a far away memory slot.

So enough bluster, what does each year look like for each manager:

We can immediately see just how much better the teams that Maddon managed SHOULD have been. They were incredibly stocked with guys that you should think would be better players. When Piniella took over in 2003 his pitching staff was actually projected to put up negative WAR, and the hitters weren’t a whole lot better. McRae faced the reverse issue in the year prior with some ok pitching and absolutely no hitting. The 2004 club had the highest expectations on the back of the best expected hitters in team history and a mediocre, but not abysmal staff and pen. Of course that was the best season in team history until 2008’s miracle run.

The Maddon-led teams showed mostly an ever-increasing accrual of talent over his entire tenure with 2011 the only year with a step down and that was entirely due to the expiring contracts on several established players and the offseason trades of a couple of others after 2010. The second highest payroll in team history was that 2010 club. The highest? This past season’s 2014 team that has already established will continue to be the highest in team history for at least one more season. Let’s put both sides together and look at this a couple of different ways:

The blue columns indicate actual accrued WAR minus expected WAR where positive means the team out-performed expectations and vice-versa. The red line shows WAR/xWAR – 1 in order to get an idea of percent increase (or decrease) over a season. Maddon’s 2008 team out-performed expectations by the biggest difference, but you’ll notice that over Maddon’s tenure, as his talent base grew better and more predictable, he wasn’t able to continue to squeeze out as much talent. He won a lot of games, but he SHOULD have won a lot of games based on expectations, and then we get to 2014. The only team that underperformed our modest projections as he gave away about 8% of their talent. Please keep in mind that I’m not assigning all the credit nor blame to these managers. My shorthand language is to phrase things in a certain manner, but I do not think anyone should think that the manager is the sole reason for these things. I think it’s interesting to compare these things across the years, however.

Piniella consistently wriggled decent actual WAR values out of the chicken shit that he was handed. Who knows what he would have done with the talent base that was set to increase over the next decade. Do you think he ever wondered what if? Well this is the first time that I’ve really thought about it and made me wonder what he would have done with the prodigious amount of talent that was set to walk through that door right after it slapped him in the ass. I’ve totaled these things up for those that prefer a table over a chart and to get an idea of what the totals look like:

To this point I’ve failed to mention one sticky wicket that takes the form of Mount RothsCrae. I glossed over it earlier, but Rothschild was fired 14 games into the season with Hal McRae assuming the reins for the other 148. As granular as thing thing already was I didn’t care to parse out player performance over this split season, particularly when the former manager received only around 9% of the games so I simply used this ratio to split out the numbers for each section:

Try not to let it ruin your day, but it does play a small part when we go to add up each managers totals:

The top portion of the table shows the total over each manager’s reign, while the bottom portion shows these things pro-rated for a 162 game season. Again, we come to praise not to bury, because what Joe Maddon just did over nearly a decade at the helm is something Rays fans are unlikely to ever see again, but part of that point is due to the incredible treasure trove of talent that fell into his lap. On a rate basis Maddon only saw about a 37% increase between his xWAR and WAR while his predecessors each showed a better rate. I think there has to be a notion that there will be diminishing returns once you get up in the Maddon stratosphere and the opposite when you see some of the abysmal talent handed to the men that came before.

I wouldn’t use this stuff as any sort of argument that Joe Maddon isn’t very good, but I think it does a good job of showing some stagnation over the years that despite fielding better teams the Rays were not seeing better results. Furthermore, 2014 looks like an out and out disaster. Again, it’s unfair to peg that all to Maddon, but when you see a team plateau and then fall apart I think it takes some of the shine off the apple. I will always appreciate what Joe Maddon did here transforming culture and nurturing talent. I’m happy to say that when my daughter is born in a few month her nickname can be Maddy and it won’t have anything to do with that lanky southpaw shoving it in the Series.

We’ll never have another like him, but it’s time to move on. Thanks for the memories, good luck, get paid while your value is through the roof, but maybe we can isolate someone that can squeeze a little more water from the rock to get us to that big ship in the sky.

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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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