Archive for Player Analysis

There seems to be quite a bit of disagreement in FanGraphs-land over what skills make for a good pinch-hitter. Some will argue that power is more important while others might say that on-base skills are more important. And while I know that it’s fashionable for the author to make a stance at the start of his article, I’m not going comply. I’m just going to unsexily dive face-first into Retrosheet.

How can we solve this problem? How do we know what skills are best for pinch-hitters? Well, we can examine the base-out states that pinch-hitters confront and then derive from those base-out states specific pinch-hitter linear weights. We will then compare pinch-hitter linear weights to league-average linear weights to see which skills retain value. Simple.

We’re also going to split the data by league, since pinch-hitting tendencies in the National League are likely going to be different than American League tendencies. I’m going to use the last five years of data, because whim. The table below, then, includes league-average linear weights followed by NL and AL pinch-hitter linear weights (aside: the run values of linear weights are from 1999-2002, per Tango’s work. This won’t make a real difference in the results, however, since we’re examining relative value of different base-out states and not overall run-value of different events).

Relative Linear Weights, 2009-2013

In the National League we can see that the value of home runs have increased slightly while walks have seen a corresponding decrease. This is because pinch-hitters often come to the plate when there are more outs than average. This sensibly decreases the value of walks and increases the importance of hurrying up and sending everyone around the bases already. This note comes with a caveat, however — the differences in linear weights are pretty small. It seems that managers in the National League are often forced to use the pinch-hitter to replace the pitcher, and therefore pinch-hitters are used in a lot of sub-optimal places.

The American league does not condone making everyone hit, however, and the impact upon pinch-hitting situations is pretty clear. The run value of home runs increases by .04 in pinch-hitting situations in the American League compared to the paltry .01 National League increase. In fact the run values of nearly all events increases — managers in the American League simply have more flexibility on when to use pinch-hitters and so they are able to deploy their pinch-hitters in base/out situations that are strategically favorable.

What does this all mean? Like everything, this simultaneously means quite a bit and not much at all. Home run value increases while walk value decreases during average pinch-hitter situations, but the change isn’t huge. If you’re a general manager looking for a bench bat and there’s a home-run guy available with a 90 wRC+ and a plate-discipline guy with a 95 wRC+, take the plate-discipline guy. What if they both have a 90 wRC+? Then take the home-run guy. The pinch-hitter linear weights here are more of a tie-breaker than a game-changer. Power is more important than walks when it comes to being a pinch-hitter, but being a good hitter is more important than power.

Roster construction is never that simple, though. Ideally a team will have both power and plate-discipline guys available on the bench and then the manager will be able to leverage both of their abilities based upon the base/out state (and also the score/inning situation, which is outside the scope of this article). Managers tend to be kind of strategic dunces, though, so I’m not sure if I see this happening. If I were in charge of anything I would supply my manager with a chart of base/out states that list the team’s best pinch-hitters in each situation. I’m not in charge, though, and even if I were I would probably be ignored.

I am in charge of this article, however, which means that I can bring it to a close. I’ll note that another valid way to do this study would be to create WPA-based weights rather than run-expectancy weights. There’s a lot more noise in WPA, but it could still create some interesting conclusions. I reckon the conclusion would be pretty much the same though — what makes a good pinch-hitter? Well, a good hitter makes for a good pinch-hitter. And a little power doesn’t hurt.

The signing of Shin-Soo Choo gives the Rangers a player with strong on-base skills, solid power, and decent corner-outfield defense. The signing also left a gaping hole in the outfield for the Reds. Choo was one of three Reds starters that got on base at an above-average clip. He was easily the first- or second-best offensive player for the Reds in 2013. While he was miscast in center field, Choo brought a great deal of value to a team that needed his particular offensive skill set.

Walt Jocketty has stated that Billy Hamilton is the new center fielder and will likely bat leadoff for the 2014 Reds. Hamilton starting in center field should come as no surprise as the Reds do not have many other options. The wisdom of Hamilton batting leadoff is at least up for debate. You can easily go look at his projections for 2014 and draw your own conclusions, but I would like to at least provide some context.

Every baseball fan knows about Hamilton’s speed. He is ferociously fast. He stole 155 bases in the minors in 2012 and successfully stole 13 bases in 14 attempts in limited major league action in 2013. Speed is nice , but it is certainly not close to the most important skill for a player in the leadoff spot. Reds fans may know this best of all from watching Corey Patterson, Willy Taveras, and Drew Stubbs flounder at the plate. Those players were wickedly fast, but as the saying goes, you can’t steal first base. None of them had the on-base skills to bat leadoff, but they found themselves there anyway because of their speed. To avoid this list of failed Reds leadoff hitters, Billy Hamilton will need to get on base enough to justify being at the top of the order. That is the obvious question: can Hamilton get on base to use that blinding speed of his to turn singles into doubles and doubles into triples? There are signs that he can but others that he shouldn’t in 2014.

The 2012 season launched Hamilton into top-20 prospect territory. He obviously broke the stolen-base record, but he also showed some ability with the bat. In a 132 games between high A and AA, Hamilton hit .311/.410/.420. He had 14 triples. His walk rate rose dramatically from the year before. Hamilton looked like a perfect leadoff hitter through two levels.

Then 2013 and AAA came. Hamilton slashed .256/.308/.343. His walk percentage dropped from 16.9% in 50 games in AA (small sample size noted) to 6.9% in 123 games in AAA. it was arguably his worst season as a professional. He looked completely overmatched at times and questions about his ability to get on base resurfaced.

So which is the real Billy Hamilton, and what does it mean for 2014? Hamilton’s ceiling is likely between his 2012 and 2013 minor league performance. In five seasons as a minor leaguer, Hamilton slashed .280/.350/.378. Coupled with his speed and potential excellent defense in center field, that slash line could make him an All-Star-caliber player. The hope is that 2013 was a product of learning a new position and a significant drop in BABIP from over .370 to .310.

Still, Hamilton was very inconsistent at the plate in 2013 and didn’t prove he could hit AAA pitching for an extended period of time. The major leagues are an obvious step up in competition, and it would be surprising to see him match his .280/.350/.378 minor league career slash line in 2014. Steamer projects him to have a .305 OBP, and after last year, it is easy to see why.

While it is very possible Hamilton could surpass gloomy projections, the Reds probably shouldn’t risk it in 2014, at least at first. It makes much more sense to see how Hamilton adjusts to major-league pitching in a less important part of the lineup (7th for instance). He would get fewer at bats and would not be so heavily scrutinized if he struggled adjusting to the level. If he performs well, he can always move up in the lineup, but the Reds likely have better leadoff options than Hamilton to begin the year.

If Hamilton plays excellent defense in center field and has a good year on the bases, he will provide solid value for the Reds. To fill Choo’s shoes, he will have to hit closer to his career minor league mark as opposed to his 2013 numbers. In 2014, that may be difficult.

There are so many problems with ERA that it’s unbelievable. I’m not going to sit here and tell you what’s wrong with ERA, though, because you’re probably smart. But there’s a problem with ERA, and it’s a problem that transcends ERA. It’s a problem that trickles down through FIP, xFIP, SIERA, TIPS, etc. etc. name your favorite stat, etc., and it’s something I don’t see talked about much.

All of our advanced pitcher metrics are trying to predict or estimate ERA. They’re trying to figure out what a pitcher’s ERA should be, and herein lies the problem: Because they could be exactly right, but they’d still be a little incorrect due to one little assumption.

This assumption–that pitchers have no control over whether or not the fielders behind them make errors–seems easy to make. Like most assumptions, however, this one is subtly incorrect. Thankfully, the reason is pretty simple. Ground balls are pretty hard to field without making an error, and fly balls aren’t. And the difficulty gap is pretty huge.

How big? Well in 2013 there were precisely 58,388 ground balls, 1,344 of which resulted in errors. On the other hand a mere 98 out of 39,328 fly balls resulted in errors. That means that 2.3% of ground balls result in errors while a tiny 0.25% of fly balls do. It’s time to stop pretending that this gap doesn’t exist, because it does.

So now that we know this, what does it mean? Well it means this: ground-ball pitchers will have an ERA that suggests they are better than their actual value, while fly-ball pitchers have the opposite effect. Pitchers who allow contact, additionally, are worse off because every time they allow contact they put pressure on their defense. They’re giving themselves a chance to stockpile unearned runs which nobody will count against them if they’re only looking at ERA derivatives. When it comes to winning baseball games, however, earned runs don’t matter. Runs matter.

I am going to call this the “pressure on the defense” effect, which will cause some pitchers to be more prone to unearned runs than other pitchers. How big is this effect? Well, not huge. The gap between the best pitcher and worst pitcher in the league is roughly three runs over the course of the season. But keep in mind that three runs is about a third of a win, and a third of win is worth about $2 million dollars. We’re not discussing mere minutiae here.

In order to better quantify this effect I have developed the xUR/180 metric, which will estimate how many unearned runs should have taken place behind each pitcher with an average defense. Below is a table of all qualified starting pitchers from 2013 ranked according this metric. I have also included how many unearned runs they actually allowed in 2013, scaled to 180 innings for comparative purposes.

Some notes:

• Groundballs are still good, they’re just not as good.
• A combination of groundballs and contact lead to more unearned runs. The pitchers at the top of the board demonstrate this.
• A combination of strikeouts and fly balls will tend to limit the impact of unearned runs, as demonstrated by the bottom of the board.
• Errors that occur on fly balls tend to be more costly than errors on ground balls. This metric accounts for that gap, but the low likelihood of fly-ball errors make this bullet point’s effect relatively negligible.
• Line drives are similar to fly ball in terms of error rate, but they tend to be less costly than fly ball errors.

I’m sure there is more to be gleaned, but the point is this: we need to stop trying to predict ERA, because ERA is not a pure value stat. We should be trying to figure out how many runs a pitcher should/should have given up, because that’s what matters. Runs matter, and who cares if they’re unearned? They’re kind of the pitcher’s fault, anyways.

Happy holidays, all.  It took me a while, but I finally have the second installment of xHitting ready.  First off, thank you to all those who read/commented on the first piece.  For those who didn’t get a chance to read it, the goal here is to devise luck-neutralized versions of popular hitter stats, like OPS or wOBA.  A main extension over existing xBABIP calculators is that this approach offers an empirical basis to recover slugging and ISO, by estimating each individual hit type.

I’ve returned today with an improved version of the model.  Highlights:

• One more year of data (now 2010-2013)
• Now includes batted-ball direction (all player-seasons with at least 100 PA)
• FB distance now recorded for all player-seasons with at least 100 PA

(There’s no theoretical reason for the 100 PA cutoff, only that I was grabbing some of the new data by hand and couldn’t justify the time to fetch literally every single player.)

I have also relaxed the uniformity of peripherals used for each outcome.  At least one reader asked for this, and after thinking about it a while, I decided I agree more than I disagree.  The main advantage of imposing uniformity was that it ensures the predicted rates (when an outs model is also included) sum to 100%.  But it is true that there are certain interactions or non-linearities that are important for some outcomes, but not others.  Including these where they don’t fully belong has a cost to standard errors/precision, and to intuitive interpretation.  To ensure rates still sum to 100%, there’s no longer an explicit ‘outs’ model; outs are simply assumed to be the remainder.

For those curious, below I display regression results for each outcome and its respective peripherals.  You can otherwise skip below if these are not of direct interest.

(The sample includes all player-years with at least 100 plate appearances between the 2010 and 2013 MLB seasons.  Park factors denote outcome-specific park factors available on FanGraphs.  Robust standard errors, clustered by player, are in parentheses; *** p$<$0.01, ** p$<$0.05, * p$<$0.1)

The new variables seem to help, as each outcome is now modeled more accurately than before (by either R2 or RMSE).  For comparison, here are the R2’s of the original specification:

• 0.367 for singles rate
• 0.236 for doubles rate
• 0.511 for triples rate
• 0.631 for HR rate

Something else I noticed: for balls that stay “inside the fence,” both pull/opp and actual side of the field matter.  Consider singles: the ball needs to be thrown to 1st base (right side of infield) specifically.  Thus an otherwise-equivalent ball hit to the left side is not the same as one hit to the right side, since the defensive play is harder to make from the left side.  Similarly, hitting the ball to left field is less conducive for triples than hitting the ball to right field.

But hitting the ball to the left side as a lefty is not the same as hitting it there as a righty, since one group is “pulling” while the other group is “slapping.”  The direction x handedness interactions help account for this.

How well do the predicted rates do in forecasting?  For singles, doubles, and triples, the predicted rates do unambiguously better than realized rates in forecasting next season’s rates.  Things are a little less clear for home runs, which I will expand on below.

Although predicted HR rate shows a slight edge in Table 1, the pattern often reverses (for HR only) if you use a different sample restriction — say requiring 300 PA in the preceding season.  (For other outcomes, the qualitative pattern from Table 1 still holds even under alternative sample restrictions.)

So home runs appear to be a potential problem area.  What should we do when we need HR to compute xAVG/xSLG/xOPS/xWOBA, etc.?  Should we:

1. Use predicted HR anyway?
2. Use actual HR instead?
3. Use some combo of actual and predicted HR?

Empirically there is a clear answer for which choice is best.  But before getting to that, let’s take a look at whether predicted home-run rate tells us anything at all in terms of regression.  That is, if you’ve been hitting HR’s above/below your “expected” rate, do you tend to regress toward the prediction?

The answer to this seems to be “yes,” evidenced by the negative coefficient on ‘lagged rate residual’ below.

So, although realized HR rate is sometimes a better standalone forecaster of future home runs, predicted HR rate is still highly useful in predicting regression.  Making use of both, it seems intuitively best to use some combo of actual and predicted HR rate for forecasting.

This does, in fact, seem to be the best option empirically.  And this is true whether your end outcome of interest is AVG, OBP, SLG, ISO, OPS, or wOBA.

Observations:

• (Option 1 = predicted HR only; Option 2 = actual HR only; Option 3 = combo)
• Whether you use option 1, 2, or 3, xAVG and xOBP make better forecasters than actual past AVG or OBP
• Option 1 does not do well for SLG, ISO , OPS, or wOBA
• ^This was not the case in the previous article, but results to that point had sort of a funky sample, having recorded flyball distance only for a partial list of players
• Option 2 “saves” things for xOPS and xWOBA, but still isn’t best for SLG or ISO
• Option 3 makes the predicted version better for any of AVG, OBP, SLG, ISO, OPS, or wOBA

End takeaways:

• The original premise that you can use “expected hitting,” estimated from peripherals, to remove luck effects and better predict future performance seems to be true; but you might need to make a slight HR adjustment.
• The main reason I estimate each hit type individually is for the flexibility it offers in subsequent computations.  Whether you want xAVG, xOPS, xWOBA, etc., you have the component pieces that you need.  This would not be true if I estimated just a single xWOBA, and other users prefer xOPS or xISO.
• A major extension over existing xBABIP methods is that this offers an empirical basis to recover xSLG.  The previous piece actually provides more commentary on this.
• Natural next steps are to test partial-season performance, and also whether projection systems like ZiPS can make use of the estimated luck residuals to become more accurate.

Finally, I promised to list the leading over- and underachievers for the 2013 season.  By xWOBA, they are as follows:

Comments/suggestions?

We have come a long way in evaluating players and yet, perception still clouds our judgment. Perception awarded Derek Jeter several Gold Gloves during years where he was a poor defensive player. Perception will likely award Nelson Cruz a hefty contract this winter. While there is no way to know for sure, I fear that perception may have played a role in the biggest trade so far this offseason: the well-documented Mark Trumbo trade.

Plenty of writers have covered why this trade looks like a poor move for the Diamondbacks so I won’t dive deeply into that. I desire to understand how Trumbo could be valued so highly (assuming the Diamondbacks feel they gave up quality for quality). Dave Cameron wrote an interesting article about how Trumbo was both overrated and underrated. He stated that Trumbo’s one great skill, breathtaking power, is a frequently overvalued skill. Kevin Towers seems to be one of those who overvalues power and made the trade based on that one skill.  But is Trumbo’s power the only reason that a team might overvalue him? With this in mind, I decided to find a comparable player and at least speculate to the perception differences that may cause a team to overvalue someone like Trumbo.

That player is Pedro Alvarez. The similarities are actually quite amazing. The following table contains combined information from the 2012 and 2013 seasons, the two years that Trumbo and Alvarez were both full-time players.

Holy smokes! Every time I look at these numbers, I am shocked at how similar these two players were over a two-year span. Trumbo is one year older and right-handed, but that’s where the differences end. Neither gets on base much or is a great defender, but Alvarez wasn’t terrible at third in 2013. They both derive their value almost entirely from their power and strike out way too much. They are the right-handed and left-handed versions of each other from an offensive standpoint.

I’ll admit that if someone had forced me to pick between the two players before doing the research, I may have gone with Trumbo. Why does Trumbo seem to get more attention than Alvarez?  Well, the markets are obviously different. Los Angeles draws a lot more attention than the finally revived corpse that is Pittsburgh baseball. What else does Trumbo have that Alvarez doesn’t? Trumbo has one giant first half in 2012 where he flashed skills he probably doesn’t have.

Pedro Alvarez’s best half of baseball was probably the first half of 2013. Alvarez hit .250/.311/.516 with 24 home runs. That is an impressive stat line, but it doesn’t show any growth in other skills outside of Alvarez’s impressive power. He didn’t get on base much more than other stretches of his career, and his average remained similar to his 2012 line of .244. He has never given anyone any reason to believe he is more than a one-trick pony.

During the first half of 2012, Trumbo hit .306/.358/.608 with 22 home runs. He was an All-Star, and some people thought he had taken a big leap forward. It was the kind of first half that can change perceptions, even though it was a small sample size. The second half proved unkind. Trumbo hit .227/.271/.359 with 10 home runs. But what a first half!

I have no idea whether Towers put any stock into Trumbo’s first half in 2012. Probably not. But it isn’t hard to see how teams could talk themselves into thinking that Trumbo has untapped potential based on that half. Regardless, the perception of Mark Trumbo as an above-average player likely comes from his undeniable power and one monster half of baseball that he has never come close to duplicating. It makes me wonder whether Towers would have given up two young players with potential for Alvarez if he had been available. Considering Alvarez is another “100-plus RBI, 30 home run guy”, he may have. But then again, he may secretly be banking on Trumbo as a real impact bat that produces in more ways than one. While there is no definitive answer to that, this comparison is another precautionary tale to overvaluing short sample sizes.

It’s something we hear all the time: “He’s a scrappy player” or “He’s always trying hard out there, I love his scrappiness.” Maybe chicks don’t dig the long ball anymore; maybe they’re into scrappiness. I’m not really in a position to accurately comment on what chicks dig though, so I don’t know.

Even from a guy’s perspective, scrappiness is great. It’s hard to hate guys that overcome their slim frames by just out-efforting everyone else and getting to the big leagues. It’s not easy to quantify scrappiness, though. Through the years it’s always been a quality that you know when you see, but there’s never been a number to back it up. Until now.

Scrap is a metric that is scaled on a similar scale to Spd, where 5 is average and anything above that is above average, and anything below 5 is below average. Here are the components that make it up (each component is factored onto a Spd-like scale, assigned a weight, and then combined with all of the other components to give a final number).

• Infield hit% — Higher is better.
• .ISO — Less power means more scrappiness.
• Spd –The ability to change a game with legs.
• balls in play% — (PA-BB-K)/PA — Go up there looking to fight.
• zSwing%. — Higher is better. Measures willingness to defend the zone.
• oSwing%. — Lower is better. These guys can’t hit the low and away pitch to deep center.
• zContact%. — Higher is better. These guys swing for contact.

Without further ado, here are the Scrap rankings of all qualified batters in 2013.

That’s quite a bit to look at. Here are a few of my takeaways:

• The general perception of a player’s scrappiness is pretty close to what this metric spits out.
• There are some surprises, such as Tulo being near the bottom. In his case it’s caused by an extremely low speed rating and a low z-swing%.
• Little dudes that run hard tend to be scrappy (duh).
• Big oafy power guys tend not to be scrappy (duh).
• Upon removing the qualified batter restriction the ‘Scrap’ leader is Hernan Perez. Tony Campana is a close second. I think we can all agree that Campana is more or less the definition of scrappiness.

This isn’t a stat that’s going to forever change how we view baseball. But this does give us a way of quantifying, however imperfectly, a skillset that we haven’t been able to before. Now we not only know that Jose Altuve is scrappy, we know just how scrappy he is. I’ll let you decide how important that is.

If you have any suggestions regarding different ways to calculate Scrap let me know in the comments. It’s a metric that requires a good amount of arbitrary significance since, well, what does it even mean to be scrappy? We’ve always had an idea, and now we have a number.

The idea for this metric was spurned on by Dan Syzmborksi on this episode of the CACast podcast, somewhere around the 75-minute mark.

In the 2014 Hardball Times Baseball Annual, Jeff Moore analyzes six teams undergoing some form of “rebuilding.” He correctly notes that the concept has become a platitude in sports media, but that it still has explanatory value. In order to highlight the utility of “rebuilding,” he parses the concept to represent different forms of practice implemented by a variety of organizations. Moore covers the “ignorance” of the Philadelphia Phillies who continue on as if their core of players wasn’t aging and Ryan Howard was ever a reliable contributor; the “recognition” of the New York Mets that they have to be patient for one or two more years before the pieces come together and, they hope, work as well as Matt Harvey’s new elbow should; the “overhauling” of the Houston Astros evident in their fecund farm system and arid big league squad; the “perpetual” rebuilding of the Miami Marlins in a different key from anyone else, most recently using the public extortion and fire sale method; the Kansas City Royals’ “deviation” by trading long-term potential for a short-term possibility; and the “competition” exemplified by the 2013 Pittsburgh Pirates as they seemingly put everything together in 2013, though it remains to be seen whether or not they will need to rebuild again sooner rather than later.

Although the Colorado Rockies are not on Moore’s radar, I think they fall into an altogether different category. They appear to be in a confoundingly stagnant state of non-rebuilding. The mode of rebuilding can be as stigmatizing as it is clichéd, and it is as if the Rockies are avoiding the appellation at the cost of the foresight it might bring. Or, I don’t know what the hell is going on, and I’m not convinced there is a clear plan.

That might sound unfair. But if we, like Moore, take the definition of rebuilding to essentially mean identifying a future window of opportunity and working towards fielding a competitive team to maximize that opportunity, but with the acceptance of present limitations, then I don’t think I’m far off. General Manager Dan O’Dowd is, inexplicably, the fourth-longest tenured general manager in all of baseball, despite overseeing just four winning clubs in 14 full seasons. The only GMs who have held their current job longer are the dissimilarly successful Brian Sabean of the San Francisco Giants, Brian Cashman of the New York Yankees, and Billy Beane of the Oakland Athletics. The possible moves that have been rumored suggest that Dan O’Dowd and de facto co-GM Bill Geivett are frozen by anything more than a one-year plan.

Let’s look at some of the possible moves that are garnering notice. Beat writer Troy Renck reports that the Rockies are eying first baseman Justin Morneau to replace the retired Todd Helton. Of all of the speculative deals, this one is most likely to happen. But what would this accomplish in the short and long-term? In the short term, it would provide a replacement for Todd Helton and possibly provide a bridge for either Wilin Rosario or prospect Kyle Parker to take over full-time at first. The long-term effects are not as easy to identify, as his contract probably wouldn’t exceed two years.

It might sound just fine, until you realize that Morneau would be a “replacement” in more than one sense. Per FanGraphs’ Wins Above Replacement (WAR), Morneau hasn’t accrued an average major-league season since the half-season he played in 2010. Hayden Kane over at Rox Pile notes that he slashed .345/.437/.618 before a concussion ended his 2010 season and most of the next, but those numbers were inflated by a .385 Batting Average on Balls in Play (BABIP), over .100 points higher than his career average. He was still well on his way to a successful season, but the effects the concussion had on his productivity cannot be overstated. Morneau accrued 4.9 war in the 81 games he played in 2010, and 0.4 since. Optimistically, if Morneau out-produces his projected line next year (.258/.330/.426, per Steamer projections), which he likely would do playing half of his games in Coors Field (except against lefties, who he can’t hit), he would at best be a league-average hitter to go along with his average defense. Sure, it would be an improvement from the lackluster production from first base in 2013, but not enough to build beyond current listlessness.

Fundamentally, I believe that the Rockies do need a bridge before easing Rosario into a defensive position where he is less of a liability or seeing what the team has in Parker. But they already have the link in Michael Cuddyer. While he’s unlikely to reproduce the career year he had in his age 34 season in 2013, having Cuddyer play out his contract sharing time at first seems to be the better allocation of resources in the short-term. In January of 2013, Paul Swydan characterized the Rockies as an organization on a “quest for mediocrity.” Signing Morneau would go a long way toward realizing that goal.

In addition to possible additions via free agency, trade rumors are aren’t helping to clarify where the team is. It has been rumored that the Rockies are interested in trading for Anaheim’s Mark Trumbo, which would also fill the hole at first base that I don’t think actually exists yet. Trumbo, a power hitter, is misleadingly tantalizing. As opposed to Morneau, Trumbo is at least on the right side of 30; similarly though, Trumbo doesn’t get on base enough to provide the offense the boost it needs, especially on the road. He’d be a virtual lock to hit 30+ home runs, but he would also be sure to have an OBP hovering around .300. It’s unclear who would be involved in such a deal, as the Angels wouldn’t be interested in the Rockies’ primary trading piece, Dexter Fowler.

Speaking of Fowler, he’s going to be traded. In an interview with Dave Krieger, O’Dowd said that the organization has given up on him. Not in those words of course—rather, he noted that Fowler lacks “edge,” which is a bullshit baseball “intangible” that doesn’t tell us anything about the player in question, but rather that the front office seeks amorphous traits that can only be identified retrospectively. Reports have the Rockies in talks with Kansas City that would result in the teams swapping Fowler for a couple of relievers, likely two of Aaron Crow, Tim Collins, and Wade Davis. This, too, would maintain organizational stagnation.

The Rockies are practicing a confounding type of non-rebuilding, wherein veterans are brought in not with the idea that they can be valuable role players (like Shane Victorino, Mike Napoli, and Stephen Drew were for the Boston Red Sox last off-season), but as immediate solutions to problems that should be viewed in the long-term. I’m not as pessimistic as I might sound. The Rockies finished in last place for the second straight season in 2013, but with just two fewer wins than the Padres as Giants, and a true-talent level of about a .500 team. The thing about teams with a win projection of about 80 is that they can reasonably be expected to finish with as much as 90 wins—and as few as 70. If the Rockies are competitive in 2014, it will likely be due to health and a lot of wins in close games. I do, however, think they can be competitive starting in 2015. That’s the rebuilding window of opportunity the team should be looking at. If they are, it won’t be because of who is playing first base or right field, or even an improvement in hitting on the road, but progress in the true source of their problems: run prevention.

Last year, only the Twins and the lowly Astros allowed more runs per game. Despite this, for the first time in a while Rockies’ fans can be optimistic about the engine of run prevention, quality starting pitching. This is an area where the team can build a clear agenda for the future. Tyler Chatwood and Jhoulys Chacin should be reliable starters for the next few years. It’s unclear how many good years Jorge de la Rosa has left in him, and it’s also unclear whether or not Juan Nicasio can be a legitimate starter. But the Rockies have two polished, nearly big-league-ready pitching prospects in Jonathan Gray and Eddie Butler—Rockies’ fans should be really excited about these two—so long as one of them is not one of the “young arms” rumored to be in play for Trumbo. If Gray and Butler can be shepherded to the big leagues in a timely manner and learn to pitch to major leaguers quickly, they could join Chatwood and Chacin for possibly the best rotations in Rockies history. And if the front office really wants to make a big free-agent splash, the answers aren’t in the Brian McCanns or Jose Abreus of the world, but in splitter-throwing, ground-ball inducing, 25-year-old starting pitcher Masahiro Tanaka. His presence would likely push a rotation in 2015-2016 and possibly beyond from dependable to exceptional. Of course, it won’t happen. The Rockies, if they bid, will be outbid, and it’s precisely starting pitchers in demand that tend to stay away from Colorado.

In a sense, every major-league team is always in some stage of rebuilding, whether they admit it or not. My point is that I think there can be power in the admission of it. De-stigmatizing the “rebuilding process” might contribute to the recognition that it’s not necessarily a multiyear process, and that being in the process is not an acknowledgement of failure. Recognition of this, which by itself should provide more foresight, should lead the organization and armchair observers like myself from a state of confusion due to the team’s pursuit of stagnation, to one of encouragement where progress can be visualized.

My article on weighting a hitter’s past results was supposed to be a one-off study, but after reading a recent article by Dave Cameron I decided to expand the study to cover starting pitchers. The relevant inspirational section of Dave’s article is copied below:

“The truth of nearly every pitcher’s performance lies somewhere in between his FIP-based WAR and his RA9-based WAR. The trick is that it’s not so easy to know exactly where on the spectrum that point lies, and its not the same point for every pitcher.”

Dave’s work is consistently great. This, however, is a rather hand-wavy explanation of things. Is there a way that we can figure out where pitchers have typically laid on this scale in the past  so that we can make more educated guesses about what a pitcher’s true skill level is? We have the data–so we can try.

So, how much weight should be placed on ERA and FIP respectively?  Like Dave said, the answer will be different in every case, but we can establish some solid starting points. Also since we’re trying to predict pitching results and not just historical value we’re going to factor in the very helpful xFIP and SIERA metrics.

Now for the methodology paragraph: In order to test this I’m going to use every pitcher season since 2002 (when FanGraphs starts recording xFIP/SIERA data) where a pitcher had at least 100 innings pitched, and then weight all of the relevant metrics for that season in order to create an ERA prediction for the following season. I’ll then look at the difference between the following season’s predicted and average ERA, and then calculate the average miss. The smaller the average miss, the better the weights. Simple. As an added note, I have weighted the importance of a pitcher’s second (predicted – actual) season by innings pitched so that a pitcher who pitched 160 innings in his second (predicted – actual) season will assume more merit than the pitcher who pitched only 40 innings.

How predictive are each of the relevant stats without weights? I am nothing without my tables, so here we go (There are going to be a lot of tables along the way to our answers. If you’re just interested in the final results, go ahead and skip on down towards the bottom).

This doesn’t really tell us anything we don’t already know: SIERA and xFIP are similar, and FIP is a better predictor than ERA. Let’s start applying some weights to see if we can increase accuracy, starting with ERA/SIERA combos.

We can already see that factoring in ERA just a slight amount improves our results substantially. When you’re predicting a pitcher’s future, therefore, you can’t just fully rely on xFIP or SIERA to be your fortune teller. You can’t lean on ERA too hard either, though, since once you start getting up over around 25% your projections begin to go awry. Ok, so we know how SIERA and ERA combine, but what if we use xFIP instead?

Using xFIP didn’t really improve our results at all. SIERA consistently outperforms xFIP (or is at worst only marginally beaten by it) throughout pretty much all weighting combinations, and so from this point forward we’re just going to use SIERA. Just know that SIERA is basically xFIP, and that there are only slight differences between them because SIERA makes some (intelligent) assumptions about pitching. Now that we’ve established that, let’s try throwing out ERA and use FIP instead.

It’s interesting that ERA/SIERA combos are more predictive than FIP/SIERA combos, even though FIP is more predictive in and of itself. This is likely due to the fact that a lot of pitchers have consistent team factors that show up in ERA but are cancelled out by FIP. We’ll explore that more later, but for now we’re going to try to see if we can use any ERA/FIP/SIERA combos that will give us better results.

There are three values here that are all pretty good. The important thing to note is that ERA/FIP/SIERA combos offer more consistently good results than any two stats alone. SIERA should be your main consideration, but ERA and FIP should not be discarded since the combo offers a roughly .05 better predictive value towards ERA than SIERA alone. It’s a small difference, but it’s there.

Now I’m going to go back to something that I mentioned previously–should a player be evaluated differently if he isn’t coming back to the same team? The answer to this is a pretty obvious yes, since a pitcher’s defense/park/source of coffee in the morning will change. Let’s narrow down our sample to only pitchers that changed teams, to see if different numbers work better. These numbers will be useful when evaluating free agents, for example.

As suspected ERA loses a lot of it’s usefulness when a player is switching teams, and FIP retains its marginal usefulness while SIERA carries more weight. Another thing to note is that it’s just straight-up harder to predict pitcher performance when a pitcher is changing teams no matter what metric you use. SIERA itself goes down in accuracy to .793 when only dealing with pitchers that change teams, a noticeable difference from the .760 value above for all pitchers.

For those of you who have made it this far, it’s time to join back in with those who have skipped down towards to bottom. Here’s a handy little chart that shows previously found optimal weights for evaluating pitchers:

Optimal Weights

Of course, any reasonable projection should take more than just one year of data into account. The point of this article was not to show a complete projection system, but more to explore how much weight to give to each of the different metrics we have available to us when evaluating pitchers. Regardless, I’m going to expand the study a little bit to give us a better idea of weighting years by establishing weights over a two-year period. I’m not going to show my work here mostly out of an honest effort to spare you from having to dissect more tables, so here are the optimal two year weights:

As expected using multiple years increases our accuracy (by roughly .15 ERA per pitcher). Also note that these numbers are for evaluating all pitchers, and so if you’re dealing with a pitcher who is changing teams you should tweak ERA down while uptweaking FIP and SIERA. And, again, as Dave stated each pitcher is a case study–each pitcher warrants their own more specific analysis. But be careful when you’re changing weights. When doing so make sure that you have a really solid reason for your tweaks and also make sure that you’re not tweaking the numbers too much, because when you begin to start thinking that you’re significantly smarter than historical tendencies you can start getting in trouble. So these are your starting values–carefully tweak from here. Go forth, smart readers.

As a parting gift to this article, here’s a list of the top 20 predictions for pitchers using the two-year model described above. Note that this will inherently exclude one-year pitchers such as Jose Fernandez and pitchers that failed to meet the 100IP as a starter requirement in either of the past two years. Also note that these numbers do not include any aging curves (aging curves are well outside the scope of this article), which will obviously need to be factored in to any finalized projection system.

It is widely talked about by announcers and baseball fans alike, that knuckleball pitchers can throw hitters off their game and leave them in funks for days. Some managers even sit certain players to avoid this effect. I decided to analyze to determine if there really is an effect and what its value is. R.A. Dickey is the main knuckleballer in the game today, and he is a special breed with the extra velocity he has.

Most people that try to analyze this Dickey effect tend to group all the pitchers that follow in to one grouping with one ERA and compare to the total ERA of the bullpen or rotation. This is a simplistic and non-descriptive way of analyzing the effect and does not look at the how often the pitchers are pitching not after Dickey.

I decided to determine if there truly is an effect on pitchers’ statistics (ERA, WHIP, K%, BB%, HR%, and FIP) who follow Dickey in relief and the starters of the next game against the same team. I went through every game that Dickey has pitched and recorded the stats (IP, TBF, H, ER, BB, K) of each reliever individually and the stats of the next starting pitcher, if the next game was against the same team. I did this for each season. I then took the pitchers’ stats for the whole year and subtracted their stats from their following Dickey stats to have their stats when they did not follow Dickey. I summed the stats for following Dickey and weighted each pitcher based on the batters he faced over the total batters faced after Dickey. I then calculated the rate stats from the total. This weight was then applied to the not after Dickey stats. So for example if Janssen faced 19.11% of batters after Dickey, it was adjusted so that he also faced 19.11% of the batters not after Dickey. This gives an effective way of comparing the statistics and an accurate relationship can be determined. The not after Dickey stats were then summed and the rate stats were calculated as well. The two rate stats after Dickey and not after Dickey were compared using this formula (afterDickeySTAT-notafterDickeySTAT)/notafterDickeySTAT. This tells me how much better or worse relievers or starters did when following Dickey in the form of a percentage.

I then added the stats after Dickey for starters and relievers from all four years and the stats not after Dickey and I applied the same technique of weighting the sample so that if Niese’12 faced 10.9% of all starter batters faced following a Dickey start against the same team, it was adjusted so that he faced 10.9% of the batters faced by starters not after Dickey (only the starters that pitched after Dickey that season). The same technique was used from the year to year technique and a total % for each stat was calculated.

The most important stat to look at is FIP. This gives a more accurate value of the effect. Also make note of the BABIP and ERA, and you can decide for yourself if the BABIP is just luck, or actually better/worse contact. Normally I would regress the results based on BABIP and HR/FB, but FIP does not include BABIP and I do not have the fly ball numbers.

The size of the sample was also included, aD means after Dickey and naD is not after Dickey. Here are the results for starters following Dickey against the same team.

It can be concluded that starters after Dickey see an improvement across the board. Like I said, it is probably better to use FIP rather than ERA. Starters see an approximate 18.9% decrease in their FIP when they follow Dickey over the past 4 years. So assuming 130 IP are pitched after Dickey by a league average set of pitchers (~4.00 FIP), this would decrease their FIP to around 3.25. 130 IP was selected assuming ⅔ of starter innings (200) against the same team. Over 130 IP this would be a 10.8 run difference or around 1.1 WAR! This is amazingly significant and appears to be coming mainly from a reduction in HR%. If we regress the HR% down to -10% (seems more than fair), this would reduce the FIP reduction down to around 7%. A 7% reduction would reduce a 4.00 FIP down to 3.72, and save 4.0 runs or 0.4 WAR.

Here are the numbers for relievers following Dickey in the same game.

Relievers see a more consistent improvement in the FIP components (K, BB, HR) between each other (11.4, 8.1, 4.9). FIP was reduced 10.3%. Assuming 65 IP (in between 2012 and 2013) innings after Dickey of an average bullpen (or slightly above average, since Dickey will likely have setup men and closers after him) with a 3.75 FIP, FIP would get reduced to 3.36 and save 3 runs or 0.3 WAR.

Combining the un-regressed results, by having pitchers pitch after him, Dickey would contribute around 1.4 WAR over a full season. If you assume the effect is just 10% reduction in FIP for both groups, this number comes down to around 0.9 WAR, which is not crazy to think at all based off the results. I can say with great confidence, that if Dickey pitches over 200 innings again next year, he will contribute above 1.0 WAR just from baffling hitters for the next guys. If we take the un-regressed 1.4 WAR and add it to his 2013 WAR (2.0) we get 3.4 WAR, if we add in his defence (7 DRS), we get 4.1 WAR. Even though we all were disappointed with Dickey’s season, with the effect he provides and his defence, he is still all-star calibre.

Just for fun, lets apply this to his 2012. He had 4.5 WAR in 2012, add on the 1.4 and his 6 DRS we get 6.5 WAR, wow! Using his RA9 WAR (6.2) instead (commonly used for knucklers instead of fWAR) we get 7.6 WAR! That’s Miguel Cabrera value! We can’t include his DRS when using RA9 WAR though, as it should already be incorporated.

This effect may even be applied further, relievers may (and likely do) get a boost the following day as well as starters. Assuming it is the same boost, that’s around another 2.5 runs or 0.25 WAR. Maybe the second day after Dickey also sees a boost? (A lot smaller sample size since Dickey would have to pitch first game of series). We could assume the effect is cut in half the next day, and that’d still be another 2 runs (90 IP of starters and relievers). So under these assumptions, Dickey could effectively have a 1.8 WAR after effect over a full season! This WAR is not easy to place, however, and cannot just be added onto the teams WAR, it is hidden among all the other pitchers’ WARs (just like catcher framing).

You may be disappointed with Dickey’s 2013, but he is still well worth his money. He is projected for 2.8 WAR next year by Steamer, and adding on the 1.4 WAR Dickey Effect and his defence, he could be projected to really have a true underlying value of almost 5 WAR. That is well worth the $12.5M he will earn in 2014.

For more of my articles, head over to Breaking Blue where we give a sabermetric view on the Blue Jays, and MLB. Follow on twitter @BreakingBlueMLB and follow me directly @CCBreakingBlue.

We all know by now that we should look at more than one year of player data when we evaluate players. Looking at the past three years is the most common way to do this, and it makes sense why: three years is a reasonable time frame to try and increase your sample size while not reaching back so far that you’re evaluating an essentially different player.

 The advice for looking at previous years of player data, however, usually comes with a caveat. “Weigh them”, they’ll say. And then you’ll hear some semi-arbitrary numbers such as “20%, 30%, 50%”, or something in that range. Well, buckle up, because we’re about to get a little less arbitrary.

 Some limitations: The point of this study isn’t to replace projection systems—we’re not trying to project declines/improvements here. We’re simply trying to understand how past data tends to translate into future data.

 The methodology is pretty simple. We’re going to take three years of player data (I’m going to use wRC+ since it’s league-adjusted etc., and I’m only trying to measure offensive production), and then weight the years so that we can get an expected 4^th year wRC+. We’re then going to compare our expected wRC+ against the actual wRC+*. The closer the expected to our actual, the better the weights.

 *Note: I am using four-year spans of player data from 2008-2013, and limiting to players that had at least 400 PA in four consecutive years. This should help throw out outliers and to give more consistent results. Our initial sample size is 244, which is good enough to give meaningful results.

 I’ll start with the “dumb” case. Let’s just weigh all of the years equally, so that each year counts for 33.3% of our expected outcome.

 Expected vs. Actual wRC+, unweighted

 Okay, so we’re averaging missing the actual wRC+ by roughly 16.5. That means that we’re averaging 16.5% inaccuracy when extrapolating the past into the future with no weights. Now let’s try being a little smarter about it and try some different weights out.

 Expected vs. Actual wRC+, various weights

Huh! It seems that no matter what we do, “intelligently weighting” each year never actually increases our accuracy. If you’re just generally trying to extrapolate several past years of wRC+ data to try and predict a fourth year of wRC+, your best bet is to just unweightedly average the past wRC+ data. Now, the differences are small (for example, our weights of [.3, .3, .4] were only .03 different in accuracy the unweighted total, which is statistically insignificant), but the point remains: weighing data from past years simply does not increase your accuracy. Pretty counter-intuitive.

Let’s dive a little deeper now—is there any situation in which weighting a player’s past does help? We’ll test this by limiting our ages. For example: are players that are younger than 30 better served by weighing their most previous years heavily? This would make sense, since younger players are most likely to experience a true-talent change. (Sample size: 106)

 Expected vs. Actual wRC+, players younger than 30

Ok, so that didn’t work either. Even with young players, using unweighted totals is the best way to go. What about old players? Surely with aging players the recent years would most represent a player’s decline. Let’s find out (Sample size: 63).

 Expected vs. Actual wRC+, players older than 32

Hey, we found something! With aging players you should weight a player’s last two seasons equally, and you should not even worry about three seasons ago! Again, notice that the difference is small (you’ll be about 0.8% more correct by doing this than using unweighted totals). And as with any stat, you should always think about why you’re coming to the conclusion that you’re coming to. You might want to weight some players more aggressively than others, especially if they’re older.

In the end, it just really doesn’t matter that much. You should, however, generally use unweighted weights since differences in wRC+ are pretty much always results of random fluctuation and very rarely the result of actual talent change. That’s what the data shows. So next time you hear someone say “weigh their past three years 3/4/5” (or similar), you can snicker a little. Because you know better.