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.

Toronto Blue Jays 1B/DH Edwin Encarnacion had another great year with the bat in 2013. He posted a .272/.370/.534 line with a 148 wRC+ that was 6th in the AL. This was on the heels of a 2012 season where Encarnacion managed a .280/.384/.557 line with a 151 wRC+.

In his late-career resurgence, Encarnacion has become the rarest of players, a power hitter that rarely strikes out. Only Chris Davis and Miguel Cabrera had more home runs than Encarnacion’s 36. The previous year, Encarnacion slammed 42 home runs.

Meanwhile, Encarnacion struck out in only 10% of his plate appearances. Only seven qualified hitters struck out at a lower rate than Encarnacion. None of them had more than 17 home runs.

In fact, you’ll have to go back to the glory days of Albert Pujols (2001-11) to find someone who matched Encarnacion’s home run total with a similarly low strikeout rate.

Here’s a look at their numbers side by side.

Pretty impressive, huh? Well, let’s dig even further. From 2001-11, the MLB average walk and strikeout rates were 8.5% and 17.3%, respectively. In 2012-13, they were 7.9%, and 19.9%, respectively. So, here are Pujols’ and Encarnacion’s numbers expressed as a percentage of the MLB average.

So if we adjust for the MLB average, Edwin Encarnacion’s home run and walk rates from 2012-13 were better than those of vintage Albert Pujols. His strikeout rate was a shade worse. If I restricted the comparison to 2013, Encarnacion would be better in all three categories.

Does this mean that Encarnacion from 2012-13 has been the offensive equivalent of vintage Pujols? Well, not quite. Let’s revisit wRC+. Pujols’ average from 2001-11 was a robust 167. Encarnacion’s wRC+ from 2012-13 is 148. Where does this big difference come from?

Pujols in-play batting average in his prime years was .311. On the other hand, Encarnacion has just a .256 in-play average from 2012-13. That’s a very big difference. Only Darwin Barney had a worse in-play batting average than Encarnacion in that time frame.

Does Pujols hit more line drives? What’s the reason for this big split? Here are their batted-ball ratios.

Pretty similar. Pujols hits more ground balls, Encarnacion does a better job of avoiding the infield fly. In fact, based on these ratios, you would expect Encarnacion to have a higher in-play average than Pujols.

Recently teams have been using a unique shift against Encarnacion, where they put three infielders on the left side of second base. Here’s a picture below.

This shift has been successful in taking away hits from Encarnacion. Since 2012, he’s hit just .222 on ground balls, compared to .262 for vintage Pujols. In 2013, just 25 of the 170 groundballs Encarnacion hit found a hole. Here’s a link to his spray chart.

On balls he pulls, Encarnacion has a .376 batting average. That might sound very good, but compare it to Pujols, who hit .477 on balls he pulled in his vintage years.

Edwin Encarnacion is an elite hitter. In terms of walks, strikeouts, and home runs, he’s every bit the hitter that Albert Pujols was during his prime years. Sure, his pull-heavy approach might allow the shift to take away some hits, but the shift can’t do anything about the balls he puts over the fence.

The Hall of Fame (HOF) voting will be announced in a month or so, and with a very competitive ballot full of worthy new players, deserving holdovers and numerous players with suspicions hovering over their candidacy, it will be one of the most compelling ballots in years. There will be no shortage of analysis in the coming month, and I’ll add to it, but hopefully in a manner that helps clarify instead of confuse.

In his wonderful book “Whatever Happened to the Hall of Fame?” Bill James laid out criteria for two measures he invented to evaluate HOF resumes. He devotes Chapter 14 to describing one of them, the HOF Standards and an additional measure, the HOF Monitor on p359-61. At the risk of being 100% incorrect, the two systems complement each other very well–the Monitor essentially measure the successful seasons (number of hits, home runs, runs scored, etc.) while the Standards measures these numbers over a career (did a pitcher win 200 games? 250? 300? Did a hitter hit 350 home runs? 400? and so on). In a perfect world, a player does well on both scales–he has a long career filled with career milestones AND has years in which he is clearly the best in the game. Putting these two factors together goes very far in helping evaluate HOF worthiness.

The tests work on two different scales–James states that anything over 100 on the Monitor and 50 on the Standards places the player in the company of those already enshrined. Therefore, that creates a fun thing to measure–just how well do HOF inductees match up with James’ measures? This graph shows pitchers of recent vintage only (from around 1960 or so) and plots them on a scatter graph on both of these measures:

Yellow dots are HOF members. Take a moment and peruse the players in the upper right quadrant, those that meet both tests for Standards and Monitor. These are truly worthy of enshrinement and the names are understood as among the best pitchers in baseball history. Roger Clemens and Randy Johnson are far right because they were power pitchers who racked up huge numbers of strikeouts per season and over a career, whereas Greg Maddux was simply a dominant pitcher who got batters out however he could. It doesn’t matter either way–any serious discussion of the best pitchers of the past 25 years includes these three pitchers, no matter how different their styles were.

The others in the upper right quadrant are Pedro Martinez, Tom Glavine and Mike Mussina. Glavine and Mussina are on the 2014 ballot and will generate no shortage of discussion, some of which might even concern their career achievements. I won’t discuss the quirks and shortcomings of HOF balloting in this post but will do so over the next week or so at my blog Beyond The Scorecard. Mussina in particular will generate tremendous discussion since he “only” won 270 games, whereas somehow Glavine’s 35 more wins is a wide chasm. Leaving aside the uselessness of the win as a stat in modern baseball (I have more thoughts on that here, for starters), it sets up a magical threshold that is exceedingly difficult to attain, and yet rewards no shortage of pitchers who missed that mark.

Nobody suggests that James’ measures should be hard and fast rules, and he himself argues on p182 that it would be a “terrible idea,” but that doesn’t mean that some element of rigor can’t be applied to the review of these pitchers to see if they’re truly amongst the best in their generation. Jamie Moyer had more career wins than Pedro Martinez–is there anyone who seriously suggests that Moyer was a better pitcher than Martinez? We don’t use metrics to create artificial (and often capricious) cutoffs as much as give nuance and context to the numbers we see. Particularly as the role of the starting pitcher has changed over the years, these types of values are even more important. So what do we do with the pitchers in the lower right quadrant? There’s plenty of precedent for enshrinement but it appears that at least in recent years, egregious errors made in the past are becoming far fewer. Even the “worst” HOF inductee on this chart, Jim Bunning was inducted by a Veterans Committee in 1996 and is far from the worst selection the HOF has made.

My real point is that James’ measures hold up remarkably well when tested against actual inductees. Like just about everything else he’s done in baseball metrics (and for the Boston Red Sox), it’s a measure that adds true value and allows us to make informed decisions as we evaluate HOF candidates. It’s been almost 20 years since he conceived these measures and perhaps time will require tinkering with the numerical values (for example, is 300 wins still a reasonable upper limit for pitching wins? If not, what should it be dropped down to?) to reflect changes in the game. But the overall structure remains very robust and does an excellent  job of matching up our remembrances with actual events. As Bill savors his third World Series title while being associated with the Red Sox, he should also be remembered as the man who attempted (and very much accomplished) something very important–helping us accurately evaluate player careers and place them in the proper context.

There are several unlabeled dots due to space:

In the lower right quadrant there are four dots between Andy Pettitte and Justin Verlander–they are (from top to bottom) CC Sabathia (just to the left of Pettitte), David Cone (left of Morris), Ron Guidry (right below Cone) and Vida Blue (above Verlander)

In the lower left quadrant there are six dots right around Jim Bunning–they are Luis Tiant (right below), Kevin Brown (just to the left of Tiant), Dwight Gooden (left of Brown), Mickey Lolich (below Bunning), Mike Cuellar (just below Lolich), Orel Hershiser (to the left of Cuellar) and Johan Santana (left of Hershiser). Other notable pitchers in that quadrant are (going down the Monitor number) David Wells, Dave Stewart, Cliff Lee, Bret Saberhagen, Frank Viola, Bob Welch, Fernando Valenzuela, Kenny Rogers and Jamie Moyer.

Be sure to visit my blog for more thoughts on the Hall of Fame and other baseball stuff

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TIPS is a new ERA estimator that I have created. The post on the estimator can be found here.

In short, TIPS is an estimator that attempts to measure pitcher skill completely independent from all other factors other than batter-pitcher relationships (removing defense, catchers, umpires, batted ball luck, etc.). The formula is:

TIPS 6.5*O-Looking(PitchF/x) – 9.75*SwStr% – 4.8*Foul% + C (around 2.60)

where: O-Looking(PitchF/x) = 1 – O-Swing% (PitchF/x), SwStr% = percent of pitches swung at and missed, Foul% = percent of contacts fouled off

The estimator was found to be the most predictive of any estimator in samples less than 70 IP.

I have taken the free agent custom leaderboards provided by Dave Cameron and ranked the pitchers by TIPS.

TIPS may not have as much power with starting pitchers, since the samples will be larger than 70 IP, but since these pitchers will be changing defense, park, and catcher, I believe it can be useful (when used with FIP and xFIP). Click this text for the starting pitcher leaderboard.

If you cannot view the google spreadsheet, here are the top free agent starting pitchers by TIPS. Yes, I know Lincecum has since signed, but he is still included.

Kazmir, Marcum, Haren, Hughes, and Johnson all look like really good value signings (when comparing their ERA and FIP/xFIP/TIPS). Scott Kazmir is someone who I believe could be a legit number 2 guy moving forward if he can keep his velocity. I know Jason Marquis had a 4.05 ERA, but he is someone you should be wishing your team does not sign.

But now on to where TIPS really shines, relievers!

Here is the RHP leaderboard and LHP leaderboard. I am also providing the full combined leaderboard:

There are a few notable FA relief pitchers. Mujica, Benoit, Nathan, Rodney, Balfour, Hawkins, and Gregg all closed this year. Crain is a pitcher who could potentially close as well. Looking at the closers, Mujica is alone in the top tier by TIPS. Then Benoit, Crain, and Nathan are second tier. Rodney and Balfour are in the next tier, while Hawkins and then Gregg are in the final tiers. Gregg in particular looks like a RP that no team should touch. Parra and Logan make for some good LOOGY signs if teams are looking for left-handed relievers. There a quite a few names in this list that would do a fine job in filling out a bullpen. It goes to show that trading for bullpen pieces might be akin to trading your brother or sister your blueberry for their strawberry when there is a pack of strawberries on the counter. A bit of a random analogy, but it makes sense. The SP crop is much thinner than the RP crop. There are no big name or potential number 1 pitchers in the FA crop, which means teams that are looking to add to the front of their rotation might have to do so through trade.

On a bit of a side note, I wanted to talk a little more about TIPS. Why does TIPS really like Mujica? It loves his amazing 44.2% O-Swing% and his 12.5% SwStr% isn’t too shabby either. O-Swing% (I use the PitchF/x value), SwStr%, and Foul% are peripherals that you should be accustomed to looking at and understanding. Foul% is not readily available, but is not too hard to calculate. What value is good? What is bad? I will explain here:

To finish this off, I’d like to say Koji Uehara is a monster. 39.2% O-Swing% (Above Excellent), 18.5 SwStr% (Above Excellent), and 60.8% Foul% (Almost Excellent).

In the Marlins deal last November, Josh Johnson was the main headlining piece along with Jose Reyes and Mark Buehrle. Then the Blue Jays added R.A. Dickey in December and the starting rotation looked to be very strong. Dickey, Morrow, Johnson, Buehrle, and Happ were all supposed to have strong seasons and hope for a 2013 World Series title was in abundance. Then came April. The rotation struggled, terribly. Josh Johnson seemed to be the worst infringer of them all. He was the worst disappointment of the season. But was he actually that bad?

Using all of the standard metrics for pitchers, Josh Johnson was brutal. He was 2-8 with a 6.20 ERA and 1.66 WHIP. He also only pitched 81 and a third innings. How could you possibly say he had a good season? Those stats look worse than 2012 Ricky Romero. If you take a look at his K/9 of 9.18 you see he had the best K/9 of his career. You also see that he had the worst BB/9 of his more recent years at 3.32. These two stats are a little deceiving in this case however. Because of his much longer innings, his K/9 and BB/9 would both be up as he faces more batters per inning. We then have to look at the rate per batter. He had a K% of 21.6%, which is just shy of his career average (not best, as K/9 suggests) of 21.9%. This makes his strikeout rate look less appealing but it is still very good. The adverse effect is applied to his walk rate, as his BB% was 7.8%. This mark is better than his last two years and better than his career average of 8.1%.

Now on to why I believe Josh Johnson will be a good starter next year and onward. In case you haven’t heard of them before, there are ERA-accompanying stats called FIP, xFIP, and SIERA. These stats try to eliminate events that are beyond the pitcher’s control (fielding independent pitching). FIP is calculated from K’s, BB’s, and HR’s to IP. xFIP is the same, except that it corrects the pitcher’s HR total to what it would be with a league average HR/FB rate. SIERA uses a more complex formula based on K%, BB%, and batted ball profiles (ground balls, fly balls, and pop ups) to approximate ERA. These three stats do a much better job of predicting future ERA than they do of current ERA. ERA fluctuates greatly from year to year and sample to sample for pitchers, while the guts of these metrics are more constant. ERA is not stable as it depends on luck in BABIP, HR/FB, and LOB as well as team defense. FIP is usually closest to the ERA of the sample, as it doesn’t account for HR/FB luck. SIERA is the best at predicting future ERA, followed closely by xFIP, FIP, and lastly, ERA.

So while Josh Johnson’s ERA is 6.20, his BABIP is an inflated .356 (compared to a career average of .305 and league average of .294) and this should regress back towards the mean. FIP has BABIP luck taken out of the equation and has Johnson with a FIP of 4.62. This is much lower than the 6.20 ERA, but 4.62 is still not very good for a pitcher of his price-tag. However FIP does not assume a league average HR/FB rate, this is where xFIP comes into play. Johnson’s HR/FB% this year is an abysmal 18.5% (compared to a 8.2% career average and 10.6% league average). It can be assumed that this will regress towards the mean as well next year. So accounting for this absurd HR/FB%, Josh Johnson had an xFIP of 3.60. That looks a little better doesn’t it? Especially since xFIP does a better job of predicting future ERA.

The one problem with using FIP and xFIP in this case however, is that they are based of rates with IP as the denominator. As I discussed earlier, due to the long nature of Josh Johnson’s innings, this would increase the K, BB, and HR per inning as more batters come to the plate. This is where SIERA comes into play as the best statistic to use in this case. SIERA, as mentioned prior, deals with rates where PA (or BF) is the denominator. It is also shown that batted ball profiles are somewhat controllable by the pitcher and have an impact on results. In most cases, xFIP and SIERA are very similar, but replacing the IP denominator with BF and including some batted ball profile gives SIERA the slight edge in predictability. Josh Johnson’s SIERA this year was 3.73, which is probably the best guess as to what we can expect his ERA to be going forward.

3.73 or 3.60 look excellent and amazing considering the results we saw. What a ray of hope! But what if he really was just more hittable this year? What if he wasn’t unlucky and batters can just hit him? This is what I will look into now.

Johnson’s injury history and the effect it has had on his velocity is well documented. He is not the same pitcher he was in ’09 and ’10.  He is a different pitcher now, but he has been this way for two years, not one. Josh Johnson is the same pitcher that he was in 2012 when he posted a 3.81 ERA for the Marlins (he might even be better). How is this possible you say? His ERA has jumped 2.39 runs! I will dive into all of his peripherals to prove that he hasn’t changed that much.

First let’s take a look at his velocity (I will be using PITCHf/x numbers for all values).

His average FB velocity in 2012 was 92.8mph, while this year it is 92.9mph. Slider velocity was 86.9mph and now is 86.1mph. Curve was 78.5mph and now is 79.1mph while his changeup was 87.6mph and now is 88.6mph. All of these velocities are very constant! There is nothing here inferring that he is more hittable than last year, let’s move on.

Let’s look at plate discipline to see if there is anything that suggests hittability. His O-Swing% (outside zone swing%) was 30.9% and now is 32.3%. This should decrease hittability if anything, since contact should be worse on pitches outside of the zone. His Z-Swing (zone swing%) is a constant 60.4% compared to last year. His O-Contact% is slightly up (59.5% to 61.9%) but this shouldn’t matter, as these pitches should be less hittable. His Z-Contact% is slightly down (90.9% to 89.6%), which should be good as it means more whiffs in the zone. His zone% in also slightly down (44.9% to 43.7%), but who cares if he doesn’t walk more batters. Lastly, his SwStr% (swinging strike%) is essentially constant (9.2% to 9.3%). Again there is nothing here to suggest that batters should be able to hit him better.

I have heard some people say that he just gets rattled when things go bad. I’d like to partially debunk this theory, as his pace (time between pitches) is essentially the same as last year (20.9s in 2012 and 21.0s in 2013). Pitchers who are rattled generally take more time between pitches. There’s not really any other stats that can prove otherwise, as all his peripherals are fairly constant.

The one main difference that is notable in his peripherals between 2012 and 2013, is his 2-seam fastball use. He has used his two-seamer 13.3% of the time compared to only 4.8% last season. This difference has come at an expense of all three of his secondary pitches, which are all slightly down in usage. Is his two-seamer a bad pitch? It’s certainly not his best. I would take pitch values from this year with a grain of salt, as they are all low due to his bad luck, but his two-seamer has been below average for three years in a row: -1.94 RAA/100 pitches (runs above average) in 2011, -2.43 RAA/100 in 2012 and -1.99 RAA/100 in 2013.  Other than his changeup since his velocity decline (which went from average to well below average), the two-seamer has been consistently his worst pitch. The fact that he is using it more is not a good thing, but this is easily corrected if it is pointed out to him. It has nothing to do with a lack of ability. His above average curve and slider have taken a hit in usage and this needs to be corrected.

Pitch selection hasn’t been too much of an issue for him in terms of strikeouts and walks however. Both his K% and BB% are trending the right direction from last year. His K% is up 0.9%, while his BB% is down 0.4%. These both suggest he has improved since last year, and his xFIP and SIERA mirror that. xFIP has gone from 3.73 to 3.60 while SIERA has improved from 3.86 to 3.73. He has been getting better at pitching with his reduced velocity, not worse (as it appears on the surface).

One counter argument to this could be that he’s just throwing more meatballs down the middle that are getting hit, but also mean he walks less and strike out more. This was partially debunked by his lower zone% and lower z-contact% from before, but I want a little more proof that this is not the case. FanGraphs, with the help of PITCHf/x, is an amazing website that, in addition to all these fancy stats, also provides heat maps for pitchers to see exactly where they are throwing the ball.

Here are Johnson’s 2012 heat maps:

And here are his 2013 heat maps:

Not much difference is there? He enjoys throwing down and away the most, and this hasn’t changed at all. In case you’re wondering, there is less yellow in 2013 because he’s thrown about half as many pitches.

Another theory I have heard would be that his pitches are straighter now. I will look into this. This actually might have a case. His movement on each pitch has decreased since last year (around .6 inches for each pitch). However, we need to look into the numbers a little deeper. PITCHf/x movement in the z-direction (up or down) excludes gravity and gives a movement number in which the ball would move without gravity. What does this mean if we have positive movement values (which Johnson does with every pitch except his curve)? It means that, without gravity, each pitch would move up. In reality, gravity is much larger than this movement force and the balls drop. So a larger positive movement number means that the ball will drop less than a smaller movement value, and therefore have less movement. Johnson’s fastball and this two-seam fastball (to a larger extent), both have less rise this year, this means they actually have more drop. His slider is about the same while his changeup and curve are showing slightly less drop. I might say this is a problem, but his curve was his best pitch this year while his changeup has been bad for 2 years anyways and should just be a show pitch. I would be more concerned if he was showing less movement in the horizontal direction, but this isn’t the case. With the exception of his changeup (which is moving less), each pitch’s horizontal movement is almost identical to 2012. All things considered, nothing here suggests that he is any more hittable, especially considering his batted ball profile.

One last thing to look at is to see if batters are getting better contact aside from high home run rates is batted ball profile. Again these almost look identical to 2012. His line drive rate is slightly up (23.6% to 24.2%). It isn’t much, but still a small concern. His ground ball rate is related and took a small hit (46.2% to 45.1%). His fly ball rate is slightly up too (30.2% to 30.7%), but that’s not a problem either. His infield fly ball rate is also up (7.2% to 8.6%) which is actually good since they are almost always an out. His infield-hit rate is up (5.1% to 5.9%) showing some more of his bad luck. Again, SIERA takes batted balls into consideration and it wasn’t too concerned with his rates with the 3.73. There are some xBABIP formulae out there that predict what BABIP should be based on batted balls. These formulae are better at suggesting if a pitcher (or batter) has changed their true talent BABIP (instead of getting lucky) then actually predicting BABIP.  Using Steve Staud’s xBABIP that uses LD%, FB%, and IFFB%, Josh Johnson’s 2012 and 2013 xBABIPs are nearly identical (.3163 to .3159). Matt Swartz’s xBABIP uses GB% and K% and yields .2894 in 2012 and .2880 in 2013. This is almost exactly the same again. This suggests that Josh Johnson’s true talent BABIP has not changed and that he has been getting very unlucky. There is no large or conclusive outliers in Josh Johnson’s stats suggesting that he his any different of a pitcher than in 2012.

Another thing that I would like to add is that Josh Johnson has been very consistent at preventing home runs and having a HR/FB rate that is less than league average. This is shown by his 8.2% career average and that he has posted HR/FB rates lower than league average in every year of his career except 2013. This causes his FIP to be consistently lower than his xFIP and SIERA (has been every year save 2013). So while xFIP and SIERA are the best estimators of ERA, Josh Johnson usually outperforms them in FIP. He had an excellent 3.40 FIP last year and was just a bit unlucky with LOB%, which cause his ERA to higher at 3.81. Using all of this information and the proof that Josh Johnson hasn’t changed, it would be safe to say that his ERA should be around 3.55 next year (if he were still in the NL) if everything keeps trending the same way.

There are two more things to consider though: league change and age. The AL ERA this year is 0.26 runs higher than the NL ERA. This can be accounted for in the 3.55, which brings him back to around 3.70-3.90. Age is another thing to consider, Josh Johnson is going from 29 to 30 years old. As a pitcher, this actually gives him an approximate 0.05 decrease in ERA. This generalization is shown in this graph from Baseball Prospectus. Taking this into consideration I believe we will see Josh Johnson post an ERA between 3.65 and 3.85 next year.

So let’s say we have Johnson posting a 3.75 ERA next year. A full season of Johnson should be around 3.0 WAR, cut his innings in half (injury risk) and that’s still 1.5 WAR. With wins being worth approximately $9M next year, Josh Johnson could realistically be worth anywhere from $13.5M to $27M, depending on injuries. A qualifying offer will be around that $13.5M. So even with a qualifying offer, the downside is that you will pay what you get, while the upside is much better. You can’t really lose. However I don’t think the Jays need to pay him $13.5M. Remember, he posted a 6.20 ERA this year. GMs around the league, as well as agents, will want to stay away from a bad, injury-prone pitcher. I believe the Jays could extend Johnson at around $11M/year over three years. At this price you could most certainly expect positive value from him. There are not really any cases like this to compare the situation with, so predicting possible contracts is a shot in the dark, but no matter the contract, I am positive it will be worth it. The Blue Jays definitely need to extend Josh Johnson as soon as possible. It is one of the best buy low opportunities they’ll ever encounter.