The key to winning at fantasy baseball is finding players who will outperform their draft position.  This will be the first of a series of articles addressing undervalued and overvalued players that you should be targeting in your draft.

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After reading Kiley McDaniel’s articles on explaining the scouting scale, I thought that I would take a different approach in trying to explain it–namely the approach of gifs.  Although the scale is normally reserved for players who haven’t lost their rookie status in the majors, perhaps a visualization can better illustrate what “major league average,” “plus,” or “below average” looks like.

To show the differences between curveballs in different positions along the scouting scale, major league curveballs must first be graded.  To do this, I pulled up Baseball Prospectus’ pitch f/x leaderboard and set it to filter for pitchers who threw at least 100 curveballs in 2014.  The way in which pitch movement is measured and the fact that lefties’ curveballs’ horizontal movement appeared to be measured lower than righties forced my methodology.

I split up righties and lefties into separate groups for analysis.  Pitch movement was recorded based on the inches a pitch broke more than the the pitch with the least break.  Total movement was recorded as the square root of the combined horizontal and vertical movement squares (C² = A² + B²).  Z Scores were recorded for each curveball’s velocity and movement, and then were added (with movement receiving a 1.5 times greater weight) to form a grade.  The grades were then transferred into Z Scores with a median of 50 and each standard deviation being 10.  Finally, the righties and lefties were combined to make a final scouting scale.

Name / Throws / Total Movement Z Score / Velocity Z Score / Scouting Grade

For our first gifs, we’ll look at two of the top curveballs on the by-the-numbers scouting scale.  At an slightly above average velocity of 79.7 mph with the highest amount of break above than the baseline (sorry, Fernando Salas), Garrett Richards’ curveball is a sight to behold and grades out as a 73:

Next, we’ll look at Tyler Skagg’s curveball, which, features great horizontal and vertical movement while maintaining average velocity.  It grades out as a 69:

With the plus-plus-type (70) curveballs out of the way, we’ll take a look at some plus (60) curveballs.  Wandy Rodriguez fits this category.  His curveball has slightly less, but similar, movement as Skagg’s but it’s lesser velocity makes it a lesser-quality pitch.  Notice how it has defined break, but it appears a bit loopy due to its velocity:

With Kevin Jepsen, we see a curveball that is graded similarly but looks much different than Wandy’s.  Although its break looks sharp because of its velocity, the total movement is slightly below average.  This pitch may actually be a slider, but Jepsen’s breaking pitches tend to run together so consider it a representative picture of his curveball.

Next, we’ll look at an average (50 on the scale) curveball.  Erik Bedard gets slightly above average break, but with below average velocity.  It has solid two-plane break, but it doesn’t look very sharp:

Next, we’ll look at a below average (40 on the scale) curveball.  Jordan Lyles has good velocity on his curveball, averaging 81.75 mph, but he also averages nearly two standard deviations less break than an average curveball in this sample.  The following is a tough angle to see the break, but it lacks the sharp break of a better curveball.

Finally, we’ll look at Jered Weaver’s well below average (24 on the by-the-numbers scale!) curveball.  His curveball was the slowest in the sample and also featured below average break (much of the perceived break is from its low velocity).  It’s still an effective pitch for him, but it’s probably a result of his height and delivery combination than his curveball.  With just about any other pitcher, it would likely be ineffective.

With Wil Myers headed to San Diego, and only a clean bill of health keeping Matt Kemp from joining him (which may or may not magically appear of LA is willing to pay a larger portion of Kemp’s remaining $105 million on his contract…), the San Diego Padres find themselves with an opportunity to move 2014 right fielder Seth Smith. Kemp’s days as an everyday center fielder have long passed, and Myers is likely better suited for a corner outfield spot as well. This leaves nowhere to play Smith, and the Padres could deal Smith to acquire some help at a spot other than the corner outfield.

The biggest issue with Smith has always been his splits. No matter how you slice it, Smith should relegated to a platoon role.

Smith’s splits:

                  RHP          LHP

wRC+     123              63

wOBA   .362            .274

ISO         .204           .109

A team that has been in the hunt for a corner outfielder this offseason is the Seattle Mariners. So far we’ve seen Seattle add Nelson Cruz to serve as the everyday DH, and over the past month we’ve seen the Mariners linked to names like Melky Cabrera, Dayan Viciedo, Justin Upton, and Kemp. The big hangup with Kemp, as well as Upton, was the issue with teams trying to pry away young pitchers like Taijuan Walker and James Paxton. Fortunately for the M’s, a player like Seth Smith would not cost them these young arms.

With Kemp and even more cash expected to head to San Diego, Upton will likely become the top corner outfielder available this offseason. Upton has just one year remaining on his current contract, and will make nearly $15 million in 2015. Meanwhile, Smith will make nearly $13 million over the next two years, and his contract contains a club option for the 2017 as well. Now we all know that Justin Upton is a better baseball player than Seth Smith, but is one year of Upton (and no Walker or Paxton for the next 6 years) better than two years of Smith and Justin Ruggiano (with Walker and Paxton still in Seattle for the next 6 years)?

Earlier this week, the Mariners acquired Ruggiano from the Cubs for minor league reliever Matt Brazis. Ruggiano is two years removed from a career year in Miami, in which he posted a 2.6 WAR in just 91 games. Even though his WAR over the last two seasons combined for just 1.3, he still profiles as an excellent platoon player. In 2013, Ruggiano posted a 130 wRC+ vs LHP, as well as a .362 wOBA. For 2014, Ruggiano’s numbers were nearly identical, posting a 129 wRC+ and a .362 wOBA vs LHP. Take a look at the career numbers below, with Smith and Ruggiano’s being their career splits:

                     Upton     Smith     Ruggiano

wRC+         121            123           128

wOBA        .359         .362          .360

ISO             .202         .204          .241

Whether or not the Mariners add Justin Upton, or Seth Smith, or go with some sort of Brad Miller/Ruggiano platoon that we’ve seen rumored, they will get solid production from that right field spot. Smith would cost them some value, and the Smith/Ruggiano platoon may not be the sexiest, but Smith would not cost them a Walker or Paxton.

Amidst the expensive December fireworks being set off by Andrew Friedman and Theo Epstein, the cash-strapped New York Mets quietly took another step towards correcting a major 2014 deficiency with the addition of John Mayberry for $1.45 million.

Removing the historically bad hitting performance of their pitching staff (they started the season with a major league record 0-for-64), the often maligned Mets lineup actually generated a respectable 104 wRC+ against right-handed pitching in 2014, good enough for 5th best in the National League.

Their offense vs. left-handed pitching was another story however as an 89 wRC+ (14th NL) and 22 HR (MLB worst) left the Mets scrapping to find runs in the late innings of games against deep lefty-heavy bullpens.  Leading the struggles vs lefties were Eric Young Jr (84 PA, 60 wRC+), Lucas Duda (125 PA, 54 wRC+), and Chris Young (83 PA, 51 wRC+).

I prepared for this first FanGraphs Community article of mine by studying Mayberry a little closer.  As a fan who has witnessed plenty of NL East action over the years, I was well aware of Mayberry’s established platoon splits.  What I wasn’t aware of was the massive amount of growth he had in 2014.

John Mayberry Splits vs LH Pitching

2011 – 6.7 BB%, 15.0 K%, 0.44 BB/K, .288 ISO, .306 BABIP, 157 wRC+
2012 – 5.6 BB%, 17.8 K%, 0.32 BB/K, .223 ISO, .289 BABIP, 116 wRC+
2013 – 7.4 BB%, 15.7 K%, 0.47 BB/K, .220 ISO, .244 BABIP, 106 wRC+
2014 – 13.4 BB%, 12.2 K%, 1.10 BB/K, .329 ISO, .214 BABIP, 151 wRC+

After 3 seasons with respectable peripherals, Mayberry took his platoon game to another level in 2014 with career-best numbers across the board except for an inexplicable .214 BABIP.  Over 534 career plate appearances against LHP, Mayberry carries a .269/.324/.533, 30 HR, 130 wRC+.

In addition to Mayberry is the aggressively acquired Michael Cuddyer (career 132 wRC+ vs LHP), and the Mets are now in position to be significantly strengthened vs. left-handed pitching without making headlines or gutting their very deep farm system.

He’s not a good enough 3B and he doesn’t hit well enough to play at any of the easier defensive spots.

1261 PA, .273/.348./450, 102 OPS+

“He” is Edwin Encarnacion, then of the Cincinnati Reds, and those are his stats through his age 24 season (2005-2007). Just three years into his major league career, Encarnacion had yet to attain 600 PA in any one season, and questions were already be raised about his viability as an every day player. The quote above comes from here, and, to be fair, it represented the judgment of only some E5 observers. But despite having the opportunity to act out one of the best baseball revenge fantasies ever, Encarnacion never fully put those doubts to rest while he was with Cincinnati. Following what seemed to be a possible breakout season at age 25 in 2008, E5’s power disappeared the following year, and the disgusted Reds shipped him midseason to America’s Hat in exchange for the Ghost of Rolen Past, who gave Cincinnati the final 3.5 seasons of his career, 1.5 of which were useful.

North of the border, Encarnacion’s power returned in 2010 even as his OBP continued to regress; his production overall rebounded to the level it had been in 2008 (109 OPS+ and wRC+). He continued to maneuver around third base as though it were a point singularity, however, so in 2011 the Blue Jays began transitioning him to a 1B/DH, giving him 92 starts in those slots as opposed to just 30 at third. The results at the plate were encouraging: his average and OBP made substantial gains without giving away too much power. Then, in 2012, Encarnacion finally went off, commencing a three year tear during which his OPS has never been below .900 and his worst HR total was 34. Today, Encarnacion can hit in the middle of any major league lineup. If Alex Anthopoulos is working for the MLB Network in 2016, it won’t be Encarnacion’s fault.

***

He started off cold as ice … before getting hurt sliding into second base.

1361 PA, .250/.331/.415, 97 OPS+

“He” is Alex Gordon, and those are his stats through his age 24 season (2007-2009). Just three years into his major league career, Gordon’s stellar offensive production in college already seemed a distant memory, and questions were being raised about his viability as an every day player. The quote above comes from here, and while that writer was ready to give up on Gordon, to be fair, many others were calling on the Royals to remain patient with the second overall pick in the 2005 draft. In 2009 Gordon’s power, never substantial in the majors to that point, really began circling the drain, and after getting off to an anemic .685 OPS start in April 2010, the disgusted Royals demoted Gordon and banished him to left. He would never appear at third base again.

He would, however, rediscover his stroke. Gordon hammered 16 homers in just 321 PAs at Omaha, good for a steak-sized 164 wRC+. He returned to The Show on July 23, and while the remainder of his 2010 season did little to quiet his critics (he finished with a wRC+ of 85, actually two points worse than the previous year), in 2011 he began a four year rampage, headlined by 96 doubles during 2011-2012, as well as ironman durability. Since opening day 2011, Gordon is third in the majors in plate appearances, behind only Ian Kinsler and Elvis Andrus. Gordon’s durability and on-base skills have made him a key offensive cog in the Royals somewhat surprising resurgence. He’ll never have to go to back to Omaha unless he has relatives there.

***

He’s been injured numerous times, suspended and has underperformed with the bat once pitchers began taking advantage of his lack of patience at the plate.

 1431 PA, .265/.323/.426, 104 OPS+

“He” is Brett Lawrie, and those are his stats through his age 24 season (2011-2014). Just four years into his career, his first 171 blistering plate appearances of his career have disappeared in the rearview, and questions are being raised about his viability as an everyday player. The quote above comes from here, and was written before Lawrie strained his oblique (once again) last year, ending his season and, as it turned out, his tenure with the Blue Jays.

Lawrie plays baseball as though he’s being chased by an enraged Sumatran tiger. In his first brief season in the bigs (when, incidentally, he replaced Encarnacion as the Jays’ starting third baseman) this paid off with a .293/.373/.580 slash line.  Since then, however, he has been unable to translate all that energy into baseball achievement. That kind of intensity can wear thin unless it’s backed by production, and Lawrie’s rate stats have gone generally backwards since 2011. He’s had a fractured finger, repeated oblique injuries, and a bad slide into second, among other injuries.  Lawrie blames the turf at Rogers Centre, but the turf lawyered up, and Lawrie’s case proved at best inconclusive.

As the career paths of Encarnacion and Gordon suggest,  one way to resuscitate Lawrie’s bat might be to move him to the left end of the defensive spectrum. That won’t happen in Oakland; after the Donaldson trade the A’s third base depth chart (Renato Nunez aside) looks like the Fallujah skyline. Billy Beane has little incentive to try Lawrie anywhere other than third. And maybe it will work. The hopeful comp here might be Gary Gaetti, whose stat line through age 24 (1981-1983) looks similar to the other guys in this post, viz:

1241 PA, .237/.293/.428, 94 OPS+

But Gaetti was already a superior defender, and became a durable, full time starter at age 23. His plate appearances are light because he only had a shotglass of coffee in 1981.

Gaetti was good. Real good: a 42 WAR career during which he amassed 2,280 hits and 360 homers while adding defensive value almost right up to the end. It’s certainly worth Beane’s time to see if he has that kind of player on his roster, especially since it appears that the A’s are going to be running a talent show rather than a pennant race next season. My guess is that if Lawrie develops at third, he’ll have slightly more bat and slightly less glove than Gaetti, though to be fair, both men had exactly the same career minor league OPS (.851).

It’s less clear whether participating in this particular rat race is the best outcome for Lawrie. Like E5 and Gordon, he might be better served by moving to a safer corner where he can concentrate on developing his offensive skills without placing his body’s soft tissue in excessive danger. I’m sure if you asked him he’d say he wants to stay at third. My guess is that Encarnacion and Gordon once thought that way too, and Lawrie’s career to this point looks more like theirs than Gaetti’s.

Brett Lawrie, like Repo Man, is always intense. I have a hard time not rooting for him; he attacks his job with an explosive, exuberant passion that would get me (and probably you) fired. I want him to succeed. I’m not at all sure he will.

Adjusting to the New Reality

The level of offense in baseball has been dropping for some time now. In the 1980s and into the early 1990s, teams scored around 4.3 runs/game (with the exception of 1987, when offense jumped up to 4.7 runs/game for one year, then went right back down in 1988). Offense started to rise in 1993 and first jumped over 5 runs/game in 1996. Run-scoring peaked at 5.1 runs/game in 2000, then leveled off to around 4.8 runs/game through 2007. Since 2008, offense has gone down steadily, with 2014 seeing an average of 4.1 runs/game. You have to go back to 1981 to find fewer runs per game in baseball (4.0 runs/game).

This has implications in the world of fantasy baseball. Consider the table below that shows the ERA in Major League Baseball by year, going back to 2001:

Some would point to PED testing for the lower level of offense, some would blame a bigger strike zone, some would peg it on the increasing number of relievers throwing 95+ for an inning or two. Whatever the reason, this is the new reality and sometimes it can be hard to adjust to new realities.

Let’s look at the numbers shown above in more detail.

Over the stretch of years from 2001 to 2009, MLB had an ERA of 4.39. Over the three-year stretch from 2010 to 2012, ERA dropped to 4.01. The last two years have seen big drops each year, from 4.01 to 3.87, to 3.74.

This has repercussions in fantasy baseball. With ERA dropping quickly, we need to reevaluate the pitchers we take on draft day and during the season.

Let’s go back to 2009, when MLB had an ERA of 4.32. The top 60 starting pitchers in ERA (minimum of 160 IP) combined for an ERA of 3.54. The median ERA for this top 60 was 3.77. There were 11 pitchers with an ERA under 3.00.

Fast forward to 2014. Last year, MLB had an ERA of 3.74. The top 60 starting pitchers in ERA (minimum of 160 IP) combined for an ERA of 3.14. The median ERA for this group was 3.33. There were 22 pitchers with an ERA under 3.00.

In 2009, the median guy in the top 60 was someone like John Danks (3.77) or Jarrod Washburn (3.78). Last year, the median guys in the top 60 were Jose Quintana (3.32) and Chris Archer (3.33). [Caveat: I know ERA isn’t the only way to judge a pitcher in fantasy baseball. I’m keeping it simple.]

Six years ago, when scouring the waiver wire, that pitcher with a 4.00 ERA was a potential pick-up. These days, you don’t want to look at that guy, he’ll just hurt your team. This may seem obvious, but it really is a change in mindset when you’re looking to improve your team. What we once thought was good is no longer good.

One of the side effects of a big drop in the run environment is the difficulty for projection systems to keep up. If we go back to the 2010 season, we can see a stark example. If a pitcher had league average ERAs in 2007 (4.47), 2008 (4.32), and 2009 (4.32), we could do a simple 3/2/1 weighted average for his three seasons and project an ERA of 4.35 for 2010. League-wide, though, ERA dropped from 4.32 in 2009 to 4.08 in 2010. Most projection systems will project ERAs that will be in line with the previous few seasons’ run environment. In this case, the projections will be well above what the actual ERAs were for the 2010 season (unless a projection system can anticipate such a drop in offense).

Let’s do the same for more recent seasons. If we take a pitcher with league average ERAs in 2011 (3.94), 2012 (4.01), and 2013 (3.87), and do a simple 3/2/1 weighted average, we get a 2014 projection of 3.93. The actual ERA in MLB in 2014 was 3.74, so pitchers as a group are going to be forecast with ERAs around 0.20 higher because the drop in offense was so drastic.

With this in mind, I looked at last year’s projections from four systems: Steamer, ZiPS, Davenport, and Oliver. I looked at all pitchers who were projected by each of the four systems who pitched 30 or more innings in 2014. There were 326 pitchers in this group and they finished 2014 with a combined ERA of 3.58. You can see how each of the projection systems forecast these players prior to the 2014 season:

When looking at the data, what you shouldn’t do is say that Davenport had the best projections. What is true is that Davenport best anticipated the run environment. Looking at the table, it would be easy to assume that Davenport and Oliver had the best projections, as they were closest to the actual ERA of this group of pitchers. In reality, if you are trying to assess which system better projected individual players, you would first want to adjust them all to the actual run environment, then compare the differences between projected ERA and actual ERA for individual pitchers.

In the case of the 326 pitchers used above, the table below shows the average absolute difference in actual ERA and projected ERA for each individual pitcher, using projections adjusted to the run environment of this group of pitchers.

Looking at it this way, it’s easy to see that the different projection systems were very close on this group of 326 pitchers and Davenport and Oliver are in the middle of the pack, with Steamer moving from the bottom to the top.

What does this mean for 2015? If you’re the type of fantasy baseball player who likes to create your own projections by combining projections from other sources, you will first want to know what level of offense those projections are expecting (ERA in this example). If you think 2015 will be much like 2014 (3.74 league-wide ERA) but the projections expect an ERA much higher or lower, you should adjust all pitchers by the amount the projections are high or low. With these new adjusted projections, you can now combine your projections.

As an example, I took those same 326 pitchers from above and compared their actual combined ERA from 2014 to their 2015 Steamer projections. This group of pitchers had a combined ERA of 3.58 in 2014. Steamer is projecting them to have a 3.84 ERA in 2015. The difference is 0.26 in ERA. I don’t know the run environment Steamer is basing their projections on, but this would suggest that it’s higher than what we saw in 2014.

Based on the disclaimer that accompanies each team’s ZiPS projections, we know that ZiPS is projecting based on the AL having an ERA of 3.93 and the NL having an ERA of 3.75. This would be a slight increase from the 2014 season (AL: 3.82 ERA, NL: 3.66 ERA) and is, essentially, a 3/2/1 weighted average from 2012, 2013, and 2014.

I looked at the starting rotations for the five teams that we have ZiPS projections for so far. There are 25 pitchers and they are projected by ZiPS to pitch 3985 innings with a 3.73 ERA. These same 25 pitchers are projected by Steamer to pitcher 4039 innings with a 3.98 ERA. Steamer is high by 0.25. Steamer projects higher ERAs for 23 of these 25 pitchers. This is a small sample of just 25 pitchers, but it would appear that you will want to adjust the Steamer pitching projections down if you do any sort of combining of projections in your fantasy baseball prep.

In addition, if you’re in a keeper league and have access to last year’s data for your league, you may want to project your keepers and potential additions for 2015 and compare your team projections to last year’s stat categories. This way, you will have an idea of how competitive your team will be. For example, I’m in an 18-team, 25-man roster league. We have nine starters on offense, four starting pitchers, and two relievers in our active lineups, and a 10-man bench that can be made up of players from any position. Teams in this league averaged around 1000 innings last season, so when I create projections, I can plug in the stats for my keepers and potential additions to see how my team looks for the upcoming season. In order to compare my projected 2015 team to 2014 stat categories, I want my projections to be adjusted to the level of offense of 2014 (in this case, ERA).

Offense in baseball has been dropping for a few years now. Successful fantasy players will have to adjust to this new reality when doing their pre-season prep work, on draft day, and when adding players from the waiver wire.

It seems that every MLB season, we witness a failed starter turn into a great reliever. The 2014 season was no different, and one of the biggest transformations came from the arm of Orioles RHP Zach Britton. Britton, a former 3rd round pick by the O’s in 2006, was twice named to Baseball America’s top 100 prospects list during his time as an Orioles’ minor leaguer. Britton would go on to join the Orioles’ rotation in 2011, posting a 4.86 ERA and a 4.23 xFIP over 46 starts from ’11-’13, while splitting time at AAA Nofolk. Due to Britton’s inconsistencies, the Orioles decided to try his hand in the bullpen for the 2014 season, and the results speak for themselves:

76.1 IP, 1.65 ERA, 2.82 xFIP, 75.3% GB rate

The ridiculous 75.3% GB rate comes after Britton posted a 55.5% rate over the previous three years. The key difference was Britton relying heavily on his sinking fastball, going from using it 69.6% of the time as a starter, to 91.5% as a reliever. As with most converted starters, Britton also saw a jump in his fastball velocity, going from an average of 92 MPH from ’11-’13, to 95.1 as a reliever. Is there another pitcher out there that could go from being a very average starter to a top notch closer? There is, and he also resides in the AL East.

Joe Kelly and Allen Craig were shipped to Boston in a 2014 trade-deadline deal that sent veteran John Lackey to the St. Louis Cardinals. In his seven starts with the Cards in 2014, St. Louis witnessed a regression from Kelly that was expected by anyone that had kept up with his peripheral statistics in the previous two seasons. Over the 2012 and 2013 seasons, Kelly posted a 3.08 ERA, which tied Madison Bumgarner and Stephen Strasburg, as well as topped teammate Adam Wainwright’s 3.39 ERA during that time frame. But when you look deeper, you see more numbers that don’t belong next to names like Bumgarner, Strasburg and Wainwright: 6.0 K/9, 4.12 xFIP, and 4.22 SIERA. Despite a fastball velocity that tied Jeff Samardzija’s 94.7, and only finished behind Strasburg and Matt Harvey’s 95.5, Kelly’s results were very mixed. In fact, they showed a great resemblance to someone else’s numbers:

               Kelly         Britton (starter)

FIP        4.11            4.25

xFIP      4.14           4.23

K/9        6.05           5.94

BB/9     3.35           4.00

GB%      52.4%        54.9%

While the numbers are not exactly identical, the results are very similar: two pitchers that had very good stuff, but were very inconsistent. As noted earlier, Britton’s move to the ‘pen was also a move to a primarily two-seam/sinking fastball that induced tons of ground balls. While it is unlikely that Kelly’s move to the ‘pen would turn him into a ground ball machine like Britton, it should be noted that Kelly already possesses an average fastball of 94.7 MPH as a starter, while Britton saw his jump from 91.6 as a 2013 starter to an average of 95.1 as reliever in 2014. A few other notable velocity spikes we’ve seen from pitchers with a history of working as a starter, as compared to  fastball velocity as a reliever:

Tommy Hunter 91.6 to 96.0

Andrew Miller 92.5 to 94.9

Wade Davis 91.8 to 93.7

Joba Chamberlain 92.5 to 94.6

Maybe Kelly puts it together as a starter this season. After all, Boston thought very highly of him if they were willing to give up John Lackey last season. Maybe we see Kelly cut down on his walk rates, and finally put together some peripheral stats that match his strong ERA numbers in 2012 and 2013. But what if he doesn’t? What if he continues to be a bottom-of-the-rotation type pitcher? Boston could move him to the ‘pen, see that 95 MPH fastball bump up to the 97-98 range, and reap similar rewards that the O’s received from Britton in 2014.

FanGraphs Wins Above Replacement is considered by many in the sabermetric community be the holy grail of WAR.  And, even though I’m writing a piece that is critical of fWAR, FanGraphs is still the first website I go to when I want to get a basic understanding of a specific player or team’s value.  Don’t view this article as an attack on fWAR or FanGraphs, both of which I use frequently; instead, consider this article as constructive criticism.

fWAR, specifically for pitchers, is riddled with minor problems that together make the metric less valuable.  In Part 1 of the series, we’re going to look at a hotly debated issue regarding fWAR that has been brought up by other readers before: the fWAR park factors.

According to the FanGraphs glossary, a basic runs park factor is used when calculating fWAR.  Because FIP models ERA, using runs park factors for FIP shouldn’t be a problem.

Unfortunately, this idea simply isn’t true.  The inputs of FIP, HR/9, BB/9, and K/9, only include about 30% of plate appearances.  Some ballparks (Citi Field for example), inflate HR/9 and FIP despite suppressing runs in general.  If Pitcher fWAR is based on FIP, FIP park factors, not runs park factors, must be used.  Below is a table comparing runs and FIP park factors for different teams/ballparks, with FIP park factor equaling ((13*HRPF)+(3*BBPF)-(2*SOPF))/(14), with all of the data coming from the FanGraphs park factors.

In addition, the standard difference between the Basic and FIP park factors was a staggering 5.5.  Clearly, using runs park factors on FIP significantly benefits and hurts certain teams’ Pitcher fWAR.

While the Marlins, Red Sox, Pirates, Twins, and Royals benefit from park factors that overestimate their ballpark’s FIP-inflating ability, the Reds, Brewers, White Sox, Yankees and Mets experience the opposite effect, falsely increasing/decreasing these teams’  Pitcher fWAR.

Looking at the team pitching leaderboards, the effect of this mistake is pronounced on several teams’ fWAR.  For example, the Mets, despite ranking 9th in the National League in FIP while playing in a ballpark that inflates FIP by 2%, rank dead last in the National League in Pitcher fWAR.  Similarly, the Red Sox rank 5th in the AL in Pitcher fWAR despite ranking 10th in the AL in FIP and playing in a ballpark that suppresses FIP by 4%.

Using FIP park factors instead of runs park factors is a simple change that would vastly improve the accuracy of Pitcher fWAR.  In the next segment of “Trying to Improve fWAR”, I’ll examine the league adjustments (or lack thereof) in both Position Player and Pitcher fWAR.

Last time, we analyzed Yu Darvish’s sliders in terms of when they projected as strikes and how pitch movement affected perception, leading batters to swing at pitches outside of the strike zone in the direction of the pitch movement. This time, we will turn our focus to four-seam fastballs. As before, we are using the 2013 data set since the algorithms for this were run before the completion of the 2014 season. To start, we can examine a four-seam fastball from Yu Darvish, his second-most thrown type of pitch in 2013, via simulation using the nine-parameter PITCHf/x data for its trajectory. The chosen fastball from Darvish was thrown roughly down the middle of the strike zone and we also track the projection of the pitch as it approaches the plate.

Note that the pitch, in this case, is simulated at one-quarter actual speed. The strike zone shown is the standard width of the plate and 1.5 to 3.5 feet vertically. The red circle represents the projection of the pitch after removing the remaining PITCHf/x definition of movement from its current location (Note that while the simulation shown above is a GIF, the actual simulation is an interactive PDF where the controls at the bottom of the image can play, rewind,  slow down, etc. the simulation. This is discussed at the end of the article for the interested reader, including a link to several interactive PDFs as well as a tutorial for the controls and the source code written in TeX). Here, the movement causes the pitch to rise, giving the pitch in the simulation a “floating” quality as it never seems to drop.

As in the previous work on sliders, we will start by splitting the four-seamers into four groups based on the pitch location and the batter’s response: strikes (pitches with a 50% chance or better of being called a strike) and balls (lower than 50% chance of being called a strike), and swings and pitches taken. Working with the projections to the front of the plate after removing the remaining movement on the pitch, we can examine how attractive (in terms of probability that the projection will be called a strike) pitches in each of these four categories, on average, are to batters incrementally as they approach the plate.

To begin, for left-handed batters versus Darvish in 2013:

For both types of pitches in the strike zone (red=taken, green=swung at), the average probability of the pitch being called a strike levels off around 20 feet, with strikes swung at peaking at probability 0.919 at 9.917 feet from home plate, then dropping to 0.917 at the plate. Strikes taken reach their maximum at the front of the plate with probability 0.869. The four-seamers swung at outside of the strike zone (blue) average around 0.5 probability of being called a strike up until around 30 feet, before dropping off. The fastballs taken outside the zone (orange) tend to project as low-probability strikes initially and remain so to the front of the plate.

We can simplify this graph to include only swings and pitches taken.

Once again, pitches swung at project as better pitches throughout than those taken. The peak for swings is at 14.083 feet with probability 0.782, and finishes at 0.777. The pitches taken keep increasing in attractiveness all the way to the front of the plate, reaching a called-strike probability of 0.332.

To further examine what is happening in these graphs, we can view the location of these projections from 50 feet to the front of home plate. The color scheme is the same as the four-curve plot above.

Focusing on the blue projections for the moment (swings outside the strike zone), the projections down and to the right of the zone are carried by movement toward the strike zone and most end up as borderline strikes. Those up and to the left project further and further outside the strike zone as they approach the plate, since their direction of movement is roughly perpendicular to the strike zone contour. To get a better idea of the number of each of the four cases in nine regions in and around the strike zone, we can fade the data into the background and replace it in each region by an arrow indicating the direction that the average projection for that area is moving and the number of pitches of that case located there.

Focusing first on the pitches in the strike zone, there is a dearth of projections in the upper-right area, which would be on the inside half of the plate to LHB. The pitches taken in the strike zone tend to skew slightly down and to the left, relative to those swung at. Note that in many of the regions around the strike zone, the samples are quite small so it may be difficult to draw any strong conclusions. With this in mind, these results can be summarized in the following table where the center cell represents the swing percentage in the strike zone and all other cells contain the percentage of swings in that region.

The region with the highest swing percentage is the strike zone, at 59%. The region with the next highest percentage is above the strike zone, which is in the general direction of movement, but here there are only nine data points to rely on for this percentage. It would seem that the regions that induce swings are those where the pitches project in the strike zone and are carried out by movement (above and above-and-left of the zone) and where the pitches project as balls but movement is carrying them toward the zone (below and below-and-right of the zone). Notice that the area below and left of the strike zone has 47 pitches thrown there and only 2 swings, which is where the movement parallels the strike zone.

It would appear, based on these observations, that the location of the pitch, relative to the direction of the movement, has an influence on generating swings outside the strike zone. As with the sliders in the previous article, we will use, as a measure of if the pitch is thrown outside in the direction of movement, the angle between the movement of the four-seam fastballs at 40 feet (the pfx_x and pfx_z variables in the PITCHf/x data set) outside the zone and a vector perpendicular to the strike zone extending to the final location of the pitch at the front of home plate. An angle of zero indicates that the movement of the pitch carried it perpendicularly away from the strike zone. Ninety degrees means that the pitch projection parallels the strike zone due to movement. A one-eighty degree angle means that the pitch is being carried by movement perpendicularly toward the strike zone. Further explanation, including a visual depiction, can be found in the link to the previous article at the top of this page.

To begin, we will look at the distribution of angle versus distance from the strike zone for all of Darvish’s four-seamers outside the zone to lefties.

The distribution, in this case, seems slightly skewed toward having pitches thrown in the general direction of movement. This visual assessment is supported by the percentages in the table (sorted by angle and average distance from the strike zone contour in feet. e.g., 0.5 = 6 inches, 0.33 = 4 inches), with nearly 29% of pitches having an angle of less than 45 degrees and over 58% with an angle less than 90 degrees. The distribution does not seem to have definitive shape.

For all MLB right-handed pitchers in 2013, including Darvish, the distribution is much more clear. There is a swell of pitches thrown with angle between 0 and 90 degrees and within six inches of the strike zone, with 37.5% thrown with an angle of less than 45 degrees, and 61.9% with an acute angle. In conjunction, as the angle increases, the average distance from the strike zone decreases. To get a better handle on the ramifications of this choice of pitch locations, we can further sort the data into swings and pitches taken.

For Darvish, nearly 44% of the pitches swung at had an angle between the vector perpendicular to the strike zone and the movement vector of less than 45 degrees. For those less than 90 degrees, this percentage jumps to nearly 70%. In addition, the average distance outside with angle less than 45% is an average of 4 inches outside whereas, overall, the average is about 3 inches in all directions. We can compare this to Darvish’s right-handed colleagues in 2013:

For MLB righties, the largest area of swings is right around a 30-degree angle. Close to half of the swings, 46.9% to be exact, occur when the angle is less than 45 degree and over two-thirds are for pitches in the general direction of movement. The average distance on four-seamers swung at outside is close to Darvish’s overall, but is almost an inch further out for Darvish for 45-degree or less angles. So for RHP to LHB, pitches thrown in the neighborhood of 30 degrees and within a half-foot of the strike zone tend to induce swings, which is also seen for Darvish. We can now look at the complement of this, pitches taken outside, to see how this distribution compares to swings.

The distribution for Darvish on pitches taken has some semblance to that for all pitches, but the percentages have dropped in all cases. In addition, the average distances across the board are over six inches outside.

For all MLB RHP in 2013, the pitches taken by LHB outside the strike zone are largely located below 90 degrees, with a large number near 60 degrees. Compared to the case of all pitches outside the strike zone, the percentages are not all that dissimilar, but the distances are slightly larger. Putting the two hexplots together to see how they form the plot for all outside pitches, we see that what appears to be one large grouping of data below 90 degrees for all pitches separates into two smaller groupings: one around 30 degrees for swings and one around 60 degrees for pitches taken.

To examine why it might be the case that pitches thrown in the direction of movement, meaning a small angle between the movement vector and the vector perpendicular to the strike zone, are swung at more frequently and are more effective at inducing swings further from the strike zone than those that are not, we can take a four-seamer thrown by Darvish above the strike zone and examine both the trajectory of the pitch and its projection. We can again simulate such a pitch (at quarter speed) via the PITCHf/x data for Darvish. Note that since the below simulation does not possess the same computational capabilities as the rest of the code, which is done in R, we use the standard strike zone as a reference rather than the 50% contour.

Based on the simulation and associated projection, we can see that the pitch projects as a strike early on and, late in its trajectory, appears to be a ball. The important observation for this is that, for some part of its flight, the pitch does appear that it may be a strike. Similarly, for a pitch below the strike zone, we see the opposite result.

One can see the problem with getting a batter to swing at a pitch such as this. It starts out as looking like a pitch in the dirt and, through its path to the plate, only slightly improves its chances of being called a strike, and at no point really gives the batter much incentive to swing at it. Thus it makes sense that a batter might swing at a four-seam fastball high above the strike zone but not one a similar distance beneath.

Performing the same analysis for right-handed batters, we again start with Darvish’s results for the four-seam fastball in terms of ball/strike and swing/take.

Here, the swing/strike curve peaks at probability 0.94 at 11.667 feet and finishes at 0.937. These probabilities are slightly higher than those for lefties at the maximum and at the front of the plate. The pitches taken in the strike zone peak at the plate with probability 0.904, compared to 0.869 for LHB. For both cases of pitches outside the strike zone, they reach their maximum very early in the trajectory and drop off afterward.

Changing to the two-curve representation for four-seam fastballs to right-handers, the swing curve reaches its apex of probability 0.814 at 19.833 feet and ends with probability 0.797 at the plate. For pitches taken, the average strike probability increases throughout the trajectory, ending at 0.411. Once again, these probabilities are higher than for left-handed batters.

As before, we can switch to the discrete data and their projections as the pitches near the front of home plate. Of note is that the pitches taken (red data points) are, by and large, down and to the right of the strike zone from the catcher’s perspective, which is in the opposite direction that the movement influences the pitches as they approach the plate. In addition, the majority of swings outside the strike zone, the blue data points, leave the strike zone in the direction of movement. Also of interest is that the pitches fill up the strike zone more against RHB, while four-seamers to LHB were lacking for the inner half of the strike zone. For the pitches swung at outside the strike zone in the opposite the direction of movement, down and to the right, they end up very close the strike zone contour, making them boarderline strikes, and thus nominally classified outside the zone. To observe these phenomena more succinctly, we can switch to a vector representation indicating the number of pitches and the direction that the projections are headed for each of the nine regions in and around the strike zone.

Of the 270 pitches in the defined strike zone, the average location of the 112 taken were down and to the right of those swung at, as represented by the red and green arrows. To quantify the percentage of swings in each of the nine regions, we can refer to the below table, aligned spatially with the data from the GIF (center square being in the strike zone).

Based on these results and for regions with more than a handful of pitches, the highest percentages of swings outside the strike zone are in the upper and upper-left regions, in the direction of movement. The lower-left corner is large as well but can be disregarded as it only contains two pitches, one of which was swung at. Also, it is hard to draw any conclusions to the left of the plate since there is no data.

We can now turn our attention to pitches outside the zone for both Darvish and other MLB righties in 2013:

First, for Darvish, the distribution of pitches, when viewed by plotting distance from the strike zone versus angle between the perpendicular vector to the strike zone and the movement vector, appears bimodal with a large grouping both above and below the 90-degree mark.

The four-seamers outside to righties are, on average, over 6 inches outside, with most thrown, 59.9% to be precise, in the opposite direction of movement. However, most of the pitches thrown in the direction of movement, 31.2%, are thrown with an angle of less than 45 degrees. Compared to LHB, the distances are greater and the percentage of pitches with an angle of less than 90 degrees is noticeably lower.

For MLB RHP, the distribution also appears bimodal, with two groupings of data near 30 degrees and 120 degrees. This roughly mirrors Darvish’s distribution, relative to angle versus distance.

As compared to Darvish, RHP threw about the same percentage of pitches with an angle of less than 45%, but more with an angle of less than 90 degrees. In all cases, the MLB RHP four-seamers outside were, on average, closer to the strike zone. Compared to pitches outside to lefties, the percentages for less than 45 and less than 90 degrees are down.

Taking the subset of pitches swung at outside for Darvish, the distribution has become closer to having a single mode near 30 degrees. Despite reaching into small sample sizes for this subset, the below table reinforces these conclusions.

While only around 30% of Darvish’s pitches were thrown with an angle of 45 degrees or less, over two-thirds of his swings outside the strike zone were in this range of angles. This increases to nearly 75% when considering four-seam fastballs thrown in the general direction of movement, meaning 90 degrees or less. Of note here is that the distance that entices a swing decreases as the movement aligns less and less with the vector perpendicular to the strike zone. Here, the distances are greater compared to left-handed batters faced by Darvish in 2013, but the percentages are up.

Switching the larger sample of all 2013 MLB RHP, we retain only one of the modes observed for all pitches. The pitches that are swung at outside are clustered down near 15 degrees and within half a foot of the strike zone.

The percentage of swings with an angle of 45 degrees or less is over 50% and, like Darvish, those less than 90 degrees are up near 75%. The distance again decreases as the angle increases and, compared to Darvish, is much closer to the zone. Versus right-handed batters, the percentages for angles 45 and 90 degrees or less are greater but the distances do not differ greatly as compared to LHB.

The other half of the data, pitches taken outside, gives us the second mode seen originally in Darvish’s data. This mode is a cluster of data above the 90 degree level.

While a quarter of the pitches taken are thrown with an angle of 45 degrees, only a little over one-third were thrown in the general direction of movement. Note that the pitches that are thrown in the direction of movement and are taken tend to average three-quarters of a foot outside, so it makes sense that they would not be swung at. The percentage of pitches taken with an angle of less than 90 degrees is down from 56.8% for LHB and, overall, the pitches are almost an inch further outside.

For the MLB data set, the second mode is located around 120 degrees.

As with Darvish, about one quarter of the pitches taken outside are at an angle of 45 degrees or less and 59% are thrown in the opposite direction of movement. When put up against the pitches taken by LHB, the percentages are down for both 45 and 90 degree or less pitches from 35.1% and 60.7%, respectively.

As with RHP versus LHB, the full distribution, in terms of the hexplots, separates into two clusters: one related to swings and one related to pitches taken. The cluster related to swings sits in the range of 15 degrees while pitches taken are closer to 120 degrees. This is similar to the case for lefties, except the cluster of pitches taken moves from the 60-degree area to the 120-degree area and the cluster related to swings moves down from 30 degrees to 15 degrees. However, in both cases, the swings appear to be separate clusters from the pitches taken.

Discussion

For four-seam fastballs thrown by Yu Darvish in 2013, the maximum attractiveness on swings is in the range of 10-20 feet in front of home plate for left- and right-handed batters, possibly tying into how long a batter can reasonably project a pitch when deciding to swing. The four-seamers also tend to be swung at outside the strike zone in the general direction of movement, which we have seen previously with sliders. This is especially pronounced for RHB vs RHP, with pitches exiting the strike zone in the direction of movement causing swings, and pitches entering the zone opposite the direct of movement being taken. By simulating the PITCHf/x data, we can get an idea of why this might be true: pitches outside thrown in the general direction of movement project in the strike zone for some period of time before projecting outside of it and pitches thrown opposite this direction project outside and, while their probability increases, these pitches never appear as strikes and thus do not usually induce swings from the batter.

Next time, we will finish up with cut fastballs from Yu Darvish and see how movement affects perception in this case. After that, we can switch to the 2014 data set and also turn the algorithm around and apply it to a batter.

PITCHf/x Simulation

For those familiar with the previous installment, we covered a slider thrown by Yu Darvish to Brett Wallace and simulated the projected pitch location in R. To better represent how the pitch projection may tie into perception, we have switched to a more visually appealing representation of simulating the PITCHf/x data in the context of the catcher’s viewpoint (we could presumably display this from the batter’s point of view as well). For the aforementioned slider to Wallace, the simulated PITCHf/x data, based on the 9-parameter model, is:

This would seem to be a better way to represent the data, including a backdrop and accurate scaling of the pitch size and location. As another example, we can simulate a random Darvish curve:

In order to make the GIFs for simulating the PITCHf/x data, we are first using TeX to write the code and then compiling it using MiKTeX with the “animate” package handling the controls. To begin, we place a reference point 6 feet, 1 inch behind the tip of home plate, roughly approximating the location of the catcher (the one inch past six feet is not important but makes the distance to the front of home plate an even 7.5 feet).  The height of the reference point is taken to be 2.5 feet in the z-direction. This is the point by which we will determine perspective. Everything will be projected into the plane at the front of home plate, spanning three feet to the left and right of center and from the ground to five feet high. For a given position of the pitch, we find the associated spherical coordinates, relative to the reference point. To figure out where to display the pitch in the frame, we track the pitch along the line formed between the pitch location and the reference point until it reaches the frame. Since the two angle measures of the spherical coordinates will not change when tracking along this line, we need only find the distance along it that places it in the frame we are displaying.

Once we have the location of the pitch in the frame, we still need to find the size of the pitch as seen from that distance. To do this, we again use the reference point and find the distance to the center and to the top of the baseball. With a third side that goes from the top to the center of the baseball, this creates a triangle. Forming a similar triangle by adding an additional third side where the frame cuts the triangle at the front of the plate, we obtain a smaller triangle contained in the larger one. Using this geometry, we can find the size that the pitch will appear at this distance using trigonometric properties of similar triangles (namely that their sides have the same ratio).

To begin the simulaton, we find the times associated with 55 feet and the front of the plate. We then find the location of the pitch in three dimensions, incrementing in time from release to strike zone and adjusting the location and the size of the pitch to appear positioned and scaled correctly in frame. The simulation in the actual PDF is at 60 frames per second, with most animations lasting around a half a second. For the purposes of creating GIFs, we slow the pitches down to one quarter this speed and capture using a program called LICEcap. The code is written so as to work for any pitch by merely swapping in the chosen 9-parameter PITCHf/x data and recompiling. The projection is shown as a red circle, and is calculated as previously discussed. All background features are scaled appropriately, in a similar manner as the pitch.

Note that while this is, in many ways, an approximation of perception from the catcher’s point of view, it functions well for our purposes of providing a decent replacement for live video since we can overlay the projection and view it from the reverse of the traditional television angle from center field. Included is a link to a Google Drive containing a collection of interactive PDFs for pitchers and pitches from 2013 and 2014. There is also an interactive guide to the controls with the given example being a Clayton Kershaw slider. Finally, the source code is included so the interested reader/programmer can input any chosen PITCHf/x parameters and compile to get a representation of the pitch, that includes distance to home plate, the velocity of the pitch, and the time since release.

On August 3, 2009, Neftali Feliz made his major league debut against the Athletics, pitching two perfect innings with four punchouts. In those innings, he mowed down hitters with 23 fastballs that averaged 99.45 mph, 4 changeups that averaged 91.13 mph, and 3 sliders that averaged 82.43 mph. He would end his rookie season with a 1.74 ERA (2.48 FP), a 33.3 K%, and a 6.8 BB%. As a 21 year-old, he already looked like a bonafide bullpen ace for the Rangers.

Fast forward to the end of spring training in 2014. Feliz is 25 years old and in the prime ages of his baseball career. And he’s starting in AAA. With a fastball that is 91-93 mph. Rangers’ General Manager Jon Daniels said of him, “He’s healthy and his work ethic has been solid, but he needs some work and the best place to get him that is in Round Rock right now. I expect he’ll be back as soon as he’s ready to help us.” A team whose bullpen for opening day included Seth Rosin thought that Feliz wasn’t ready to contribute for them out of the gate. Clearly something was off.

On August 1, 2012, Neftali Feliz underwent Tommy John surgery. Tommy John surgery generally requires at least 12 months for recovery, and he was back in the majors by September 2013. He averaged 94.19 mph with his fastball during his 6 games in September 2013. While his velocity was a step down from his 97+ mph heat in 2009-2011, pitchers often have to slowly build their arm strength up again to pre-surgery levels and there was no reason to believe he wasn’t on track to doing so. When his velocity failed to reach that level through most of his 2014 campaign, though, it became unclear if he would ever regain his pre-surgery stuff.

By some measures, his time in AAA was a success. He struck out 9.73 batters and walked only 2.51 per 9 innings. He produced a 3.14 ERA compared to the Pacific Coast League’s league-wide 4.64 ERA. His biggest problem was home runs—he gave up 6 in only 28.2 innings. When was he called back up to the major league squad on July 4, there were reasons to be cautiously optimistic that he could find some success again as a reliever. The Rangers noted that he was throwing in the mid 90’s some games while in others he would sit in the low 90’s.

Feliz didn’t exactly dominate during his early outings. Through July 23, he had pitched 10.1 innings with only 4 strikeouts, 3 walks, and 2 home runs given up. Yet, out of the playoff race, Texas dealt their closer Joakim Soria to the Tigers and anointed Feliz their new closer. While it’s possible that the team merely liked his shiny ERA at the time over his FIP (2.61 to 5.75), perhaps they started to see some signs of life in him. Regardless, his 1.69 ERA and 13 saves out of 14 save opportunities the rest of the way probably made them feel validated in their decision. With his end of the season performance, it appears likely that he will be the Rangers’ opening day closer.

Projecting into 2015, Feliz’s 4.90 FIP and -.1 WAR from 2014 provide red flags. His home run rate also look to be an issue. His extreme flyball tendencies (51.1 FB% versus 27.3 GB%) resulted in 1.42 HR per 9 innings despite a fairly ordinary 11.1 HR/FB% rate. His 17.2 K% and 9 BB% doesn’t exactly inspire confidence, either. Steamer isn’t a fan and projects him for .1 WAR in 65 innings.

But there are reasons for optimism, too. He kept up his low BABIP streak at .176 (.215 career) thanks to his impressive 20 IFFB% (17.8 career). Among relievers with 200 innings since his debut, he has the lowest BABIP, the 6^th lowest LD%, and the highest IFFB%. Steamer projects him for a .284 BABIP next year, but I’m willing to bet his will be much lower than that figure and will continue to let him beat his FIP by around a full run.

Next, we’ll look at his home run rate. His 2014 figure was the highest of his career, caused primarily by his 11.1 HR/FB% (6.9 career). What may have caused that? Well, it may have been caused at least in part by his changeup. A changeup is a pitch designed to fool hitters who are looking for a fastball: it is supposed to be thrown with identical arm speed as the fastball to make it harder to pick up, and then its velocity and/or movement difference makes it effective. For a pitcher throwing in the upper 90’s with hitters already struggling to catch up to their fastball, a changeup may be less effective because the velocity reduction may sometimes help the hitter instead of hurting him (of course, there are exceptions). When Feliz was throwing in the upper 90’s in 2009-2011, he threw his changeup just 4.4 percent of the time. When Feliz was throwing in the low to mid 90’s in 2014, he threw his changeup 12.4 percent of the time. For his career, opponents have a .212 ISO against his changeup compared to a .119 ISO against his fastball.  In 2014, hitters had a .429 ISO against the pitch, including 3 of his 5 home runs given up on the year.

His velocity provided another reason for optimism as well. While his early- to mid-season velocity wasn’t great, he improved as time went on: in July he averaged 92.88 mph; in August he averaged 93.7; and in September he averaged 95.81. The ISO against his fastball decreased each month as well (from .107 to .107 to .053), even as he increased his usage of his fastball (from 64.7% to 77.36 to 77.78).  His velocity increase had an added bonus as well: it allowed him to use his changeup less (from 14.72% to 11.32 to 3.17). The biggest question is whether he can maintain his September velocity, or even improve upon it.

Overall, I don’t think Neftali Feliz is a safe bet to be great in 2015. But I do think that he has a real chance to be much better than the projections project him to be. To end this post, I’ll post a few gifs of Feliz at his best in 2014:

* All pitch usage, velocity, and movement numbers are obtained from Brooks Baseball. All pitch results numbers are obtained from Baseball Savant.