## Statcast, Scouting, and Statistics: An Objective Look at the 20-80 Scale for Hitters

The 20-to-80 scale is one of the core tenets of baseball scouting and allows evaluators to quickly interpret a player’s skillset. Kiley McDaniel wrote an excellent series of articles back in 2014 (and provided an update this past November) explaining the scale, and while the whole series is easily worth a read, one of the key notes is as follows:

The invention of the scale is credited to Branch Rickey and whether he intended it or not, it mirrors various scientific scales. 50 is major league average, then each 10 point increment represents a standard deviation better or worse than average.

On the surface, the scale is fairly easy to understand, but somewhat harder to conceptualize what each grade actually looks like. For example, how frequently does a hitter with a 45 power grade hit a home run? How does a 60 run grade translate to Sprint Speed? I decided to investigate, drawing inspiration from a 2013 article by Mark Smith. The idea of an objective 20-to-80 scale, while not a new concept, is worth revisiting at this time because of changes in the run environment and the development of new player evaluation techniques, most notably StatCast. We’ll begin with a brief rundown on methodology before looking at each tool:

Methodology

As McDaniel noted, the 20-to-80 scale largely mirrors a normal distribution, with each 10-point grade jump representing an increase of one standard deviation. Following this logic, the first step towards creating an objective scale is finding “major league average.” This in and of itself presents a bit of a challenge, as major league average differs from the average of all regular (qualified) major league players. Taking the average of only qualified major league regulars would result in a skewed distribution, while including all major leaguers would result in far too many outliers (for example, a player batting .000 or .750). Therefore, the trick lies in finding a sample representative of more than just regulars but also one that isn’t easily skewed by tiny sample size variations. Many of the cutoffs selected are admittedly arbitrary but allow for a reasonable sample of players receiving significant time in the major leagues, and are largely based off of Russell Carleton’s excellent work on reliability. These cutoffs will be detailed with each statistic provided.

Returning to the methodology, once the “major league average” was established for each parameter, the standard deviation of the sample was calculated. The average was assigned a grade of 50, and each 5-point increase on the 20-to-80 scale resulted in the addition of half a standard deviation to the previous value. Likewise, a 5-point decrease corresponded with a decrease from the higher value by half a standard deviation. The following chart illustrates this pattern:

All data spans 2016 to 2018 and represents cumulative averages over that span. When applicable, each tool grade also provides a representative example player, as well as the number of players in the sample that fall under that tool grade. These examples are meant to be purely illustrative and are in place to simply give an idea of what each tool might look like rather than exist as a hard-and-fast grade. For FanGraphs-sourced data, cumulative data is directly pulled while cumulative three-year data from Baseball Prospectus and Baseball Savant was calculated with the use of weighted averages. Without further ado, let’s look at the tools:

Hit

For the hit tool, we’ll look at a number of measures, varying from traditional to StatCast-based. In order, those parameters are strikeout rate, batting average, batting average on balls in play, expected batting average, and contact rate. Strikeout rate and contact rate grades were calculated based on all non-pitchers with at least 200 PA between 2016 and 2018 (easily clearing Carleton’s standard of 60 PA for K%), while batting average was based off players with at least 910 at-bats (matching up with Carleton’s standard), and expected batting average and BABIP are based off all players with at least 820 balls in play over that span. Here’s a look at the hit tool across the league:

Power

Power can be measured in a number of ways, but we’ll look at four primary measures: home run rate, isolated power, average exit velocity, and barrels per batted ball event. Any player with at least 200 PA qualified for the sample for the ISO and HR% grades, while players with at least 100 batted ball events qualified for the StatCast metrics. It’s worth noting the absence of 20 or 25 power grade players, but this is possibly indicative of the emphasis teams place on hitting for power. It is nearly impossible for a player to provide enough value elsewhere to make up for a complete lack of power, as evidenced by the lack of extremely low power hitters in the league. Power may be one of the tools McDaniel mentioned in his primer on the scouting scale that doesn’t exactly follow a normal distribution.

Speed

Speed is a simpler tool to look at than just about any other tool thanks to the development of StatCast Sprint Speed. Any player season with 50 or more max effort runs (2016-2018) was considered for the sprint speed component of the tool. It is also worth adding baserunning into the mix as McDaniel notes that “baserunning and good jumps out of the batter’s box are also folded into the run grade.” The baserunning grade is based on a sample of all players with at least 150 times on base over the three-year span and the parameter is scaled to 150 times on base for ease of comparison.

Defense

While the hit, power, and speed tools are all fairly easily measured and interpreted, the defense and throwing tools present a new set of challenges. For one, it doesn’t make sense to compare players at vastly different positions on the same scale. For this purpose, I’ve broken down defense into three groups: catchers, infielders, and outfielders. Of the three groups, outfielders were the most straightforward. Outs Above Average provides a solid look at outfield defense (based on players with at least 1900 innings in the outfield, 2016 to 2018) but is absent for other positional groups. Additionally, the throwing components of DRS and UZR are more easily separated from the fielding-based components for outfielders, allowing for a separate defense grade (based on range and plays made) from the throwing grade.

This is similarly possible for catcher defense, but the catcher fielding grade is complicated by the myriad of factors that make up suiting up behind the plate. The catcher defensive parameters include non-throwing DRS, blocking runs, and framing runs. For infielders, it’s much more difficult to break down throwing runs from fielding ones, and for this purpose it is safest to simply present the two together. All Defensive Runs Saved and Ultimate Zone Rating data is from FanGraphs and includes any players with 2,000-plus innings played in the infield between 2016 and 2018. All UZR and DRS-based defensive metrics are scaled to 1,000 innings played. Catcher framing is scaled to per 5,000 framing chances while blocking is scaled to 2,500 blocking chances, based on all catchers with 1,000 defensive innings over the last three seasons (as are all catching defensive metrics for the purpose of this study).

Infielder Defense/Throwing

Outfielder Defense

Catcher Defense

Throwing

As discussed above, while there is no throwing grade for infielders, there are somewhat promising measures with which to evaluate outfielder and catcher throwing ability. Outfielder throwing is based on the arm run components of DRS and UZR, scaled to 1,000 innings. Catcher throwing includes both StatCast arm strength and caught stealing runs, scaled to 1,000 innings. Outfield metrics are based on all players with at least 2,000 defensive innings in the outfield while catcher throwing is based on all players with at least 1,000 innings caught since 2016. It is worth noting that the somewhat high cutoff for outfielders led to a number of players (Khris Davis, Derek Dietrich) that scored especially poorly in terms of throwing to not qualify for the sample, likely because teams have simply tried to avoid putting notably poor defenders and throwers in the field. Here’s a look at the throwing grade:

Outfielder Throwing

Catcher Throwing

It’s worth wondering whether the lack of catchers at either extreme in terms of defense and throwing is simply due to the limited sample of players or is being caused by a different phenomenon. It’s possible that the clustering effect is explained by the fact that terrible catchers either don’t remain behind the plate often or don’t make the major leagues. Whether this effect is due simply to a small sample, less than comprehensive defensive metrics, or something different, it is certainly interesting to observe compared to other positions.

Conclusion

For how often the 20-to-80 scale is discussed in baseball circles, it is interesting to note that it is rarely objectively analyzed to create a rough estimate of what each grade might look like at the major league level. The advent of StatCast has allowed for objective analysis of more tools than ever before and will hopefully continue to do so as the technology continues to develop. While my attempt at creating an objective view of the 20-to-80 scale is undoubtedly imperfect, the results certainly provide an interesting shorthand look at many of the measurable aspects of the scale.

Statistical data (AVG, BABIP, ISO, HR%, BsR, all DRS and UZR components, Contact%) from FanGraphs, pulled December 11, 2018. Catcher framing and blocking data taken from Baseball Prospectus, pulled on December 11, 2018. StatCast data (xBA, Exit Velocity, Sprint Speed, Barrels/BBE, OAA, Catcher Throwing Velocity) from Baseball Savant, pulled August 23, 2018. Cutoffs for metrics largely based on Russell Carleton’s work on reliability.

This piece was originally published on December 27, 2018 at the CheckSwings baseball blog.

## Best of the Bench in 2018

Every year, nearly every team in baseball receives significant contributions from unexpected sources. The 2018 campaign was no exception, with a number of players putting up productive seasons while primarily coming off the bench. For the purposes of this article, we’ll define a bench player as any player with (roughly) 100-plus plate appearances over the course of the season that did not appear as a team’s primary starter at any one position. Additionally, I sorted out players that were traded (or acquired) midseason (Manny Machado for example) or called up to start full time but did not record enough time to count as the team’s primary starter at their position (per Baseball Reference). Players with 500-plus PA were excluded from consideration on account of extensive playing time (apologies to Chris Taylor, Matt Kemp, Jurickson Profar, and Ben Zobrist), and one player was selected at each position, along with honorable mentions. Without further ado, let’s look at the best of the bench in 2018:

Catcher: Elias Diaz, Pirates

Honorable Mentions: Tyler Flowers (ATL), Luke Maile (TOR)

Diaz certainly served as a key bright spot during an up-and-down season for the Pirates, filling in admirably for the oft-injured Francisco Cervelli. The young Venezuelan had somewhat of a breakout campaign in 2018, posting a 114 wRC+ while contributing positive defensive value behind the plate. Overall, Diaz put up a solid 2.0 WAR in 277 PA on the season. After a weak offensive showing in 2017, Diaz appears to have made notable strides in plate discipline this season, improving his walk rate from 5.5% to 7.6% while dropping his strikeout rate from 19.0% to 14.4%. This decline in strikeout rate represents one of the 10 largest such drops in the league between 2017 and 2018 among players with at least 200 PA in each season. Combined with Cervelli’s excellent output, Diaz helped guide the Bucs to a league-leading 5.3 WAR from catchers:

While Flowers and Maile each put up solid seasons in their own right, Diaz ranked among the top 10 bench players in all of baseball by WAR this season and established himself as one of the better backup catchers in the game. Read the rest of this entry »

## Examining 2018’s Biggest Pitch Repertoire Changers

Every season, pitchers and pitching coaches across the league tinker with pitch arsenals, with varying effectiveness. This series examines the pitchers who have most significantly changed their arsenals in 2018, beginning with starting pitchers who have added new pitches.

This season, a handful of big-name pitchers have added a new weapon to their arsenal, including Nationals ace Max Scherzer. Below, we’ll look at Scherzer and his pitch adding companions in detail, in order of their new pitch.

Cutters

Max Scherzer, Nationals (Statistics through July 29th)

Even in the midst of a relatively down season for the Nationals, Mad Max has put together another terrific season, currently sitting at a 2.30 ERA and 4.8 fWAR. Already a three-time Cy Young Award winner, the ace righty has added a new weapon to his pitch mix this season. Scherzer has added a cutter, a pitch he toyed with in 2015, to his arsenal, and used it fairly regularly this season after not employing it at all last season. Scherzer’s pitch mix for the last two seasons is displayed in the following table:

As the table shows, Scherzer’s fastball, curveball, and changeup usage have remained about the same (as have the velocities on each pitch) while he’s shifted his focus away from his slider and towards his cutter. The new pitch is averaging 88.4 mph, a few ticks faster than the slider. Scherzer’s located both pitches similarly, throwing both pitches primarily low and on the left-hand batter’s box side of the plate. The cutter (pitch usage chart below on the left) has been used more inside to lefties, while the slider (right) has been kept low and away to righties.

However, there is one key difference between Scherzer’s cutter and slider usage: of the 242 cutters he’s thrown, only 7 have been to righty hitters, while only 3 of the 375 sliders he’s thrown have been to lefties. The development of his cutter has allowed Scherzer to avoid using his slider against lefties (who have slugged .368 off it career, compared to .270 vs righties) while keeping a four-pitch mix in play. While the pitch has only been about league average this season (with a weighted pitch value of 0.11 wCT/c), it may provide him a better weapon against lefties than his slider has. To this point, lefties have actually slugged .450 off the cutter this year (in a small sample), although they’re only hitting .183 against the pitch, and his overall line vs LHH is much improved this season (.189/.252/.349 with a .260 wOBA compared to .213/.299/.392 with a .299 wOBA in 2017). Only time will tell, but Scherzer’s been even better this season, and it may be in part due to his new pitch.

Carlos Martinez, Cardinals (Statistics through July 29th)

Another NL fringe contending team’s ace, another new cutter. Martinez began tinkering with a cutter this spring and has carried it over into the regular season. The hard-throwing righty has turned the cutter into a significant part of his arsenal, ranking 26th out of the 148 starting pitchers to throw at least 50 innings this season in cutter usage. Martinez has shown one of the most drastic changes in pitch mix of any starter over the past two seasons, with the cutter being the largest catalyst for this change.

As you can see in the table above, Martinez’s new pitch has largely come at the expense of his fastball, which has dropped in usage by 12%, marking the 3rd largest decrease in fastball usage between 2017 and 2018 of any starter in the sample. The Dominican righty has also leaned less on his slider since developing the cutter, which sits between 90-91 mph, about three ticks below his usual fastball and seven up from his slider. Martinez utilizes both a sinker and a four-seam fastball in addition to the cutter and uses each fastball in a different part of the plate. As illustrated in the pitch usage charts below, he tends to stick low and away (to a righty hitter) with the cutter (left plot), locates the sinker mostly down and inside (middle plot), and lives up in the zone with his four-seamer (right).

Martinez’s newfound cutter (an above average pitch with a wCT/c of 0.73) has also given him a nice complement to his slider, which he primarily uses low and away to righties and down and into lefties, but as mentioned sits at a much lower velocity than the cutter. It is worth noting that Martinez has rarely used the cutter against righties but has, in fact, used it as his go-to pitch versus lefty hitters, according to Brooks Baseball. Thus far, CarMart’s cutter has been an effective weapon against southpaw swingers, who are batting only .220 with a measly .305 slugging percentage on the offering. Additionally, Martinez has done a much better job overall of handling lefties on the season, holding them to a .228/.350/.332 slash (good for a .307 wOBA) after allowing a .260/.342/.441 slash (.337 wOBA) to opposite-handed opponents last season. Although Martinez has taken a step back against righties this season (allowing a .301 wOBA in 2018 compared to .263 last year), it seems that Martinez has an effective new weapon in his arsenal.

Sonny Gray, Yankees (Statistics through July 29th)

While both other pitchers discussed so far have had success this season, Gray has struggled to a 5.08 ERA on the campaign and has slipped down the depth chart in a Yankees rotation he was brought in to stabilize at the deadline last year. It’s worth noting that Gray is the most recently moved of the pitchers discussed, and his deal to the Yankees may well play into his changing pitch mix. Since the start of last season, the Yankees rank last in the major leagues in fastball usage, with just 39.3% of deliveries recorded as fastballs. Following this trend, Gray has seen a drastic shift away from using his fastball since donning the pinstripes, with his FB% dropping more than any other pitcher from last season. A comparison table of Gray’s pitch mix the past two seasons illustrates the change in pitch mix for the former Vanderbilt Commodore:

There’s a lot to unpack here, obviously starting with the cutter usage. Gray’s cutter usage ranks 17th in baseball among starters with 50+ IP this season, after not being utilized in 2017. As discussed earlier, the FB% is way down, as is the changeup usage, giving way to an increase in curveball frequency. Gray’s slider usage remains largely unchanged, holding steady in the mid-to-upper teens. In 2017, all of his pitches graded positively, with the slider standing out the most (1.06 wSL/c), but this season has been an entirely different story, with every pitch besides the curve (0.94 wCB/c) grading as below average and the cutter grading especially poorly at -1.77 wCT/c (to say nothing of the changeup, which has graded poorly but not terribly in the past, clocking in at -6.60 wCH/c, although small sample size should be noted here). There’s been plenty written about Gray’s struggles already this season, but it’s probably worth noting that his shift toward the cutter may not be helping him.

Slider

Jameson Taillon, Pirates (Statistics through August 3rd)

After missing a portion of the 2017 season to battle cancer, the Pirates righty is in the midst of a very solid season, running a 3.58 FIP/2.1 WAR through his first 22 starts. Some of this success may in part be chalked up to Taillon’s new slider, which he debuted in earnest during his May 27 start (written up here by Rotographs’ Paul Sporer) against Martinez’s St. Louis Cardinals after dabbling with it in a few earlier starts. Taillon has since made the slider a significant portion of his arsenal, utilizing it 13.5% of the time thus far in 2018. His pitch mix across the past two seasons is displayed below:

As Taillon has added a slider to his repertoire, its usage has come primarily at the expense of his curveball and sinker, which has seen the most dramatic drop in usage. Despite this decline, Taillon is still carrying a solid 49.2% groundball rate (up slightly from last year’s 47.3%). This may be in part due to the fact that Taillon’s new slider has generated a high rate of grounders (generating a GB% of 52.38%, per Brooks Baseball). The new pitch stands out in another way as well: thus far in 2018, Taillon’s slider ranks third in the majors (among qualified pitchers) in slider velocity at 89.9 mph. Taillon has thrown 174 sliders against right-handed hitters this season and has primarily located low and away, while most of the big righty’s sliders against opposite-handed hitters have been down and in, as shown in the zone profiles below (vs left on the left, vs right on the right):

Although it’s impossible to discern exactly how much of an impact the pitch has had on Taillon’s improvement this year, it is worth noting that the pitch ranks 18th among qualified pitchers in Fangraphs’ Pitch Value among sliders at 1.40 wSL/c. Although right handed hitters have had success against the pitch thus far (.263 BAA with a .491 SLG), the new pitch has devastated lefties this season, who are hitting a measly .160 with a .240 slugging percentage off the pitch. Taillon’s overall line against lefties is also much improved compared to last season (.321 wOBA this season vs. .355 in 2017), although it’s worth noting that Taillon’s numbers from last season are likely distorted by a rough second half following his return from cancer treatment. He’s also been more effective vs. righties and seen improvements in pitch value on both his fastball and curveball as well, possibly due in part to the new threat of his slider. After displaying strong talent and remarkable perseverance last season, Jameson Taillon has added a new weapon to an already strong arsenal en route to a very strong 2018 season.

Curveball

Patrick Corbin, Diamondbacks (Statistics through August 4th)

Coming off a solid but unspectacular 2017 season (4.08 FIP), Patrick Corbin seems to have taken a major step forward in his walk year, compiling 4.3 wins above replacement on the back of a 2.56 FIP through 141.1 innings pitched this season. The lefty has seen his strikeout rate jump more than nine percent from last season (21.6% in 2017, 30.7% in 2018), and has done so with a new weapon in his arsenal: a curveball he seems to have debuted in his April 17th start against the division rival Giants. Corbin has used the pitch a little over a tenth of the time (10.6% of his deliveries to be exact) and has seen it become his third option in a pitch mix that heavily features sliders and fastballs. Here is Corbin’s pitch mix over the past two seasons:

Corbin’s fastball (averaging 90.5 mph this season) and slider (81.6 mph) usage have remained largely the same, as the addition of Corbin’s curve has come largely at the expense of his changeup, which the lefty rarely uses. The new curve has averaged 73 mph on the season, coming in about nine ticks slower than Corbin’s primary breaking ball. Per Brooks Baseball, Corbin’s curveball seems to be of the 12-6 variety and has been used almost exclusively against opposite handed hitters, who have seen 218 of the 219 deliveries registered as curveballs by Brooks. Additionally, it is worth noting that over half of the curves Corbin has thrown have been to start an at bat, and that the pitch has resulted in the highest percentage of strikes of any pitch Corbin throws (although the slider isn’t far behind). Corbin has located most of his curves down and away to righties, further contrasting to the slider (below right), which the soon-to-be free agent has buried down and in against righty opponents.

Although opponents have batted .294 and slugged .471 against the pitch in a small sample this season, it appears to be a more effective weapon against righties than the now-infrequently-used changeup, against which righty opponents have batted .339 and slugged .617 over the course of Corbin’s career. Corbin has also subdued righties much more effective overall this season than in the past, having held them to a .245 wOBA in 2018 compared to a career (including 2018) .324 line. It certainly seems plausible that Corbin’s shift away from changeups to opposite-handed hitters and towards early count curveballs (161 of the 218 curves to righty batters have come in 0-0, 1-0, or 0-1 counts) has helped him to more effectively dispatch opposite-handed opponents than ever before. Fangraphs’ Pitch Values also seem to offer support for his idea, grading Corbin’s curve as a positive pitch (0.38 wCB/C), whereas the changeup has graded as negative in every one of Corbin’s six seasons (with a net value of -2.27 wCH/C). Additionally, both Corbin’s slider and fastball have played up this year to the tune of career-best pitch values, possibly due to the threat of a third positive pitch against righty hitters. This has allowed Corbin to become a more well-rounded pitcher during an excellent season and helped pave the way to a potentially lucrative offseason deal.

Data courtesy of Fangraphs and Brooks Baseball. Zone profiles all from catcher’s perspective, courtesy of Brooks Baseball. In instances where pitch type disagreements existed, Fangraphs pitch data was prioritized over Brooks Baseball. Pitch Mix tables based on Fangraphs data.

## Using History and Steamer to Predict the Comeback Player of the Year Award

While the race for the Comeback Player of the Year (CPOTY) award is nowhere near as fierce or publicly anticipated as the races for major awards like MVP, Cy Young, or Rookie of the Year, it’s still an award rich with history that recognizes some of MLB’s best bounceback seasons. Here, we’ll look at the history of the award, and use some of the trends in the historical data to identify some candidates for the award this upcoming season.

In 1965, the Sporting News gave out its first set of CPOTY awards to Pirates pitcher Vern Law and Tigers first baseman Norm Cash. The award was created to recognize a player who “re-emerged on the baseball field during a given season,” although this ambiguous definition has led to some questionable selections (notably 2001 Ruben Sierra over Juan Gonzalez) and debate over what it truly means. The award is given annually to one player in each league, and is typically given to either a player returning from injury or one coming off a down season to return to a level of success previously achieved in their career. The award has been given by two bodies throughout its history, as the Sporting News presented it from 1965 to 2006, while MLB has given out the award since 2005. Over the life of the award, 106 total player seasons have been recognized, and a few players have won twice.

Looking at a handful of trends within this sample allows us to identify what characteristics of player seasons correlate with winning the award, and therefore may allow us to formulate decent guesses as to what players might have a strong chance to contend for the award in the coming seasons. Some of the more important characteristics of CPOTY award winners include (but aren’t necessarily limited to) performance (both past and in the winning season), whether the player was injured in the season preceding their comeback, the player’s position, and team success. Let’s dig in and look at these trends to construct an ideal profile for a Comeback Player of the Year favorite, then look at what players might fit the bill in the upcoming season.

Performance

For the sake of simplicity, we’ll divide the performance category into three sections: past success (defined as two seasons prior to the comeback season), down season (defined as the season immediately prior to the comeback year), and the comeback year itself. While this isn’t perfect, this division will allow us to easily view the swings in performance that are associated with the award and look for current players that fit that mold. To examine a player’s performance, I looked at WAR for each of the seasons in question because it is a good general guide for player value and encompasses not only ability but also playing time to a degree, since it is a counting stat. For the purposes of this award, a counting stat like WAR is more important than a rate stat like wRC+ or UZR/150 because some winners won the award following a solid but injury plagued season. Performance was considered both by looking at the dataset for the three season groups (2 years prior, 1 year prior, and year of) as well as for the differences between the 2 years prior performance vs the year prior performance and year prior vs year of performance. Below is a box-and-whisker plot showing the distributions of the three year datasets, with WAR on the Y-axis:

As might be expected, the comeback season group yielded the most value of the three groups, followed by the past success season and then the down season. For the past success season, the middle 50% of values fell between approximately 0.5 WAR and 3.0 WAR, meaning that these seasons typically produced solid but rarely spectacular results. The middle 50% of values for the down season group fell between about 0 WAR and 1.5 WAR, meaning that most seasons in this group produced relatively middling or less value. It is also notable that the median is much closer to the lower quartile (0 WAR) than the higher quartile, and this skewing is because many of these down seasons saw players miss most or all of their season, leading to a significant number of players accumulating near 0 WAR in their down season. Finally, the middle 50% of bounceback seasons saw WAR values between 2.0 WAR and 5.0 WAR, meaning that most winners produced at least above average if not significantly above average value in their comeback season. The following table also shows the mean and median values for the three datasets (also broken down by certain time periods):

 WAR Breakdown 2 YP YP Yof Average (Total) 2.09 0.78 3.55 Median (Total) 2.05 0.35 3.35 Avg (Since 85) 2.07 0.43 3.56 Med. (Since 85) 2.05 0.10 3.10 Avg (Since 05) 2.31 0.40 3.73 Med. (Since 05) 2.15 0.20 3.65

Another way I evaluated performance was by looking at the differences in performance from year to year between the first two years (past success and down season) and the most recent two years (down season to comeback season). As expected, the first group saw a significant drop in performance while the second group typically saw a significant increase, often larger than the initial decrease. The following box-and-whisker plot shows the distribution of both sets of data, while the data table shows the mean and median values.

 WAR Change Diff. Mean 2YP to YP -1.33396 Mean YP to Yof 2.822642 Median 2YP to YP -1.05 Median YP to Yof 2.6

So our ideal candidate will have put up at least solid value during their past success season, lost a significant chunk of that value the next season, and then experienced a big bounceback the following season, posting solid to excellent value. According to Steamer’s projections, there are 23 hitters and 12 pitchers (two relievers, 10 starters) expected to follow this pattern with a bounceback 2018.

Injury

The next key component of the award is the player’s injury status during the season immediately preceding his comeback. While comebacks from injury have become more prevalent over the life of the award, injury comebacks were hardly recognized early on. The two following graphs will show the number of injury comebacks vs non-injury comebacks over time along with the difference between the two categories and the percent of injured winners over time. (Disclaimer: a good portion of this injury data did come from Wikipedia because I couldn’t find much historical injury info elsewhere, so some of it may be a little inaccurate but should not be so much so that the trends change.)

As you can see, the percentage of total winners of the award coming off injury has increased significantly as time has passed, with now nearly half of the award winners coming off injury. The difference has shrunk from a peak of 32 in 1989 to only 12 following 2017’s winners. The trend is even more stark when looking at the data broken up into specific time frames:

 Injury Breakdown Yes No Total 47 61 Since 1985 41 25 Since 2005 19 7

Since MLB took over the award in 2005, the trend has flipped entirely, with injury comebacks making up 73% percent of winners in that span. While there could be other complicating factors at play here, such as increased DL placements since the early days of the award, it still seems clear that suffering an injury during the preceding year has a strong tie to winning the award.

Position

The next characteristic of CPOTY winners is position. For whatever reason, certain positions are disproportionately represented amongst award winners. Here is a breakdown of the winners by position, in table and pie chart form:

As you can see, the award is most frequently given to starting pitchers, followed by first basemen and designated hitters. Middle infielders and catchers have rarely won the award, while outfielders, third basemen and (especially recently) relievers have received their share. Besides the dominance of starting pitchers, the most striking stat is the prevalence of designated hitters winning the award. While they make up only 11.32% of total winners, it is important to keep in mind that DHs have only been eligible to win 45 potential awards (the number of awards given in the American League since the establishment of the DH rule), so they have won 26.67% of the awards for which they have been eligible, a shocking number for players that only add value on one side of the ball.

Possible explanations for the dominance of certain positions may lie in other factors. Since the award has typically been given based on offensive production without as much regard for defensive value, it makes sense that players at bat-first positions would win the award more frequently than those at defensively oriented positions. Additionally, catchers typically accrue fewer plate appearances than players at other positions, and therefore have less opportunity to accumulate shiny counting stats than designated hitters. Another possible explanation may lie in the fact that a history of prior success is typically a prerequisite to win the award, and that older players are more likely to have an extensive track record of success. Since the award leans toward older, more experienced players, the award is more often given to players at less valuable defensive positions because players tend to move down the defensive spectrum as they age, so more older players are occupying less valuable positions while younger guys handle the tougher assignments. There are certainly other possible explanations for this trend, but some combination of these factors may play a part in the trend of bat-first players winning the award.

It may be tougher to explain the dominance of starting pitchers winning the award. It’s possible that pitcher success may be more subject to season-to-season volatility than hitters (while I haven’t been able to find any statistical studies proving this, it may be an interesting area of future research I’m considering pursuing). Another explanation might lie in the fact that every team typically rosters five starting pitchers and only one starter at each offensive position, but the difference seems stark enough at positions like catcher and shortstop that this seems unlikely. Maybe more pitchers suffer major injuries, causing them to miss significant time? There seems to be some credence to this theory, as only 13.11% of hitters played between 0 and 10 games in their down season, while 20.51% of starters pitched 5 or less games. It’s also possible that the sample still isn’t big enough and that this positional skewing is largely due to random variation. Whatever the case, it seems fair enough to weigh this trend at least a little bit going forward, so in predicting possible 2018 winners we’ll give the edge to starting pitchers, first basemen, and designated hitters.

Team Success

A final factor that has seemingly been of some importance in winning the award has been team success. While nothing about the award necessitates that the player plays on a good team, CPOTY winners have disproportionately come from winning teams. The following table displays some important statistics in terms of team success for award winners, most notably the mean and median team winning percentage, along with the percent of award winners playing on teams with certain win benchmarks. A .615 WP is roughly 100 wins over 162 games, .585 is 95, .555 is 90, .525 is 85, and 81 is .500.

 Team Success Mean WP 0.537594 Median WP 0.552 % over .615 6.60% % over .585 16.98% % over .555 50.00% % over .525 68.87% % over .500 78.30%

As you can see, both the mean and median winning percentages for teams featuring a comeback player significantly exceed .500 and exceed it by enough that this difference can’t simply be attributed to the contributions of the comeback player in most cases. Even more strikingly, nearly 80% of winners played for teams that finished over .500, and nearly 70% of winners played for borderline playoff contenders or better (85+ wins). The histogram below illustrates the distribution of team winning percentage for players winning the award since its inception:

The data is fairly skewed left, with very few award winners playing on truly terrible teams and a very large portion of CPOTY winners playing for teams in the 89 to 94 win range. While it is true that there aren’t necessarily a ton of winners on elite teams, I think it might be fair to chalk that up to the fact that are simply less elite teams than just good teams, so it isn’t that players on elite teams are less likely to win, just that there are less elite teams than good ones historically.

There’s no way to definitively answer why the award voting swings so heavily towards players on winning teams, but the data shows that this is indeed the case. Maybe voters believe that playing on a good team is part of a good comeback. It’s possible that players having bounceback seasons on winning teams are just more visible than those playing on teams going nowhere and therefore unfairly benefit in the voting. Another possibility is that voters are still relying on team-dependent stats like runs scored, runs batted in, pitcher wins, and saves, and guys on worse teams have less opportunity to rack up these stats. Perhaps there’s another driving reason, but clearly the award has historically favored guys playing on winning teams.

After combing through the data, a few characteristics of CPOTY winners have stuck out. A pattern of solid value->drop in value->return to solid-to-excellent-value stands out, as does the recent trend of awarding the CPOTY award to a player returning from injury. An ideal CPOTY candidate would also play on a projected contender and be a starting pitcher, first baseman, or designated hitter. While a player doesn’t necessarily need to meet all of these criteria to win the award and there are some good candidates who don’t (Greg Bird, Mark Trumbo, Dansby Swanson, Alex Reyes, Carlos Gonzalez, etc.), these characteristics have certainly been favored in the voting. Now it’s time to delve into the question of what players might have a good shot at taking home a comeback player of the year award next year.

After looking through the aforementioned group of 23 hitters and 12 pitchers, I decided to cut the sample down some by removing guys that aren’t really ticketed for regular duty next year, don’t project especially well, or never really broke out in the first place. This removed an additional six hitters, leaving 17 hitters and 12 pitchers. The following table further details each player’s candidacy in each of the criteria discussed earlier, sorted by position (Team W% is projected for 2018):

Just looking at the two lists, they seem like pretty good groups of names for CPOTY contenders. Davis, Cabrera, Machado, Ramos, Hernandez, and Price especially stick out in the AL, while Eaton, Syndergaard, Cueto, Bumgarner and Cespedes seem like good bets in the NL. Personally, I’d lean towards Syndergaard in the NL and Machado (or Cabrera if Machado is dealt to the NL) in the AL. It’s certainly possible that the award winners this year don’t come from these lists, but based on historical trends, these 29 players seem like solid favorites to take home the Comeback Player of the Year award in 2018.

FanGraphs leaderboards and player stats, Baseball Reference Player Pages, and Wikipedia for injury new were heavily used to do research for this post.

## On Drew Smyly, Michael Pineda, and the History of Signing Injured Free-Agent Pitchers

About 12 hours apart, news of two very similar moves broke out of Chicago and Minnesota, as the Cubs agreed to terms with Drew Smyly while the Twins signed Michael Pineda. Both pitchers inked two-year deals with \$10-million guarantees and additional incentives based on innings pitched, but the two deals shared an even more important similarity: both pitchers underwent Tommy John surgery this summer and seem unlikely to contribute significantly during the 2017 campaign. Both clubs are clearly betting on a return to health and productivity in 2019 for the two still relatively young pitchers, as evidenced by the financial distribution of the contracts. Pineda is only owed \$2 million for the upcoming season but will receive \$8 million in 2019, while Smyly will be paid \$3 million next year but will pull in \$7 million the following year. Since both pitchers underwent surgery around the same time, during the middle of the summer, it seems unlikely that either will throw pitch in the coming season.

While uncommon, these types of deals certainly aren’t entirely unprecedented. The Kansas City Royals have inked three pitchers with similar situations over the past few years, with varying degrees of success. These contracts, given to Luke Hochevar and Kris Medlen in 2015 and Mike Minor the following season, seem to represent the most relevant examples of such a deal. While Minor was non-tendered by the Braves following repeated shoulder issues, both Medlen and Hochevar underwent Tommy John surgery the previous year. All three pitchers would appear for the Royals in the major leagues over the life of their deals, albeit with differing results. Hochevar would appear in 89 games for the Royals, and accumulate only marginal value, as he posted a FIP around 4.00 and tallied only 0.3 WAR combined before succumbing to thoracic outlet syndrome surgery. Kansas City declined their option over Hochevar last winter, who became a free agent and sat out 2017 recovering.

Medlen would also return to pitch in 2015, making eight starts and seven relief appearances for Kansas City. He saw an uptick in walks and a downturn in strikeouts compared to his previous work, but overall pitched his way to a 4.01 ERA with similar peripherals and rang up half a win of value. 2016, however, would not be so kind to Medlen, as he was shelled to the tune of a 7.77 ERA while walking more batters than he struck out and battling a shoulder injury. He would sign a minor-league deal with the Braves after the season, but would not return to the majors. Although he did not appear with the Royals in 2016 after struggling in AAA, Minor marks the largest success story of the three. Over 65 relief appearances, Minor registered a 2.62 FIP and was worth 2.1 WAR out of the bullpen. He recently signed a three-year contract with the Rangers to return to a starting role.

In total, the Royals invested \$25.75 million in the three pitchers and saw them accumulate a grand total of 2.9 WAR, with most it coming from Minor. This works out to a \$/WAR figure of \$8.88 million per win, which is slightly higher than the \$8 million per win value assumed of the free-agent market. Based on these three deals, it would appear that this type of signing is not a bargain, but rather an overpay on average. However, it isn’t fair to make such an assumption without looking at a larger sample of data. If we classify a similar deal as one in which a team signed a pitcher that was injured at the time of the signing and expected to miss at least part of the following season and either signed a major-league deal or a two-year minor-league pact, that leaves us with 18 similar signings since 2007. One of these signings, Nate Eovaldi, has yet to return from his injury but should in 2018, so we won’t include him in the sample.

These 17 signings correlate to 25 player seasons following injury, with 24 of those representing guaranteed contract years, as well as one option year (Joakim Soria, 2015). The breakdown of these player seasons by games, innings pitched, strikeouts, walks, earned runs, and WAR are presented in the table below:

 G IP K BB ER WAR Total 447 725.2 606 246 347 6.9 Mean 18 29 24 10 14 0.27 Median 7 20 15 6 10 0

Altogether, when on a big-league mound, the group pitched to a 4.30 ERA to go along with a 7.52 K/9 and a 3.05 BB/9, numbers not entirely dissimilar from, say, Dustin McGowan or Sal Romano in 2017. So even the healthy group put together fairly middling results, but it’s also important to remember that eight of these player seasons wouldn’t see the player throw a single big-league pitch, and therefore provided no value to the club. Let’s plot the distribution of value produced by WAR:

That 2.1 WAR recorded by Minor last season was the highest figure of any player season in the sample, and besides Mike Pelfrey’s 2013 season, no other player season really comes close. Of the 10 player seasons recorded by primarily starting pitchers, only Pelfrey’s season even came close to average production, as every other starter either wasn’t durable or good enough to rack up any significant value. On the relief side, Minor and 2014 Joakim Soria both excelled, but no other relief season (out of the 15 in the sample) even crossed the 0.5 win threshold. As with the Royals pitchers earlier, it is important to look at these deals from a value standpoint. We can do this by calculating \$ per WAR for the whole sample to find a mean, and for each deal to find a median, and visually represent the distribution. Overall, teams invested a total of \$78 million in these 25 player seasons, with \$71 coming in guaranteed money and \$7 million in Joakim Soria’s club option. All minor-league deals to MLB veterans were assigned a dollar value of \$333,333 for ease of calculation. Bonuses and incentives were ignored from this figure, as it is very difficult to find these details of the player contracts and few of these seasons would reach such incentives. As we saw above, the sample produced a total WAR of 6.9. This means that on average, teams paid \$11.3 million per win when committing money to injured pitchers in hopes of a bounceback, well above the market rate of \$8 million per win in free agency. Based on some quick calculations, teams paid that \$78 million for production worth \$55.2 million, for a net loss of \$22.8 million. Let’s now look at the value gained/lost for each contract (in millions of \$):

As you can see, only five such contracts actually generated positive (above market value of \$8 million per win), while the remaining 12 contracts provided their team with below-market value. The mean loss per contract is \$1.34 million, while the median is represented by the Phillies’ \$700k loss on Chad Billingsley. While neither number is outrageously high, both figures only serve to reinforce the fact that teams have generally lost more often than they have benefited from inking an injured pitcher.

None of this is necessarily to say that the Pineda, Smyly, and Eovaldi contracts are doomed or that no team should ever make this type of investment, but simply to look at how similar deals have worked out in the past. Admittedly, the sample is hardly big enough to make any sort of definitive conclusion, but the overall trend on these “bargain” signings isn’t pretty. Both Smyly and Pineda are better pitchers than most in the sample, so it is entirely possible that they (along with Eovaldi) could significantly shift the outlook on these types of deals in the future. Whether this trio of pitchers can buck the trend or will follow in the footsteps of their predecessors will certainly be an interesting, if minor (pun intended) storyline to watch over the next few seasons.

FanGraphs.com leaderboards, Baseball-Reference transaction data, and MLBReports Tommy John surgery database were all used extensively for this research.