Yes. Probably. If you’re in a hurry, you can now go do whatever you should be doing instead of reading about no-longer-prospecty baseball players. But if you’re not in a hurry, know this: there’s a chance he can survive, if only a small chance.

Kyle Drabek is rowing against a mighty tide that seeks to dash what’s left of his career against the jagged rocks. The former Phillies first-round pick (don’t fret, Ruben-haters — the really good guys in this draft were already gone) had a mediocre minor league career followed by a wretched major league one. Over 177 injury-plagued innings, Drabek has been a TTO arsonist: 6.1 K/9, 5.7 BB/9, and 1.2 HR/9. His career ERA/FIP is 5.27/5.42. When hitters die and go to heaven, they face him every night. And yet the Arizona Diamondbacks recently signed him to a minor league deal. What might they be thinking?

Drabek has been through Tommy John surgery twice, in 2007 and again in 2012. Here’s a list of repeat TJ offenders — you can sort on “Back to playing” to see who, well, made it back to playing. It’s largely a grim list, but there are some pitchers who came back to perform decently. As you can see from perusing the list, and this post, the vast majority of those are relievers.

Drabek’s major league performance so far suggests little more should be expected of him. In the expansion era there have been 73 starting pitchers who “achieved” a FIP of 5+ in at least 150 innings before age 28. Here they are — Drabek checks in at #27.  The name that jumps out most prominently on this list is Joe Nathan:

Through age 27         IP        ERA        FIP         K/9          BB/9          HR/9

Nathan                       187       4.61       5.72          5.6            5.2               1.4

Drabek                       177       5.27        5.42         6.1             5.7               1.2

Entering his age-28 season, Nathan was a failed starter with one shoulder arthroscope to his credit. That year the Giants converted him into reliever, and after season’s end converted him into A.J. Pierzynski. Nathan went on to rack up the eighth-most saves in MLB history, which is a pretty fair achievement even if you aren’t into saves.

How did he do it? Unfortunately, pitch-by-pitch data isn’t available back to the years (1999-2000) when Nathan did most of his damage as a starter. But a look at Nathan’s pitch values nevertheless suggests some clues. One big clue in particular: his slider was devastating. By fleeing to the bullpen, Nathan probably was able to add a little heat to the slider, and perhaps able to throw it more often. Hitters would only see him once as a reliever, but this may not have been a huge factor, since hitters were beating Nathan like a drum as soon as the cute guest-PA-announcer-kid finished shouting “play ball!” Get to him a third time, though, and it was like walking to the plate with a plutonium-corked bat.

One can imagine that being in the bullpen enabled Nathan to add some velo and subtract the amount of times he threw his weaker pitches. Salomon Torres presents a similar profile: a failed starter in the 1990s, Torres disappeared off the baseball earth for a few years before resurfacing with the Pirates as an effective reliever in 2002. Torres’ out pitches were the slider and splitter; he featured both as a reliever evolving toward the latter as he aged.

Justin Miller also made the transition from awful starter to solid reliever. Like the others, he did not wholly abandon a pitch when moving to the pen, but he placed greater emphasis on his — you guessed it — excellent slider, at the expense of his not-so-excellent fastball. Miller didn’t add much velocity in becoming a reliever; it seems to be the change in pitch selection that helped him turn the corner.

These career paths might be helpful signs for Drabek, but in at least two senses they aren’t: unlike the other guys mentioned above, Drabek lacks a carrying pitch. Nathan had an excellent slider, as did Miller and (at times) Torres, even when the rest of their pitches were failing them. Drabek’s pitches are all below average, so he appears to lack a safe base from which to make his bullpen transition (although to be fair, all of Drabek’s pitching stats suffer from the pain and embarrassment of small sample size). By the numbers, the curve is the least bad of his offerings; perhaps focusing on becoming a fastball-curve guy would benefit his development.

Focusing on the curve brings us to Drabek’s second problem: TJ survivors appear to struggle with breaking pitches. Throwing more curves at this stage of his career may be the last thing Drabek can (or is willing) to do. It’s not impossible: Jason Isringhausen leaned heavily on the curve as he remade himself into an elite closer. But there aren’t a lot of examples here.

Perhaps Drabek can develop his changeup, his second-least-bad pitch. It appears that, following his surgery, he tried to emphasize his cutter, a pitch that hasn’t been kind to him yet, but an approach that did help The Beard to become fearworthy. Perhaps a superior pitching coach could help Drabek, but here’s who the Snakes just hired.

Despite these difficulties, the Diamondbacks nevertheless have an incentive to pick through the Drabekian rubble to see if they can salvage any value. Even with their spiffy new TV deal the D’Backs will always be no better than second-tier in terms of resources and attendance, especially problematic with the Dodgers juggernaut in the same division. Finding cheap pitching hand-me-downs will enable the organization to invest elsewhere (as it is already doing with the lineup).

Given both Drabek’s limited major league success and his limited major league appearances, deciding how to reconstruct him may be even more difficult than such projects usually are. Drabek doesn’t have huge platoon splits, and while for now that means both southbats and northbats will feast on his pitches, over the longer haul it may mean that he could be useful as a swing man or multi-inning reliever.

The Diamondbacks have had success in re-imaging double-TJ survivor Daniel Hudson as a reliever, but he had already had much more success as a starter than any of the other pitchers mentioned in this post (except Tommy John himself). Indeed, the Snakes may move Hudson back to the rotation next year. But perhaps the work with Hudson has given the organization some clues for how to deal with a much more challenging project.

I’m rooting for Drabek, but I’m taking the under.

Projecting starting pitcher performance has proved more difficult than projecting hitter performance, mostly because pitcher skill level and performance tends to be more volatile. Another issue is that pitcher performance indicators are heavily reliant on batted-ball outcomes. This means a team’s defense and luck (e.g., softly hit balls that drop for hits) become a large part of run prevention, all of which are mostly out of the pitcher’s control. This realization has led to the development of a variety of pitching statistics that attempt to reduce pitcher performance into metrics that rely on outcomes only under pitcher control, such as walks, strikeouts, and home runs (e.g., Fielding Independent Pitching, FIP). Given that these metrics are the state of the art in terms of summarizing and describing a player’s past performance (not necessarily predictive measures; see Dave Cameron’s 2011 article here), it is useful to develop ways to attempt to predict these metrics from prior predictive statistics. As such, the goal of the current analysis was to develop prediction models using various regression tree methods that best predict starting pitcher performance metrics.

Data
Data for these analyses were compiled from several different sources, including Fangraphs.com and by using the ‘Lahman’ and ‘Retrosheet’ packages in R. Data were aggregated from the prior three seasons (2012-2014), as well as the 2015 regular season. The final data set included average performance statistics of starting pitchers from 2012-2014 who also pitched at least 50 innings during the 2015 season (N=127). The primary outcome was 2015 pitcher Wins Above Replacement (WAR). Predictors included aggregated values of over 30 performance metrics from the prior three seasons, including standard and advanced statistics (e.g., K-BB%), batted-ball measures (e.g., GB%), quality of contact statistics (e.g., hard contact %), and PITCHf/x measures (e.g., average fastball velocity).

Analytic Approach
The goal of this analysis was to use several different data modeling techniques to develop models that best predicted pitcher performance during the 2015 season from pitching data from the 2012-2014 seasons. Three separate techniques were utilized that fall within the general family of Classification and Regression Tree (CART) methods. CART methods use search procedure algorithms to find variables that are most important for prediction, then, determine the best possible cut point on the selected predictor in order to subset the data into multiple predictor spaces (Breiman, Friedman, Olshen, & Stone, 1984; Steinberg & Cola, 2009). These procedures allow for non-linear associations and higher order interactive effects. Regression trees were grown using several different packages in R, including the rpart and party packages. These packages are capable of growing large regression trees, but also include cost complexity and control parameters that allow for the assessment of over fit and tree size reduction. Next, a technique known as boosting using the gbm package in R was used to identify the predictors of highest importance for predicting pitcher performance. Although similar to ensemble CART methods that re-sample data to grow multiple large regression trees (e.g., bootstrap aggregation), boosting is a slow learning algorithm that grows regression trees sequentially, not independently. Each tree is fit to the residuals from the previous tree in order to isolate the misfit and re-shape the regression tree.

Results
First, the complete dataset was split in half in order to create training and test data sets. Next, the training data was used to fit a regression tree predicting 2015 WAR from all variables in the dataset. In the first model, liberal control parameters were set for the size of the tree, meaning a large tree was grown that selected all the best possible predictors. Each chosen predictor was then optimally split until each pitcher could be placed into a terminal node. The results from the initial model demonstrated that average strikeout rate per plate appearance (K%) was the best predictor of WAR with an optimal split of 22.39%. The initial model R2 demonstrated that 97% of the variance in WAR could be explained by this regression tree. Despite the high amount of variance explained, this model has likely over fit the data. In other words, the model is overly fit to the empirical data set, which means the model is too complex and unlikely to replicate across other samples. Reducing the size of the tree, or pruning the tree, will result in higher bias, but will reduce variance in the predicted values.

Initial Regression Tree Overfit to the Sample Data

In order to determine the optimal tree size (i.e., prune the tree) cost complexity pruning using 10-fold cross validation was done on the training data set. Based on the model deviance, the optimal tree size was determined to be between 4 and 6 terminal nodes. After pruning the tree, the R2 was reduced to .68, but the mean square error (MSE) was also reduced from 6.8 to 3.6 in the training data set. Next, the optimized tree was fit to the test data set, which produced an R2 of .57 and a MSE of 1.4. Surprisingly, after the initial split on K% the next-best predictors were related to quality of contact statistics (go here for more detailed information). Although there is a large amount of measurement error in these variables, it is still interesting these measures are predictive of WAR.

An inherent problem with regression trees is that continuous predictors with more unique values are more likely to be chosen because they contain a higher number of possible split points. The party package in R attempts to control for this issue by taking into account the distributional properties of the predictors (Hothorn, Hornik, & Zeileis, 2006). As such, similar models were fit predicting 2015 WAR using the party package in R. Results were similar to the model using the rpart package, which found that average strikeout rate was the best predictor with a split of 22.3%. However, it was determined that the data only required one optimal split, partitioning pitchers into those who were above and below a strikeout rate of 22.3% (see Figure below). Although this model explained significantly less variance in WAR (R2 =.29) than the larger tree, this model is likely to have higher stability and predictive utility in new samples.

Optimized Regression Tree using the Party Package

Finally, boosted regression trees were fit to the data to examine the optimal predictors of 2015 WAR. The number of trees (B=1,700) was chosen by examining the decline in the squared error loss for the out of the bag sample. The shrinkage parameter was set to λ =.001 with an interaction depth of d=1. For the training data the MSE was 1.79 and the R2 was .59. The model was then tested against the left-out half of the dataset (test dataset), which produced a MSE of 1.98 and an R2 of .55. Given the small differences in the R2 value and MSE for the test and training data sets, this model appears to show relative consistency. The most important predictors were determined by the importance function in the gbm package. Average strikeout rate, average fastball velocity, and average strikeouts per plate appearance minus walks per plate appearance were the most important predictors of 2015 WAR. To see a list of the relative influence of all variables refer to the table below.

Order of Variable Importance Predicting 2015 WAR

Based on these results it is clear that K% is a strong predictor of future WAR, which is not surprising because pitcher WAR is based on FIP (derived from K, BB, HR outcomes). Average fastball velocity and K% minus BB% also came out as a relatively strong predictors of WAR in the boosted regression tree models. Quality of contact was found to be an important predictor, but more analysis should be done in other samples to see if these measures have consistent predictive ability.

It was a defining moment in the 2015 World Series. Proven closer Jeurys Familia watches as a slow chopper is hit to third baseman David Wright. Wright glances back to Eric Hosmer on third and throws to first. Without hesitation, Hosmer sprints home and beats out a wild throw from first baseman Lucas Duda to stun the crowd at Citi Field and tie the game in the 9th. Hosmer made a big play on a big stage. But that wasn’t the only time in his career, the 2015 season, or even that World Series that Hosmer has shown a flair for the dramatic in big moments.

So I did a little digging.

The statistic “Clutch” quantifies how much better or worse a batter performs in high-leverage situations compared to a neutral situation. This does not necessarily make or break a good player. However, the statistic can show the track record of a player’s ability (or inability) to elevate his own game in big moments. The scale is centered at an average player clutch rating of 0 and typically ranges from -2 to 2 in any given season, with 2 being considered both a rare and excellent rating.  This statistic is better used to look at what has happened in the past rather than predict the future. To my surprise, Hosmer has dominated the clutch leader boards the last five years. Not Miggy, not Longo, not Big Papi. Hosmer.

Since his debut season in 2011, Hosmer has a cumulative clutch rating of 5.49 while the league average has been -0.38 according to fangraphs.com. The second-highest in that time span? Jacoby Ellsbury at 4.41, over a full point away. To this point in his career, Hosmer has a cumulative clutch rating that ranks 22nd in baseball history. He is ahead of legends such as Ken Griffey (5.35), Rickey Henderson (4.91), and fellow Royal George Brett (4.79). His 2015 campaign that yielded a clutch rating of 2.17 was one of the top 100 greatest clutch seasons ever recorded (tied for 63rd). Although there is no way to prove Hosmer will remain a clutch hitter, he is currently on pace to smash the all-time highest career clutch rating set by Hall-of-Famer Tony Gwynn (9.49).

So what makes Eric Hosmer clutch at the young age of 26? His high baseball IQ. He is constantly aware of the situation and knows what he has to do to produce runs for his team. Let’s use this past World Series as an example. In Game 1, Hosmer came to the plate in what was easily the one of the biggest at-bats of the season. Bottom of the 14th. Tie game. Bases loaded. Nobody out. Infield in. Seasoned veteran Bartolo Colon on the mound.

(Video copyright of MLB Advanced Media)

See what he did there? He knew the infield was in and he knew he could not do two things: strike out or hit the ball on the ground. He had to get the ball deep in the air, despite having a GB% of 52.2% in 2015. He gets a fastball in the heart of the zone and lifts the ball into right, deep enough to get the winning run home and record the seventh walk-off of his career.

The next day, he comes up in another big situation against young pitching phenom Jacob deGrom. Bottom of the 5th. Tie game. Runners on second and third.

(Video copyright of MLB Advanced Media)

With Escobar’s speed on second, Hosmer knew all he needed was a base hit to give the Royals a two-run lead. He sticks with a slider that caught a little too much of the bottom half of the plate and ropes a grounder up the middle. The 26-year-old didn’t let the moment get to him and over-swing, but instead took what the pitch gave to him. Clutch.

Although Royals fans have seen rather inconsistent numbers from Eric Hosmer through the first five years of his career, it is not overlooked how important he has been to bringing a winning atmosphere back to Kansas City as they had hoped he would. After all, he was the third overall pick in 2008 MLB amateur draft. But big expectations and big moments don’t scare Hosmer. He’s been the most clutch hitter in the league.

Moving on from an analysis of starting pitchers, we move to relievers.

Relief pitchers happen to be the easiest group of players to project as their final salary is nearly entirely driven by saves although for non-closers, holds become very important to differentiate between setup men (who make slightly more) and middle relievers.

For a RP who is arbitration-eligible for the first time, here are the statistics that correlate most with eventual salary:

Career SV: 83.28%

Platform SV: 79.07%

Career WPA: 38.15%

Career SV%: 35.60%

Career fWAR: 35.18%

Platform SV%: 27.06%

Platform SO: 25.75%

When initially looking for player comps, these are statistics we are going to focus on. Keep in mind that although ERA is not listed, it is nonetheless important as ERA is still one of the default statistics used during a hearing and one of the first bases for comparison.

Note: WPA and Shutdowns (SD) have strong correlations, however those two stats are not widespread enough to be used during a hearing. My model includes WPA, but does not include SD as the inclusion of SD de-emphasized the importance of saves while it inflated the salaries of situational relievers. While ideally that should be the way salaries are determined, that does not happen in practice so it made sense to omit SD from the model.

Let’s use Indians closer, Cody Allen, as an example of a first-year-eligible reliever. Cody Allen is arbitration-eligible for the first time going into 2016 with 3 years and 76 days of service time (3.076). In his platform season (2015), Allen recorded 34 saves with a 89.47 SV%, 99 SO and a 2.99 ERA. Over his career, Allen has compiled 60 saves with a 84.51 SV%, 4.19 WPA, 5.0 fWAR and a 2.64 ERA. The objective here is to find the players who avoided arbitration by signing a 1-year contract with statistics that are most similar to Allen’s. The more recent, the better. The best way to do that is to set a floor and a ceiling and then work your way towards the middle.

First, let’s look at David Aardsma’s 2009 platform season (old, but still useful). Like Allen, Aardsma was an effective closer with high save totals and a strong ERA. Aardsma recorded 38 saves, 80 SO with a 2.52 ERA. Over his career, Aardsma had compiled 38 saves with a 80.85 SV%, 2.25 WPA, 1.5 fWAR and a 4.38 ERA. Although the platform stats are very similar, Allen’s career numbers are far superior. Therefore, we can definitively state that Allen should receive more than Aardsma did. As such, Aardsma’s 2010 salary of $2.75 million should be the floor.

Next, let’s look at Greg Holland’s 2013 platform season. Like Allen, Holland was an effective closer with high save totals and a very strong ERA. Holland recorded 47 saves with a 94.0 SV%, 111 SO and a 1.21 ERA. Over his career, Holland had compiled 67 saves with a 88.16 SV%, 7.87 WPA, 6.9 fWAR and a 2.41 ERA. Although their career numbers are relatively close, Holland had a dominant platform season that surpassed Allen in every way. Therefore, we can definitively state Allen should receive less than Holland did. As such, Holland’s 2014 salary of $4.675 million should be the ceiling.

Given the above, Cody Allen is likely to receive somewhere between $2.75 million and $4.675 million. Now that we have a range, let’s find someone towards the middle.

In 2013, Ernesto Frieri recorded 37 SV with a 90.2 SV%, 98 SO and a 3.80 ERA. Over his career he recorded 60 saves with an 89.55 SV%, 5.62 WPA, 2.3 fWAR and 2.76 ERA Those numbers are quite similar across the board with both players having an identical career save total and only 3 more platform saves. Frieri’s 2014 salary was $3.80 million so we can determine Allen will receive a similar amount. Andrew Bailey ($3.9 million in 2012) is a decent comp as well.

As for my model, Allen projects to receive $3,595,732 +/- $130,998 which is perfectly in line with the comps above. MLBTradeRumors projects him at $3.5 million so both of our models are very close here (and will be most of the time).

For a player who has already been through the arbitration process before, the valuation is completely different as career statistics are no longer used the 2nd, 3rd, 4th, etc. time around (except in a few rare cases).

For a RP who has previously been through the arbitration process, the stats that correlate most with eventual salary are:

(1) Platform SV: 70.40%

(2) Platform fWAR: 41.36%

(3) Platform RA9-WAR: 36.58%

(4) Platform SV%: 34.79%

(5) Platform WPA: 34.34%

(6) Platform SO: 30.04%

For example, let’s look at Reds closer Aroldis Chapman who is arbitration-eligible for the third time going into 2016. As an Arb-2 going into 2015, Chapman received a $8.05 million salary. That figure includes everything he had done in his career up to that point. Thus, when determining his 2016 salary, we don’t need to focus on previous seasons. We need only determine what his 2015 season was worth and give him a raise. In his platform season (2015), Chapman recorded 33 saves with a 91.67 SV%, 116 SO, 1.99 WPA, 2.4 fWAR, 2.7 RA9-WAR and a 1.63 ERA. We want to find the players whose stats are most similar to Chapman.

First, let’s discuss Juan Carlos Oviedo’s (formally known as Leo Nunez) 2011 platform season where he recorded 36 saves with an 85.70%, 55 SO, 1.07 WPA, 0.1 fWAR, 0.2 RA9-WAR and a 4.06 ERA. Although Oviedo was fortunate enough to record more saves, Chapman was the far better player overall; so much so that, despite having fewer saves, we can determine that Chapman will definitely receive a larger raise than the $2.35 million raise Oviedo received going into 2012. Therefore, we can consider a raise of $2.35 million to be his floor. Oviedo is the perfect example of how important saves are (for arbitration purposes) when it comes to relievers.

Next, let’s look at Heath Bell’s 2010 platform season (again old, but useful still) where he recorded 47 saves with a 94.0 SV%, 86 SO, 4.49 WPA, 2.3 fWAR, 2.6 RA9-WAR and a 1.93 ERA. Like Chapman, Bell was an All-Star closer with virtually identical numbers except for WPA and SV, where Bell clearly outproduced him. Moreover, Bell was named the NL reliever of the year. As such, Bell’s raise of $3.5 million going into 2011 should be the ceiling.

Given the above, Aroldis Chapman is likely to receive a raise somewhere between $2.35 million and $3.5 million for a final salary between $10.4 million and $11.55 million.

Chapman is a perfect example of why first determining a range is important as Chapman represents a type of player who just has not been through the arbitration process in this service group before. Since 2006, there has not been a closer who recorded less than 40 saves with dominant numbers. Looking at saves we have Chris Perez (39 saves – $2.8 million in 2013), Brandon League (37 saves – $2.75 million in 2012), Jonathan Papelbon (37 saves – $2.65 million in 2011) and Joel Hanrahan (36 saves -$2.94 million in 2013). Somewhere around those numbers and perhaps a bit higher is what we should expect.

My model projects that Chapman should receive a raise of $2,743,587+/- $152,366 for a total 2016 salary of $10,793,587+/- $152,366, although I think my projection underestimates the impact his dominant numbers will have despite the lowish save totals (due the lack of comps). I would expect a raise of around $3 million. MlbTradeRumors is projecting a raise of $4,850,000 for a total salary of $12,900,000, which not only surpasses Heath Bell’s raise, but shatters Jim Johnson’s record-setting raise for a non-first-year reliever of $3,875,000 when he recorded 51 of 54 saves in 2012. Given the importance of saves and the relative unimportance of the other stats, I don’t see how such a high number is possible. Nonetheless, Chapman is a very interesting case study as he has the potential to change the way relievers are viewed during the arbitration process.

Next up: position players.

Brandon Crawford is coming off an All-Star season in which he not only won his first Gold Glove, but his first Silver Slugger as well. The last to win both awards in San Francisco? Barry Bonds in 1997. Although Crawford may not have all the tools that Bonds did, he has come a long way since he made his debut at shortstop for the Giants in May of 2011. Crawford entered the league projected as a shortstop with plus defense, but also as an offensive project. So what sparked him to have the second-most home runs (21) among all shortstops, more than his totals from 2013 and 2014 combined, and a SLG% of .462 that led the all other qualified shortstops in the league by more than 20 points? An aggressive approach at the plate paired with slight mechanical adjustments. Consider Crawford’s Z-Swing%:

Now consider his hard-hit%:

These graphs, courtesy of data from FanGraphs, tell an interesting story. For the first four years of his career, Crawford’s Z-Swing% and hard-hit% had a direct correlation. In the first two years of his career, Crawford had a Z-Swing% that was barely above average in the league and a hard-hit% that was below average. Last year, however, his Z-Swing% skyrocketed to more than 8% above league average and he had a hard-hit% that was, for the first time in his career, above average. Yet, there is something odd about his recent success at the plate.

Crawford was not making more contact than in the past; he had just improved on the quality of contact with his new swing and more aggressive approach. Last year, he posted the 16th-worst SwStr% (percentage of swings and misses) in the league at 13.6% and a below-average 73.6% Contact%. Crawford also showed more aggression on pitches outside the zone, posting an O-Swing% (percentage of swings on pitches outside the zone) of 35.2% which is also worse than the league average of 31.8%. All of these were the worst numbers in their respective categories for his young career.

Despite all of this, his aggression at the plate and his change in mechanics led him to become a top power hitter at his position last year and a legitimate threat in the second half of the Giants batting order. Although the trend in these numbers may be hard to fully validate due to the small sample size, the new-found pop in the bat could make Crawford a much more valuable player (as finding power among shortstops in today’s league is a rarity). If his Z-Swing% and hard-hit% continue to be linearly related, Crawford may very well continue his progress in 2016 and bring power to a Giants lineup that was fourth to last in total home runs last season. One thing is for certain: his flow will remain among the game’s elite.

(pearlswithplaid, pearlswithplaid.blogspot.com)

This article was co-authord by Daanish Mulla (@DanMMulla)

A New York Times article by John Branch in October 2015 discussed the elusive definition of the pitching term “stuff”. Talk of “plus stuff” and feelings of “all the stuff being there” was scattered throughout the article. Despite interesting commentary discussing the ability for pitchers to over-power hitters, there was no true definition of the nastiness of a pitcher’s stuff.

Earlier this November, Eno Sarris wrote an article examining who had the best changeup in the 2015 season. This was evaluated by looking at the difference in speed and movement with respect to the pitcher’s fastball. This made us think, to truly quantify “stuff”, you would first need to understand what goes into a pitcher having a truly dominant repertoire.

Our definition of a pitcher’s “stuff”, or their overall nastiness, was based on three different factors: 1) fastball velocity; 2) change of velocity of a secondary pitch with respect to the fastball; and 3) movement with respect to the fastball. We downloaded all of FanGraphs’ PITCHf/x data from 2008 to 2015 to attempt solving this problem.

For a pitch to qualify for this analysis, it had to be thrown by an individual pitcher at a frequency equal to, or greater than, the average frequency for that pitch to be thrown throughout the entire data set. For example, in our data set, the curveball was thrown at an average of 12% of the time by all pitchers. Thus, a pitcher’s curveball was only considered if it was thrown at a frequency of greater than or equal to 12%. We then determined the maximum and minimum velocity for all eligible pitches for each pitcher. Working off of the fastball, we then determined the maximum change in movement in both the X direction, and the Z direction, for any qualifying pitches. We then calculated the maximum resultant movement for these values. Z-scores were then calculated and summed from the following factors to get a final pitcher “stuff” score: 1) maximum velocity; 2) change in velocity between maximum and minimum velocity; and 3) maximum resultant movement.

Here is an example as to how a pitcher with elite stuff performed in this analysis. David Price had a great year with the Blue Jays and Tigers. From FanGraphs data, his maximum pitch velocity was 94.1 mph, and the minimum pitch velocity was 85.2 mph – a difference of 8.9 mph. Working off the fastball, the greatest x direction break on a pitch was 15.1”, and the greatest z direction break was 10.9”.  This produced a resultant change in movement of 18.6”.

These values translated to a z scores for velocity, change in velocity, and resultant movement of 0.969, -0.08, 0.91, resulting in a stuff value of 1.80. Comparatively, another Blue Jays starter who struggled in 2015 was Drew Hutchinson. Hutchison had a fastball velocity of 92.4 mph, an offspeed pitch of 84.3 mph, an x direction break of 7.1, and a z direction break of 9.8. Corresponding z scores for velocity, change in velocity, and resultant break were 0.392, -0.24, -0.08, resulting in a stuff value of 0.1.

To break down how well our stuff rating was performing, we correlated stuff with K/9. Pitchers included in this analysis were all starting pitchers who pitched 90 innings in a season, between the 2008 and 2015 season. Average stuff and average K/9 was calculated during this time. Overall, the correlation was r = 0.42 (Figure 1). For the sake of these graphs, knuckleballers Tim Wakefield and R.A. Dickey were not included, as the stuff metric had them rated lower than -4 per season.

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Figure 1. Stuff vs K/9, between the 2008 and 2015 MLB season.

Here’s the top 25 starting pitchers from the 2015 season ranked by their stuff. While overall, we think this is a good starting point for evaluating a pitcher’s repertoire, there are a few notable pitchers that the stuff calculation doesn’t seem to do justice. Chris Archer, who has had his slider called one of the best pitches in all of baseball, has only a 1.12 stuff value, and is ranked as having the 67^th best stuff. Max Scherzer, who threw two no-hitters, is ranked as only having the 60^th best stuff.

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Table 1. Top 25 stuff for pitchers, with raw data on velocity and break

What’s worth stressing however, is that this metric serves to evaluate the individual pitches within their repertoire. There are pitchers which would be scouted to have the ability to throw hard, with lots of break. Pitching is clearly an art form that involves more than those two things, thus players like Mark Buerhle (-2.7), are clearly someone who has mastered the art of pitching, without having great stuff.  When comparing stuff against xFIP, correlation coefficients are smaller (r = -0.33) (Figure 2). Much like K/9 does not directly predict pitcher success, neither does stuff.

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Figure 2. Stuff vs. xFIP, between the 2008 and 2015 season.

We believe there’s great use for this metric. We think this metric can provide insight into how stuff changes with age, how stuff changes after a pitcher is injured, and how it can let a coach know when a player has returned to pre-injury form, and how a pitcher’s consistency with their stuff relates to success. As with any ranking that appears on the FanGraphs website, we’re sure that there will be debate – however, we are looking forward to the input from the community into how we can improve this technique.

References

Branch, J. (2015). The Mysteries of Pitching, and All That ‘Stuff’. Posted online, October 3, 2015. http://www.nytimes.com/2015/10/04/sports/baseball/the-mysteries-of-pitching-and-all-that-stuff.html

Sarris, E. (2015). The Best Changeups of the Year by Shape and Speed. Posted online, November 9, 2015. http://www.fangraphs.com/blogs/the-best-changeups-of-the-year-by-shape-and-speed/

In case you missed Part One of my AFL notes, I covered Clint Frazier, Dominic Smith, Ian Clarkin and five other interesting prospects playing in this year’s Arizona Fall League. Just a disclaimer: I was out in Arizona for fun, and I wasn’t paying too much attention to defensive ability for any one player. These scouting reports would be more complete if I was actually scouting for an MLB organization.

Austin Meadows, L/L OF (PIT)
He’s 6’2″/200 and a plus athlete. He hit .307/.357/.407 in the Florida State League (High-A) and won’t turn 21 until next May. I was excited to watch him play, given the hype around his name, but I wasn’t impressed. Maybe he’s tired, or maybe he doesn’t care about the Fall League, but in those eight PAs, there wasn’t a single one that instilled faith in me. Despite a noticeable confidence approaching the box, he seemed almost apathetic at the plate. I can’t remark on him as a defender, but he’s clearly a good athlete, so I doubt corner outfield would give him too much trouble. With McCutchen, Marte, and Polanco ahead of him though, he’s got a tough road to major-league playing time. In my opinion, the Pirates would be smart to trade him during the offseason and improve their 2016 MLB roster.

Yandy Diaz, R/R 3B (CLE)
Pretty impressive stature; listed at 6’2″…weight could be anywhere between 185-205, but he has above-average athleticism for his size. A Cuban-born defector with fairly natural motions at third base, and good arm action making the throws across the diamond. He turned 24 in August, but he had a pretty solid year at AA (.315/.412/.408). With his size, you’d expect him to hit for more power, but he compensates with the type of plate discipline that may allow him to stick around until something clicks. Based on body alone, I’d compare him to a young Edwin Encarnacion (not anywhere the raw power, though). By no means is Diaz a surefire MLB contributor, but his main detractor is something he has a real chance to build upon. With the frame he has, I still see room for the power to develop, and he could turn into a quality everyday MLB third baseman.

Alex Blandino, R/R INF (CIN)
I just didn’t see the ballplayer in this guy. He made one fairly challenging play at second base, but he had so many empty plate appearances; swinging at first-pitch breaking balls, or taking called third strikes down the heart of the plate. In a hitter-friendly league, he seemed like an automatic out. Blandino was a first-round pick in 2014, and had a pretty solid year at High-A in 2015, but he’ll be 23 to start the season next year. I’d be surprised if Blandino ever lives up to his first-round price tag. Insert pun about him being a ‘bland’ prospect.

Brett Phillips, L/R OF (MIL)
One of the key pieces in the deal that brought Carlos Gomez to Houston, Phillips has a shot to contribute for the Brewers as soon as Opening Day 2016. He’s similar to Clint Frazier, with slightly less muscle mass (and power). As one of my friends noted, “He’s a good downhill runner.” As with Frazier, Phillips strikes out a bit too much, and it could easily be the difference in Phillips being a Quad-A player and an MLB regular. The tools are impressive, though; I could see some Alex Gordon seasons in him.

Adam Brett Walker II, R/R DH/1B (MIN)
Built like a tight end; 6’4″/230+. Great athleticism for his size, yet he was limited to DHing in the AFL. He needs to shorten his swing. His hands drop, causing the barrel of his bat to loop through the zone. He swings and misses at way too many pitches because of a weak top hand. I had essentially written him off after 11 PA…and then he hit a ball over 450 feet.

Photo Credit: Buck Davidson (@BuckDavidson)

The raw power is enormous. At least a 7. When he gets his pitch, he hits it a long way. He hit 31 HRs and stole 13 bases at Double-A Chattanooga, showing the power and athleticism are very real, but only to the tune of a .239/.309/.498 triple-slash as a 23-year old. He lead the Southern league in K-rate, striking out 35% of the time. He’d be lucky to hit .150 in the Majors right now, but he’s not unfixable. If Minnesota’s player development staff can get him to fix his swing plane, this guy could theoretically hit 40 home runs.

Gary Sanchez, R/R C (NYY)
Another big guy, Sanchez looks the part of a major-league catcher. 6’2″/220+, with decent athleticism and an average arm. He’ll never be a stud defensively, but he could theoretically stick as a 120-game catcher. He displayed some pretty lively power, driving home runs to left- and right-center. I spoke with a cranky, yet knowledgeable, Yankees fan who didn’t think much of Sanchez, but I’d be happy to have him in my system. He turns 23 this December, and he’s already posted a .295/.349/.500 slash at AAA Scranton/Wilkes-Berre. He could become more of a 1B/DH type with age, but his bat seems good enough to be around average for a DH. If he can stick behind the plate, the Yankees have a very valuable asset on their hands. He could develop into a .260/.340/.450 catcher.

(UPDATE) Jurickson Profar, switch-hitting MI (TEX)
I had the pleasure of seeing his first PA back my first day out there…and (true story), he laced a double to right field and as he was sliding into second base, Nate Orf turned to the dugout and simply said, “He’s back.” The respect this guy gets from his peers is enough to justify the hope alone. Profar’s already had a taste of the dream, whereas many of these guys are working to get there. His swing looks as natural as ever. When I was in attendance, it was a hit parade for the former LLWS Champion. Texas will have a very nice problem come Opening Day 2016, with Odor, Andrus, and Profar. Where he’ll play defensively, I’m not too sure (left field?), but I’m fairly certain he’ll be right back on track come April of next year. Kid’s a special talent.

Overall general thoughts on the AFL from a fan’s perspective: it’s incredible. I stayed with a few friends in the greater Phoenix area, and we split a $120 ‘family pass’ — which permits entrance for up to six people to any and all of the AFL games for the season (including the Fall Star Game and the championship). The four of us were able to attend nine games each in a seven-day span, and we sat in the first row behind home plate or the dugout every time, all for $30 a piece…that’s a little over $3 a game(!). I was able to witness a literal team-wide drum circle going on in the Surprise Saguaros dugout, which rallied them to a dominant 18-3 victory over the Glendale Desert Dogs. The entire week was a fantastic experience at such a reasonable price. If you have a family and want to take your kids to a bunch of professional baseball games, and take in ‘the future of baseball’, do yourself a favor and book a trip for the Arizona Fall League for the same price as taking the family to a regular season MLB game.

I have some more thoughts, particularly on a handful of pitchers, so I’ll be writing up another (shorter) post in the next few days. Again, thanks for reading.

With the World Series over and all the free agents declared it’s now time for my second-favorite part of the MLB season: the offseason. The 2015 free-agent class is pretty deep and includes some elite players. In this article I wanted to figure out a way to determine monetary value for the top three starting pitchers available this year: Zack Greinke, David Price and Johnny Cueto. All of them are aces and certainly heading for a big pay day but I wanted to develop a way of using the recent big contracts pitchers have signed and the production of great players in the past to determine what kind of pay day these guys are heading for.

Since 2009 there have been nine pitchers to sign a major deal: Clayton Kershaw, Max Scherzer, Justin Verlander, Felix Hernandez, C.C. Sabathia, Jon Lester, Zack Greinke, Cole Hamels and Matt Cain. (I didn’t include Masahiro Tanaka because he didn’t face big-league hitting until he signed his contract.) The average salary amount for these contracts was $168 million and had an average year length of about 5-6 years. When we’re looking at contracts there are many things to consider but two of the biggest factors has to be dollar and year amount. For all three of these pitchers, this may be their last big contract, so maximizing potential is crucial. Every team would love to add a pitcher of their caliber but not every team is in a position to pay for them. That’s part of the reason I wanted to figure out a way to see what dollar amount these pitchers’ production has warranted so far, in comparison to the big contracts signed since ’09 and speculate what can be expected of them for the length of the contract.

To figure out the dollar amount I looked at the nine players’ contracts and figured out the average yearly salary for each individual. I then took that number and divided it by their career WAR, essentially figuring how much it cost the team for the player’s WAR production. Here are the results I got (in millions).

Clayton Kershaw – $5.2m
Justin Verlander – $7m
Felix Hernandez – $6.5m
Jon Lester – $8.9m
C.C. Sabathia – $6.7m
Cole Hamels – $7m
Matt Cain – $9.4m
Zack Greinke – $7.7m
Max Scherzer – $7.5m

I averaged out the numbers, rounded off and got $7.3 million per WAR created. I then took that 7.3 number and multiplied it by Greinke’s career WAR to get, 27.7. So theoretically a year of Zack Greinke pitching is roughly $27.7 million. For David Price it’s $29.2 million and for Johnny Cueto it’s $21.1 million. It’s hard to predict where the market will go once teams start the bidding war, and I’m sure some team is willing to pay above the WAR value to ensure they get their man but for now I’m going to use these numbers to speculate year amount and production.

To determine the amount of years each player could receive, I decided to compare their career production with that of a similar type of pitcher. Let’s start with Zack Greinke. For Greinke I went with Greg Maddux as a comparison; obviously Greinke throws harder but I felt their command of the strike zone and pitches put Maddux and Greinke in the same boat. Below I’ve compared Greinke’s first 12 years in the big leagues to Maddux’s and I certainly think they’re close.

Zack Greinke      Greg Maddux

ERA = 3.49          ERA = 3.06
IP = 2,092.1         IP = 2,596.7
BABIP = .299       BABIP = .283
WAR = 3.8           WAR = 5.5
K/9 = 7.97            K/9 = 6.27
BB/9 = 2.37          BB/9 = 2.23
FIP = 3.52            FIP = 3.06
HR/9 = .92           HR/9 = .49

At age 32 Maddux had a better WAR than Greinke and threw about 500 more innings, but the latter may work in Greinke’s favor. The next part will help determine how many years a team can reasonably expect Greinke to pitch at an elite level. I looked at Maddux’s career numbers from age 32-38 and these were the results.

Greg Maddux (Age 32-38)

ERA = 3.21
IP = 1,581.6
BABIP = .285
WAR = 5.3
K/9 = 6.18
BB/9 = 1.50
FIP = 3.46
HR/9 = .81

As you can see from the results, Maddux was still pitching at an elite level from ages 32-38. From the ages of 39-41 however, you have a different story.

Greg Maddux (Age 39-41)

ERA = 4.20
IP = 827
BABIP = .291
WAR = 3.5
K/9 = 4.93
BB/9 = 1.39
FIP = 3.88
HR/9 = .91

Still good enough to be a major-league pitcher but a far cry from his prime. For Greinke’s situation I think you can expect a similar outcome, so a contract of 6 years at $166 million would be incredibly reasonable for a team. But this is America and money talks; whichever team is willing to pay the elite price tag for more then six years, I think, will be the winner of his services. A seven-year contract between $27-$29 million would be palatable and completely plausible but I think you start to handcuff yourself as a team going for eight years at that rate. Greinke had a dominant 2015 and if there ever was a time for him to test the open market, it’s now. We’ll see what teams are willing to shell out for him but for now let’s move on to David Price.

Unlike Greinke, David Price has never had a chance to test the open market and after another stellar season in the big leagues, Price is gearing up for a big pay day. As I mentioned before Price has a WAR value of about $29.2 million per season and at the age of 30 could see a lengthier contract then Greinke. To figure out future production I could only go with another tall, hard-throwing left-hander by the name of Randy Johnson. Price has eight years under his belt and his comparison to Randy Johnson looks something like this.

David Price          Randy Johnson

ERA = 3.02          ERA = 3.44
IP = 1,439.8         IP = 1,457.8
BABIP = .275       BABIP = .279
WAR = 4              WAR = 4
K/9 = 8.34            K/9 = 9.78
BB/9 = 2.43          BB/9 = 4.46
FIP = 3.30            FIP = 3.43
HR/9 = .80           HR/9 = .76

Price and Johnson compare very well, with Johnson having the advantage in K/9 but Price’s BB/9 is significantly better. Both have a WAR of 4 and nearly identical IP, BABIP, FIP and HR/9. Over the next eight years Johnson went on to be one of the most dominating pitchers in the game and during that stretch had some of the greatest seasons we’ve seen from a pitcher, period. Here are his numbers from 1996-2003.

Randy Johnson (’96-’03)

ERA = 2.93
IP = 1,660.8
BABIP = .308

WAR = 7
K/9 = 12.04
BB/9 = 2.79
FIP = 2.85
HR/9 = .94

This was by far the prime of Johnson’s career and although Price may not put up those types of numbers, he has a good shot of coming close. An 8-year deal for $233 million would be a steal if Price could come close to Johnson’s numbers. Price’s situation is similar to Greinke’s whereas whichever team is willing to pay elite prices for the most years will probably win out. Like Maddux, if you look at the back end of Johnson’s career, you’ll see the decline in results. Still effective for a major-league pitcher but not worth the elite money they once were.

Randy Johnson (’04-’09)

ERA = 4.00
IP = 1,011.6
BABIP = .290

WAR = 3.8
K/9 = 9.09
BB/9 = 2.21
FIP = 3.70
HR/9 = 1.21

Again, whichever team is willing to pay the elite price tag for these years of Price’s career will probably be the winner. It’s a gamble for sure to exceed eight years but eight elite seasons of David Price might be worth a year or two of mediocre Price. This brings us to our last top-tier starting pitcher and the one who perhaps stands to gain the most by being in the same class as Greinke and Price: Johnny Cueto.

First off, I want to say that I think Cueto is a great pitcher and one who deserves the “ace” title, and I know he’s spent most of his career in a hitter-friendly ballpark, but I don’t think his numbers warrant the price tag that Greinke and Price may receive. That being said, pitching is crucial for success in the big leagues and there are only a few top-tier pitchers available via free agency. A team that loses out on Greinke and Price could very well overpay for Cueto’s services to ensure they get one of the best available. For comparison I decided to use Jake Peavy; although Peavy is still playing I think his time as the ace for San Diego and his funky delivery pair nicely with Cueto. Here are the comparisons for the two pitchers through the first eight seasons of their careers.

Johnny Cueto          Jake Peavy

ERA = 3.31            ERA = 3.34
IP = 1,418.7           IP = 1,360.1
BABIP = .272         BABIP = .286
WAR = 2.9             WAR = 3.7
K/9 = 7.35              K/9 = 9.00
BB/9 = 2.65            BB/9 = 2.94
FIP = 3.87              FIP = 3.46
HR/9 = .94             HR/9 = .90

Through similar innings pitched Cueto and Peavy have comparable ERA, BABIP, WAR, BB/9, FIP and HR/9. The WAR value that I came up with for Cueto was $21.1 million per season, a number I think he can certainly get for a number of years. He’s only 29 and unlike Greinke and Price, may be able to sign two major contracts in his career if he can maintain elite status throughout the first one he is about to sign. If he were to sign a four- or five-year deal (4 years/$84 million or 5 years/$105), it’s not crazy to think that a team will pay the elite price tag for another three or four years of a quality Johnny Cueto.

The red flag I see with Cueto is the amount of innings he’s thrown; at 29 he’s only 21.1 innings away from David Price’s total of 1,439.8. As is the case with Jake Peavy, injuries completely derailed effectiveness and Peavy quickly went from “ace” to a 3rd or 4th starter. I’m not saying Cueto is destined to get hurt — his chances are the same as anyone, but paying the high price required to get him makes the possible injury sting even more. Here are the numbers Jake Peavy has put up over the past 6 seasons.

Jake Peavy (’10-’15)

ERA = 4.06
IP = 893.8
BABIP = .281
WAR = 2.3
K/9 = 7.39
BB/9 = 2.31
FIP = 3.82
HR/9 = 1.04

As I mentioned above, injuries greatly affected Peavy’s last six seasons and that’s not the best situation to compare future production from Cueto but it could be a caution to whichever team signs him as to the other end of the spectrum. We all hope for the best but you have to plan for the worst and shelling out $21m+ per season for those types of numbers doesn’t necessarily make sense.

Again I think Cueto is in a great position here, he’s young enough to sign a big deal and still have the potential to land another one down the road. It just depends on effectiveness and health; if both of those stay on his side, he should have no problem getting another big contract around 34 or 35.

After it’s all said and done, we’ll truly know the answer and that’s part of the fun. Speculating how much, how long and where players will end up helps get through the grueling winter months and I, for one, love it. Let me know what you think below and as always, thanks for reading.

This increasing time of games has been cited by Major League Baseball to be a deterrent to fans, jeopardizing ticket sales. Total game time has increased between 2.85 hours in 2004, rising to 3.13 hours in 2014. In 2015, MLB implemented rules to help speed up game time. These rules included forcing batters to stay in the batter’s box during at-bats, and decreasing the time between innings to 2 minutes and 30 seconds. Back in April, after the first few weeks of the season had passed, MLB reported success on their initiatives, stating that if current paces were maintained, average game time would drop below the 2.92-hour mark for the first time since 2011.

A more dramatic possible change was to implement a pitch clock, forcing pitchers to throw their next pitch within 20 seconds of receiving the ball back from the catcher. Currently, the rulebook states (Rule 8.04) that pitchers should throw their next pitch within 12 seconds of receiving the ball from the catcher. However, this rule is not enforced. FanGraphs presents data on the time between pitches, called Pace, which is calculated by taking the total time in an at-bat, and dividing it by the number of total pitches. Between 2010 and 2014 (for pitchers who threw at least 50 MLB innings), the slowest pitchers were Jose Valverde in 2012 (32.4 seconds), Joel Peralta in 2012 (32.3 seconds), and Joel Peralta in 2014 (32.1 seconds). The fastest pitchers were Mark Buehrle in 2010 (16.4 seconds), Mark Buehrle in 2011 (15.9 seconds), and (drum roll please… ) Mark Buehrle in 2015 (15.9 seconds). However, what goes into a pitcher’s selected pace? Focus on execution of their pitch? Embracing the glow of the national spotlight? There hasn’t been much (if anything) to describe the relationship between a pitcher’s self-selected pace and pitching performance.

I looked at the average pace for all pitchers who threw a minimum of 50 innings in years 2010 through 2015. The time between pitches increased steadily between 2010 and 2014, rising from 21.9 seconds in 2010, to 23.5 seconds in 2014. In 2015, the influence of the new pace-of-play initiatives could be seen, with pace decreasing to an average of 22.2 seconds between pitch. Definitely a step in the right direction from MLB’s perspective, but how did this impact pitching performance?

Focusing on xFIP for all pitchers from the same cohort (a minimum of 50 IP), a trend existed for xFIP to decrease between years 2010 and 2014 – an inverse relationship compared to pitching pace. In 2010, the average xFIP was 3.98, compared to 3.60 in 2014. In 2015, xFIP increased to 3.84.

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Is this truly a reflection of pitchers requiring an extra second or two to steady themselves and prepare to throw their best possible pitch in a given situation – or are other factors in play? From a physiological perspective, reducing the time between physical efforts can result in an increased accumulation of muscle fatigue. A recent paper published in the journal of Sports Sciences by Wang and colleagues (2015) found pitchers in a fatigued state were less able to throw strikes. A possible explanation of this relationship is found between increased pitching pace and decreased xFIP.

Major League Baseball will surely press forward with what is best for the game, and the business of baseball. It would be worthwhile for coaches, pitchers, and player’s union representatives to further investigate how pitchers self-select their pace between pitches. Further work is required to establish if there are any negative health consequences associated with decreasing the time between pitches. This should be completely ruled out before any further initiatives are taken by the MLB to speed up the game of baseball.

References

Seven days, nine games. Eight guys who stood out to me.

These aren’t necessarily the best players I saw in the Arizona Fall League (AFL), just the eight guys who I’m particularly bullish on. Alex Reyes has a lights-out fastball/curveball combo, and some of the best mound presence I’ve ever seen from a 20-year old, but he’s been written about by many others…so I decided to write about these eight.

1. Lewis Brinson, R/R OF (TEX)
6’3”+, long, athletic outfielder with a frame that has plenty of room to fill out. Above-average speed and athleticism, enough to probably steal 20-25 bases at the big-league level today. Good approach at the plate, takes his hacks, and doesn’t waste any at-bats. Despite a fairly wiry body, Brinson still has impressive in-game power (400+ ft. HR to center)…once his frame fills out, this kid very well could be an All-Star. He strikes me as being a surefire MLB contributor, and he has the upside of a Starling Marte or Jason Heyward. Brinson won’t turn 22 until next May, and he’s already had a very impressive campaign at AA, plus 37 PA at Triple-A in which he mashed to a .433/.541/.567 line. I didn’t really know much about him prior to the AFL, but I was impressed from his first plate appearance. My personal favorite player at the AFL this year.

2. Dominic Smith, L/L 1B (NYM)
He’s a professional hitter. His approach is very refined, his swing is incredibly simple. Very impressive hitter given he only turned 20 this past June. He’s a big boy though, with a body similar to Michael Conforto or even a slightly lighter Kyle Schwarber. Given he’s pretty bulky and left-handed, his defensive flexibility is limited to first base. I don’t believe he’s had any time in the outfield, but I suppose he’s athletic enough to play a reasonably below-average LF. With that said, his bat will play for any of the 30 teams. He’s a very balanced hitter and has a stroke with almost no extraneous motion. Short and compact swing, and he really uses his thick lower half to drive the ball. The second night I was out there, I saw him hit a ball about 420-430 feet with that same easy stroke — the ball just sounds pretty coming off his bat. Others have raved about his ability, and it’s pretty easy to tell there’s some serious talent there just by looking at his fall-league numbers, but he has the mechanics to support it. However, I did see him struggle a bit against lefties, including a downright ugly check-swing strikeout on a sidearm slider about six inches outside. The lower-end of the spectrum for Smith might be a James Loney-type platoon first baseman; best-case scenario is a pure .300 hitter who develops some legit 30+ HR power during the prime of his career (maybe a lefty-only Victor Martinez).

3. Clint Frazier, R/R OF (CLE)
He’s not quite 6-foot, but Frazier looked like one of the strongest guys in the AFL. Seriously, his forearms are huge. Above-average tools pretty much across the board, Frazier is a natural athlete. He doesn’t quite have the range to stick in center, but he’s been playing there for the Scottsdale Scorpions. Considering he just turned 21 two months ago, Frazier is a very impressive player. I wouldn’t be surprised at all to see him crush the ball in Double-A next year, and work his way into at least a cup of coffee in Cleveland. He still swings and misses a bit too much, particularly on breaking stuff down, but he consistently drives the ball, and has the type of speed to turn some doubles into triples. There’s no doubt he’ll be a major leaguer, but if he continues to whiff too much, he’s probably more of a Travis Buck-type 4th outfielder. I liked what I saw though, so I’m pretty bullish; I see him being something like a right-handed Kole Calhoun with a bit more stolen-base ability.

4. Ian Clarkin, LHP (NYY)
Besides Alex Reyes, Clarkin might’ve the most intriguing pitcher I saw in the AFL. With that said, my immediate thoughts were Clarkin’s  a 3-starter with #2 upside. Very clean, very smooth, fundamental delivery…similar in robotic-nature to Cliff Lee. Very nice life on his fastball, and while I didn’t have a gun, I’d guess Clarkin was sitting 91-92, and touching 94 when he needed to. From a 20-year old lefty, that’s pretty fantastic.

Drafted 33rd overall in 2013, Clarkin sat out the entire 2015 season with elbow inflammation. Clearly, the Yankees should be concerned his UCL might be a ticking time bomb, but they should be very pleased to have this lefty in their system. His secondary stuff was good enough, mixing in an above-average curveball with an average change and cutter. While he doesn’t have the lights-out type of stuff some other guys have, Clarkin brought a very mature approach to the mound. As long as he stays healthy, look for him to develop into a dependable lefty starter with some standout seasons (a la Gio Gonzalez with a little more command). At worst, he’s a solid lefty bullpen arm, but I think injuries would be the only thing standing in the way of him being a starter.

5 & 6. Jack Reinheimer, R/R SS (ARI) and Tyler Smith, R/R 2B/SS (SEA)
These two guys are ballplayers, flat out. In my head, I lumped them together before discovering they were both drafted by the Mariners in 2013 (5th and 8th rounds, respectively). They started the 2015 season as the middle-infield combo at Double-A Jackson. Reinheimer’s the more natural defender, with a legit chance to stick at short, whereas Smith is primarily more of a second baseman going forward. Luckily for Smith, they were separated in June when Reinheimer was acquired by Arizona in the deal that brought Mark Trumbo to Seattle.

Of the two, I prefer Reinheimer overall, but Smith flashed a bit more power, pulling a ball about 380 feet for a low-flying home run that left the park in a hurry. They both have athletic bodies (6’1/186 and 6’0/190) and an instinctive feel for the game, but Reinheimer is a full year younger. He also has a slightly better hit tool — enough that I could see him hitting .280 during his prime. He’s not a franchise shortstop you build around, but he’s a very nice piece for the Diamondbacks to have. He has a solid approach at the plate, an athletic stance, and the swing plane to spray line drives around the field. He reminds me of a better version of Cliff Pennington (a 3.5 win player with Oakland in 2010), whereas Smith is more of a Cliff Pennington version of Cliff Pennington. Both should contribute at the MLB level in the near future.

7. Ramon Torres, switch-hitting 2B/SS (KC)
The first game I went to was a Thursday afternoon matchup between Surprise and Scottsdale at the Giants’ Spring Training complex. I got there early enough to catch the second half of Surprise’s infield/outfield, and there was really only one player who stood out to me. The only natural defender I saw was Ramos Torres. He was playing second base, and he made all the plays easy; ranging to his left or right, it didn’t seem to slow down the 5’9/155 Royal. His middle-infield counterparts (including Yadiel Rivera and Aledmys Diaz) left a lot to be desired, but Torres looked very fluid. The multi-million dollar question becomes: can he hit? I hadn’t ever heard of this guy, so I was able to scout him without presumption. What I saw that day was not very impressive. A seemingly weak, left-handed contact swing that didn’t instill much confidence. Granted, the kid’s only listed at 5’9/155, but it hardly looked like the type of swing that could keep you above the Mendoza line in the big leagues. 0-4 with 2 K’s; I left the park saying, “If only he could hit.”

Good thing I got to see him play multiple games. Two days later he crushed a home run, right-handed, probably close to 385 feet. It was his only hit of the day (1-5 with a BB), but the power he generated was enough to make me stand up out of my seat. The next time he played, three days later, I was also in attendance. Batting second in the best lineup in the AFL, Torres displayed some serious pop from the left-side. This time, pulling a ball for a stand-up triple off the right field fence at spacious Salt River Field. This ball might’ve gone even further, given the power alleys are 390 feet away. Impressive in-game pop from both sides of the plate for a natural up-the-middle defender? Yes, please. I’m having a hard time putting an exact comp on Torres, but I could see him having the upside of a switch-hitting Elvis Andrus, with a good chance of at least being a respectable utility infielder like Adam Rosales.

8 (Bonus). Nathan Orf, R/R INF (MIL)
25 year old utility-type infielder and AFL “taxi squad” member (essentially, roster filler only active on Wednesday and Saturday games to give the ‘real’ prospects some extra rest). Nate Orf is a ballplayer through and through. Despite only reaching AA this year at the not-so-tender age of 25, Nate Orf was one of the most impressive players out in Arizona. Only 5’9”, Orf is about as easy to overlook as Dominic Smith is to notice. Orf seemed to get a hit every time he was up, spraying the ball all over the field and showing an advanced eye at the plate. Granted, Orf was facing pitchers some 3-to-5 years younger than him, but he seemed to hit line drives to the opposite field at will. He also made some nice defensive plays at third, and while he’ll never have the power to be much of an everyday corner infielder, Orf has the type of approach to the game that will make him an excellent bench player in the Major Leagues someday soon. Easily one of the most fun guys to watch while I was out there.

I saw JP Crawford and AJ Reed, and neither did anything to ‘wow’ me — though, Crawford’s multiple errors came pretty close. The only thing that surprised me was how big Crawford is. He’s every bit the 6’2/180 he’s listed at, and has the frame that makes me think he could be a 20+ HR threat at some point in his career. I’ll be putting together a Part II because there are a few other guys I’d like to cover, both negative and positive, including Austin Meadows, Brett Phillips, Yoan Lopez, Chance Sisco, and many others. Thanks for reading.