Archive for November, 2015

Get Nasty: Quantifying a Pitcher’s “Stuff”

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 67th best stuff. Max Scherzer, who threw two no-hitters, is ranked as only having the 60th 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.


Branch, J. (2015). The Mysteries of Pitching, and All That ‘Stuff’. Posted online, October 3, 2015.

Sarris, E. (2015). The Best Changeups of the Year by Shape and Speed. Posted online, November 9, 2015.

Revisiting Vegas

Before the season began, I wrote an article comparing the Vegas odds of each team winning the World Series to the projected standings according to Steamer. This is a look back at that comparison.

Using the Vegas odds of winning the World Series and the Steamer-projected standings, there were some strong plays on the board before the season began. Let’s look at each division, in chart form, starting with the NL West. The first table shows the Steamer pre-season projections. The second table shows the actual standings.

RDif=Run differential
RS/G=Runs scored per game
RA/G=Runs allowed per game
EXT W=Wins greater or fewer than Steamer projected

What I wrote then: It’s interesting that Vegas is really excited about the Padres, at least compared to the Rockies and Diamondbacks, who don’t project to be that much worse but who face significantly longer odds. With the Giants’ recent success, they are probably the best play here. Even if you don’t think they can beat out the Dodgers for the division, they’ve proven that they can make a run if they get into the playoffs as a wild card team. Of course, this is an odd-numbered year, so you might want to save your money and look elsewhere.

What actually happened: Steamer nailed the top of the division, picking both the Dodgers and Giants to win just one fewer game than they each did. The Diamondbacks and Padres were flipped, with the Diamondbacks winning five more games than projected and the Padres falling five games short. The Rockies came in way under. Vegas was right about the Dodgers being the favorites, with the Giants having the next-best odds, but the hype around the Padres at the beginning of the year proved to be unfounded and the Diamondbacks finished better than 120 to 1 odds would have predicted.

What I wrote then: The play here is the Pittsburgh Pirates. They are projected to be just a game off the division lead, but with odds at 30 to 1. In a world full of parity, every team in baseball would have a .500 record and 30 to 1 odds and there would be no supermodels. That would be a sad, sad, world. In this world, the Pirates are projected to be better than .500 and should have better odds than 30 to 1. Meanwhile, Vegas is excited about the Cubs, giving them 14 to 1 odds (they opened at 45 to 1). Some of you may remember that in Back to the Future, the Cubs won the 2015 World Series (in a 5-game sweep over Miami) after starting the year with 100 to 1 odds. This could be the Cubs’ year, McFly!

What actually happened: Steamer nailed the order of this division, right down to the gap between the top three teams and the bottom two. In the upper half of the NL Central, the Cardinals and Cubs shared the third-best odds in the National League and finished 1st and 3rd in overall win-loss record. The Pirates, on the other hand, finished with the second-best record in the NL but Vegas had them tied for eighth with the Marlins at 30 to 1 odds before the season. The Brewers and Reds both disappointed, but the Reds were particularly bad. They entered the season with 70 to 1 odds but finished the season with just 64 wins, one more than the Philadelphia Phillies, who were giving 300 to 1 odds back in April.

What I wrote then: There aren’t any real good plays here. As good as the Nationals look now, especially after acquiring Max Scherzer, it would be foolish to put any money on a major league team at 5 to 1 odds to win the World Series. There’s just too much unpredictability come playoff time. None of the teams in this division have appealing odds, unless your name is Lloyd Christmas, in which case you have to jump all over the Phillies at 300 to 1 (“So you’re telling me there’s a chance?”).

What actually happened: So much for those 5 to 1 odds in Vegas for the Washington Nationals. I hope you didn’t put too much money on them. Vegas was optimistic about the Nationals, as you would expect, but also gave the Marlins nearly the same odds as the Mets. The Mets made it all the way to the World Series, while the Marlins were 20 games under .500. The Phillies were the longest of longshots to win the World Series and finished with the worst record in the National League.

What I wrote then: There’s no love for the Tampa Bay Rays in Vegas, with odds of 75 to 1 in what still looks like a tight division. The Rays opened at 35 to 1. Apparently, Las Vegas does not like their recent moves. Based on Steamer projections, the Rays look like your best longshot option of any team in baseball.

What actually happened: At 14 to 1, the Red Sox were tied with the Seattle Mariners for the second-best odds of any American League team, with only the Los Angeles Angels topping them. The Red Sox (and Mariners) finished well below Steamer’s expectations. In the case of the Red Sox, the pitching didn’t hold up their end of the bargain. On the other hand, the Toronto Blue Jays had worse odds than nine other teams in the AL but finished with the second-best record in the league. They had nine more wins than Steamer projected.

What I wrote then: No team jumps out here, but if I had to pick one, I’d take the Indians at 25 to 1. They look to be right there with the Tigers to win the division, but with slightly worse odds, so you’d get a bigger payout if they went all the way.

What actually happened: I picked the Indians as the team to take a chance on, but everyone now knows the Royals were the best play. The 2015 World Champion Kansas City Royals were given 25 to 1 odds before the season started. Those odds placed the Royals behind six AL teams and tied with two others. They ended up with 14 more wins than projected by Steamer. The Tigers were the anti-Royals, finishing with 11 fewer wins than projected. The Tigers’ 20 to 1 odds were in the top six in the league and they finished with the second-worst record. The team with the longest odds in the AL, the Twins, actually made a run at a wild-card spot and had seven more wins than projected by Steamer.

What I wrote then: I guess when you lose Josh Donaldson, Brandon Moss, Jeff Samardzija, Jon Lester, and Derek Norris, your odds to win the World Series should get worse, but 60 to 1, really? Steamer still has Oakland in the mix for the AL Wild Card and just 5 games back of the Mariners for the division.

What actually happened: Based on their 68-94 record, the Athletics deserved their pre-season 60-to-1 odds, but they weren’t as bad as their record. They had a run differential that was better than the Mariners, who won eight more games than the A’s. The Angels (10 to 1), Red Sox (14 to 1), and Mariners (14 to 1) were the top three favorites in the AL in Vegas before the season started and they finished, 6th, 11th, and tied for 12th, respectively, in wins. The Angels were within range of a wild card spot and actually had one more win than Steamer projected, but the Mariners were big disappointments in Vegas and compared to their Steamer projection. They had 13 fewer wins than Steamer projected. The 50 to 1 Rangers had the worst Vegas pre-season odds of any team that went on to win their division.

The following chart shows the teams in each league with their pre-season Vegas odds, their Steamer projected win-loss record, and their actual win-loss record.

What I wrote then: The Pirates have worse odds than the Padres and Mets, neither of whom are projected to contend for the Wild Card or even finish .500. Aye, this be the National League team you should wager your doubloons on and win some booty!

What actually happened: The Pirates weren’t a bad play, really. They did win 98 games. They just ran into the Jake Arrieta Experience in the one-game wild card matchup with the Cubs.

Based on pre-season Vegas odds, the top five teams in the National League were the Nationals, Dodgers, Cardinals, Cubs, and Giants. Three of those five made the post-season. Steamer, on the other hand, had a top five of the Nationals, Dodgers, Cardinals, Pirates, and Cubs, giving them four of the five post-season teams. Both Vegas and Steamer missed out on the Mets.

The Vegas pre-season odds did a good job of identifying the league’s worst teams. Five teams finished with fewer than 70 wins and they all had odds of 60 to 1 or worse before the season started. The 120 to 1 Diamondbacks were the exception among the teams expected to struggle in 2015, as they surprisingly won 79 games.

What I wrote then: In the American League, your best options are the Athletics and Rays, and possibly the Blue Jays. The A’s are right in the mix for the wild card, yet have the same odds as the Houston Astros and Atlanta Braves. The Rays are projected to be nearly as good as the A’s and have even worse odds, better than only four teams in all of baseball—the Phillies, Diamondbacks, Rockies, and Twins. The Blue Jays don’t look to be as good a play as the A’s and Rays but, like the Pirates, they have longer odds than other similarly competitive teams.

What actually happened: It turned out the A’s and Rays were not good plays, but how about those Blue Jays?

The Vegas pre-season odds suggested a top six of the Angels, Mariners, Red Sox, Tigers, Orioles, and White Sox, with all given odds of 20 to 1 or better. None of the six made the playoffs. You have to get down to the 25 to 1 Yankees and Royals to find a playoff team and they were joined by the 30 to 1 Blue Jays, 50 to 1 Rangers, and 60 to 1 Astros. Steamer projected a top seven that included the Mariners, Red Sox, Tigers, Angels, Indians, Blue Jays, and Athletics, all with 84 wins or more. Only the Blue Jays were a playoff team among this group.

The bottom line is that baseball is difficult to predict. Eleven teams had better odds than the World Series Champion Kansas City Royals and four teams had the same odds as the Royals. Yet, it was the Royals hoisting the World Series trophy when all was said and done.

Evaluating the Gap Between ERA and FIP

Fielding Independent Pitching (FIP) has displayed an ability to accurately measure a pitcher’s true skill. FanGraphs describes FIP succinctly as “a measurement of a pitcher’s performance that strips out the role of defense, luck, and sequencing, making it a more stable indicator of how a pitcher actually performed over a given period of time than a runs allowed based statistic that would be highly dependent on the quality of defense played behind him…”

This definition recognizes three factors that may differentiate the runs a pitcher is expected to surrender (FIP) versus the runs a pitcher actually surrenders.

  • Defense
  • Sequencing
  • Luck

FIP removes these factors by only measuring the events that are within control of the pitcher and therefore accurately reflect the skill of the pitcher. These events are strikeouts, walks, batters hit by pitch and home runs. All other events, which are balls put into play, may result in outs, bases, runs, or errors, but are outside the pitcher’s complete control.

The general measure of over- or under-performance of a pitcher’s true skill is ERA-FIP. ERA measures the earned runs given up by a pitcher based on all the events that happen, opposed to FIP’s measurement of runs given the limited events over which a pitcher has complete control. Therefore, the variance between ERA and FIP is attributed to the three factors noted above: defense, sequencing and luck.

But how much of the difference between pitching results and pitching skills are attributable to defense, sequencing, and luck, respectively? And shouldn’t the opponent get some credit for widening the gap between ERA and FIP, either to the benefit or detriment of the pitcher?

I compared Ultimate Zone Rating (UZR), Defensive Runs Saved (DRS), and FanGraphs’ Defensive Runs Above Average (DEF) to ERA-FIP for each team season between 2005–2015 to try to understand the effect of defense on pitching results.

All the metrics have similar correlations, but DRS has the highest adjusted r-squared (correlation coefficient) value (.39), which measures how much of the variance in ERA-FIP is correlated by the defensive metric. FanGraphs’ DEF was right behind DRS (.37) and UZR had an adjusted correlation coefficient of (.34).

The result was somewhat surprising, because DRS and UZR do not factor in positional adjustments (UZR also does not measure catcher or pitcher defense). These metrics measure a player against the average player at that player’s position. They do not measure the difficulty of the position in comparison to other positions.

DEF does apply positional adjustments. FanGraphs uses UZR, not DRS, as the metric they apply the positional adjustments to in order to determine DEF. (see notes below for further explanation of positional adjustments)

Still, the non-positionally adjusted DRS correlates most closely to ERA-FIP. However, it does seem that the advantage over DEF is negligible.

All in all, defense, considered alone, appears to explain 35–40% of a team’s ERA-FIP.

I chose to use a team’s Run Expectancy based on 24 base-out states (RE24) to measure the effects of sequencing. RE24 measures the change in run expectancy between the time a batter comes to the plate and the run expectancy after the plate appearance. The up and down of these changes will reflect the sequence of events experienced by each team (see notes below for further explanation of RE24).

The relationship between ERA-FIP and RE 24 has a similar correlation coefficient (.38) as ERA-FIP and the defensive metrics. Sequencing seems to play a role nearly equal to defense in determining the over- or under-performance of pitchers.

Defense and sequencing are not exclusive though. The reason that the single in the bottom of the 9th occurred is likely related to the fact that the shortstop and/or third baseman did not have enough range to get to the groundball hit between them. Therefore, I measured the correlation of ERA-FIP to defense and sequencing.

Again, DRS+RE24 (.54), DEF+RE24 (.53), and UZR+RE24 (.51) all yielded similar adjusted correlation coefficients.

This suggests roughly 50% of the difference between ERA and FIP are correlated to defense and sequencing. The other half of the difference is not the great unknown, but it’s (sort of) immeasurable.

Luck is part of the other half of the gap between ERA and FIP, but is luck really 50% of what separates a pitcher’s result from a pitcher’s skill?

The skill of the opponent in running the bases is probably a greater part of the other 50% than luck is. This was on display in the playoffs, whether it’s Lorenzo Cain scoring from first on a single, Daniel Murphy taking third base from first base on a walk, or one of the other examples of aggressive (and smart) baserunning witnessed throughout the playoffs. These events change run probabilities and create runs. These base running events tend to be less noticed during the 162-game season, but they still happen.

Some of the ability for catchers and pitchers to prevent stolen bases is cooked into the defensive metrics, but not much else is. FanGraphs’ Base Running (BsR) measures the baserunning abilities of players and teams, from an offensive perspective, but to my knowledge there is no accumulated stat to measure opponents’ BsR. The data is out there. The same measures used to determine BsR would only have to be aggregated from the perspective of the pitching team.

A measure of Opponents’ BsR would likely cover a good amount of the uncorrelated variance between ERA and FIP. There would still be a lot of luck left in play, but probably not as much as there is thought to be now.

AFL Thoughts, Part 2: Meadows, Profar, etc.

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.

Determining the Market Value for Greinke, Price and Cueto

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.

Hardball Retrospective – The “Original” 1907 Philadelphia Phillies

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Therefore, Rusty Staub is listed on the Astros roster for the duration of his career while the Athletics declare “Shoeless” Joe Jackson and the Blue Jays claim Tony Fernandez. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.


OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams


The 1907 Philadelphia Phillies    OWAR: 56.2     OWS: 349     OPW%: .527

Based on the revised standings the “Original” 1907 Phillies finished in a tie for fourth place, only six games behind the front-running Cubbies. Philadelphia paced the National League in OWS and OWAR.

Sherry Magee batted .328 with a League-best 85 RBI and a team-leading 37 Win Shares. Elmer Flick supplied a .302 BA and legged out 18 three-base hits. Nap Lajoie rapped 30 doubles and pilfered 24 bases. The keystone combo of Ed Abbaticchio and Kid Elberfeld swiped 57 bags. Roy A. Thomas posted a .374 OBP and led the League in walks for the seventh time in eight seasons. “Silent” John Titus provided a solid option as a fourth outfielder, belting 23 doubles and 12 triples while hitting at a .275 clip.

Nap Lajoie places sixth among second basemen according to Bill James in “The New Bill James Historical Baseball Abstract.” Teammates listed in the “NBJHBA” top 100 rankings include Magee (21st-LF), Flick (23rd-RF), Thomas (29th-CF), Kid Gleason (72nd-2B), Elberfeld (75th-SS) and John Titus (76th-RF).

Roy Thomas CF 2.55 20.78
Nap Lajoie 1B/2B 7.5 30.2
Sherry Magee LF 7.13 37.68
Elmer Flick RF 4.95 34.39
Kid Elberfeld SS 2.9 21.36
Fred Jacklitsch C 0.84 8.17
Ed Abbaticchio 2B 2.27 20.54
John Titus RF 2.16 23
Doc Marshall C 0.44 2.67
George Browne RF 0.39 12.1
Mickey Doolin SS 0.06 12.08
Paul Sentell SS -0.06 0.02
Red Dooin C -0.21 7.72
Del Howard LF -1.08 7.34
Kid Gleason 2B -1.44 1.12

Doc White fashioned a 2.26 ERA and a 1.058 WHIP while topping the leader boards with a 27-13 record. Tully Sparks delivered a 22-8 mark with a 2.00 ERA and 1.026 WHIP as he completed 24 of 31 starts. Johnny Lush (10-15, 2.68) and “Smiling” Al Orth (14-21, 2.61) rounded out the Phillies’ rotation. George McQuillan (4-0, 0.66) yielded only three earned runs in 41 innings pitched during his inaugural campaign.

Doc White SP 4.37 23.84
Tully Sparks SP 3.63 23.54
Johnny Lush SP 0.53 12.13
Al Orth SP -0.06 15.29
Harry Coveleski RP 0.7 2.75
King Brady RP -0.02 0.13
George McQuillan SP 2.32 7.19
Fred Burchell SP -0.09 0.27
Jesse Whiting RP -0.28 0
John McCloskey RP -0.58 0
Bill Duggleby SP -1.42 1.9
Bill Bernhard SP -1.54 0

The “Original” 1907 Philadelphia Phillies roster

NAME POS WAR WS General Manager Scouting Director
Nap Lajoie 2B 7.5 30.2
Sherry Magee LF 7.13 37.68
Elmer Flick RF 4.95 34.39
Doc White SP 4.37 23.84
Tully Sparks SP 3.63 23.54
Kid Elberfeld SS 2.9 21.36
Roy Thomas CF 2.55 20.78
George McQuillan SP 2.32 7.19
Ed Abbaticchio 2B 2.27 20.54
John Titus RF 2.16 23
Fred Jacklitsch C 0.84 8.17
Harry Coveleski RP 0.7 2.75
Johnny Lush SP 0.53 12.13
Doc Marshall C 0.44 2.67
George Browne RF 0.39 12.1
Mickey Doolin SS 0.06 12.08
King Brady RP -0.02 0.13
Paul Sentell SS -0.06 0.02
Al Orth SP -0.06 15.29
Fred Burchell SP -0.09 0.27
Red Dooin C -0.21 7.72
Jesse Whiting RP -0.28 0
John McCloskey RP -0.58 0
Del Howard LF -1.08 7.34
Bill Duggleby SP -1.42 1.9
Kid Gleason 2B -1.44 1.12
Bill Bernhard SP -1.54 0

Honorable Mention

The “Original” 1978 Phillies   OWAR: 57.7     OWS: 320     OPW%: .547

Clashing with the Expos and the Bucs into the final week of the ’78 season, Philadelphia emerged in third place, only two games behind Pittsburgh. The Fightin’ Phillies led the circuit in OWAR and placed runner-up to the Pirates in OWS. Greg “The Bull” Luzinski launched 35 moon-shots and knocked in 101 baserunners. First-sacker Andre Thornton blasted 33 long balls, tallied 105 RBI and scored a personal-best 97 runs. Larry Hisle delivered a .290 BA with career-bests in home runs (34) and RBI (115). Mike Schmidt struggled through a sub-par season at the dish but played stellar defensive at the hot corner, winning his third of nine consecutive Gold Glove Awards. Shortstop Larry Bowa contributed 27 steals and a .294 BA while backstop John “Bad Dude” Stearns pilfered 25 bases. Fergie “Fly” Jenkins furnished a record of 18-8 with a 3.04 ERA and 1.080 WHIP. Dick Ruthven provided 15 wins with a 3.38 ERA. Mike G. Marshall anchored the relief corps with 10 victories and 21 saves.

On Deck

The “Original” 2001 Mariners

References and Resources

Baseball America – Executive Database


James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

Mostly Useless Information About the World Series In the Wild Card Era

We could easily call my decision to publish an article with playoff predictions using a brand-new theory about previous success predicting future success ballsy (or stupid). To summarize, research by Rosenqvist and Skans (2015) [1] showed that golfers who barely qualified for a golf tournament would go on to have more success in future tournaments than golfers who barely missed the cut in the same tournament. Seemingly accidental success created confidence, which led to more success in the future. So, using this logic, I wanted to see if this same phenomenon occurred at the team, rather than the individual level. The attempt was to predict all divisional victors from this year’s 2015 MLB playoffs using previous playoff experience and success as the predictor. As it turns out, the teams with more experience/success were only 1 for 4 in the first round of the playoffs.

This time, instead of making predictions, I did the smart thing and looked at previous trends. Instead of using the first round of the playoffs (which arguably is more erratic given that it’s only a five-game series), I focused solely on the World Series. I totaled all the previous playoff experience, age, and WAR for every player on each 25-man World Series team roster in the Wild Card Era (1995 – 2015, n = 42 teams).

WAR doesn’t predict the winner of the World Series

Is this old news? I don’t know. Tallying up a team’s WAR correlates with the actual number of wins that team will have by the end of the regular season (somewhere around r = .82 last time I checked), but it doesn’t correlate with the victor of the World Series. In fact, 13 out of the last 21 (62%) World Series victors had average WARs lower than their opponent’s.

Differences in experience at the team level relate to the duration of the World Series

The difference in previous playoff experience between the two World Series teams is a good predictor of the number of World Series games that will be played in a series. Specifically, at the team level, the greater the difference in the average previous playoff series won (r = -.45, p < .05, n =21), the average number of World Series appearances (r= -.45, p < .05, n =21), and the average number of World Series titles (r = -.46, p < .05, n =21) between the two teams, the less World Series games played that year. You’re saying, “yeah but what about the 2014 World Series that went 7 games when the seasoned Giants played the inexperienced Royals?” It’s just a trend, not a guarantee.

Other tidbits

  • The higher the average of previous World Series appearances across both World Series teams, the higher number of television viewers (r = .45, p < .05).
  • The World Series victor with the highest average WAR per player was the 1998 Yankees (m = 2.57); the lowest WAR was the 2006 Cardinals (m = 1.26).
  • Oldest World Series victors were the 2000 Yankees (m = 30.7); youngest were the 2002 Angels (m = 27.4).
  • Most experienced victor was also the 2000 Yankees (96% of the team had previous playoff experience), and least experienced were the 2002 Angels (0%).

More needs to be understood about this theory

There was however, no relationship between previous playoff experience and that year’s World Series outcome. In terms of playoff experience, the results from Rosenqvist and Skans could not be replicated in this setting. Baseball isn’t golf, and baseball isn’t an individual sport, it’s a team sport. Perhaps the average and/or aggregate levels of experience within a team might manifest differently than for an individual. So, too, are there other ways to operationalize this hypothesis of previous experience/success, so I wouldn’t write this off as a done deal. We’re still a long ways away from determining how and if this theory occurs within the context of baseball – more research into the theoretical underpinnings is always the answer.

Back to the drawing board.

[1] Rosenqvist, O. & Skans O.N. (2015). Confidence enhanced performance? – The causal effects of success on future performance in professional golf tournaments. Journal of Economic Behavior & Organization, 117, 281-295.

Pace Yourself: The Relationship Between Pace and xFIP

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.



Lin-Hwa Wang, Kuo-Cheng Lo, I-Ming Jou, Li-Chieh Kuo, Ta-Wei Tai & Fong- Chin Su (2015): The effects of forearm fatigue on baseball fastball pitching, with implications about elbow injury, Journal of Sports Sciences, DOI: 10.1080/02640414.2015.1101481

Seven Days In the Desert: My Favorite Prospects From the AFL

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.

KC’s Secret Sauce: Age-Defying Player Development

The 2006 Kansas City Royals went 62-100, tallying the team’s fourth 100-loss record in five years. In June of this particular season, owner David Glass hired a new general manager, Dayton Moore. ESPN’s first reaction was that Moore could have waited for the GM job with the Braves, who unlike the Royals were an “admired organization.” Jason Whitlock, who was in the midst of a 16-year stint as a writer for the Kansas City Star, declared that Moore was owner David Glass’ new scapegoat, and would soon be undermined by Glass’ “cheapness and incompetence.”

It took Dayton Moore a while to gain respect in Kansas City. In January of 2014, the last off-season that Moore’s Royals would endure without a World Series appearance, Royals Review mocked Moore’s tenure in Kansas City, which still only had one winning season.

Dayton Moore’s professional history before moving to Kansas City seemed to be the opposite of what a modern general manager was supposed to be. While the league’s front offices frantically shifted towards advanced statistics, Moore’s background was in scouting and player development. He established an excellent analytics office in the Royals’ organization, but his expertise and focus were drafting and signing promising young athletes and patiently developing the right type of team.

It worked, slowly. In 2009, they won 65 games. Then they won more in 2010 — and again in 2011, 2012, 2013, 2014, and 2015.

For six straight seasons, the Royals won more games than in the year before. They are the only team since World War II to do that. And they did it by being better than the rest of the league at exactly what Moore was supposed to do: develop talent.

In 2013, Jeff Zimmerman found that aging curves in baseball were changing. The fascinating and significant article showed that, in general, hitters no longer improved throughout their 20’s.

Dayton Moore’s Royals did not get the memo, and this may be what sets them apart from the rest of the league more than anything else. Sure, their bullpen is historically dominant. Yes, their solid and spectacular defense is remarkable. Their contact ability is extreme.

But all of these factors ignore something else that the Royals do historically well. Their hitters keep getting better, at points in their career when they are not supposed to.

From the ages of 23 to 26, Alex Gordon had a wRC+ of 93. That is not supposed to get better. From the age of 27 to 31, his wRC+ has been 123.

From the ages of 25 to 27, Lorenzo Cain had a wRC+ of 86. That is not supposed to get better. In his age 28 and 29 season, his wRC+ has been 121.

From the ages of 22 to 25, Mike Moustakas had a wRC+ of 82. This year, at age 26, it was 124.

This year, career 104 wRC+ hitter Eric Hosmer hit 125 at the age of 25.

Possibly most importantly for the 2015 team, Kendrys Morales followed up a 71 wRC+ season at the age of 31 with a 131 mark this year.

These players are not extremely old. None of them are at a point in their careers where they should be falling off a cliff, but recent history suggests that they should be stagnating, at best. But that’s not what the Royals have done.

Since Dayton Moore’s first full season in 2007, there have been plenty of mid-career surprises.

To create an aging curve, I measured the difference in wRC+ between consecutive seasons in which players had at least 300 PA. Then, I made every season relative to the performance at age 26, because that appears to be the first year of plateauing for the whole MLB. (Note: The results are not identical to Zimmerman’s).

From 2007-2015, with Age 26 being normalized to 100, here is what we get:

Aging Curve

Royals players dramatically improve from their early 20’s all the way until they turn 30. While the sample size is not huge, the consist improvement is remarkable. Kansas City’s players show greater improvement than MLB average at age 25, 26, 27, 28, and 30. This feat is doubly difficult when you consider that a lucky season at age 27 should naturally show a decline at age 28.

Dayton Moore and the Royals organization are rightly being showered with praise after their second consecutive World Series appearance and their seemingly invincible run through the 2015 playoffs. But their formula of success is not a frozen-in-time snapshot of the 2015 team. Player production in one year does not define the strengths of the Royals organization. Rather, the possibility that it could be even better next year does.