The 2013 Baseball Prospectus Organizational Top 10 prospects had quite an impact on the 2017 season. This impact includes the Milwaukee Brewers, who saw major trade Tyler Thornburg, roster-drop Scooter Gennett, and final rotational season for Wily Peralta define their in-house 2013 class. Jonathan Villar, Domingo Santana, Lewis Brinson, and Josh Hader made varying organizational strides among the out-of-org 2013 prospects acquired by Doug Melvin and David Stearns. In terms of WARP, the out-of-organization guys outperformed the homegrown 2013 list, and to add insult to injury, some of the Brewers previous organizational depth played quite well elsewhere (here, Mitch Haniger joins Gennett).
Brewers 2013 Top 10 | 2017 Production |
---|---|
Wily Peralta | 57.3 IP with 6.16 DRA (-0.5 WARP) |
Johnny Hellweg | Pittsburgh Minor Leagues & Unaffiliated ball |
Victor Roache | Traded to the Dodgers |
Jorge Lopez | Served as organizational depth call-up (2.0 IP) |
Clint Coulter | Brewers minor leagues |
Tyler Thornburg (Boston) | Traded / Did not play (Injury) |
Taylor Jungmann | Brewers Minor Leagues |
Mitch Haniger (Seattle) | 410 PA with .284 TAv (2.2 WARP) |
Tyrone Taylor | Brewers Minor Leagues |
Scooter Gennett (Cincinnati) | Released / 497 PA with .299 TAv (2.1 WARP) |
Jonathan Villar | Astros 2013 Top 10 / 436 PA with .242 TAv (0.9 WARP) |
Domingo Santana | Astros 2013 Top 10 / 607 PA with .306 TAv (3.4 WARP) |
Lewis Brinson | Rangers 2013 Top 10 / Graduated to MLB (55 PA) |
Josh Hader | Orioles 2013 Top 10 / 47.7 IP with 3.79 DRA (0.7 WARP) |
Entering the 2017 season, the Washington Nationals seemingly solidified their batting order by acquiring Adam Eaton, the second-best position player from this prospect class in 2016 (Nolan Arenado was best). Eaton was promptly injured to start the season, ending his potential run at matching his incredible 2016 value, but teammate Anthony Rendon was ready to step up (in 2017, Rendon would be second-best to Arenado). Corey Seager and the aforementioned Arenado both worked to lead their respective teams to the playoffs. Alongside those expected stars, Jose Ramirez joined his teammate Francisco Lindor to lead Cleveland in an effort to defend their American League pennant. The playoffs teams are loaded with the who’s who of this prospect class; Gary Sanchez (5.3), Carlos Correa (4.6), Didi Gregorious (4.3), George Springer (4.2), deadline trade Sonny Gray (4.2), Byron Buxton (4.1), and Yasiel Puig and Alex Wood (3.6 each) all produced strong Wins Above Replacement Player (WARP) value for their respective playoff clubs.
Related Reading: Refining WARP and OFP Pricing
Together the organizational Top 10 from 2013 produced 216.3 WARP in 2017, which was good for approximately 22 percent of MLB production for the season. What is rather interesting about this class is that five seasons in, the number of MLB players dipped from 178 in 2016, to 175 in 2017. Alongside the “who’s who” above, there’s quite a blast from the past in the “yet to reach the MLB” side of this prospect class: Bubba Starling, Clint Coulter, Courtney Hawkins, Duane Underwood, Kyle Zimmer, Stryker Trahan, Victor Roache, Austin Wood, Hak-Ju Lee, and Tyrone Taylor are just a few of the names that fans (especially Brewers fans) might recognize. Of course, some members of the class are just reaching the MLB, as Josh Hader and Lewis Brinson did for Milwaukee in 2017. Max Fried, Nick Delmonico, Jorge Bonifacio, and Lucas Sims were other 2017 debuts from this prospect class.
As a group, these prospects have produced more than 760 WARP at the MLB level during their respective careers.
2013 Top 10 Summary | Players | MLB Players | WARP | Per Player | Total ($M) | MLB Only ($M) |
---|---|---|---|---|---|---|
70 OFP | 29 | 28 | 160.4 | 5.5 | $38.7 | $40.1 |
60 OFP | 123 | 95 | 323.4 | 2.6 | $18.4 | $23.8 |
50 OFP | 146 | 109 | 277.4 | 1.9 | $13.3 | $17.8 |
All | 298 | 232 | 761.2 | 2.6 | $17.9 | $23.0 |
As some of these prospects work to build or expand their legend through (hopeful) playoff success, it is worth looking into the completed 2017 season by these prospects in order to learn how a prospect class progresses over time. By tracking this class over five seasons, one can ask, “How do young prospects perform during their initial seasons?,” and additionally, “How likely are prospects to improve once they reach the MLB?,” or simply, “How many prospects become good MLB players?” These are crucial questions for the Brewers as they exit their rebuild and enter the stage of truly developing their youngest, (hopefully) most impactful potential at the MLB level:
What should be expected of the Brewers’ 2017 top prospect class as they develop at the MLB level?
First and foremost, what is telling about the 2013 prospect class is how quickly many prospects reach the MLB and exit the MLB. From this prospect class, 232 players reached the MLB at some point over the last five seasons. However, as mentioned above, only 175 players from this prospect class worked in the MLB during the 2017 season. So, it must first be emphasized that while the Top 10 organizational prospects as a group are the most elite prospects, within the top 5 percent of all minor leaguers, many of these players will not have long or impactful careers. This should not necessarily be surprising, as according to Baseball Reference Play Index the vast majority of MLB players hardly achieve 1.0 career WAR (1.2 WAR places batters and pitchers within the top third of all-time players); but, it should be underscored as a requisite warning against prospect list hype. Brewers fans will recognize Johnny Hellweg, Sean Nolin, and Garin Cecchini as Top 10 2013 prospects that fit this mold. Top 10 organizational prospect status is not a guarantee for a long career, or even anything more than a cup of coffee in some cases.
Second, while the number of 2013 Top 10 organizational prospects working in the MLB declined in 2017, the average WARP for these MLB players also declined. Granted, the decline in WARP was from 1.3 to 1.2, which basically means that the level of performance for these players largely remained the same from year-to-year. Basically, what ought to be read into this statistic is the fact that there is no clear narrative about improvements as a group for these prospects. Once in the MLB, there is no clear path for Top 10 prospects to continually improve or expand their WARP; roles fluctuate, injuries and ineffectiveness occur, and in some cases performance levels simply fluctuate. Viewing the time-series shifts for these players can demonstrate the volatility of year-by-year performance upon reaching the MLB.
The following table tracks the largest year-to-year WARP declines from 2016 to 2017 for prospects from the 2013 organizational Top 10:
TimeSeries | Change1 | Change2 | Change3 | Change4 | Change5 | Change6 | AbsoluteChange | WARP | WARPGenerated |
---|---|---|---|---|---|---|---|---|---|
Adam Eaton | 0.7 | -0.9 | 3.3 | 1.5 | 2.9 | -6.8 | 16.1 | 16.4 | 32.5 |
Noah Syndergaard | 0.0 | 0.0 | 0.0 | 4.1 | 1.5 | -4.9 | 10.5 | 10.4 | 20.9 |
Aaron Sanchez | 0.0 | 0.0 | 0.8 | 0.3 | 2.6 | -4.3 | 8.0 | 5.0 | 13.0 |
Jonathan Villar | 0.0 | 0.0 | 1.2 | -0.4 | 3.9 | -3.8 | 9.3 | 7.6 | 16.9 |
Joc Pederson | 0.0 | 0.0 | -0.1 | 1.3 | 2.2 | -3.0 | 6.6 | 4.9 | 11.5 |
Tommy Joseph | 0.0 | 0.0 | 0.0 | 0.0 | 1.1 | -2.8 | 3.9 | -0.6 | 3.3 |
Jackie Bradley | 0.0 | -0.3 | 1.2 | 1.2 | 2.1 | -2.7 | 7.5 | 8.4 | 15.9 |
Gregory Polanco | 0.0 | 0.0 | 1.5 | 1.3 | 0.2 | -2.6 | 5.6 | 7.8 | 13.3 |
Yordano Ventura | 0.0 | 0.5 | 2.8 | 0.1 | -0.9 | -2.5 | 6.8 | 9.7 | 16.5 |
Tyler Glasnow | 0.0 | 0.0 | 0.0 | 0.0 | 0.3 | -2.5 | 2.8 | -1.9 | 0.9 |
Maikel Franco | 0.0 | 0.0 | -0.3 | 2.4 | -0.4 | -2.4 | 5.5 | 2.8 | 8.3 |
Christian Yelich | 0.0 | 1.5 | 1.3 | 0.4 | 2.1 | -2.4 | 7.7 | 15.8 | 23.4 |
Jose Iglesias | -0.3 | 2.3 | -2.0 | 0.4 | 2.0 | -2.3 | 9.3 | 4.6 | 13.9 |
Jeurys Familia | 0.2 | -0.1 | 1.4 | 0.7 | -0.1 | -2.1 | 4.6 | 6.1 | 10.7 |
Tony Wolters | 0.0 | 0.0 | 0.0 | 0.0 | 1.6 | -2.1 | 3.7 | 1.1 | 4.8 |
Addison Russell | 0.0 | 0.0 | 0.0 | 1.6 | 2.2 | -2.0 | 5.8 | 7.2 | 13.0 |
Chris Beck | 0.0 | 0.0 | 0.0 | 0.0 | -0.2 | -1.9 | 2.1 | -2.3 | -0.2 |
Nomar Mazara | 0.0 | 0.0 | 0.0 | 0.0 | 1.5 | -1.9 | 3.4 | 1.1 | 4.5 |
Jake Odorizzi | -0.1 | 0.3 | 1.5 | 2.6 | -1.1 | -1.8 | 7.4 | 10.7 | 18.1 |
Kyle Gibson | 0.0 | -0.9 | 4.1 | 0.8 | -3.5 | -1.8 | 11.1 | 5.5 | 16.6 |
Tyler Thornburg | -0.5 | 0.9 | -0.4 | -0.2 | 1.9 | -1.7 | 5.6 | 1.4 | 7.0 |
Wily Peralta | 0.7 | 0.2 | 0.8 | -2.5 | 1.9 | -1.6 | 7.7 | 3.1 | 10.8 |
Chad Bettis | 0.0 | -0.2 | -0.7 | 2.5 | -0.1 | -1.6 | 5.1 | 1.9 | 7.0 |
Michael Foltynewicz | 0.0 | 0.0 | -0.1 | -0.1 | 1.7 | -1.5 | 3.4 | 1.2 | 4.6 |
Zack Wheeler | 0.0 | 1.0 | 1.7 | -2.7 | 0.0 | -1.4 | 6.8 | 2.3 | 9.1 |
Randal Grichuk | 0.0 | 0.0 | 0.0 | 2.5 | 0.1 | -1.3 | 3.9 | 6.4 | 10.3 |
David Dahl | 0.0 | 0.0 | 0.0 | 0.0 | 1.3 | -1.3 | 2.6 | 1.3 | 3.9 |
Billy Hamilton | 0.0 | 0.5 | 2.7 | -2.6 | 1.8 | -1.3 | 8.9 | 7.8 | 16.7 |
Francisco Lindor | 0.0 | 0.0 | 0.0 | 3.3 | 2.9 | -1.2 | 7.4 | 14.5 | 21.9 |
Tyler Naquin | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | -1.2 | 2.2 | 0.8 | 3.0 |
Trayce Thompson | 0.0 | 0.0 | 0.0 | 1.1 | -0.5 | -1.1 | 2.7 | 1.2 | 3.9 |
Justin Grimm | 0.0 | -0.3 | 1.2 | 0.1 | -0.1 | -1.1 | 2.8 | 2.3 | 5.1 |
Matt Davidson | 0.0 | -0.2 | 0.2 | 0.0 | 0.0 | -1.1 | 1.5 | -1.3 | 0.2 |
Daniel Norris | 0.0 | 0.0 | 0.0 | 0.3 | 0.1 | -1.1 | 1.5 | 0.0 | 1.5 |
Matt Wisler | 0.0 | 0.0 | 0.0 | 0.1 | 0.3 | -1.1 | 1.5 | -0.2 | 1.3 |
Cody Asche | 0.0 | 0.6 | 0.8 | -1.5 | 0.4 | -1.1 | 4.4 | 1.4 | 5.8 |
Jorge Soler | 0.0 | 0.0 | 0.7 | -0.8 | 1.1 | -1.1 | 3.7 | 1.5 | 5.2 |
Luis Sardinas | 0.0 | 0.0 | 0.0 | -0.3 | 1.1 | -1.1 | 2.5 | 0.2 | 2.7 |
Nolan Arenado | 0.0 | 2.4 | 1.9 | 3.1 | 0.4 | -1.0 | 8.8 | 28.7 | 37.5 |
Kevin Gausman | 0.0 | 0.9 | 0.6 | 0.5 | 1.1 | -1.0 | 4.1 | 9.6 | 13.7 |
Wil Myers | 0.0 | 2.1 | -2.4 | 1.1 | 2.8 | -1.0 | 9.4 | 8.8 | 18.2 |
This is not necessarily a case where the volatility can be explained away as a function of young and inexperienced players finding their respective paths within the MLB. The average 2013 Top 10 organizational prospect that has reached the MLB already has more than three seasons of play under their respective belts. Granted, this counts partial seasons the same as full seasons, but the point remains that volatility is not simply an aspect of inexperience for this cohort. Certainly, the largest declines in 2017 performance can be explained by injury in many cases (Adam Eaton, Noah Syndergaard, Aaron Sanchez, and Joc Pederson), but there are also players like Jonathan Villar, Jackie Bradley, Gregory Polanco, Maikel Franco, and Christian Yelich high on the volatility list. Yet even if “injury volatility” is viewed as somewhat more “legitimate,” or perhaps outside of the control of the player, it nevertheless remains a serious aspect of volatility and should be considered when fans, analysts, and teams are assessing prospect classes.
It is a real question to aks whether or how injuries between 2017-2021 to Lewis Brinson, Brett Phillips, Brandon Woodruff, Josh Hader, and other top Brewers prospects, will impact contending chances or roster-building strategies for Milwaukee (it may seem audacious to suggest, but indeed injury is an aspect of the game for which teams should prepare. For example, this is one reason Brewers fans should not be quick to trade away from the Ryan Braun, Keon Broxton, Lewis Brinson, Brett Phillips, and Domingo Santana outfield stockpile).
The following table exhibits the most volatile 2013 Top 10 organizational prospects by summing the absolute value of annual WARP shifts:
TimeSeries | Change1 | Change2 | Change3 | Change4 | Change5 | Change6 | AbsoluteChange | WARP | WARPGenerated |
---|---|---|---|---|---|---|---|---|---|
Shelby Miller | 0.3 | 1.2 | -2.2 | 5.6 | -6.3 | 1.2 | 16.8 | 4.4 | 21.2 |
Adam Eaton | 0.7 | -0.9 | 3.3 | 1.5 | 2.9 | -6.8 | 16.1 | 16.4 | 32.5 |
Anthony Rendon | 0.0 | 1.3 | 4.2 | -4.5 | 2.7 | 2.6 | 15.3 | 17.8 | 33.1 |
Yasiel Puig | 0.0 | 3.9 | 2.3 | -4.7 | 0.5 | 1.6 | 13.0 | 17.2 | 30.2 |
A.J. Pollock | 0.2 | 1.1 | 0.9 | 3.1 | -4.9 | 2.4 | 12.6 | 12.2 | 24.8 |
Marcell Ozuna | 0.0 | 1.6 | 1.6 | -2.7 | 3.0 | 2.6 | 11.5 | 14.9 | 26.4 |
Julio Tehran | -0.4 | 2.2 | 2.3 | -3.5 | 2.8 | 0.0 | 11.2 | 15.7 | 26.9 |
Sonny Gray | 0.0 | 1.8 | 2.8 | 0.6 | -3.5 | 2.5 | 11.2 | 17.5 | 28.6 |
Kyle Gibson | 0.0 | -0.9 | 4.1 | 0.8 | -3.5 | -1.8 | 11.1 | 5.5 | 16.6 |
James Paxton | 0.6 | 0.8 | -1.1 | 2.2 | -2.5 | 3.7 | 10.9 | 8.5 | 19.4 |
Marcus Stroman | 0.0 | 0.0 | 3.4 | -3.1 | 3.2 | 0.9 | 10.6 | 11.6 | 22.2 |
Noah Syndergaard | 0.0 | 0.0 | 0.0 | 4.1 | 1.5 | -4.9 | 10.5 | 10.4 | 20.9 |
Alex Wood | 0.0 | 0.8 | 3.3 | -3.4 | 0.7 | 2.2 | 10.4 | 10.6 | 21.0 |
Mike Zunino | 0.0 | 1.1 | 2.2 | -3.6 | 1.9 | 1.4 | 10.2 | 8.7 | 18.9 |
Wil Myers | 0.0 | 2.1 | -2.4 | 1.1 | 2.8 | -1.0 | 9.4 | 8.8 | 18.2 |
Jonathan Villar | 0.0 | 0.0 | 1.2 | -0.4 | 3.9 | -3.8 | 9.3 | 7.6 | 16.9 |
Jose Iglesias | -0.3 | 2.3 | -2.0 | 0.4 | 2.0 | -2.3 | 9.3 | 4.6 | 13.9 |
Gerrit Cole | 0.0 | 2.4 | -0.1 | 2.3 | -2.9 | 1.5 | 9.2 | 14.2 | 23.4 |
Billy Hamilton | 0.0 | 0.5 | 2.7 | -2.6 | 1.8 | -1.3 | 8.9 | 7.8 | 16.7 |
Nolan Arenado | 0.0 | 2.4 | 1.9 | 3.1 | 0.4 | -1.0 | 8.8 | 28.7 | 37.5 |
Dan Straily | -0.3 | 2.1 | -2.7 | 0.8 | 0.8 | 1.5 | 8.2 | 3.4 | 11.6 |
Aaron Sanchez | 0.0 | 0.0 | 0.8 | 0.3 | 2.6 | -4.3 | 8.0 | 5.0 | 13.0 |
Wily Peralta | 0.7 | 0.2 | 0.8 | -2.5 | 1.9 | -1.6 | 7.7 | 3.1 | 10.8 |
Chris Archer | 0.7 | 1.0 | 1.1 | 3.5 | -1.3 | 0.1 | 7.7 | 21.6 | 29.3 |
Christian Yelich | 0.0 | 1.5 | 1.3 | 0.4 | 2.1 | -2.4 | 7.7 | 15.8 | 23.4 |
Jackie Bradley | 0.0 | -0.3 | 1.2 | 1.2 | 2.1 | -2.7 | 7.5 | 8.4 | 15.9 |
Corey Seager | 0.0 | 0.0 | 0.0 | 1.9 | 4.7 | -0.9 | 7.5 | 14.2 | 21.7 |
Jake Odorizzi | -0.1 | 0.3 | 1.5 | 2.6 | -1.1 | -1.8 | 7.4 | 10.7 | 18.1 |
Francisco Lindor | 0.0 | 0.0 | 0.0 | 3.3 | 2.9 | -1.2 | 7.4 | 14.5 | 21.9 |
Travis d’Arnaud | 0.0 | 0.2 | 2.1 | 1.6 | -2.5 | 0.6 | 7.0 | 9.8 | 16.8 |
Zack Wheeler | 0.0 | 1.0 | 1.7 | -2.7 | 0.0 | -1.4 | 6.8 | 2.3 | 9.1 |
Yordano Ventura | 0.0 | 0.5 | 2.8 | 0.1 | -0.9 | -2.5 | 6.8 | 9.7 | 16.5 |
Jedd Gyorko | 0.0 | 1.3 | -1.3 | 0.7 | 2.7 | 0.7 | 6.7 | 9.5 | 16.2 |
Joc Pederson | 0.0 | 0.0 | -0.1 | 1.3 | 2.2 | -3.0 | 6.6 | 4.9 | 11.5 |
Jose Ramirez | 0.0 | 0.3 | 0.4 | -0.2 | 2.3 | 3.4 | 6.6 | 10.5 | 17.1 |
Danny Salazar | 0.0 | 1.5 | -0.3 | 2.9 | -1.2 | -0.6 | 6.5 | 12.0 | 18.5 |
Scooter Gennett | 0.0 | 1.8 | -1.4 | -0.6 | 2.2 | 0.3 | 6.3 | 6.3 | 12.6 |
Chris Owings | 0.0 | 0.0 | 0.3 | -2.1 | 2.8 | 1.1 | 6.3 | 1.6 | 7.9 |
Michael Wacha | 0.0 | 1.3 | 0.1 | 1.5 | -2.7 | 0.5 | 6.1 | 6.5 | 12.6 |
Trevor Rosenthal | 0.4 | 1.9 | -1.4 | 0.6 | -1.6 | 0.1 | 6.0 | 5.0 | 11.0 |
Tyler Skaggs | -0.9 | 0.4 | 2.2 | -1.7 | 0.2 | 0.6 | 6.0 | 1.3 | 7.3 |
George Springer | 0.0 | 0.0 | 2.6 | 1.0 | 1.5 | -0.9 | 6.0 | 15.5 | 21.5 |
Jonathan Schoop | 0.0 | 0.0 | -0.6 | 1.6 | 0.0 | 3.7 | 5.9 | 6.1 | 12.0 |
Addison Russell | 0.0 | 0.0 | 0.0 | 1.6 | 2.2 | -2.0 | 5.8 | 7.2 | 13.0 |
Austin Hedges | 0.0 | 0.0 | 0.0 | 0.7 | -1.0 | 4.1 | 5.8 | 4.2 | 10.0 |
Jarred Cosart | 0.0 | 1.3 | 1.4 | -1.8 | -0.7 | -0.5 | 5.7 | 4.8 | 10.5 |
Matt Adams | -0.3 | 1.4 | 0.1 | -1.4 | 2.0 | -0.5 | 5.7 | 4.9 | 10.6 |
Aaron Hicks | 0.0 | 1.0 | -0.1 | 0.6 | -1.8 | 2.2 | 5.7 | 5.0 | 10.7 |
Tyler Thornburg | -0.5 | 0.9 | -0.4 | -0.2 | 1.9 | -1.7 | 5.6 | 1.4 | 7.0 |
Carlos Correa | 0.0 | 0.0 | 0.0 | 2.6 | 2.5 | -0.5 | 5.6 | 12.3 | 17.9 |
Tony Cingrani | 0.1 | 1.3 | -2.7 | 1.4 | -0.1 | 0.0 | 5.6 | 0.3 | 5.9 |
Gregory Polanco | 0.0 | 0.0 | 1.5 | 1.3 | 0.2 | -2.6 | 5.6 | 7.8 | 13.3 |
Maikel Franco | 0.0 | 0.0 | -0.3 | 2.4 | -0.4 | -2.4 | 5.5 | 2.8 | 8.3 |
Delino DeShields Jr | 0.0 | 0.0 | 0.0 | 1.7 | -1.8 | 2.0 | 5.5 | 3.5 | 9.0 |
Rob Brantly | 0.4 | -2.2 | 1.8 | -0.5 | 0.5 | 0.0 | 5.4 | -1.9 | 3.5 |
Gary Sanchez | 0.0 | 0.0 | 0.0 | 0.0 | 2.6 | 2.7 | 5.3 | 7.9 | 13.2 |
Kolten Wong | 0.0 | -0.4 | 1.0 | 2.5 | -0.4 | -0.8 | 5.1 | 7.9 | 13.0 |
Nate Karns | 0.0 | -0.1 | 0.2 | 2.7 | -1.8 | -0.3 | 5.1 | 4.5 | 9.6 |
Chad Bettis | 0.0 | -0.2 | -0.7 | 2.5 | -0.1 | -1.6 | 5.1 | 1.9 | 7.0 |
J.T. Realmuto | 0.0 | 0.0 | 0.0 | 0.6 | 2.9 | 1.5 | 5.0 | 9.1 | 14.1 |
What is most intriguing about this group of prospects is that five seasons from the publication of these lists (2013-2017), the overall value expectations of each Overall Future Potential (OFP) category can be outlined. I published a discussion on this basic valuation on Sunday, in order to emphasize the usefulness of using WARP and OFP to interpret player value in monetary terms. I discussed the shortcomings of these statistics at length there.
2013 Prospect Org Top 10 | MLB | AvgWARP | AvgValue | 70Context | 70Value | 60Context | 60Value | 50Context | 50Value |
---|---|---|---|---|---|---|---|---|---|
2011 | 3 | 0.2 | $1.2 | -0.1 | $0.7 | 0.2 | $2.8 | -0.2 | $0.0 |
2012 | 35 | 0.0 | $0.3 | -0.1 | -$0.7 | 0.1 | $1.0 | 0.0 | $0.1 |
2013 | 87 | 0.6 | $4.3 | 0.6 | $8.5 | 0.1 | $5.0 | -0.2 | $2.9 |
2014 | 123 | 0.8 | $5.5 | 0.2 | $7.2 | 0.3 | $7.7 | -0.3 | $3.5 |
2015 | 157 | 1.1 | $7.4 | 0.9 | $13.6 | 0.0 | $7.1 | -0.2 | $6.0 |
2016 | 178 | 1.3 | $8.9 | 1.1 | $16.5 | -0.2 | $7.6 | -0.1 | $7.9 |
2017 | 173 | 1.2 | $8.7 | 0.7 | $13.3 | 0.0 | $8.5 | -0.2 | $7.3 |
Summary | 756 | 0.7 | $5.2 | 3.3 | $82.1 | 0.5 | $43.3 | -1.2 | $19.3 |
Risk | 77.9% | 0.6 | $4.02 | 96.6% | $45.7 | 77.2% | $25.0 | 74.7% | $18.1 |
Key conclusions:
- 70 OFP prospects can be defined as potential superstars that not only perform very well during their first MLB seasons, but also have significant ceiling yet to attain after those initial years. In this regard, though, this category is perhaps most risky in terms of cashing out their top ceiling value versus their more realistic, depreciated value.
- 60 OFP prospects can viewed similarly, although there is significantly less range between their initial MLB performance (within 5 years of their appearance in the organizational Top 10) and absolute ceiling.
- 50 OFP prospects are quite intriguing, as they are the riskiest in terms of returning prospects to the MLB, and also returning quality MLB performances. However, they are the least risky in terms of reaching ceiling at the MLB level (although the depreciation from “average MLB regular” grade as a prospect to “replacement role or quality depth” at the MLB level is quite steep)
In terms of judging one prospect class, it should be stated that the Baseball Prospectus team made largely and significantly correct evaluation decisions in grading players. If one wishes to protest the inclusion of Nolan Arenado as a 50 OFP, for example, it should be stated that Arenado’s class also includes prospects like Tyrone Taylor (athletic prospects with MLB roles fizzling out), as well as 35 players with negative MLB WARP and another 15 with WARP between 0.0 and 0.5 (i.e., 50 replacement players). One can also return to the original Arenado scouting profiles from 2013, and understand the context of a top prospect that was not necessarily living up to contextual expectations at that time (there are valuable lessons to be learned here, too).
There will indeed be some 50 OFP prospects that overcome their scouting shortcomings and play up to their strengths while adjusting at the MLB level, but the 50 OFP performance by the 2013 class should show why this is not a given; one needs to wade through 50 replacement players and 37 players yet to reach the MLB to land one Arenado from the 50 OFP class. By contrast, the 70 OFP prospect class is filled with fewer misses, as 28 of 29 prospects from this OFP rank reached the MLB already, with many quickly posting fantastic performances (see Carlos Correa, Carlos Martinez, and Francisco Lindor, with others reaching solidly above average performance levels (Gerrit Cole, Xander Bogaerts, among others, come to mind here).
Although it is tempting to hang on to the idea that players can transcend their OFP grades, it should be noted that while there are indeed cases of such transcendence, that transcendence comes at the cost of risky development across scouting categories. Furthermore, using time-series analysis and comparing OFP categories against average performance leaves clear conclusions about the general impact of certain levels of prospect talent. The benefit of working with these assumptions and data is that not only can the potential impact of prospects be estimated at the MLB level, but that level of performance can be calculated to assess the risk of developing that prospect as opposed to trading that prospect. Where players with clearer roles, potentially less volatile production, and otherwise favorable surplus value scenarios are available via trade, teams should not hesitate to trade prospects to secure that production. There is no “silver bullet” through which teams are developed by using prospects, save those prospects that are so impactful as to occupy the highest reaches of the scouting rankings.
Photo Credit: Benny Sieu, USAToday Sports Images