Your Team’s Prospects Are Probably Not Going to Work Out

Serious prospect hounds know that only about 10% of minor leaguers ever participate in a Major League game in their career. However, even the most discerning fans can be deluded into believing that their team’s farm system can overcome the odds and build a perennial contender based on their farm system alone.

I decided to investigate how much average WAR a prospect generates based on their ranking in Baseball America’s Prosect Handbook. I used a similar process in a previous article where I calculated the amount of WAR based on the next six seasons of a player’s career since being listed instead of when a player makes their Major League debut. This means that players closer to the Majors get a boost to their value, since they will have more opportunities to accumulate WAR than players in the lower minors.

Next, I grouped the players by their ordinal ranking in their organization from the 2001 to 2015 seasons and calculated each group’s average WAR to create the visualization below.

That is a steep decline, but it is not unexpected. Most prospects that ascend to the top of their team’s list have flourished in the lower minors or have a higher pedigree than their minor league compatriots. Many top prospects are also perceived as being closer to big league ready. It makes sense that these types of players would produce more value given my methodology. Players that are ranked lower can still be successful in the Major Leagues, but the profusion of prospects that fail to make it to the Majors keeps these group’s average much lower than the higher ranked players in an organization.

This is a decent start, but it does not account for differences in an organization’s minor league depth. The fifth ranked prospect for a rebuilding team with plenty of depth is likely more talented than a fifth ranked prospect in a competing team’s depleted farm system. To account for the quality differences between farm systems, I created a heat map of average WAR produced with the player’s ranking in the organization on the y-axis and the team’s farm system ranking on the x-axis.

As expected, higher values are in the top half of the heat map. The highest values appearing in the top left-hand corner. If Baseball America’s rankings are an accurate representation of minor league talent, the highest ranked players in the most talented farm systems should produce the most value.

Average WAR is a reasonable place to start. However, with so many prospects in my dataset failing to reach the big leagues, the distribution of player WAR value is heavily skewed to the right. When dealing with skewed datasets, it is more appropriate to use median values instead of average values. This is because outliers can heavily influence your results and the median calculation helps to mitigate the effects that outliers can have on your dataset. The next heat map was created the same way as the previous one, but with each group’s median WAR instead of average WAR.

Woof. This chart is much bluer than the previous one, but the pattern is similar with the top of the chart producing the most value. This graphic shows a more desolate view on prospect valuation, but front offices and fans do not necessarily care about summary statistics. They care about their individual player and how he will do in the future. It is nice to know the odds of success from the past, but it is not necessarily predictive of a player’s future. Players outperform baseball industry projections all the time. Who is to say that your team does not have a diamond in the rough? The next heat map attempts to provide a realistic showing of a best-case scenario for each prospect ranking. Each cell is the maximum amount of WAR produced in their respective cohort.

This graphic shows why GMs are reluctant to trade away their prospects. The general trend remains the same, but there are far more yellow and green boxes dispersed throughout the chart. Nobody wants to be known as someone who trades away a young star for three months of a role player. This heat map shows that Major League contributors can come from almost anywhere.

I am interested in players that the industry has overlooked. I believe it would be beneficial to identify these types of players to see if there is a possible blind spot in prospect valuation. The first thing I decided to do was limit the dataset to include only players that were ranked eleventh in their organization or lower. I could have drawn the line anywhere in the top 30, but the line chart from earlier seems to start to level off around this point and ten seems like a logical cutoff point.

The next step was to determine how much accumulated WAR is considered a success. I landed on 10 WAR being considered as a success. This cutoff is arbitrary, but I wanted to pick a lower cutoff value to accommodate for players that are in the lower levels of the minors and several years away from the Majors. These younger players may be as talented as older players but since I am not adjusting for a player’s team-controlled seasons, inexperienced players do not have the same opportunities to generate as much value as their teammates who are closer to the Majors. By keeping my threshold low, I should be able to mitigate some of the bias in my dataset.

There were 97 players who met my criteria and 127 occurrences of players ranked eleventh or lower that produced over 10 WAR in six seasons. With 25 players doing it twice, Denard Span doing it three times and Josh Donaldson doing it four times! Below is a summary table of each occurrence with an accompanying bar chart.

Year	Player	Position	Amateur Type	Team Rank	Rank in Organization	Highest Level Played	WAR
2001	A.J. Pierzynski	Position Player	HS	15	18	MLB	11.4
2001	Aaron Harang	Pitcher	4Yr	11	27	A+	12.2
2002	Aaron Harang	Pitcher	4Yr	19	16	AA	17.0
2011	Adam Eaton	Position Player	4Yr	22	30	Rk	14.6
2012	Adam Eaton	Position Player	4Yr	4	12	AA	15.1
2009	Alex Avila	Position Player	4Yr	28	20	A	10.8
2005	Andre Ethier	Position Player	4Yr	8	16	A+	11.2
2011	Andrelton Simmons	Position Player	JC	2	15	Rk	12.8
2007	Asdrubal Cabrera	Position Player	INTL	10	15	AAA	12.2
2007	Austin Jackson	Position Player	HS	7	18	A	11.7
2005	Ben Zobrist	Position Player	4Yr	22	16	A-	12.1
2006	Ben Zobrist	Position Player	4Yr	20	16	A+	18.5
2003	Bill Hall	Position Player	HS	16	18	MLB	10.7
2014	Blake Snell	Pitcher	HS	20	14	A	11.1
2001	Brandon Webb	Pitcher	4Yr	29	27	A	16.8
2002	Brandon Webb	Pitcher	4Yr	23	26	A+	22.4
2006	Brett Gardner	Position Player	4Yr	17	13	A-	14.7
2009	Brett Gardner	Position Player	4Yr	15	13	MLB	20.6
2011	Brian Dozier	Position Player	4Yr	13	30	A+	16.4
2001	Brian Lawrence	Pitcher	4Yr	8	11	AAA	11.3
2003	Brian McCann	Position Player	HS	2	28	Rk	14.9
2006	C.J. Wilson	Pitcher	4Yr	16	14	MLB	11.2
2007	Carlos Ruiz	Position Player	INTL	21	13	MLB	14.4
2012	Charlie Blackmon	Position Player	4Yr	16	11	MLB	16.9
2004	Chien-Ming Wang	Pitcher	INTL	27	12	AA	10.4
2003	Chone Figgins	Position Player	HS	5	28	MLB	15.8
2004	Chris Young	Pitcher	4Yr	30	19	AA	12.1
2014	Cody Bellinger	Position Player	HS	14	14	Rk	15.4
2015	Cody Bellinger	Position Player	HS	3	20	Rk	16.6
2013	Collin McHugh	Pitcher	4Yr	26	24	MLB	11.4
2013	Corey Dickerson	Position Player	JC	20	13	AA	10.5
2011	Corey Kluber	Pitcher	4Yr	7	26	AAA	21.4
2002	Covelli Crisp	Position Player	JC	30	18	A+	15.2
2003	Covelli Crisp	Position Player	JC	1	26	MLB	16.8
2003	Curtis Granderson	Position Player	4Yr	12	18	A-	17.2
2011	Dallas Keuchel	Pitcher	4Yr	26	23	AA	12.0
2012	Dallas Keuchel	Pitcher	4Yr	17	21	AAA	14.2
2006	Dan Uggla	Position Player	4Yr	2	29	AA	20.3
2003	David Bush	Pitcher	4Yr	6	14	A+	10.1
2003	David DeJesus	Position Player	4Yr	26	19	AA	13.6
2013	Dellin Betances	Pitcher	HS	11	19	MLB	11.2
2014	Dellin Betances	Pitcher	HS	18	26	MLB	11.3
2005	Denard Span	Position Player	HS	4	14	A	10.0
2007	Denard Span	Position Player	HS	8	13	AA	15.1
2008	Denard Span	Position Player	HS	18	20	AAA	18.3
2002	Dontrelle Willis	Pitcher	HS	1	21	A-	20.8
2001	Erik Bedard	Pitcher	JC	27	19	A	10.5
2005	Freddy Sanchez	Position Player	4Yr	18	13	MLB	14.9
2006	Geovany Soto	Position Player	HS	15	16	MLB	12.6
2007	Geovany Soto	Position Player	HS	18	17	MLB	13.8
2015	German Marquez	Pitcher	INTL	17	25	A	13.3
2008	Ian Desmond	Position Player	HS	9	14	AA	10.7
2009	Ian Desmond	Position Player	HS	21	19	AA	14.8
2013	Jake deGrom	Pitcher	4Yr	26	11	A+	26.2
2006	Jamie Shields	Pitcher	HS	10	12	AAA	20.3
2003	Jason Bay	Position Player	4Yr	20	12	AA	15.9
2004	Jayson Werth	Position Player	HS	8	17	MLB	16.6
2008	Jonathan Lucroy	Position Player	4Yr	21	16	Rk	22.4
2004	Jonathan Papelbon	Pitcher	4Yr	23	14	A-	10.2
2011	Jose Altuve	Position Player	INTL	26	28	A+	19.0
2013	Jose Ramirez	Position Player	INTL	24	23	A	21.0
2009	Josh Donaldson	Position Player	4Yr	3	13	A+	13.9
2010	Josh Donaldson	Position Player	4Yr	12	14	AA	22.6
2011	Josh Donaldson	Position Player	4Yr	28	12	MLB	30.5
2012	Josh Donaldson	Position Player	4Yr	26	20	MLB	35.6
2007	Josh Hamilton	Position Player	HS	12	30	AA	25.2
2004	Josh Johnson	Pitcher	HS	14	24	A	10.8
2005	Josh Johnson	Pitcher	HS	14	11	A+	16.4
2006	Josh Willingham	Position Player	4Yr	2	11	MLB	13.3
2007	Justin Masterson	Pitcher	4Yr	9	13	A-	12.2
2010	Kenley Jansen	Pitcher	INTL	24	14	AAA	10.9
2014	Ketel Marte	Position Player	INTL	25	20	A+	11.6
2011	Kevin Kiermaier	Position Player	JC	3	26	Rk	11.1
2013	Kevin Pillar	Position Player	4Yr	12	21	A+	10.3
2014	Kevin Pillar	Position Player	4Yr	15	20	MLB	11.9
2013	Khris Davis	Position Player	4Yr	22	16	AAA	11.5
2012	Kole Calhoun	Position Player	4Yr	18	20	A+	13.1
2013	Kole Calhoun	Position Player	4Yr	30	11	MLB	13.1
2014	Kyle Hendricks	Pitcher	4Yr	4	11	AAA	18.0
2010	Kyle Seager	Position Player	4Yr	11	30	A+	17.6
2015	Lance McCullers	Pitcher	HS	10	11	A+	10.7
2005	Luke Scott	Position Player	4Yr	22	17	AA	11.1
2006	Luke Scott	Position Player	4Yr	20	15	MLB	11.3
2001	Mark Ellis	Position Player	4Yr	11	17	AA	10.7
2003	Mark Hendrickson	Pitcher	4Yr	6	13	MLB	10.5
2003	Matt Cain	Pitcher	HS	11	11	Rk	11.8
2011	Matt Carpenter	Position Player	4Yr	24	11	AA	20.5
2012	Matt Carpenter	Position Player	4Yr	12	12	MLB	23.8
2002	Matt Holliday	Position Player	HS	24	11	A+	14.3
2003	Matt Holliday	Position Player	HS	25	16	AA	20.2
2015	Max Kepler	Position Player	INTL	2	12	A+	10.8
2014	Mike Clevinger	Pitcher	JC	30	17	A	10.8
2015	Mike Clevinger	Pitcher	JC	23	22	A+	11.5
2005	Mike Napoli	Position Player	HS	1	29	A+	10.0
2006	Mike Napoli	Position Player	HS	3	11	AA	14.3
2008	Mike Stanton	Position Player	HS	14	11	A-	14.7
2001	Morgan Ensberg	Position Player	4Yr	10	15	MLB	15.4
2010	Neil Walker	Position Player	HS	16	26	MLB	15.9
2003	Nick Swisher	Position Player	4Yr	22	11	A+	11.2
2011	Noah Syndergaard	Pitcher	HS	4	24	Rk	10.0
2012	Odubel Herrera	Position Player	INTL	2	27	A	10.4
2015	Odubel Herrera	Position Player	INTL	22	12	AA	10.9
2006	Pablo Sandoval	Position Player	INTL	18	15	A-	12.6
2010	Paul Goldschmidt	Position Player	4Yr	27	13	Rk	20.9
2011	Paul Goldschmidt	Position Player	4Yr	22	11	A+	25.8
2002	Rich Harden	Pitcher	JC	19	21	A-	10.2
2005	Ricky Nolasco	Pitcher	HS	10	19	AAA	11.9
2013	Robbie Ray	Pitcher	HS	16	18	A+	10.2
2015	Robbie Ray	Pitcher	HS	6	11	MLB	11.8
2004	Russell Martin	Position Player	JC	2	18	A	18.9
2009	Ryan Hanigan	Position Player	4Yr	14	16	MLB	16.5
2002	Ryan Howard	Position Player	4Yr	11	15	A-	11.6
2004	Scott Baker	Pitcher	4Yr	5	19	A	10.7
2005	Shane Victorino	Position Player	HS	20	19	MLB	16.4
2006	Shane Victorino	Position Player	HS	22	14	MLB	21.9
2001	Ted Lilly	Pitcher	JC	7	18	MLB	10.4
2014	Tommy Pham	Position Player	HS	7	23	AAA	15.6
2015	Tommy Pham	Position Player	HS	15	15	MLB	15.5
2001	Travis Hafner	Position Player	JC	13	12	A+	16.1
2002	Travis Hafner	Position Player	JC	8	16	AA	18.3
2013	Travis Shaw	Position Player	4Yr	6	23	AA	10.0
2015	Trevor Story	Position Player	HS	8	12	AA	17.9
2011	Tyler Flowers	Position Player	JC	27	17	MLB	11.0
2008	Will Venable	Position Player	4Yr	12	15	AA	11.5
2013	Yan Gomes	Position Player	4Yr	24	27	MLB	13.6
2005	Yovani Gallardo	Pitcher	HS	3	16	A	11.5
2004	Zach Duke	Pitcher	HS	11	15	A	12.6

The first thing that stands out is that there are far more position players than pitchers. I do not know for certain why this is the case, but I have several theories. The first is that pitchers are fragile by nature and they are more likely to be injured and unable to generate as much WAR as position players. Another theory is that it may be easier to scout pitchers than position players and that the more successful pitchers are ranked higher and are excluded from my dataset.

Another observation is that almost half of the players attended college and there are relatively few international players that achieve stardom. Of the 97 players in my dataset, forty-five of them attended a four-year university and there are only eleven international players. However, his does not necessarily mean that college players are more likely to exceed their prospect ranking.

From 2001 through 2015, over 40% of players that were ranked between eleven and thirty in their organization attended college. This is the most prevalent type of amateur experience by far. It stands to reason that they would have more people accrue 10 or more WAR.

It appears that international players are less likely to reach 10 WAR. There is roughly the same amount of ranked high school and international players, but there are 30 high schoolers that reached 10 WAR compared to only 11 international players. I do not really have a good explanation for this phenomenon, but I do find it interesting.

The final observation I have is from the position player bar chart. I find it interesting that players with AAA as their highest level are the least represented group. The only explanation I can offer is that perhaps players that reach AAA but not the Majors are perceived as having lower ceilings and their teams decided they were not worthy of being called up in September when rosters expanded. This could artificially lower the AAA group and increase the size of the MLB group. If rosters did not expand in September, I think that there would be far more players in the AAA group and fewer in the MLB group.

Conclusions

• Most prospect value is concentrated in the top of a team’s farm system, but value can come from any ranking position.
• Position players are more likely to outperform their ranking than pitchers.
• College position players are the most prevalent lower ranked prospects to accrue 10 or more WAR.
• International players are the least likely to generate 10 or more WAR.

Evaluating prospects is a difficult endeavor and I hope that this article helps to illuminate the types of players that are typically overlooked by prospect evaluators.

Click here for GitHub code