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.

YearPlayerPositionAmateur TypeTeam RankRank in OrganizationHighest Level PlayedWAR
2001A.J. PierzynskiPosition PlayerHS1518MLB11.4
2001Aaron HarangPitcher4Yr1127A+12.2
2002Aaron HarangPitcher4Yr1916AA17.0
2011Adam EatonPosition Player4Yr2230Rk14.6
2012Adam EatonPosition Player4Yr412AA15.1
2009Alex AvilaPosition Player4Yr2820A10.8
2005Andre EthierPosition Player4Yr816A+11.2
2011Andrelton SimmonsPosition PlayerJC215Rk12.8
2007Asdrubal CabreraPosition PlayerINTL1015AAA12.2
2007Austin JacksonPosition PlayerHS718A11.7
2005Ben ZobristPosition Player4Yr2216A-12.1
2006Ben ZobristPosition Player4Yr2016A+18.5
2003Bill HallPosition PlayerHS1618MLB10.7
2014Blake SnellPitcherHS2014A11.1
2001Brandon WebbPitcher4Yr2927A16.8
2002Brandon WebbPitcher4Yr2326A+22.4
2006Brett GardnerPosition Player4Yr1713A-14.7
2009Brett GardnerPosition Player4Yr1513MLB20.6
2011Brian DozierPosition Player4Yr1330A+16.4
2001Brian LawrencePitcher4Yr811AAA11.3
2003Brian McCannPosition PlayerHS228Rk14.9
2006C.J. WilsonPitcher4Yr1614MLB11.2
2007Carlos RuizPosition PlayerINTL2113MLB14.4
2012Charlie BlackmonPosition Player4Yr1611MLB16.9
2004Chien-Ming WangPitcherINTL2712AA10.4
2003Chone FigginsPosition PlayerHS528MLB15.8
2004Chris YoungPitcher4Yr3019AA12.1
2014Cody BellingerPosition PlayerHS1414Rk15.4
2015Cody BellingerPosition PlayerHS320Rk16.6
2013Collin McHughPitcher4Yr2624MLB11.4
2013Corey DickersonPosition PlayerJC2013AA10.5
2011Corey KluberPitcher4Yr726AAA21.4
2002Covelli CrispPosition PlayerJC3018A+15.2
2003Covelli CrispPosition PlayerJC126MLB16.8
2003Curtis GrandersonPosition Player4Yr1218A-17.2
2011Dallas KeuchelPitcher4Yr2623AA12.0
2012Dallas KeuchelPitcher4Yr1721AAA14.2
2006Dan UgglaPosition Player4Yr229AA20.3
2003David BushPitcher4Yr614A+10.1
2003David DeJesusPosition Player4Yr2619AA13.6
2013Dellin BetancesPitcherHS1119MLB11.2
2014Dellin BetancesPitcherHS1826MLB11.3
2005Denard SpanPosition PlayerHS414A10.0
2007Denard SpanPosition PlayerHS813AA15.1
2008Denard SpanPosition PlayerHS1820AAA18.3
2002Dontrelle WillisPitcherHS121A-20.8
2001Erik BedardPitcherJC2719A10.5
2005Freddy SanchezPosition Player4Yr1813MLB14.9
2006Geovany SotoPosition PlayerHS1516MLB12.6
2007Geovany SotoPosition PlayerHS1817MLB13.8
2015German MarquezPitcherINTL1725A13.3
2008Ian DesmondPosition PlayerHS914AA10.7
2009Ian DesmondPosition PlayerHS2119AA14.8
2013Jake deGromPitcher4Yr2611A+26.2
2006Jamie ShieldsPitcherHS1012AAA20.3
2003Jason BayPosition Player4Yr2012AA15.9
2004Jayson WerthPosition PlayerHS817MLB16.6
2008Jonathan LucroyPosition Player4Yr2116Rk22.4
2004Jonathan PapelbonPitcher4Yr2314A-10.2
2011Jose AltuvePosition PlayerINTL2628A+19.0
2013Jose RamirezPosition PlayerINTL2423A21.0
2009Josh DonaldsonPosition Player4Yr313A+13.9
2010Josh DonaldsonPosition Player4Yr1214AA22.6
2011Josh DonaldsonPosition Player4Yr2812MLB30.5
2012Josh DonaldsonPosition Player4Yr2620MLB35.6
2007Josh HamiltonPosition PlayerHS1230AA25.2
2004Josh JohnsonPitcherHS1424A10.8
2005Josh JohnsonPitcherHS1411A+16.4
2006Josh WillinghamPosition Player4Yr211MLB13.3
2007Justin MastersonPitcher4Yr913A-12.2
2010Kenley JansenPitcherINTL2414AAA10.9
2014Ketel MartePosition PlayerINTL2520A+11.6
2011Kevin KiermaierPosition PlayerJC326Rk11.1
2013Kevin PillarPosition Player4Yr1221A+10.3
2014Kevin PillarPosition Player4Yr1520MLB11.9
2013Khris DavisPosition Player4Yr2216AAA11.5
2012Kole CalhounPosition Player4Yr1820A+13.1
2013Kole CalhounPosition Player4Yr3011MLB13.1
2014Kyle HendricksPitcher4Yr411AAA18.0
2010Kyle SeagerPosition Player4Yr1130A+17.6
2015Lance McCullersPitcherHS1011A+10.7
2005Luke ScottPosition Player4Yr2217AA11.1
2006Luke ScottPosition Player4Yr2015MLB11.3
2001Mark EllisPosition Player4Yr1117AA10.7
2003Mark HendricksonPitcher4Yr613MLB10.5
2003Matt CainPitcherHS1111Rk11.8
2011Matt CarpenterPosition Player4Yr2411AA20.5
2012Matt CarpenterPosition Player4Yr1212MLB23.8
2002Matt HollidayPosition PlayerHS2411A+14.3
2003Matt HollidayPosition PlayerHS2516AA20.2
2015Max KeplerPosition PlayerINTL212A+10.8
2014Mike ClevingerPitcherJC3017A10.8
2015Mike ClevingerPitcherJC2322A+11.5
2005Mike NapoliPosition PlayerHS129A+10.0
2006Mike NapoliPosition PlayerHS311AA14.3
2008Mike StantonPosition PlayerHS1411A-14.7
2001Morgan EnsbergPosition Player4Yr1015MLB15.4
2010Neil WalkerPosition PlayerHS1626MLB15.9
2003Nick SwisherPosition Player4Yr2211A+11.2
2011Noah SyndergaardPitcherHS424Rk10.0
2012Odubel HerreraPosition PlayerINTL227A10.4
2015Odubel HerreraPosition PlayerINTL2212AA10.9
2006Pablo SandovalPosition PlayerINTL1815A-12.6
2010Paul GoldschmidtPosition Player4Yr2713Rk20.9
2011Paul GoldschmidtPosition Player4Yr2211A+25.8
2002Rich HardenPitcherJC1921A-10.2
2005Ricky NolascoPitcherHS1019AAA11.9
2013Robbie RayPitcherHS1618A+10.2
2015Robbie RayPitcherHS611MLB11.8
2004Russell MartinPosition PlayerJC218A18.9
2009Ryan HaniganPosition Player4Yr1416MLB16.5
2002Ryan HowardPosition Player4Yr1115A-11.6
2004Scott BakerPitcher4Yr519A10.7
2005Shane VictorinoPosition PlayerHS2019MLB16.4
2006Shane VictorinoPosition PlayerHS2214MLB21.9
2001Ted LillyPitcherJC718MLB10.4
2014Tommy PhamPosition PlayerHS723AAA15.6
2015Tommy PhamPosition PlayerHS1515MLB15.5
2001Travis HafnerPosition PlayerJC1312A+16.1
2002Travis HafnerPosition PlayerJC816AA18.3
2013Travis ShawPosition Player4Yr623AA10.0
2015Trevor StoryPosition PlayerHS812AA17.9
2011Tyler FlowersPosition PlayerJC2717MLB11.0
2008Will VenablePosition Player4Yr1215AA11.5
2013Yan GomesPosition Player4Yr2427MLB13.6
2005Yovani GallardoPitcherHS316A11.5
2004Zach DukePitcherHS1115A12.6

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

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