Minors To The Majors: Hitter Prospect Grades (Part 2)

I will continue to help define how to value a prospect for fantasy purposes. Last week, I examined how major league position players’ production lines up with the standard scouting grades. Today, I go the other way and look at how graded prospects perform in the majors.

I believe I am making this study about two years too soon. I would love for there to be more MLB information after the player received his grades and his 4-5 year production. I don’t have that luxury right now. I feel any answer I come up with will be a nice anchoring point but will need to be adjusted later.

To do this study, I took the grades given by Baseball America (2011 to 2014) and MLB.com (2013 to 2014). With each of these players, I looked at those who had 300 plate appearances in their career. With this fairly encompassing group, I would only able to match of 118 seasons. In some of these cases, the same player was compared. For example, both BA and MLB had their own 2013 grades for Xander Boegaerts. Like I said, a person can shoot about 20 different holes in this study, but I am just working with what I have been given so far.

Now before I get to the study, here is a quick reminder of the grades and values most scouts use and the values I calculated for speed and defense last week.

Batting Prospect Grading Scale
Overall Tool Is Called Batting Avg Homers Speed Score WAR’s Defensive Value Hitter WAR
80 80 0.32 40 >9.1 >22 Top 1-2 7
75 0.31 35-40 8.4 to 9.1 20 to 22 Top 2-3 6
70 Plus Plus 0.3 30-35 7.6 to 8.4 16 to 20 Top 5 5
65 0.29 27-30 6.9 to 7.6 12 to 16 All-Star 4
60 Plus 0.28 23-27 6.1 to 6.9 8 to 12 Plus 3
55 Above Avg 0.27 19-22 5.4 to 6.1 4 to 8 Above Avg 2.5
50 Avg 0.26 15-18 4.7 to 5.4 0 to 4 Avg Regular 2
45 Below Avg 0.25 12-15 3.9 to 4.7 -4 to 0 Platoon/Util 1.5
40 0.24 8-12 2.4 to 3.9 -8 to -4 Bench 1
35 0.23 5-8 1.7 to 2.4 -12 to -8 Emergency Call-Up 0
30 0.22 3-5 < 1.7 < -12 Organizational -1

I will be concentrating on the three fantasy-relevant grades, Batting, Speed, and Power. Remember that a decent Glove score is needed to get to the majors and a player’s Arm value determines where they can play on the field.

Power Grade

This is the easiest of the values to explain how I got the formulas. I lined up the Power grade and the home runs hit pro-rated to 650 PA and got the player’s home run production. After some early suspect results, I got some of the best results of the three values I examined. For Power, I was able to get two different equations to predict home runs via a Power grade. For one method, I bucketed the home run totals by Power grade and then ran a best-fit line on the points. For the other equation, I did a basic best-fit line. Here are the two equations.

Bucket method (r-square of 0.94)
HR/650 = .40*Power Grade – 2

Best fit line (r-square of 0.31)
HR/650 = .35 * Power Grade + 0.57

Here is how the above formulas line up with the expected values.

Power Grade to MLB Home Runs
Power Grade Actual Bucket Best-Fit Line
80 >40 30 29
75 35-40 28 27
70 30-35 26 25
65 27-30 24 23
60 23-27 22 22
55 19-22 20 20
50 15-18 18 18
45 12-15 16 16
40 8-12 14 15
35 5-8 12 13
30 3-5 10 11

The results are kind of what I expected with a heavy regression to the mean value with a 55-grade lining right up. I would use the bucket value as a nice easy formula and it lines up nicely. Power is done, now on to Speed.

Speed Grade

After using our version of Speed Score last week to convert Speed grades to a comparable value, I determined I needed to use stolen bases to make the data fantasy relevant. I had to do a little fuzzy math to get Speed Score converted to stolen bases per 650 plate appearances. I eventually came up with this formula:

Stolen Bases = .523*Exp(Speed Score *.501)

You will notice the “Exp” value. This exists because the number of stolen bases increases exponentially as a player’s Speed Score increases. As you will soon see, the exponential increase is the same when looking at the actual values. Again with stolen bases, I was able to get a bucketed best-fit line and a bucketed line.

Bucket method (r-squared = 0.89)
Stolen Bases = .504*Exp(0.05621 * Speed Grade)

Best Fit method (r-squared = .60)
Stolen Bases = .318*Exp(0.06223*Speed Grade)

The Grade and stolen base numbers line up really nice and here are how the various values compare.

Speed Grade to MLB Stolen Bases
Speed Grade Speed Score Speed Score Range Bucket Method Best-Fit Line
80 >9.1 > 50 45 46
75 8.4 to 9.1 35 to 50 34 34
70 7.6 to 8.4 24 to 35 26 25
65 6.9 to 7.6 17 to 24 19 18
60 6.1 to 6.9 11 to 17 15 13
55 5.4 to 6.1 8 to 11 11 10
50 4.7 to 5.4 6 to 8 8 7
45 3.9 to 4.7 4 to 6 6 5
40 2.4 to 3.9 2 to 4 5 4
35 1.7 to 2.4 1 to 2 4 3
30 < 1.7 0 3 2

A small bit of a regression to the mean at the top and bottom of the tables to the mean, but decent values none-the-less.

Batting Grade

What a mess. When Kiley McDaniel still worked for us, he wrote a six-part series on determining a player’s batting Grade. Determining this grade is the hardest in baseball.

If a person wants the short and easy answer, use .257 for the batting average (average of all hitters). Now, here is some jumbled math to get to a value around .257.

For batting, I came up with three equations using the bucket method, the best-fit line, and a multi-linear regression using all three grades. First, for the bucket grade, I had to throw out the 2014 75 batting grade Baseball America gave Byron Buxton. It was causing a negative correlation and just causing a mess of things. After getting the value out of there, the best-fit line for the buckets lines up great.

When I ran just a best-fit line, I did end up with a positive slope (higher Batting grade meant a higher AVG), but the r-squared was 0.03. Looking over the various hitter misses, I noticed the faster players had lower than expected averages because speed is probably not being taken into account. So then I ran a multiple-variable correlation for batting average using the Speed, Batting, and Power grades. With the extra inputs (all significant), I have an r-squared of 0.08. Not good at all.

Additionally, as you will soon see, all lines’ slopes are fairly small. Here are the equations I ended up with:

Bucket Method (r-square = 0.08)
AVG = .00069 * Batting + .220

Best Fit Line (r-square = .03)
AVG = .00065 * Batting + .2205

Three Factors Included (r-square = 0.08)
AVG = .232 + .000804 *Batting – .000476 * Power + .000126 * Speed

The bucket and best-fit equations almost work out to the same values. Now here is a table to compare the values. For the three bucket equation, I used a 50 grade for both power and speed.

Batting Grade to MLB Batting Average
Batting Grade Batting Avg Bucket Best Fit Line Three Variables
80 0.320 0.275 0.273 0.279
75 0.310 0.272 0.269 0.275
70 0.300 0.268 0.266 0.271
65 0.290 0.265 0.263 0.267
60 0.280 0.261 0.260 0.263
55 0.270 0.258 0.256 0.259
50 0.260 0.255 0.253 0.255
45 0.250 0.251 0.250 0.251
40 0.240 0.248 0.247 0.247
35 0.230 0.244 0.243 0.243
30 0.220 0.241 0.240 0.239

All the projected values are about the same with the correct value happening at Grade 45 (.250 AVG). It is not surprising to see more regression to the mean with a variable stat like batting average. I think the one time I may jump to the third equation is if a hitter’s Power or Speed grade is on the extreme end.

I think the one time I may jump to the third equation is if a hitter’s Power or Speed grade is on the extreme end.

Adding Runs and RBI

Truthfully, the above work is a decent start to get an idea of a prospect’s production, but if you are going to create a projection a person should have an idea of the Runs and RBI the hitter will produce. A few years back, I created such a formula which looked to get the total number. The total is key for me because the generally stays the same, the ratios can change depending on lineup position. So running a simple multiple linear regression over the last 5 years’ worth of data (min 300 PA) here is a formula to get the total of Runs and RBI (r-square = 0.76).

Runs+RBI = 2.15 * HR + 365 * AVG + 13
Runs = (2.15 * HR + 365 * AVG + 13) * .525
RBI = (2.15 * HR + 365 * AVG + 13) * .475

Additionally, not all Runs have a RBI associated with them (e.g. scoring on a passed ball) so I have included the formulas for just Runs and RBI.

Putting everything together

So we have all the needed formulas, even though some are suspect. How about putting them to use? I collected all the hitter’s grades from MLB.com’s top 200 draft prospects (getting this data has been fairly easy this postseason during all the commercials). I used the above bucket equations to create a future projection and used my 2016 fantasy value equation to rank them.

MLB.com’s Prospect Grades to MLB Stats
Name Batting Power Speed Defense Arm HR SB AVG Runs RBI Fantasy Value
Taylor Trammell 45 50 70 60 45 18 26 0.251 75 68 18.8
Thomas Jones 45 45 70 60 50 16 26 0.251 73 66 18.0
Corey Ray 55 55 60 55 50 20 15 0.258 79 71 17.8
Blake Rutherford 55 55 60 50 50 20 15 0.258 79 71 17.8
Buddy Reed 50 40 70 60 60 14 26 0.255 71 65 17.6
Nonie Williams 45 50 65 50 60 18 19 0.251 75 68 17.5
Kyle Lewis 55 60 50 50 50 22 8 0.258 81 73 17.2
Conner Capel 50 45 65 55 55 16 19 0.255 74 67 17.0
Joshua Lowe 50 50 60 55 60 18 15 0.255 76 69 16.8
Akil Baddoo 50 50 60 50 40 18 15 0.255 76 69 16.8
Tre Carter 45 45 65 55 55 16 19 0.251 73 66 16.7
Brandon Marsh 45 50 60 55 60 18 15 0.251 75 68 16.5
Will Benson 45 55 55 55 55 20 11 0.251 78 70 16.4
Mickey Moniak 55 45 60 60 50 16 15 0.258 74 67 16.3
Alex Kirilloff 50 55 50 50 55 20 8 0.255 78 71 16.2
David Martinelli 50 50 55 55 50 18 11 0.255 76 69 16.0
J.B. Woodman 50 50 55 50 55 18 11 0.255 76 69 16.0
Bryan Reynolds 50 50 55 55 40 18 11 0.255 76 69 16.0
Heath Quinn 45 55 50 50 55 20 8 0.251 78 70 15.9
Ronnie Dawson 45 55 50 45 40 20 8 0.251 78 70 15.9
Nick Senzel 55 50 50 55 55 18 8 0.258 77 69 15.7
Nolan Jones 55 50 50 50 50 18 8 0.258 77 69 15.7
Cole Stobbe 55 50 50 45 50 18 8 0.258 77 69 15.7
Tyler Fitzgerald 45 50 55 50 55 18 11 0.251 75 68 15.7
Stephen Wrenn 50 35 65 60 40 12 19 0.255 69 63 15.6
Will Craig 55 55 35 40 60 20 4 0.258 79 71 15.5
Sheldon Neuse 50 50 50 45 60 18 8 0.255 76 69 15.4
Lucas Erceg 45 55 45 50 60 20 6 0.251 78 70 15.4
Ben Rortvedt 50 55 40 50 55 20 5 0.255 78 71 15.4
Joshua Palacios 50 40 60 50 50 14 15 0.255 71 65 15.3
Delvin Perez 50 40 60 60 60 14 15 0.255 71 65 15.3
Anfernee Grier 50 40 60 60 45 14 15 0.255 71 65 15.3
Hunter Bishop 50 40 60 50 40 14 15 0.255 71 65 15.3
Carter Kieboom 55 50 45 50 55 18 6 0.258 77 69 15.3
Nick Banks 55 50 45 50 50 18 6 0.258 77 69 15.3
Drew Mendoza 55 50 45 50 60 18 6 0.258 77 69 15.3
Gavin Lux 50 45 55 55 55 16 11 0.255 74 67 15.3
Javon Shelby 45 50 50 40 55 18 8 0.251 75 68 15.1
J.C. Flowers 45 40 60 50 60 14 15 0.251 71 64 15.0
Austin Hays 50 50 45 50 60 18 6 0.255 76 69 15.0
Hudson Sanchez 50 50 45 45 55 18 6 0.255 76 69 15.0
Walker Robbins 55 50 40 55 60 18 5 0.258 77 69 15.0
Ulysses Cantu 55 50 40 40 55 18 5 0.258 77 69 15.0
Jameson Fisher 55 50 40 45 40 18 5 0.258 77 69 15.0
Zack Collins 50 55 30 40 45 20 3 0.255 78 71 15.0
Ryan Boldt 55 40 55 55 45 14 11 0.258 72 65 14.9
Willie Abreu 40 50 50 50 50 18 8 0.248 75 67 14.8
Avery Tuck 40 50 50 50 50 18 8 0.248 75 67 14.8
Andy Yerzy 40 55 40 40 50 20 5 0.248 77 70 14.8
Tyler Ramirez 50 45 50 50 45 16 8 0.255 74 67 14.7
Chris Okey 50 50 40 50 50 18 5 0.255 76 69 14.7
Chad Mcclanahan 50 50 40 40 55 18 5 0.255 76 69 14.7
Peter Alonso 45 55 30 45 50 20 3 0.251 78 70 14.7
Jake Fraley 50 35 60 55 40 12 15 0.255 69 63 14.6
Nick Quintana 50 50 35 40 60 18 4 0.255 76 69 14.4
Reid Humphreys 45 50 40 45 60 18 5 0.251 75 68 14.4
Bo Bichette 45 50 40 45 45 18 5 0.251 75 68 14.4
Sean Murphy 45 50 40 55 70 18 5 0.251 75 68 14.4
Bobby Dalbec 40 55 30 45 60 20 3 0.248 77 70 14.4
Boomer White 55 40 50 50 50 14 8 0.258 72 65 14.3
Joe Rizzo 55 45 40 40 50 16 5 0.258 74 67 14.3
C.J. Chatham 50 40 50 50 60 14 8 0.255 71 65 14.0
Luis Curbelo 50 40 50 45 55 14 8 0.255 71 65 14.0
Blake Tiberi 50 45 40 45 50 16 5 0.255 74 67 14.0
Christian Jones 45 50 30 45 45 18 3 0.251 75 68 14.0
Garrett Hampson 50 30 60 50 45 10 15 0.255 67 61 13.9
Cam Shepherd 55 40 45 45 55 14 6 0.258 72 65 13.9
Matt Thaiss 55 45 30 40 45 16 3 0.258 74 67 13.8
Colton Welker 45 45 40 55 55 16 5 0.251 73 66 13.7
Colby Woodmansee 50 40 45 45 50 14 6 0.255 71 65 13.6
Carlos Cortes 55 40 40 45 50 14 5 0.258 72 65 13.5
Brett Cumberland 55 40 40 40 45 14 5 0.258 72 65 13.5
Jacob Robson 50 20 65 55 40 6 19 0.255 62 56 13.4
Nick Solak 55 30 55 50 40 10 11 0.258 68 61 13.4
Tres Barrera 40 50 20 50 55 18 2 0.248 75 67 13.4
Mario Feliciano 50 40 40 45 50 14 5 0.255 71 65 13.2
Andrew Knizner 45 45 30 45 50 16 3 0.251 73 66 13.2
Bryson Brigman 50 30 55 50 45 10 11 0.255 67 61 13.1
Brennon Lund 50 30 55 50 45 10 11 0.255 67 61 13.1
Logan Ice 50 40 35 50 50 14 4 0.255 71 65 13.0
Connor Justus 45 35 50 55 55 12 8 0.251 68 62 13.0
Cooper Johnson 40 45 30 60 65 16 3 0.248 72 65 13.0
Trever Morrison 45 30 55 50 50 10 11 0.251 66 60 12.8
Daniel Bakst 50 40 30 45 50 14 3 0.255 71 65 12.8
Jeremy Martinez 50 40 30 45 45 14 3 0.255 71 65 12.8
Will Smith 50 30 50 55 60 10 8 0.255 67 61 12.6
Stephen Alemais 50 30 50 55 60 10 8 0.255 67 61 12.6
Grae Kessinger 45 35 45 55 60 12 6 0.251 68 62 12.6
Jake Rogers 40 40 30 65 60 14 3 0.248 70 63 12.2

The list generally went by the draft order, but the one major different I notice is how some of the top speedsters, with some other skills, Taylor Trammell and Thomas Jones, have moved to the top. It would be tough for me to take them over Corey Ray (even with the injury) or Kyle Lewis in adynasty leagues, but they do give me some deeper targets.

I am finally done for this week. I am probably should have divided the work into two articles, but I think it is nice to have the procedure all in one place. As always, ask away because I will probably stop with hitters and move forward to pitchers.





Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won three FSWA Awards including on for his MASH series. In his first two seasons in Tout Wars, he's won the H2H league and mixed auction league. Follow him on Twitter @jeffwzimmerman.

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lexomatic
Member

When I try duplicating your work on players (mostly) not on the list, things go wrong when I try to add R/Rbi. For example, Bo Bichette gets me Totals of 27 R 25 Rbi… the same with JB Woodman. Avg and HR and SB totals I get the same result as your list. Where am I going wrong? I’m using the individual formulas.