Best players at their experience level

by KerryWhisnant

Who are the best offensive players at this point in their career, among players who are currently active? Younger players on such a list might very well turn into the stars of tomorrow. More established players on the list are building their resume for possible admission to the Hall of Fame.

For the last two years I have presented a list of the active players who had the most career Runs Created with the same or more plate appearances. Here the list is updated for 2010 (the data comes from with a minimum of 50 PA required:

PA RC Player RC/PA
9757 2005 Manny Ramirez 0.205
9654 1826 Chipper Jones 0.189
8234 1679 Todd Helton 0.204
6782 1506 Albert Pujols 0.222
6661 1200 David Ortiz 0.180
6065 1052 Adam Dunn 0.173
5867 1014 J.D. Drew 0.173
5350 953 Mark Teixeira 0.178
5089 938 Miguel Cabrera 0.184
4313 807 Matt Holliday 0.187
4298 737 Jason Bay 0.171
3765 702 Ryan Howard 0.186
3518 628 Prince Fielder 0.179
3372 619 Hanley Ramirez 0.184
3292 576 Kevin Youkilis 0.175
3153 506 Brad Hawpe 0.160
2939 478 Jayson Werth 0.163
2547 466 Ryan Braun 0.183
2470 384 Dustin Pedroia 0.155
2322 376 Luke Scott 0.162
1870 373 Joey Votto 0.199
1840 302 Evan Longoria 0.164
1804 268 Mike Napoli 0.149
1728 255 Justin Upton 0.148
1570 244 Nelson Cruz 0.155
1552 228 David Murphy 0.147
1519 219 Martin Prado 0.144
1269 214 Carlos Gonzalez 0.169
1146 182 Andrew McCutchen 0.159
1089 154 Casey McGehee 0.141
1079 151 Ryan Raburn 0.140
1054 146 Colby Rasmus 0.139
916 144 Seth Smith 0.157
871 114 Joe Inglett 0.131
779 107 Drew Stubbs 0.137
623 98 Jason Heyward 0.157
575 86 Matthew Joyce 0.150
527 74 Chris Dickerson 0.140
509 72 Neil Walker 0.141
460 70 Buster Posey 0.152
396 58 Mike Stanton 0.146
385 54 Chris Johnson 0.140
287 45 Logan Morrison 0.157
238 36 Randy Ruiz 0.151
198 33 Micah Owings 0.167
192 32 Carlos Santana 0.167
173 26 Mitch Moreland 0.150
158 23 Lorenzo Cain 0.146
155 19 Sam Fuld 0.123
99 17 Casper Wells 0.172
84 13 Juan Francisco 0.155
73 9 John Mayberry 0.123

This is a very interesting list. Most of the players near the top are often mentioned as possible future Hall of Fame candidates, and have been on the list before. Established players who were not on the list last year include Jason Bay, Prince Fielder, Kevin Youkoulis, Brad Hawpe and Dustin Pedroia. Younger players on the list include Mike Napoli, Nelson Cruz, Martin Prado, Carlos Gonzalez, Casey McGeHee, Colby Rasmus, Drew Stubbs, Jason Heyward, Buster Posey and Mike Stanton.

There are also some surprising absences from the above list, the most conspicuous being Alex Rodriguez, who is trumped by Manny Ramirez. We can make a list of the players with the second highest RC for players with the same or more PA, to see who just missed the cut. Rather than showing all the numbers, I simply give the names here (again 50 PA minimum, in order of decreasing PA): Alex Rodriguez, Jim Thome, Bobby Abreu, Vladimir Guerrero, Jason Giambi, Jim Edmonds, Lance Berkman, Jorge Posada, Chase Utley, Travis Hafner, Nick Markakis, Nick Johnson, Brian McCann, Andre Ethier, Ian Kinsler, Josh Willingham, Josh Hamilton, Shin-Soo Choo, Ben Zobrist, Matt Diaz, Geovany Soto, Pablo Sandoval, Kendry Morales, Ben Francisco, Chris Coghlan, Will Venable, Chris Davis, Mike Morse, Gaby Sanchez, Travis Buck, Ike Davis, Jed Lowrie, Nolan Reimold, Starlin Castro, Tyler Colvin, Pedro Alvarez, Danny Valencia, David Freese, Kyle Banks, Josh Thole, Mat Gamel, Max Ramirez, Dayan Viciedo, Juan Miranda, Brent Clevlen and Wilson Ramos.

Some of the #2 players were on the top list last year, but were superceded by someone who passed them in RC but still had fewer PA: Utley (passed by Holliday), Hafner (Howard), Markakis (Youkoulis), McCann (Braun), Zobrist (Upton), Morales (McCutchen), Sandoval (Gonzalez) and Chris Davis (Inglett).

This second list is a very fine group as well, and include some potential Hall of Fame candidates – steroid issues aside — but I think most people would say that overall the players on it are a step down from the first list.

We can continue the process by finding players with the third highest RC for players with the same or more PA (this time 500 PA minimum to keep the list somewhat shorter): Scott Rolen, Magglio Ordonez, Alfonso Soriano, Troy Glaus, Pat Burrell, Mike Sweeney, David Wright, Carlos Pena, Justin Morneau, Joe Mauer, Curtis Granderson, Dan Uggla, Ryan Zimmerman, Jack Cust, Ryan Garko, Jay Bruce, Mike Fontenot, Garrett Jones, Dexter Fowler, Brett Gardner, John Baker, Austin Jackson, Ryan Shealy and Brennan Boesch. Mauer and Zimmerman were on the top list last year, replaced by Fielder/Ramirez and Hawpe, respectively. Clearly it’s much harder to find Hall of Fame caliber players on this last list, although one or two might be considered a premier player at their position.

Sometimes a great player is overshadowed by a better contemporary, and, on the other hand, not all players on the first list are great since there is not strong competition at all PA levels. But these lists provide an interesting perspective on who the better players are by experience level, and is a different way of looking at player rankings.

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8 Responses to “Best players at their experience level”

  1. Chuck Says:

    I love the definition of Runs Created;

    “A set of formulas developed by Bill James and others that estimates a player’s total contributions to a teams run totals.”

    At least we can assume, based on the quote, that RC actually HAS a formula.

    Unlike WAR.

  2. Raul Says:

    I have the baseball abstract. I’ll try to find the formula.

  3. Raul Says:

    Actually, crap. I left the book back in New York.

  4. Hartvig Says:

    I have to admit that I had no idea who Joe Inglett was. I’m sure I’ve probably seen him play (on television, at least) some time but the name was completely new to me.

  5. KerryWhisnant Says:

    That’s one reason I used RC — I figured it would have a better reception here. The drawback is that it doesn’t include fielding — although if you don’t trust the fielding metric, that could be a plus. :-)

    There are a number of RC formulas, differing in sophistication. The one I use is

    RC = A*B/C,


    A = H + BB + HBP – CS – GDP
    B = 1.125*1B + 1.69*2B + 3.02*3B + 3.73*HR + .29*(BB+HBP-IBB)
    + .492*(SB+SF+SH) – .04*SO
    C = AB + BB + HBP + SF + SH.

    So it includes SB, CS, SF, SH, SO and GDP. I’m not sure what bb-ref (the source of this analysis) uses – it may not be quite as complicated.

    The simplest (original) form is OBP*SLG/PA, the idea being that scoring runs is a combination of getting on base and driving them in. All RC formulas are non-linear (i.e., if you increase OBP and SLG by 10%, your RC increases by more than 10%), which is actually very realistic. My simulations all exhibit nonlinear behavior.

  6. KerryWhisnant Says:

    Regarding Inglett, I didn’t either. Since his career OPS+ is 97, he is one example of how a player can slip into a top position if there is no real competition at his experience level. That happens mostly at the lower PA levels, although I am amazed at how J.D. Drew has managed to make the top list all three years — in fact, I have carried the analysis back further and he’s been on it for the last six years.

  7. Chuck Says:

    Kerry @ #5,

    Too much work, bud.

    Anything which takes that much work or that much fudging of numbers isn’t worth the time and leads to questionable results.

    I’ll take the orginal form, thankyou very much, or even a form of Total Average.

    Nice work, as usual, good to see you posting here again.

  8. KerryWhisnant Says:

    I goofed, the simple form is OBP*SLG*AB (the OBP and SLG both already divide by PA or AB, so you have to multiply by AB to compensate).

    “Kerry @ #5, Too much work, bud.”

    Hah! bb-ref is not using the simple form, but it might be closer to it than the one shown above. They’re not that different from each other, the complicated one is more of a tweak than a major change.

    “Anything which takes that much work or that much fudging of numbers isn’t worth the time and leads to questionable results.”

    While that can certainly be true sometimes — especially when the data is sparse, you can contort yourself to fit it but it isn’t meaningful — in this case there is a ton of data to fit and the refinement really does improve the overall fit. You also have to do a reality check — do the refinements make sense? In this case they do: SB are good, CS, GDP, SO are bad (the latter just a little).

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