Hitter Evaluations: 2B 2012 Talent Projections

With the core background work done on my hitter evaluations, I have decided to apply the process to second basemen for 2012. This ranking is based off of all player’s stats given the same number of PA. This a look to see which players are the most talented. You can follow the process with this spreadsheet.

Background and and Procedures (skip if desired)

To get a 2012 projection (columns R to X), I used a one-part weighting of 2011 stats (Column J to P) and a two-part weighting of the 2011 ZIPS projections (column B to H). I hoped to include most 2012 2B-eligible players (10 games at 2B in 2011). If there is a second baseman you want added, let me know.

Note: There was not a ZIPS projection for Tsuyoshi Nishioka so I created one. It was the average values of the other second basemen.

The standard values were changed to rates (columns AA to AI) to be used in further calculations.

Next, I created a projection using the five standard stats (AVG, HR, RBI, Runs, SB) for each of the second basemen based on 600 PA (columns AK to AQ). Besides these five stats, Hits, Ks, BBs and OBP can be determined using the information at hand. The values were then weighted using Zach Sander’s formula for finding fantasy above replacement values (columns AS to AW).

Note: We understand that the process is flawed when comparing different positions, and are working on updating it, but the calculator seems to work just fine for one position.

Finally, the individual overall rankings are calculated (columns AY and AZ).

2012 2B Rankings (based off of 600 PA):

Rank Name HR Runs RBI AVG SB Ranking Value
1 Ian Kinsler 23 78 78 0.270 26 7.3
2 Dustin Pedroia 16 80 80 0.301 18 7.2
3 Robinson Cano 23 79 79 0.299 5 5.7
4 Chase Utley 20 76 76 0.270 16 4.5
5 Brandon Phillips 17 72 72 0.281 17 3.5
6 Michael Young 14 75 75 0.300 5 3.3
7 Ben Zobrist 18 74 74 0.252 18 2.9
8 Dan Uggla 29 76 76 0.244 3 2.5
9 Rickie Weeks 22 73 73 0.248 13 2.1
10 Brian Roberts 11 69 69 0.262 29 2.1
11 Kelly Johnson 20 72 72 0.251 13 1.6
12 Michael Cuddyer 17 73 73 0.269 8 1.6
13 Daniel Murphy 11 72 72 0.292 8 1.5
14 Ryan Raburn 23 72 72 0.262 5 1.5
15 Howie Kendrick 14 69 69 0.282 15 1.5
16 Martin Prado 13 70 70 0.283 4 0.3
17 Neil Walker 15 70 70 0.268 8 0.2
18 Ryan Roberts 15 69 69 0.244 17 -0.1
19 Aaron Hill 19 68 68 0.253 10 -0.3
20 Omar Infante 7 68 68 0.289 5 -1.0
21 Tsuyoshi Nishioka 13 67 67 0.258 12 -1.1
22 Maicer Izturis 7 67 67 0.273 13 -1.1
23 Chone Figgins 2 62 62 0.244 32 -2.3
24 Danny Espinosa 18 64 64 0.223 17 -2.6
25 Gordon Beckham 13 65 65 0.250 7 -2.7
26 Johnny Giavotella 6 64 64 0.264 13 -2.7
27 Orlando Hudson 8 65 65 0.249 12 -2.8
28 Jason Kipnis 11 65 65 0.258 7 -2.9
29 Ryan Theriot 2 63 63 0.271 15 -3.0
30 Mark Ellis 10 63 63 0.254 12 -3.0
31 Justin Turner 8 64 64 0.264 7 -3.1
32 Sean Rodriguez 17 63 63 0.225 13 -3.4
33 Robert Andino 11 62 62 0.248 13 -3.5
34 Dustin Ackley 7 64 64 0.246 8 -4.1
35 Darwin Barney 2 61 61 0.273 9 -4.6
36 Jemile Weeks 5 58 58 0.244 19 -5.2

I will not go over the list today because I will begin looking at it in detail over the next few weeks. In the mean time, let me know if you have any suggestions or questions.




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Jeff writes for FanGraphs, The Hardball Times and Royals Review, as well as his own website, Baseball Heat Maps with his brother Darrell. In tandem with Bill Petti, he won the 2013 SABR Analytics Research Award for Contemporary Analysis. Follow him on Twitter @jeffwzimmerman.


44 Responses to “Hitter Evaluations: 2B 2012 Talent Projections”

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  1. Guancous says:

    That chart really seems to punish 2nd year players. Ackley, Kipnis and Jemile Weeks seem low.

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    • phoenix2042 says:

      sophomore slumps much? i think ackley can beat a .248 BA…

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    • Jeff Zimmerman says:

      The ZIPs projections hated them.

      Ackley was projected with a 0.244 average and 0.280 BABIP

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    • harpago17 says:

      Just out of curiosity, what’s the reason for giving the prior year ZIPS projections twice the weight of the actual 2011 stats? I think that’s probably what’s driving down the value of 2nd year players so much.

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      • Jeff Zimmerman says:

        Most weighting systems use a 5,4,3 (or 8,5,4,3) weighting for the last 3 years. It should actually be a 1.4 to 1 weighting. The projection needs more weight. I added a little more weight to projection because it adds in some regression. Another problem is the projections are near 600 PA and the players are only at 300 PA for 2011. The weighting is closer to almost 4:1 for the rookies.

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  2. Ray says:

    Uggla with under 30 HRs? He is the Adam Dunn of 2bman, no?

    I also don’t like the Cano projection….he could go 30-100 and possibly approach 35 HRs next year. His power is trending up.

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  3. Andrew says:

    That’s a ridiculously pessimistic projection for Cano and Pedroia. I understand that it’s based on 600 PAs (Cano’s had at least 670 for the past three seasons), but still, if either of them only put up around 80 runs/rbi in that many PAs I would be shocked. Does it factor in the quality of the players hitting around them at all? The problem with this chart is that not enough variables are accounted for, therefore we see very little variation between the very best and very worst second basemen – everyone ends up with around 68 runs and RBI. It’s also tough to see 36 second basemen getting 600 PA. I don’t fully understand the methodology, but an eye test will reveal that it seems very flawed.

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  4. Brad Johnson says:

    The R/RBI totals smell extremely hinky. I’d be less critical if we weren’t holding PA constant for this exercise. The HR, SB, and AVG number are probably of some use, but the R/RBI should just be discarded. I’ll set the line on R^2 to actual performance for R/RBI at .175. Any takers?

    Clearly the first problem is that there’s no attempt to project lineup slot. Ian Kinsler is not going to go 78/78 in R/RBI. He’s going to score way more R than RBI. Similarly, Uggla will drive in a lot more runs than he’ll score.

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  5. EJ says:

    So Cano, the 3 hole hitter in the Yankees lineup is only gonna have 79 RBI’s next year? Kinsler’s power seems low too. I don’t personally care for this list for a whole lotta reasons, too many of which to ramble on in this post…

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      • abreutime says:

        Jeff, I recommend you either combine R and RBI into 1category, or apply the batting order adjustments based on expected order. You can also apply the team runs adjustment. I think it’s silly to present these “preliminary” numbers at all, since they are obviously not meant to be indicative of anything.

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      • abreutime says:

        Also, why predict R+RBI together? The problem is that factors that can contribute to one (Walks contributing to Runs) might have adverse effect on the other (putting balls in play probably increases RBI). The only way that BB would likely contribute positively to RBI is probably caused by interactions with other elements in your model.

        Sorry I didnt notice this stuff earlier, as I just now looked at the google spreadsheet. Seeing the positive coefficient on BB in the RBI predictor gave me pause.

        Lastly, if you are giving projections for fantasy advice, it makes no sense to normalize to 600 PA. I can only understand this decision if you have zero methods for projecting PA, but you’ve apparently developed methods based on batting order. I recommend using them. Maybe present both the normalized version and the one based on projected PA.

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      • Jeff Zimmerman says:

        I am looking at the player’s talent and this all I want to look at in October. 1/3 of these players will probably be on other teams or lose their job by the time ST comes around. The top 10 may have secure jobs, but the rest of them could change their roles and teams easily.

        This is how the players would perform on an average team with an average spot in the lineup.

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      • abreutime says:

        So maybe Runs and RBI should be withheld from this presentation, since they are so context dependent. Including them “as is” undermines your work, since everyone projects to a narrow range and R=RBI.

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      • tyke says:

        what does “an average spot in the lineup” entail exactly?

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  6. Dave S says:

    Chone Figgins, leading all MLB 2bmen in SBs next year…

    you heard it here first.

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  7. Ed says:

    “I am looking at the player’s talent and this all I want to look at in October. 1/3 of these players will probably be on other teams or lose their job by the time ST comes around. The top 10 may have secure jobs, but the rest of them could change their roles and teams easily.”

    Jeff, this comment begs the question of why even do this analysis now.

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    • Jeff Zimmerman says:

      To give people as much information as possible for now.

      In one league I have Kelly Johnson, Cuddyer and Neil Walker. 2 will become FAs at the end of the season. It will give an idea of their potential.

      Also as information become available, the adjustment to the numbers can be made.

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      • Luke says:

        I appreciate the efforts, Jeff. I’d already compiled my initial 2012 rankings at every position. It’s never too early!

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  8. bobby says:

    When using the average and standard deviations of the whole group…will you be using the same amount for other 1 starter positions also(C,1B,SS,3B)? Differences in the total amount in the group can artificially give players more or less value by mistake yes?

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  9. Dustin says:

    How do you project Rickie Weeks to only hit 22 hr with a .248 BA?

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  10. robertobeers says:

    Freddy Sanchez?

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  11. eduardo says:

    Nishi may not hit 3 career homers let alone 13 in 1 year.

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  12. Just Jim says:

    Out of curiousity, If Allen Craig somehow manages to get a full time job out of spring training, and considering he qualifies for 2nd, where would you put him in your rankings?

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  13. Eric Dykstra says:

    Figgins won’t even get 100 PA next year.
    I’d take Kipnis and Ackley top 10.

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  14. Total Dominication says:

    So Cano will have is worse year since 2008? By far? And pedroia will regress a ton as well?

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    • Jeff Zimmerman says:

      This is the number for 600PA, hitting 5th for an average team, like the Indians.

      Let’s take Cano for a second.

      Take these numbers and assume all the surrounding circumstances stay the same (to early to tell how this will work out with many of the players)

      He gets 671 PA
      Yankees score 5.35 R/G
      Cano bats fifth all season
      (All of these he has no control over)

      Using the adjustments from here, he ends up with:

      26 HRs, 107 Runs, 114 RBIs, 0.299 AVG and 5 SB

      for some bizarre reason he was traded to Minnesota who averaged 3.85 R/G and had a team 0.306 OBP vs 0.340 (less PA). He would get on average 660 PA and his numbers would change to:

      25 HR, 75 Runs, 80 RBIs, 0.299 and 5 SB

      The top players are going to look worse, but if they were stuck at Houston or Minnesota last year, their numbers would be below the numbers above.

      Anyone knows the top few 2B are going into next year will be good. I am looking for the diamonds in the rough and how changing teams will effect the player’s numbers.

      Finally, with 2B the top 3 all play for high power offenses, so their numbers take huge drop off when compared to the rest of the league.

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      • Jamie says:

        A couple things:

        1) Cano’s not going to be traded to Minnesota, neither are any of the top 10 SB’s, so why are these fictious numbers relevant?

        2) Assuming these numbers are relevant, do your calculations take into account the increased R/G and OBP that the “average team” would have if Cano/Pedroia, etc. was batting 5th for them?

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  15. harpago17 says:

    I misunderstood the point of your article, and now I want you to admit your numbers are wrong!

    (just trying to join in with the general theme in comments section here)

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  17. Corey says:

    this is idiotic.

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  20. DaBulls says:

    Contrary to the spam just above me and the whiners too lazy to read the introduction about team, lineup et al adjustments, this is great work.

    I’ve been toying around with projection systems recently, seeing if I could get increased accuracy on rate figures by regressing Marcel, CAIRO, ZiPs, etc., but haven’t been able to get r^2 north of ~0.55 and standard error of about 1.1 hr per 100 PA. Seeing as this translates to a +/- of 6 HR over an average season, the difference between Ryan Howard and Freddie Freeman, I didn’t find my combinations particularly helpful.

    I’m curious if you could(/would) retroactively run your projections using 2010 stats and 2011 ZiPs on 2011 actuals (rates, as to eliminate injuries etc.) and report the r^2 and stdevs…?

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    • Jeff Zimmerman says:

      You are probably about as close as you are going to get. The numbers move that much (see Adam Dunn).

      One key I have found it to combine 3 projection systems together (I am not sure if 3 is the right number). Tom Tango’s battle of the projection systems has shown that the composite is better than just 1 system.

      I may get back to it later, but we are about to start the 2012 fantasy writeups so time is short. If I haven’t gotten back to this question by Xmas, email me at wydiyd ~ hotmail and I will look into it. -Jeff

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