Ranking 2B By Fantasy Talent
In last week’s article on 2B, I discussed briefly a method I use for ranking players by talent. This week I will show the method along and the rankings. I loosely based this method on the the Mayberry Method introduced in the 2010 Baseball Forecaster. The Mayberry Method looks at the player’s talent and expected playing time to give them a fantasy value. I stripped the analysis down further to just look at the player’s talent.
Here is the method that is used. The original Mayberry Method looked at a hitter’s projected AVG, HR, SB and PA and ranked each player according to a formula. To get the player’s hitting ability, I stripped plate appearances from the equation because a player has no control over plattoons, batting order and talent around them. Also plate appearances directly effect the number of Runs and RBI’s the player will generate in a season.
Besides stripping out PA, I didn’t want HR and AVG to be given the same weight as SB. I feel that HR and AVG should be weighted more as each contributes directly to RBIs and Runs. So I weighted AVG and HR twice that of SB. Next, I converted HRs and SBs into rate stats, HR/PA and SB/PA.
Finally I took the 5 categories and plugged them into our own Zach Sander’s formula for finding fantasy above replacement values. For the values, I went with a Wisdom of the Crowds approach and used a weighted average of 4 different projection systems.
After following the above method, here are the pre-season rankings I came up with:
| LASTNAME | FIRSTNAME | Average | Average | Home Runs | Home Runs | Stolen Bases | Total |
| Cano | Robinson | 1.98 | 1.98 | 1.15 | 1.15 | -0.77 | 5.48 |
| Kinsler | Ian | 0.64 | 0.64 | 0.97 | 0.97 | 1.26 | 4.49 |
| Utley | Chase | 0.75 | 0.75 | 1.32 | 1.32 | 0.11 | 4.26 |
| Phillips | Brandon | 0.48 | 0.48 | 0.87 | 0.87 | 0.93 | 3.62 |
| Pedroia | Dustin | 1.70 | 1.70 | -0.13 | -0.13 | 0.15 | 3.29 |
| Raburn | Ryan | 0.14 | 0.14 | 1.11 | 1.11 | -0.27 | 2.24 |
| Johnson | Kelly | 0.36 | 0.36 | 0.77 | 0.77 | -0.04 | 2.23 |
| Wigginton | Ty | 0.25 | 0.25 | 1.17 | 1.17 | -0.91 | 1.93 |
| Roberts | Brian | 0.59 | 0.59 | -0.58 | -0.58 | 1.90 | 1.91 |
| Prado | Martin | 1.64 | 1.64 | -0.44 | -0.44 | -0.75 | 1.67 |
| Uggla | Dan | -0.47 | -0.47 | 1.72 | 1.72 | -0.90 | 1.60 |
| Young, Jr | Eric | -0.03 | -0.03 | -0.91 | -0.91 | 3.42 | 1.55 |
| Hill | Aaron | -0.47 | -0.47 | 1.56 | 1.56 | -0.82 | 1.35 |
| Weeks | Rickie | -0.58 | -0.58 | 1.08 | 1.08 | 0.33 | 1.32 |
| Aviles | Mike | 0.92 | 0.92 | -0.33 | -0.33 | 0.02 | 1.20 |
| Kendrick | Howie | 0.75 | 0.75 | -0.47 | -0.47 | 0.15 | 0.72 |
| Infante | Omar | 1.31 | 1.31 | -0.87 | -0.87 | -0.56 | 0.32 |
| Beckham | Gordon | -0.03 | -0.03 | 0.39 | 0.39 | -0.41 | 0.32 |
| Rodriguez | Sean | -1.42 | -1.42 | 1.48 | 1.48 | -0.13 | -0.01 |
| Polanco | Placido | 1.03 | 1.03 | -0.84 | -0.84 | -0.75 | -0.37 |
| Sanchez | Freddy | 1.03 | 1.03 | -0.79 | -0.79 | -0.94 | -0.47 |
| Uribe | Juan | -0.80 | -0.80 | 0.90 | 0.90 | -0.88 | -0.69 |
| Zobrist | Ben | -0.97 | -0.97 | 0.40 | 0.40 | 0.46 | -0.70 |
| Walker | Neil | -0.47 | -0.47 | 0.28 | 0.28 | -0.34 | -0.74 |
| Espinosa | Danny | -1.86 | -1.86 | 0.93 | 0.93 | 0.60 | -1.26 |
| Theriot | Ryan | 0.48 | 0.48 | -1.64 | -1.64 | 0.96 | -1.37 |
| Lopez | Felipe | 0.09 | 0.09 | -0.84 | -0.84 | 0.11 | -1.40 |
| Callaspo | Alberto | 0.48 | 0.48 | -0.81 | -0.81 | -0.89 | -1.56 |
| Schumaker | Skip | 0.70 | 0.70 | -1.15 | -1.15 | -0.66 | -1.57 |
| Keppinger | Jeff | 0.59 | 0.59 | -0.98 | -0.98 | -1.03 | -1.83 |
| Figgins | Chone | -0.58 | -0.58 | -1.65 | -1.65 | 2.52 | -1.94 |
| Lowrie | Jed | -1.03 | -1.03 | 0.21 | 0.21 | -0.99 | -2.62 |
| Scutaro | Marco | -0.19 | -0.19 | -0.89 | -0.89 | -0.46 | -2.63 |
| Hall | Bill | -2.25 | -2.25 | 1.02 | 1.02 | -0.30 | -2.78 |
| Hudson | Orlando | -0.30 | -0.30 | -0.93 | -0.93 | -0.38 | -2.85 |
| Casilla | Alexi | -0.53 | -0.53 | -1.60 | -1.60 | 1.22 | -3.03 |
| Hairston | Jerry | -1.42 | -1.42 | -0.33 | -0.33 | 0.16 | -3.33 |
| Brignac | Reid | -1.47 | -1.47 | -0.15 | -0.15 | -0.56 | -3.80 |
| Ackley | Dustin | -1.03 | -1.03 | -0.98 | -0.98 | -0.54 | -4.57 |
When compared to the list that we put out at the beginning of the year, here are a couple interesting tidbits.
– Using this method for ranking players, Aaron Hill and Dan Uggla are nearly the same player, all HR, no SB or AVG. Uggla is being drafted about 70 spots earlier in the drafts compared to Hill, mainly based on increased opportunites to drive in and score runs.
– Eric Young, Jr. looks to be a fairly productive fantasy player based mainly entirely on his SB, but may not be given a chance to start since his teammate Jose Lopez is entrenched at 2B.
This method by itself should not be used to fully evaluate players in fantasy baseball because it doesn’t take into account playing time. Increased playing time allows a player to accumulate more of the 4 counting stats normally used for hitters. It is though a good method to find talented players not playing everyday and if they are given the opportunity to shine, they could out produce other players.
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 four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR twice, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.
If anyone is entrenched at 2B ahead of EY Jr., it’s Jose Lopez and not Ty Wigginton. Lopez has started every game there so far. But yeah, a lot has to change before Young sees playing time.
Fixed
Mark Ellis isn’t even worth listing?
A few issues with this:
1. Where exactly are these talent levels coming from? They seem to be very heavily biased by last years’ performance, rather than the longer track record. Or are there people out there who really believe that Figgins, Uggla, and Aaron Hill will really end up with similar AVG at the end of the season? (Sings: “Which one of these things, is not like the others…?”)
2. The doubling of AVG and HR seems pretty arbitrary.
– Runs is best correlated to things like OBP. SB correlates to OBP, because your number of steals is highly based upon the number of times you can get on base in the first place (i.e. you can’t steal first base). Tons of sluggers post pretty pedestrian Run values.
– Likewise, RBI seems to have very little to do with AVG but is mainly driven by slugging (hence the value of guys like Howard, Dunn, etc). That slugging doesn’t have to be in the form of HR, however, doubles work just fine (just ask Nick Markakis).
– Wouldn’t it make more sense to figure out some “true talent” estimates of RBI and R based upon typical usage, rather than just throwing extra weight onto a couple of talents randomly?
3. This method will consistently overrate platoon players. Since these players get the advantage of friendly matchups (raising their stats) and you take away any PA adjustment, they’re always going to look better than they should. While you say that players don’t have any control over their platoons- they do. Players end up in platoons because they can’t hit righties or lefties, 90% of the time. If you want to assume everyone is playing for a year, you’d need to break down their platoon components (PA vs righties and PA vs lefties), then weight their performance based upon the typical distribution of pitchers they’d face?
1. 2011 Zip projections for the hitter’s AVG
Figgins: 0.263
Hill: 0.257
Uggla: 0.259
Pretty much the same, 4 point difference. I used an average of 4 projection systems. Zips wasn’t even one since it wasn’t released yet.
2. I really wanted to just use AVG and HR, but felt I couldn’t dump SB.
RBIs are driven by many factors. Would Ryan Howard have as many RBIs if he batted 9th for the Royals?
Other factors could always be added in, but I am just trying to keep it simple. Getting RBIs and Runs, unless you hit a home run, assume others are on base and hitting after you to hit you in. I know for a fact that Billy Butler can hit a ton of doubles, he gets half the RBIs he should if he was on another team.
I feel that if I can win AVG, HR and SB, R and RBIs won’t be to far behind (I have found that R and RBI can get unbalanced – trade time). I am just tired of chasing RBIs and Runs. Pretty much the same as chasing Wins, seems like a losing cause to the rest of the stats.
I do adjust the values for possible PA (including batting order), injury and surrounding players later.
3. Agree on this point.
I do something similar to find “playing time upside” in my deeper leagues. But as stated above, OBP & SLG play an important role in R & RBI.
Why not just convert projected R & RBI to per PA while you’re at it?
FWIW,
Overrated players using your system:
Wigginton, J.Lopez, Kendrick, Prado, Cano
Underrated:
Zobrist, Weeks, B.Roberts, Utley
Regardless, the utility of this methodology is limited.
It gives a glimpse of what is possible with full playing time for players (if they aren’t destined to platoon), which players could be worth teaming up in a fantasy platoon situation, the strength of potential call-ups, etc.
As stated above, RBIs and Runs rely on the output of others, I am just looking at the single player involved, not those around him. SLG and OBP, may make for nice subs for one of the AVG and HRs
Was this built upon Sanders’ flawed formula for which positional Standard Deviations are used for each category and not the overall population’s? If it is, this analysis is very flawed.
Also, as mentioned, the doubling the weights of HRs and AVG are arbitrary. I would expect some quantitative justification for it. Not just a gut feeling.
This is exactly when Sanders’ formula works. The formula doesn’t work when you’re trying to compare second basemen to outfielders, but it does work if you’re simply comparing second basemen to second basemen.
Just arbitrarily glancing at the unsorted statistics, I’d say that the distribution is pretty close to normal and the analysis should be legit. There’s enough players in the pool to make me happy.
Rather than arbitrarily saying that “avg and hr contribute more to RBI and RUN,” it’d take you twenty five seconds to look up the correlation coefficients in R. Mind you, I bet you’re not *too* far off and people are causing a fuss because you skipped the methodology portion.
I like stuff like this because it puts each player in the perfect situation — It’s basically a “Value to the Yankees” formula. Take every second basemen, guarantee him health and stick him in the same spot in the Yankees line-up so that they get the same amount of PA. Given those constants, who should the Yankees purchase to be their five hitter?
Anyways, I like when people dick around with math. It doesn’t have to be hard and fast, it can be fun too. At least it gets the discussion rolling. I think you just have to make sure that everyone knows that you’re dicking around and not presenting concrete proofs.
I really don’t know how you can do that other than preface the article with, “Listen guys, we’re just shooting the shit here. If you have an idea go ahead and spit hot-math-flames at us. No one’s presenting this as their thesis, so try not to tear me a new asshole”
I should read all comments before I begin responding. Good points.
If I get the time, I will get you the R(-squared) for the RBI and Runs to AVG. I may play around with a bit and see if adding OBP and SLG are better.
Jeff, I’m not one to care. Unless you start titling your articles “SURE FIRE, INDISPUTABLE, TRUTH TRAINS OF MATH” I’m not going to get my panties in a knot. Although, I would thoroughly enjoy a fangraphs series with that very title.
Your first task: Accumulate a large sample size and report back to me the coefficients for white players and announcers describing them as “hardworking/hustle” and black players and the term “athletic.”
O/U is currently set at 0.87
You’re incorrect. You use the mean from the positional population but to give proper overall context to the player’s value, you need to use general population standard deviations.
Tom Tango has written at length about this. The link to his article can be found in the comments in Sanders original article.
Lewis, I’m very correct, but I think we’re disagreeing over what we’re actually measuring, and in turn, differing over the methodology.
If we are only measuring the value of a second baseman relative to other second basemen, the method used is completely correct. If, as you’re suggesting, we’re measuring the value of second basemen relative to other second basemen with the belief that a second basemen doing well in AVG (or whatever) should be put into the context of how the other positions produce, then we would do exactly what you’re suggesting.
Should a second basemen be rewarded for flashing a skill that’s common in the major leagues, but uncommon at his position? Well, when measuring a player’s overall worth to a baseball club, the answer is yes as you suggest.
For the purpose of this comparison, I don’t know if that’s what we’re measuring. I assumed that we were measuring overall talent of second basemen, not relative to the major leaguer, but relative to his peer group with no need for contextualizing skills that are either common or uncommon across the entire player universe.
Honestly, I would be completely satisfied with the article using your methodology instead, but you’d be getting a different answer (although it probably won’t be too different). You’ll be getting value to an MLB team, rather than talent compared to peer group.
Woops. “answer is no, as you suggest”
By stripping PAs away, not only did you take off the platoon, but also one other big factor in fantasy- health. Isn’t keeping yourself healthy a part of talent?
Agree, I just want the player’s ability when they are batting.
Nishioka?
Never mind, 2010 stats not projections.
OR! THE MATH BECAME SELF-AWARE AND PROJECTED A BROKEN LEG.
Hill has two steals already. I think he has decent speed. Wouldn’t surprise me if he ends with 15.