Prospectus Idol Entry

For the second week in a row, Will Carroll complains about an article that includes some technical detail (but, frankly, not all that much). That's a fine position to have, but it seems strange coming from a staff member at Baseball Prospectus, which is known for - and frequently submits its willing readers to - pretty sophisticated and technical statistical analysis.

Tim's piece is, to my mind, very good. He explains a *very* valuable fantasy tool clearly and with ease. Especially for fantasy players who are relatively new to the game, this article has the potential to have a pretty dramatic effect on how they approach a season. It's hard to see how that doesn't meet the goal of the week's competition. While the more technically naive among us will have to do some work to make use of Tim's method, it's not too much work: given the tools available, calculating percentages (basically) is easy.

So, I don't get Will's problem: Tim compliments BP readers by assuming they're smart, but he doesn't go so far as to assume they're degree students in a statistics program. As I understand, that's been the tone BP has been trying to strike for as long as it has been around. What's the complaint?

Not only does this article show people how to create their own fantasy rankings (an underrated aspect to victory in leagues with unique settings), it explains the basics of what they're all about. One of my favorites this week.

Side note: Pivot tables are WAY easier to create and use than most people realize. Find 15 minutes, a dataset you're interested in and someone to walk you through them. You'll be thankful you do.

What I would really like to see -- either from a BP Idol contestant or a BP author/intern -- is a *tutorial* on how to do the kind of advanced analysis we love reading about. Tell us how to create a Retrosheet Database and manipulate it using Excel.

A step-by-step article for people who haven't been to math grad school would be really helpful.

Since I haven't had that kind of training (though I'd like to), I agree with Will that Tim's article lost me at the VLOOKUP line. I read through to the end and appreciated Tim's insight. But telling readers that "you can do it" without telling them *how* to do it is, imho, a mistake.

I'll bone up on Pivot Tables and VLOOKUPS and give you a thumbs up. You're aiming for the high end of the BP community with regard to statistical analysis, and, even if I don't normally use those Excel functions, I want a writer capable of writing at this level to win.

This is actually just what I wanted to read, if I would appreciate more detail on the specifics in the author's future editions. If you work excel for a day job like me, but have yet to try to really apply it to do interesting things for baseball analysis, this is worthwhile stuff.

I'm a bit of an Excel wizard, so I'll admit my bias and say I didn't get stumped with pivot tables and vlookups.

Very minor quibbles:
Many people consider Pujols a top three or top pick in almost any format. I think you needed to elaborate on why you think he is not a top three pick or else you'll lose a lot of readers.

It might have helped the word count to just send the "Note to Baseball Prospectus: " to customer service. Either way, it's not a good idea to mention a word count as being a limiting factor.

Since some statistics are more scarce than others, I think you could've discussed the possibility of the reader to add a weight to MPV by adding in a multiplier to a category like stolen bases.

I know one of the finalists got some negative comments for using a title and subtitle to start up an article theme but I didn't mind it there, nor here.

A bigger quibble
I'll admit I'm a bit confused which "team" you are using to compare Branyan/Wright.... I assume it's the "Team Total 4552" line, but then I don't see how the difference between a team+wright OBP of .350 and a Team Total OBP of .341 comes out to a "14%" difference. Is that a .009/.350 or something?


Personally, I found the article easy to understand and think I would've understood the methodology behind MPV even if I didn't know Excel. I think the tone of the writing was strong, fun, and a pleasure to read. I like how you indicate that databases would be downloaded. I would even recommend that you could create a mock spreadsheet to attach to the article that people could download. That way, you spend more time discussing MPV without scaring people off with Excel. Even then, the concepts discussed were easy to understand and most of the examples (besides the math on the Wright/Branyan section) were well presented. I also like that you defined the kind of league you used based on style, roster size, and categories used.

Another great job, and another thumbs-up from me.

Some criticism

As far as I can tell much of the comparison between Wright and Branyan was based on at bats which resulted in much of the difference as clearly in a daily league a replacement level player even in those spots for 200 ABs would have greatly boosted the rate stats.

Secondly I do have a problem with using this value as the only tool as first of all you do not necessarily maximise the value of your team by taking the best ranked player and it would be better to use this system to tier the players and look where the drop off from one player to another is which isn't necessarily the same as the one with the highest score.

Overall though I thought the stats were fine and followed through well but I did feel that perhaps more effort could have gone into what happens if you take say Josh Hamilton over Ian Kinsler say for the rest of the draft to give people a flavour of how it was used in practice.

So some critisicm but I enjoyed the article and voted it up

Good stuff. I started doing this type of analysis myself when I first learned spreadsheets, then lacking any knowledge of vlookups or pivot tables. I guess I had assumed much of the BP constituents had done the same. The skills I honed doing this for my hobby has somewhat led me to my current profession (Finance) and prove to be valuable skills used on a daily basis there.

For the unitiated, pivot tables allow a very quick summary of data based on fields you choose. Vlookups are a way to link to datasets together when you have fields in one that you want to use with fields from another, or if you simply want to combine the data. Both are relatively simple to use and worthwhile to learn if you have any inclination to do statistical analysis - they will make the task much less daunting.

I use pivot tables in particular frequently to help in my Diamond Mind league - help decisions around lineup construction (both who to start and in what batting order), what opposing hitters to pitch around, and pinch hitting and bullpen decisions. Extremely valuable tools.

Simply perfect. GREAT work and much appreciated by a player in a less-than-traditional league.

Not a thumbs-up for me, because of a flaw in the "baseline-calculation" section. You reference that league context is important, and totally ignore it in your baseline calculations, preferring instead to reference every player to the average (qualifying) player. You instead should be looking at a league-specific replacement level.

As a quick example, consider the value of a full-time but poor-hitting catcher in either a one-catcher and a two-catcher 12-team league. In the one-catcher league, this catcher should have very little if any value, as the replacement-level catcher in this league is probably full-time (or close) and similarly useless without his chest protector. In a two-catcher league, this player is relatively valuable, as whomever you can find on the waiver wire is likely a part-time or reserve catcher.

This issue will also come to a head when there are differences in the variances of performance across positions, when one player is hands-down above the rest at his position, and a variety of other situations. The rest of the article was good, but if a fantasy player followed your advice in any league where the average (200 PA+) player was too far off from that league's replacement level, they'd just plain draft the wrong players.

Thumbs up but barely. I loved his 1st 2 pieces which puts this one over the edge. This article finishes strong and really hammers the topic home nicely.

I really liked this article. And I'm really disappointed in Will's analysis of it.

For a lot of us, BP was an introduction to sabremetrics, and taught a lot of us to think differently, expand our thought processes, realize that there were more than a few ways to do and/or analyze something, etc. We didn't have Bill James, because James wasn't writing as much anymore, but we did have BP, Neyer, Shandler, etc.

I didn't get everything on the technical side. But I did realize I'd be able to learn, and it made it seem not so scary. That gave it a thumbs up on it's own.

I am a Scoresheet guy, not a Rotisserie/category type fantasy baseball player. However, this article quite kept me interested all the way through. I have never heard of Pivot Tables or VLookup, but had only a little trouble following this article and think it may well be a sensible approach to valuing players – except I do have a few qualms.

It requires a fair amount of work to stay on top of your necessary updates. I’m not sure how practical that is, but if your Pivot Tables make it a cinch, then I guess I should sign up for those lessons.

The part I didn’t understand is where you get your percent improvement over the baseline. I didn’t come up with the same percentages as you. For example, calculating Team-Wright’s .improvement in OBP: T-W’s .350 – baseline .341 is .009. .009 / .341 is .026 or a rounded 3%. You say 14%.

You also say, “Note that the percent improvement calculation for OBP is compared to increasing the OBP over a reasonable low-level of .275.” No, I do not see that either. .350 - .275 = .075 which is 28% of .275. That’s not 14% either. How about .075/.341? No, that’s 22%.

The other problem I have with this essay is that your baseline is a composite of average starters for your league rather than the bottom of the possibilities. That may well be more suitable, but you do not explain why. Most of us are used to looking at Value Over Replacement, so to do otherwise requires an explanation.

Great article, Tim. Obvious thumbs up. One possibility for future improvement (I realize that your main point here was not to come up with the perfect measure): I think you might be able to get a more useful number than the MPV one you suggested. Fantasy players don't really care that they added 10% (on average) to their categories because 10% means different things in different categories. Do you think using standard deviations for each category (and then averaging) makes more sense?

I don't know my elbow from VLOOKUP, but I still liked the submission. I'm wondering how much of this information is already available to us through BP's extaordinary PFM tool. And in case it's not obvious, that is a compliment, not a criticism. Thumbs up.

Remember, it's a total vote count as a go-forward. I vote thumbs up for my six best so that what I consider to be the 9th best article has a lesser chance of sneaking past the fourth... Decent read, thumbs up for TK this week...

i've read almost all of the articles so far but have yet to post a comment. As a fantasy player who's competed in multiple leagues for the past six years or so, this article is exactly what i'm looking for. i would like to see something attached here that allows me to input my players into this format. i'm pretty illiterate when it comes to this stats stuff but i understand exactly what he is trying to do and i know for a fact that this is something that could help me in every single one of my fantasy leagues. it really takes the guessing out of decision-making.

excellent job and i look forward to your future pieces.

I love this article. I completely disagree with Will that it has too much math/stats. The concept itself was interesting and I didn't find the analysis difficult to follow at all. The fantasy world is really missing this kind of article. There are way too many "Pick up Aaron Hill from your free agent pool" articles and not enough DIY articles. Great work and thumbs up!

First of all, great article. It took things I try to do each season (usually unsuccessfully) and approached them from a perspective I wasn't considering, and explained it really well to boot.

But I do have one question. In calculating the position list you put Branyan at third but also put him at first because he was slotted there by the Mariners for 2009. Branyan isn't going to be drafted at both positions--you can only draft him once. And since he's likely going to be drafted as a third baseman, why should he be included in the 1B list at all? Including him in both spots treats him like two separate players for the analysis.

Branyan may be a bad example, since the position difference between first and third is close enough that he could be moved to first depending on how the draft proceeded. But what about guys like Russell Martin or Pablo Sandoval? Do they get included at third base even if they're never going to play there on anyone's team?

Excellent article Tim. I've done a lot of these spreadsheet analyses myself for my leagues, but never hit on the idea of using percentage of stats. I always found myself adding an arbitrary points total and got tripped up on "calculated" stats.

I would be curious your take on value in keeper leagues, based on growth, attrition, and decline rate. My two major leagues are long-term keeper with prospect drafting, and I have had a heck of a time assigning value to this sort of thing.

Pivot tables *are* basic. Easily one of the better articles of the week.

The day I discovered VLOOKUP is the day I went from competing every year in my league to winning (or placing second) every year. Anyone who doesn't use Excel is not a serious fantasy player.

This article is adequate for Fantasy 101, but a bit too introductory for the average BP reader. I feel like if you didn't already understand everything he mentioned, you really wouldn't get much out of a BP subscription.

I hope we will continue to have access to this article at BP, because I'm going to want to read it again next winter when I'm prepping for my draft.

For the record in a points leauge that counts walks, and the difference, Based on PECOTA between Pujols and the number two 1B, was about the same as the difference between Cabrera and the tenth best 1B (Morneau)

I am a bit late to the comments party here, but I'll chime in quickly. Perhaps it is just me, but I think that Tim's introduction is misleading in terms of what he actually covers. It is almost as if he set out to write a very comprehensive article, started writing a draft, but then as soon as he saw his word count closing in on the maximum, he slapped on the "oh well, I guess I'm done now" conclusion and turned in his work.

I think that the base content itself is pretty good for readers who are new to valuation, but as a stand-alone piece of work, it is lacking a smoothly-flowing intro > body > conclusion structure that properly sets the reader's expectations, delivers against them, and leaves them with something tangible to “do on their own” (as promised).

All that said, Tim showed enough potential with this that he earned my third of three thumbs up votes for this week. Even though he explained only a small fraction of what a person REALLY needs to know about the DIY process, he at least convinced me that he understands the DIY process well enough to potentially serve as an instructor on the topic. As someone who has played this role (informally) elsewhere, I know that mastering the process yourself is only part of the job – being able to fully break down the custom valuation components and clearly communicate the technical how-to is the only way to wean a reader off of pre-published or systematically-generated dollar values. I would be curious to read a more in-depth “training” from Tim on this.

I have two big arguments against this system as useful fantasy player evaluator.

First as another reader pointed out it fails to establish a proper replacement level player. It simply compares players to those at the same position who are projected to have 200 ABs. Where does this number come from? Is it right? Answer, no. To see why it's wrong lets use a simple example of a league that only counts HRs. Using Branyan and Wright as above Wright is projected to have 32 HRs to Branyan's 26. If we used 200 ABs as our baseline and 30 hitters qualified, lets say the average 3B would get 15 HRs. This would make Wright 113% better than the average 3B and Branyan isn't too far behind at 73%. In relative terms, Wright is about 50% better than Branyan. But this is a 20 team league so there should only be 20 3Bs (ignoring the UT/CI spot). So if we now only use the top 20 3Bs we should probably expect an average of 20 HRs. Now Wright is still 60% better than average but Branyan is a much smaller 30% better than average. And in relative terms Wright is now two times better than Branyan. So as you can see, what you set the replacement value at has a great effect on the relative value of players.

The second complaint is that it appears to ignore the effect of plate appearances on OBP/SLG. A player who puts up an OPS of .900 over 600 PA can actually improve a team's OPS more than a player with an OPS of 1.000 over 200 PA, depending on the team's OPS and PAs. Also the fact that you arbitrarily choose .275 and .350 as baselines for OBP and SLG seem to suggest that there is very little rational statistical basis for this method.