Commenters in my last post asked how to determine position scarcity in FanGraphs Points leagues, and another reader, Kris, suggested using box plots. I figured “hey, that’s a good idea for a post.” So, here is a box plot based on Marcel projected 2011 performances for starters at each hitting position (using the numbers of players per position that Zach used here).
Every year, a number of starting pitchers get drafted higher than they should because of their successful performances the year prior. Call me crazy, but I tend to avoid these pitchers. Why? Because the expected cost outweighs the projected output. Instead, I set my sights on pitchers still on the upswing, and even a few coming off disappointing seasons who are likely to bounce back.
Not every pitcher can be Roy Halladay or CC Sabathia, guys who can actually sustain their peaks across multiple years. But that doesn’t stop owners from latching onto a pitcher following a big season, or even an outlier season, hoping that said pitcher has established a new talent level. In most cases, though, the wave has already crested.
This strategy gets tricky because it requires: 1) distinguishing between pitchers still capable of better and those about to take a step back; and 2) accepting that there are simply some pitchers you won’t own come draft day. The five below fall into that category for me this year.
While most fantasy owners fall in love with players who broke into the elite a year ago (like Carlos Gonzalez and Andrew McCutchen) or get carried away with unearthing the next didn’t-see-that-coming talent, the best way to find cheap value on draft day is to keep tabs on vets whose fantasy reps took a hit following a down year. Like these three NL leftfielders with power-hitting track records.
This is the third part in a published four part series. If you’re interested in reading the Update, click through!
If you haven’t done so, please make sure to read the first two pieces on Fantasy Value Above Replacement from earlier today.
Before we get into the auction conversion, there are a couple notes that need to be addressed.
Weighing Rate Stats – We can all agree that a batter that hits .300 in 600 at-bats is more valuable than one who does it in 400 at-bats, right? We have to take this into account and adjust for AB’s and IP in rate stats. Simply multiple a players normal z-score in the batting average category by their AB total. We’ll call this wBA. Once that is done for every player at that position, you take the z-score of those wBA numbers and you have your final batting average value. You do the same with ERA and WHIP as well, using IP instead of AB. It can actually end up helping some players with poor ERA numbers, because having a 5.00 ERA isn’t as bad if it’s only 150 innings. On the flip side, power hitters who play everyday and have a bad batting average will be penalized even more for it.
Pitcher Adjustment – Because pitchers can only contribute to four stat categories (SP don’t get saves, and it’s always best to disregard RP wins), we need to curb their value. I do this simply by multiplying their FVAAz number by 0.8 to reflect the ⅘ ratio.
Inputs – Please note, and this is a biggie, that this system is only as good as its inputs. If your projections are way off, the rankings might be, too.
Now that we’ve gotten that out of the way, we can take a look at how to convert FVARz into auction values.
Auction Conversion
The auction conversion formula takes into account the number of players on each team, as well as the simple idea that every player taken in an auction has to cost at least a dollar. The conversion formula is below.
[(Team Budget – (1*no. of players per team)) / number of players per team] * (z-score above replacement / average z-score for above-replacement players) +1 = Dollar Value
Note: “Draftable” player are considered to be players who are projected to produce at a level above replacement level.
In our case, a 12-team standard league, it would look something like this:
[(260-(1*23))/23]*(FVARz / average FVARz for above-replacement players) + 1
If you want to make things easier, you can substitute 3.0 for “average FVARz for above-replacement players.” The number varies a bit year-to-year, but it is usually around 3.0. So our final formula is
10.3*(FVARz/3)+1
This formula works because it makes two things clear: The average player should be payed the average amount of money a team can spend on a player, and a replacement level player is worth $1.
A Small Sample Using Marcel
In case you’re having a hard time visualizing this whole process, I have done a small sample of FVARz using the Marcel projections found right here on FanGraphs. The sample is done using a 400 AB minimum, and the positions are done simply based on what Marcel says.
If you want to view the sample, click here and your wish will be granted.
Thanks
While I was the only one doing any direct work on this project, many others helped me out by letting me bounce ideas off them, and other such things. So, let me thank Michael Jong and Joel Goodbody, as well as FanGraphs’ own David Appelman and Eno Sarris.
This is the second part in a published four part series. If you’re interested in reading Part Three, and the Update, click through!
If you haven’t already, make sure to read the introduction to Fantasy Value Above Replacement from earlier this morning.
Step Two: Adjusting for Positional Replacement Levels
Theory 2: There will be more outfielders drafted than any of the other position players, and there will be more 1B drafted then any other infield positions.
Replacement levels are taken on a positional basis. While it may seem that it is just doubling up on the positional adjustment, it is still important to do it this way. If we only standardized replacement level across positions without accounting for the number of players drafted, we could end up with very uneven numbers. There will be more outfielders drafted than any other position players, and more first baseman drafted than any other infielders. We have to reflect that in this number, and this is how we do it.
A replacement level player is defined as “a player who is available on the waiver wire in a majority (50.01%) of leagues.” This will mean that some players taken in the last couple rounds will be at (or below) replacement level, and that is just fine. When I originally created this system, I had it set for 246 players above replacement level. According to the crowdsourcing data that I collected, you guys agree that the numbers should indeed be 246, since the last 2.5 rounds of a 23 round standard draft are replacement level or below.
Because the overall replacement level doesn’t necessarily help up pinpoint the levels at each position, I did a sampling of mock drafts this offseason to help come up with the numbers. Below is a list of how many players are considered to be above replacement level at each position.
C: 12
1B: 23
2B: 18
3B: 17
SS: 16
OF: 62
SP: 62
RP: 36
The data was found using a sampling of ESPN and Yahoo mock drafts to try and prevent one site’s bias from screwing with the data.
When we set a replacement level at each position, we do so by forcing the 13th catcher (for instance) to be a replacement level player. The FVAAz number will change every year depending on the strength of the position, so we just simply adjust using the 13th catcher instead of a set number. We use the 13th catchers’ FVAAz, and add the respective value (or subtract, in some cases) to force their Fantasy Value Above Replacement to equal zero. We then use the same factor and add (or subtract) it to every other player’s FVAAz at the position.
We now have our FVARz, and once we have one for every player at every position, we can directly compare these players. Now, regardless of position, a player with a FVARz of 10 is more valuable in drafts than a player with a FVARz of 9.
Next, we’ll look at how to convert FVARz numbers into “auction dollars,” as well as going over some semi-random notes.
This is the first part in a published four part series. If you’re interested in reading Part Two, Part Three, and the Update, click through!
In real-life baseball analysis, we have tools such as WAR to help us boil down a players’ contributions to a single number. Yet, in fantasy baseball, no such tool exists, sans a few “player raters” out there. The goal of this project was to create a number that would allow us to easily compare players’ values to each other in an effort to create accurate rankings before drafts begin. Today I will be rolling out a Fantasy Value Above Replacement metric, henceforth known as FVARz.
Using z-scores as the underpinning of this system can help keep things objective and accurate across the board. Simply start with a projected line in the players’ 5×5 categories and include it among the rest of the players at that position. Then, within each category, take the sum of the z-scores – the number of standard deviations away from the mean a player’s statistic is – and that determines the players’ value above the positional average. We then adjust for replacement level at that position, and you have a final number you can take to the bank. We can then use those overall value numbers to compare across positions, and even convert them into auction dollars.
For the sake of simplicity, all of these numbers are done for 12-team standard roto leagues, but the data can easily be manipulated to reflect a different number of teams, positional requirements, or player pools.
Step One: Comparing Inside the Position
Theory 1: Players can only play the positions that the game engine allows them, so those other players at their position are their only competition for a roster spot (sans UTIL).
All players’ raw numbers are compared only to those in their position grouping when it comes to overall value, because players can only play the position that the game engine allows. It does not make sense to directly compare Albert Pujols’ raw numbers to Robinson Cano’s raw numbers, for example, because they cannot occupy the same spot on your roster. Their raw data needs to be adjusted before we can directly compare them.
This is important because it is the way to calculate “position scarcity,” a key component in fantasy valuation. We can all agree that hitting 25 homers as a catcher is much more valuable than hitting 25 homers as an outfielder, and this will reflect that.
So compare the players’ numbers inside each position by using z-scores. Take the positional average and standard deviation in each 5×5 category, and calculate z-scores for each player’s performance in those 5×5 categories (we’ll talk more about how we properly do batting average and other rate stats later). We then just total up those z-scores, and call that out as our Fantasy Value Above Average, or FVAAz.
It is important to be consistent when deciding which players make it into your positional player pools. It is easiest to set an AB minimum (or IP for pitchers) and use it across the board in order to guarantee consistency. You can pick whatever number you like, but I’d recommend using a minimum around 400 AB’s.
Next, we’ll look at how we set our positional replacement levels, and why we do so.
We’ve been dissecting ottoneu one – the flagship league that spawned the FanGraphs fantasy game – and it seems we’ve been having fun doing it. Oh, and of course, someone usually ends up with a free team for the inaugural year. That might have something to do with why people find it fun.
One of the reasons that the game has resonated with so many players is the mix of valuation and keeping. Go into the auction like many have done before, go home with a value player as anybody worth their salt in auction leagues is capable of doing, and then at the end of the year, you are faced with salary inflation and arbitration. Now you have a new set of issues to ponder.
Was that player, who was a value at $x, worth $x+2 after a year of aging? Or does the risk put forward by his extra year of age eliminate that surplus value? You’re a general manager at the winter meetings pondering trade ideas. You’re examining your projections, and valuing the projected numbers. The advantage you have over your real-life GM is the fact that you, at any moment, can cut a player you don’t feel is performing up to his cost.
So we come to the keeper decisions made in ottoneu one – you can keep anyone you like, provided the price is right. Remember, the guys in this league are all FanGraphs readers like you, so let’s not get too snarky. They’ll be watching, and they’re all just trying to win their leagues like the rest of us. But, it’s still worth a discussion.
For a year of a free ottoneu/FanGraphs fantasy team, argue which hitter and which pitcher of the following actual keepers was the worst decision of this current offseason. Best argument wins.
Oh, and for context, I’ve added screenshots of the most expensive players in ottoneu one – this way you get to see how sweet the leaderboard looks, too. Pick one hitter and one pitcher in your comment:
Miguel Cabrera ($52)
Mark Teixeira ($46)
Jay Bruce ($30)
Andre Ethier ($26)
Justin Verlander ($44)
Johan Santana ($37)
Chad Billingsley ($28)
Ted Lilly ($17)
I haven’t always felt this way, but auctions are pretty sweet. How often does someone have to grab that sleeper from you just because of the vagaries of the snake draft before you start thinking about auctions anyway? How great is it to decide a player is worth something, plus or minus, and then go toe to toe with someone that has a similar evaluation of that player? It’s an exciting process, and it really makes you put your player valuation money where your mouth is.
Adding keeper functionality to an auction league just makes even more sense. As I migrated from snake to auction in my fantasy preferences, I’ve also migrated to keeper leagues. Aren’t we trying to play at being GMs here? In a way, we must be approximating the thrill of running a team, and a keeper auction league is probably the closest you can get without hitting the sim leagues – which are cool, but I’d rather leave defense out. It’s just so tough to evaluate.
In any case, as the title indicates, my personal fantasy baseball trends have led me to ottoneu, the new fantasy game at FanGraphs. I hope you enjoy the game as much as I will. Let’s play another “Trade Tree” game, shall we? This seemed popular the first time around. It’s pretty sweet that all of these trades would show up on the respective player pages in your league, and it does a good job of illustrating how ottoneu game play goes.
For a free year (one team) of ottoneu fantasy, argue who got the most value for their Dan Haren in ottoneu one, the flagship league of the game.
Team 1: Traded $19 Haren during the 2009 season for $7 Joba Chamberlain, $5 Dexter Fowler and $5 Justin Smoak
Team 2: Traded $21 Haren before the 2010 season with $19 Adam Jones for $5 Shin-Soo Choo
Team 3: Traded $23 Haren before the 2011 season for $12 Clay Buchholz, $6 Martin Prado and $4 Jason Kipnis
*Before you knock any team too harshly, remember to appreciate the context in which these trades took place. I know you don’t have team data, but look at the year at least. Also, this is for 4×4 ottoneu (OBP, SLG, HR, R / ERA, WHIP, K, HR/9)
You may have heard that FanGraphs got a fantasy game. It’s hard to contain my excitement, but let’s start with some pithy slogans!
No People Herding!
This auction dynasty is open year-round, so commissioners don’t have to email everyone to remind them to get their keepers in, and what were their players’ prices again, and what did they want to do with those two hurt guys again and what happened to Charlie and did Jim change his email or is he just on his annual three-week trek to the Harry Baals Community Center for that Baccarat Tournament?
No, the game is there waiting. It’s always there.
This year, I was given the opportunity to be in the presitigious Scoresheet BL (short for “Both Leagues”) Kings fantasy league. It’s a mixed league, but with a roster size of 35 players, and competition from 23 of the brightest analysts around, it’s going to get pretty intense.
Scoresheet is a bit of a different animal than your normal fantasy league. Rather than just adding up the stats for your players like most leagues do, Scoresheet allows you to manage your entire roster and plays simulated games based on your roster construction. You can do the obvious things like set your lineup and rotation, but you can also set when starters are pulled, when relievers enter and when to use pinch-hitters. It can be pretty addictive, and ever since I had given up a team a couple of years ago, I had been wanting to get back into a league. Mission accomplished.
Read the rest of this entry »
