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.

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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.
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The upcoming season will be pretty exciting at RotoGraphs. We’re bringing on some new faces to mesh with the old and expanding our coverage. This post, however, is about what YOU want.

So, please use the comments section to tell us what you’d like to see more of at RotoGraphs. More in-depth pieces about single players? More rankings information? More mailbags? More work on leagues with different settings? More pro and con?

Less of any of this?

Let us know. We aim to please.

Chasing wins in fantasy baseball sometimes seem futile, but if pursued in a logical way, they can be gained. Playing sub-par pitchers may increase win and strikeout totals, but they puts a drain on WHIP and ERA. By looking at the pitcher’s talent level and knowing the offense of the pitcher’s team, the chances of getting a win can be determined. The following are formulas to help estimate a pitchers win total.

First, all the qualified starters that didn’t switch teams from 2010 were matched with their team’s average runs scored per game. Then a linear regression was run comparing the player’s ERA, his run support and his actual winning percentage. The following equation was created:

Projected Winning % = 0.112(Run Support)-0.105(ERA)+0.446
with an R-squared = 0.827

With this equation, the expected number of wins can be estimated with just a couple more pieces of data. First, the number of starts that lead to a decision (win or loss) for games in 2010 was 70% with the bullpen getting the rest. Second, the number of GS will have to guesstimated using playing time projections and injury history. With this information, a projected number of wins can be calculated:

Projected Wins = 0.7 * Games Started * Projected Winning %

Going back over the 2010 numbers, the average difference between the number of games won and the predicted number of games won was 1.89 with a standard deviation was 2.24 wins.

For example, here is how Felix Hernandez’s win total would compare if he pitched for different teams during 2010. He was able to get 13 wins with a 2.47 ERA in 34 games with a team that average scoring 3.13 runs a game. With those numbers, he was projected to win 13.3 games. Now if he played for the Yankees and got their run support (5.23), his wins would have been around 18.9. If he had only got just 4.0 runs of support, he would have been closer to 15.6 wins.

Normally, trying to accumulate wins is a tough proposition. With a little knowledge of the pitcher and his team’s offense, the amount of wins the pitcher gets can be somewhat predicted.

Pitchers can’t control every aspect of the game around them including the the defense behind them. A team’s defense can effect a pitcher’s WHIP and ERA by letting more batted balls turn into hits (increasing WHIP) therefore leading to more runs allowed (increasing ERA). The following is a look at how much a team’s defense could effect a pitcher’s ERA or WHIP.

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This summer I played in the deepest league I’ve ever played in, a 20-team mixed league with traditional 5×5 scoring plus OBP and Quality Starts*. It basically came down to who had the best injury luck and made the shrewdest waiver wire/free agency pick ups (i.e. whoever grabbed Jose Bautista first). My offense was fine, propped up by Robinson Cano, Jayson Werth, Hanley Ramirez, Carlos Gonzalez, and (eventually) David Ortiz. I can’t say the same about my pitching staff.

Cole Hamels and (especially) Wandy Rodriguez started slowly before having monster second halves, and the same was true for Brian Matusz. Dallas Braden was solid yet unspectacular (I had him on the bench for the perfect game figuring the Rays would hit him around), but Ben Sheets was pretty much a flop before getting hurt. My most consistent starter all year was C.J. Wilson. My weekly ERA and WHIP pretty much sucked, and the wins were scarce.

My team was still competitive thanks to the offense and luck, but the pitching staff needed work. I started to pursue trades rather aggressively in early-June but after a week or two I gave up. Quality pitching was hard to come by in this league and everyone knew it, so if you wanted a good starter you were going to have to overpay. Frustrated but in need of some kind of fix, I gave up on starters and instead turned to the free agent pool for middle relievers. Not closers and not necessarily setup men either, but guys that pitched a fair amount of innings with high strikeouts rates.

In the first week of Operation Middle Reliever I grabbed Hong-Chih Kuo (this was long before Jonathan Broxton fell apart), Arthur Rhodes, Darren Oliver, Mike Adams, and a rookie just breaking in by the name of Jonny Venters. All five had sky high strikeout rates at the time and were getting a boatload of work, so I figured it was worth a shot. Here’s what they gave me that first week…

15 IP, 11 H, 6 R, 5 ER, 7 BB, 19 K (3.00 ERA, 1.20 WHIP)

The numbers would have been better if it wasn’t for Rhodes’ first meltdown of the season, a 0 IP, 3 H, 3 ER effort that put the always scary “inf” in his ERA and WHIP columns for a few days. Aside from that, the overall production is pretty damn good, basically the same as adding two good starters to my staff. Our league carried 3 SP, 2 RP, and 3 P starting spots, so from that point on I had four or five middle relievers going every night. As the season progressed I got a better handle on things and leveraged my roster spots by keeping track of workloads (via Daily Baseball Data). The production was solid and best of all, there’s a seemingly limitless supply of these kind of relievers available. If someone got hurt or hit a rough patch, a capable replacement was just a few clicks away. Trust me, those were just the first five relievers I picked up, there was another dozen or so that came in and out as the season went on.

There’s a downside as well. Blow-ups like the one Rhodes had are inevitable and can screw up your week rather easily. If two relievers have performances like that, I basically done for the week. I was also close to punting QS, though things improved there once Hamels, Wandy, and Matusz hit their strides late. Roster efficiency was another issue; I needed four or five guys to give me the production I could be getting from two or three. I was pretty desperate for help, but the strategy worked. I finished the year with the best regular season record in the league but ultimately lost in the Championship Round.

While I recommend going heavy on quality middle relievers in deep leagues, my best advice to make sure you have good starters. I know it’s a helluva lot easier said than done, but I wouldn’t go into the season counting on bullpeners to carry my pitching staff. If you need help at midseason and aren’t willing to meet to asking price for starters in a trade, grab some high strikeout relief arms to tide you over. Don’t try to catch lightning in a bottle with sketchy starters.

* Not a fan of QS in fantasy, probably won’t use them again.