I’m happy to announce the fantasy values have come to FanGraphs+! Based on Steamer projections, these values are for standard, OBP, and “only” leagues based on the following descriptors: 12 teams, 23 starting lineup slots, $260 budget. They will appear on the projections’ leaderboard for FanGraphs+ subscribers ($4.99/yr).

The methodology for these values has long floated around the site, but there are a few minor changes that have been made to better the accuracy and efficacy of these values.

The replacement levels have been altered to cover a full league’s worth of players. In the past, we had assumed that the last round of two contained replacement players, but discarding this assumption leads to fuller, more accurate auction values. Now, the top 276 players are worth a combined $3120, which is the full budgeted amount for a 12-team auction draft.

In the past, we’d limited results to players that met a certain at-bat or innings threshold. This is no longer the case. However, there is still need for a baseline uninfluenced by low counting stats, the league averages and standard deviations were calculated using the players with at least 350 ABs or 40 IP.

FanGraphs+ Giveaway!

Name the player the following player capsule describes in the comments and win free access to FanGraphs+!

The 33-year-old [Player] is past his “prime.” While he used to provide some power ([number less than ten] homers in 2010), he looks to be done. He never hit for average (.232 career), but it dropped to [number less than .232] this last year. Two pitchers, Hamels (.217) and Kershaw (.207), had better averages in 2012 (70 minimum plate appearances). Also, the power disappeared, as he ended the season with only one home run. Finally, he stole two bases which was only one off of his career high. Basically he is not good enough to play and when he does, he produces no value. (Jeff Zimmerman)

In what is likely to be my last hitter Pod Projection, Desmond Jennings topped the list of remaining vote getters. The sophomore was a bit of a disappointment last season and was hampered by a knee injury that cost him nearly a month. I have been a big fan of Jennings in the past. Let’s find out if we can expect him to rebound this year and enter the upper echelon of fantasy outfielders.

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Before diving back into this, after our long weekend away, I want to let you know what to expect this (hopefully this) week: Part 5 is clearly here right now, and Mike and I are working on parts 6 and 7 as we speak. In Part 6, I’ll be looking at the equations Mike came up with the other day along with one other equation that smarter statistical minds than mine are currently developing. Ideally, part 6 will be something of a wrap on our attempt to find an equation for expected HR/FB within a season. In Part 7, Mike will take a deeper look at a handful of players, and see how useful this new equation really is.

Today, I’ll be looking at another piece of the puzzle – what happens in future years to guys who show breakout distance on their fly balls.

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Yesterday, seemingly out of nowhere, the Astros acquired first baseman Chris Carter from the A’s. Carter was a one-time top prospect in the White Sox system, boasting elite power and above average plate patience. Of course, that came with its share of strikeouts, making Carter the prototypical slugging first base prospect. Carter is no spring chicken though, he is already 26 years old as he moves onto his third team. Sadly, last year represented his most expanded audition in the big leagues, even though he recorded just 260 plate appearances. His massive power was on full display, though, as he launched 16 homers to notch a .275 ISO. So now in Houston, does he shoot up sleeper lists?

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It’s been nearly two weeks since I posted my last Pod Projection after the journey to estimate/project HR/FB ratio took over my life. Surprisingly, Jason Heyward tallied nary a vote when I first asked you readers who you wanted me to publish a projection breakdown for next. Instead, fellow RotoGrapher Howard Bender requested Heyward, while kickin’ rocks of course. Big things were expected of Heyward as a rookie and big things are still expected. As such, I thought he was a hitter more than worthy of a statistical dissection.

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**For those of you who have listened to the Fantasy Baseball Roundtable radio show in the past, I wanted to share that we are officially back on the air! And if you have never listened, well here’s your chance to hear my manly voice talking about nerdy stats. Listen live every Wednesday night at 9-10 PM EST.

The quest continues! On Monday, Chad Young and I set out on a journey to try utilizing the fly ball and home run distances and angles found on Baseball Heat Maps in an attempt to answer several questions regarding a hitter’s HR/FB rate. As expected, we found a strong correlation between it and batted ball distance. But, distance alone wasn’t telling us the whole story. Chad decided to incorporate batted ball angle and the previous season HR/FB rate and that certainly improved our equation. Then yesterday I took that another step further and found that including the HR/FB rate from two seasons ago was even better. But, I wasn’t satisfied. A hitter’s HR/FB rate in 2012 should not be affected by how he performed in the metric in previous years. We pinpointed what we thought may be one of the major hindrances and that was the way the angle data was presented as an average.

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On Monday, I set the groundwork for a quest to try to predict a hitter’s HR/FB ratio utilizing the new data first made available last year that tells us the average distance of a hitter’s batted balls. Our goal was to answer several questions:

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Yesterday, Mike Podhorzer shared part one of this epic journey and teased that today we would be incorporating batted ball angle to see if that helps our model. I am, in fact, going to do that today.

But first, I am going to take a quick step back and let you know my reasons for doing this. Next, I’ll introduce some data on batted ball angle, as noted above.

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As a forecaster of baseball player performance for usage in my fantasy leagues, I am always looking for new data to incorporate into my projections and new methods for predicting outcomes. Home runs are a result that excites all, even chicks dig ’em. But how can we better determine whether Chase Headley’s breakout, for example, was for real? The answer may lie in newly available data collected by our own research extraordinaire Jeff Zimmerman.

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Tying for the second most number of votes for a full Pod Projection was Blue Jays third sacker Brett Lawrie. After sky high expectations resulting from an exciting debut that mixed power and speed, the sophomore seriously disappointed in his full season follow-up. Lawrie missed a month of action due to an oblique injury and missed time here and there due to various other nicks and bruises. You have to wonder how healthy he was all season and if those issues affected his performance. This is just another example of why projecting player performance is so difficult. We have to speculate about injuries and their effects and we may never know if we were correct.

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