2012 Pod Hitter Projections: An Introduction

For the past 10 years or so, I have generated my own projections to use for my fantasy baseball drafts. As you can imagine, the spreadsheet has become more complex every year as I have incorporated more and more formulas in an attempt to increase the precision. Unlike the projection sets you are familiar with, such as ZiPS, CAIRO, Bill James, etc, I don’t actually have a system. It would be fantastic to have the programming and mathematical chops to be able to develop a full-fledged system that takes previous year’s stats and a host of other factors into account and instantly spit out a projection. Unfortunately, my method is very time consuming as I literally project every player by hand, poring over countless metrics on FanGraphs, ESPN Home Run Tracker, StatCorner, and Rotoworld, and in Ron Shandler’s Baseball Forecaster.

Though initially these projections were used solely for fantasy purposes and therefore primarily included just starters for my relatively shallow 12-team mixed league, I have increased the number of projections due to my participation in Tom Tango’s Forecasters Challenge for the past three years (for those curious, my projections were listed as FantasyPros911 in 2009 and 2010). Projecting back-up catchers is as much fun as you think it is!

I thought it would be an informative and thought-provoking exercise to spotlight interesting players that I have recently projected and explain my process for projecting each metric. We always read about different projection systems and methods, most times calculated by a computer, but we usually only see the results and don’t get to learn why a player was projected the way he was.

I will be starting with hitters first and then in a month or so will switch over to the pitchers. Before I dive into individual players, I want to describe how I develop each hitter projection fantasy category by category, explaining exactly what metrics I project that ultimately result in the final projection for each fantasy category.

Batting Average

I first project the hitter’s contact rate ([at-bats – strikeouts] / at-bats), which generally remains pretty steady for a hitter’s career. Sometimes a hitter’s rate really dives or jumps one year, which I will look into further, but for the most part, will project the rate to return close to his previously established level.

I then project the hitter’s batted ball breakdown, since we know that line drives fall for hits more often than ground balls, which fall more often than fly balls. These percentages will affect the BABIP projection.

Next, I project two Baseball Forecaster metrics, PX (power index) and Spd (statistically scouted speed index), which are used as part of the publication’s expected BABIP formula. A higher PX and Spd will positively affect expected BABIP.

I then project HR/FB ratio, which I will discuss a little more in the home run section. This affects batting average for obvious reasons — home runs are hits, so all else equal, the more homers, the higher the batting average. In addition, home runs are a component of the second expected BABIP formula I use that was published on The Hardball Times several years ago.

Last, and most importantly, I project BABIP. I use the Baseball HQ and THT expected BABIP results as a guide, and find it most useful for rookies or young players with limited BABIP history. I pay more attention though to the hitter’s own history, and research indicates that the metric declines with age.

All these numbers get thrown into various formulas and eventually a batting average projection pops out.

Home Runs

Home runs are simply the product of how often the hitter makes contact, what percentage of those balls he makes contact with are fly balls and how many of those fly balls go over the fence.

We already discussed contact rate and fly ball rate above. Fly ball rate actually rises as a hitter ages, which gives us Jason Heyward fans some hope!

The “how many fly balls go over the fence” part is measured by HR/FB ratio. Besides historical marks, I look at ESPN Home Run Tracker, as I have found in prior research that I have conducted that a hitter with a relatively high number of “Just Enough” home runs are more likely to regress.

Throw the contact rate, fly ball percentage and HR/FB rate projections into a blender and a home run projection smoothie comes oozing out.

RBI and Runs

These two categories are completely manual. I have stumbled upon a couple of formulas to project RBI, but they either required way too much additional work, or I didn’t feel it added any accuracy. For these, I simply look at past years, take expected batting order slot into account, any lineup changes (addition of Albert Pujols) and any other changes that might factor into these numbers, like a projected increase in power for the hitter.

Stolen Bases

Like RBI and Runs, stolen bases is another totally manual projection. I have considered going all out and trying to project the Baseball Forecaster metric stolen base opportunity percent ([SB + CS] / [BB + singles]) along with success rate, but I am not sure the benefit outweighs the extra time required.

Instead, I simply examine those two metrics, along with the Spd metric described above (not the same as Spd on FanGraphs), and try to determine how real last season’s stolen base total was. I then take any team philosophy and batting order changes into account. I also look at age, which is important, because a player’s speed obviously declines with age.

Stolen bases can be tough to project though, because you never know when a non-speedster, like Jeff Francoeur last year, will suddenly decide to steal. And then you have guys like Matt Holliday, who went from 11 to 28 to 14 steals from 2007 to 2009.

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So that’s the gist of how I project hitters. In the coming weeks, I will be posting on some of the more interesting players, going through the process of exactly how I projected each metric and resulting fantasy statistic.