2012 Pod Projections: Prince Fielder

Here at FanGraphs, we are doing our best to make sure we publish as many articles about Prince Fielder’s move to the Motor City as possible. We want to ensure that there is absolutely no temptation to go anywhere else to find an angle that we missed (because we couldn’t have missed an angle, we covered them all!). So to continue on with the behind the scenes look at my projections, it is only fitting that Fielder is the next man in the spotlight.

Previous projection articles:
Dustin Ackley
Asdrubal Cabrera
Paul Goldschmidt

At-Bats: 575. One of the easiest projections I will ever have to make. Fielder has been amazingly healthy and consistent, posting at-bat totals ranging narrowly between 569 and 591 over every full season he has played. Now with the opportunity to DH every so often, he may actually reach 600 for the first time in his career.

Contact Rate: 79%. Last year, he posted a career best 81% rate, an impressive jump from his previous season’s 76% mark. Contact rates are usually quite consistent and it’s not often a player sees such a spike and maintains it. Before the trade, I would have projected a drop back to 78%, which is a bit higher than previous seasons, giving him some credit for the possibility of real improvement. However, Miller Park actually inflated left-handed strikeouts by 7% last year, while Comerica reduced them by 5%, so I decided to bump up the projection another 1%.

GB%/LD%/FB%: 41%/19%/40%. Not as easy a call as one might expect, despite batted ball distribution being relatively stable and dealing with a veteran. Fielder’s FB% declined to a career low 37.1% last year, after being in the low-to-mid 40% range since 2007. In my experience, this usually ends up being just a one season fluke, so I projected a rebound close to his career average FB% of 40.7%. Remember, fly ball rate is an important component of his ultimate home run projection.

BABIP: .305. For someone with massive power and a league average line drive rate, it is a bit surprising that his career BABIP is right at .300. Is his rotundness that much of a factor? Well, since 2006, the league average IFH% has been about 6.1%, while Fielder has notched infield hits at just a 2.9% rate. This is obviously no surprise, but certainly takes a couple of percentage points off his expected BABIP. Anyhow, over the last three years, his BABIP has averaged about .304, with his two over .300 seasons coming in those seasons. My two expected BABIP formulas spit out .316 and .311, so my projection is almost identical to last year with the Brewers.

HR/FB Ratio: 20%. As is usually the case with the hitters I have posted on so far, the HR/FB ratio generates the most curiosity. Normally, I would compare the left-handed home run park factors of Miller and Comerica Parks and then divide the result by two to take into account that only half Fielder’s games come at home. But earlier this morning, Eno Sarris published a handy chart that even takes away park factors into account (thanks Eno for saving me from my laziness!). Now I can simply project Fielder as if he were still a Brewer and then just multiply that by the reduction he will see due to the different park factors.

The move to Detroit will cost him about 6.5% of his home runs. Fielder has alternated between 18% and 22%-24% over the past five seasons, so I probably would have projected him for 21% this year in Milwaukee, with no real mathematical reason for picking that number. Applying the park factor and then rounding, we arrive at the 20% HR/FB projection, for only a slight decrease, but a decline nonetheless.

RBI and Runs: 115 and 95. I don’t see much of a difference for projected output here after the move. Depending on how Milwaukee would have arranged their lineup, the 1-2 guys both there and in Detroit aren’t exactly on base machines. Hitting behind Miguel Cabrera is obviously a nice thing, but since he’s so damn good himself, he will obviously be clearing the bases on frequent occasions, taking opportunities away from Fielder. Austin Jackson and current projected second slot hitter Brennan Boesch are projected for not much better than a league average OBP.

Since I think his overall production will take a slight hit because of the park switch, my RBI projection is lower than last year’s 120, but he still benefits slightly from an actual hitter in the ninth spot, rather than a pitcher. For the runs projection, the slightly lower expected performance is offset by hitting in the AL and in a lineup that was a bit better than Milwaukee last year in runs scored.

SBs: 1. How did he managed to steal 7 bases over his first full season?! He hasn’t even attempted more than 5 since.

Below is my final projected batting line, along with Bill James, RotoChamp and Fans projections for comparison. Remember, the other projection sets likely reflect his projected totals as a Brewer. Actually, maybe not, since he was a free agent, so not sure how potential team changes are taken into account.

Pod 575 0.285 36 115 95 1 79% 0.305 20% 41%/19%/40%
Bill James 562 0.285 38 109 93 1 79% 0.300 ? ???
RotoChamp 575 0.294 38 112 117 1 80% 0.311 ? ???
Fans (49) 567 0.287 36 134 112 3 80% 0.303 ? ???

For the most part, the projections are reasonably similar, though the Fans’ RBI projection is quite a bit optimistic, while their and RotoChamp’s run projections are insane. No idea how RotoChamp arrived at their runs scored projection.

Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.

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Matt Hunter

The RBIs and Runs just turn out that way on the fan projections, even with about the exact same stats that you have. We don’t choose em. Is there a reason for that? Seems like the fan projections are just taking the other hitting stats and finding an expected R and RBI total, so I wonder why yours are so much lower.