When analyzing players, I want more than a one-line projection, I want a range of possible outcomes. The problem with comps is that they are time-intensive. Once the worksheet is setup, a person can select and download the desired output in a few seconds. Here is the outputted table from the example procedure explained below.

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The smiles appear before the response.

Ask a few Orioles hitters for their immediate reactions to news that the club is moving in the left field wall, and their approval can be seen on their faces.

The above quote came from the Orioles caravan and got me thinking about how projections incorporate three new park changes.

I’ve seen the park changes referenced in articles and pods for reasons to fade or target certain players. I lean on projections and assume that they incorporate dimensions into account when they create their projections. If the changes are already accounted for, I don’t want to overrate affected players. After looking over various projections, most seem to take the changes into account, but some haven’t yet. Read the rest of this entry »

It’s time to dive into more hitter projection comparisons after examining playing time a while back. In this article, I’ll show which projections are best in the standard roto categories (R, RBI, HR, SB, and AVG) and at the end, look at ways playing time projections could be improved.

For all the background information on the test I used (RMSE) and data sample reference the first article. These tests take forever to run and at some point, I kept getting the same answers (smartly aggregating the projections), so I stopped running any new ones for hitters. Here are the results for the tests I ran.

Test 1: Batting Average

The only hitter rate stat, batting average, begins the analysis.

While some individual projections are near the top, the averages and aggregators stay strong.

Test 2: Home Runs

Here is how the projections performed when looking at the raw number of home runs.

With Paywall #7 and Marcels crushing the playing time estimates, it’s no surprise they are at the top. And here are the home projections prorated per plate appearance.

The big surprise was that Marcels remained near the top even when the home runs were turned into a rate stat. The aggregators held up but weren’t as strong as previous tests.

Test 3: Stolen Bases

Here are the stolen bases projections ranked by the raw number

A new order of projections on this raw stat … well besides the averages performing near the top. Here they are as a rate stat.

That’s some domination by the aggregators by taking the top five spots.

Test 4 Runs plus RBI (R+RBI)

I combined the two because the results were consistent (aggregators kicking ass) and I just wanted to see if any of the results stood out like with stolen bases.

Nothing changed. Correctly guessing playing time allows a projection to dominate these rankings. It’s time to move on.

Conclusions on Hitter Projections

The answer is simple, get an aggregation of projection. ATC and Zeile already do the combination. Or a person could use Tanner Bell’s projection aggregator to personally control the inputs and weighting.

Additionally, if combining projections, I would not pay up for any with Razzball, ZiPS, THE BAT X, and Davenport all performing great.

Test 5: Where Projections Miss

Note: I cut and diced the available information in what seemed a 100 different ways. The following are the two best examples I found for why projections miss. I’m sure there are better ways to improve projection playing but I haven’t havent figured them yet.

From some of my unpublished work, I have determined that projections miss based on age, previous playing time (proxy for health), and talent (projected OPS). I wanted to find out why Marcels performed better than the standard projections. In 2024, here are the players the 4 Big Projs projected for more playing time than Marcels.

A table full of prospects (e.g. Carter, Cruz) or injured players (e.g. Lewis, Hoskins). This verifies some of my previous findings that players with checkered playing histories miss their playing time projections.

Dropping the playing time on hurt guys is not hard but it is tougher with guys like Elly. If he plays every day, he’s a steal.

For the next example, I grouped hitters by their Marcel playing time projection and combined plate appearances from the previous two seasons. Then I compared our 2021 to 2024 projected Steamer plate appearances to the actual number. Here are the results.

Steamer’s plate appearance projections perform great for regulars or bench bats. The players between those two are the toughest to estimate with over 500 PA in each previous season needed for a solid playing time projection.

While I focused on our Steamer projections, all of the other projection systems over project playing time compared to Marcels. They likely had the same issues. A fantasy manager might need to some way take into previous playing time while making future estimates.

For right now, I don’t know the right answer. As a group, it is a little embarrassing that a simple formula kicked everyone’s collective ass in playing time. In the previous article, I mentioned adding in a computer projection (e.g. Marcels) to temper expectations. I stand by that observation. For now, that’s all I can recommend.

Well, I got my yearly, “Talk to the Boss First Before Publishing” article out of the way halfway through January. I started looking into hitter playing time and previously they were just one column in one of the tables. This year, we dove into why our projections came in near the bottom with some computer-generated projections beating them. Besides the results, there is a ton of other information so if someone blows off the specific results, at least read the summary.

Collection Information

Last season I collected about 20 projections right before the final last weekend when most fantasy managers draft. This is when projections needed to be their best. Here is the tweet I sent to mark when I pulled them.

Mark time now…

— Jeff Zimmerman (@jeffwzimmerman) March 21, 2024

In all, I collected 20 different projections. Eight were not freely available to the public. They will be just be labeled Paywall X. Here are the ones people could freely get from the internet.

• ATC (aggregate of other projections)
• Baseball-reference’s Marcels
• Clay Davenport
• Draft Buddy
• THE BAT X
• FanGraphs Depth Charts (aggregate of Steamer and ZiPS)
• Fantasy Pros Zheile (aggregate of other projections)
• Fantrax
• Razzball (Steamer Based)
• Razzball Grey (Grey’s personal projections)
• Steamer (FanGraphs)
• ZIPS

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The 2025 edition of The Process is now available. Like last year, only the appendix will have new studies and research. I wrote several studies with additional contributions from Jenny Butler, Patrick Davitt, Bryan Fitzgerald, Jake Maish, Carlos Marcano, Roger Strong, and Adam Warner. Read the rest of this entry »

1. I must have the auction/draft order done before traveling.

Every year the Tout Wars auction is held in New York City. I knew I would get to town 24 hours before my auction and planned to create my auction strategy. On my flight, I turned my knee and could barely walk by the time I got to my room. I spent the next 24 hours dealing with the knee. Read the rest of this entry »

Over the break, I’m going to run a few quick studies. If you want to request, add it to this Twitter thread.

I am running a few quick fantasy baseball studies over the next few days. I'll take suggestions. The two I have planned:

1. If a hitter's AVG is below his projection, how much rebound is expected?

2. How much does a month's ERA/FIP/xFIP matter when looking forward?

— Jeff Zimmerman (@jeffwzimmerman) July 16, 2024

The final study I’ll run is looking at how much stock should a person have in a month of pitching stats. I wanted to understand these correlations since I quote them a ton. The usual suspects top the list with an interesting find on WHIP projections. Read the rest of this entry »

Over the break, I’m going to run a few quick studies. If you want to request, add it to this Twitter thread.

I am running a few quick fantasy baseball studies over the next few days. I'll take suggestions. The two I have planned:

1. If a hitter's AVG is below his projection, how much rebound is expected?

2. How much does a month's ERA/FIP/xFIP matter when looking forward?

— Jeff Zimmerman (@jeffwzimmerman) July 16, 2024

Over at the Pull Hitter Patreon, there was a discussion on starting pitcher Wins from shi … bad teams (e.g. Paul Skenes). Here are the results I found. Read the rest of this entry »

Over the break, I’m going to run a few quick studies. If you want to request, add it to this Twitter thread.

I am running a few quick fantasy baseball studies over the next few days. I'll take suggestions. The two I have planned:

1. If a hitter's AVG is below his projection, how much rebound is expected?

2. How much does a month's ERA/FIP/xFIP matter when looking forward?

— Jeff Zimmerman (@jeffwzimmerman) July 16, 2024

Someone I know said they drafted for batting average (AVG) this spring, but their team is struggling in the category. They wondered how much upward regression should be expected. This is a simple study with a reasonable answer, some but not all. Read the rest of this entry »

With two prominent players, Mike Trout and J.T. Realmuto, just needing meniscus surgery, I examined hitter production before and after surgery. With a limited sample, some struggles should be expected. Read the rest of this entry »