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 »

Real-life general managers face economic pressures of all kinds. It must be stressful to be in charge of it all; managing revenue, navigating relationships with the owner(s), the coaches, the players, the fans, deciding whether a player is “worth” the money he’ll likely win in an arbitration hearing, trying to figure out if you can fit in some greens to your diet. Ottoneu managers can face economic pressures too. For some reason, we actually enjoy fabricating the economic stressors that those real-world GM’s face. Why? Well, it’s fun to pretend. But also because we think we might actually be able to do the job ourselves. Be careful what you wish for, you may have to justify cutting a player like Freddie Freeman from your team because you thought it would be a good “economic” decision. Get your speaking notes ready for FanFest, prepare yourself for the press conference, ditch the greens, grab whatever’s still sitting under the heat lamp at the closest “Grab N’ Go!” It’s time to justify the offseason decisions you “had” to make.
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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.

We know that batter wOBA has a relatively higher correlation than many other metrics. However, it isn’t even higher because there are still many factors outside a batter’s control that influence the final mark. So let’s review the hitters who both underperformed and overperformed their xwOBA marks last year. If we assume a similar level of underlying skills and performance, this makes for the quickest list of hitters who will either enjoy a wOBA surge or decline this coming season.

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About two weeks ago, Jordan Rosenblum introduced us to his new projection system, OOPSY, which is now available on every player page that has received a forecast. The system incorporates Baseball Savant data, along with Stuff+, which made me very curious to see which players it was more bullish and bearish on than the Depth Chart projections (which are now 100% matching Steamer, since ZiPS haven’t been published yet). So let’s review the names that fall into each of these groups.

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One of my favorite traditions every winter is to peruse our Steamer600 projections and dream about some potential breakthrough seasons. This puts everyone on equal footing playing time-wise since it is the most difficult factor to consistently project as Rob Manfred simply refuses to turn off injuries in the global settings of the game!

Here are some of my favorite potential breakouts using these numbers:

CATCHER (they do make an exception at C where it’s 450 PA since very few Cs log 600)

Gabriel Moreno | ARI, 194 ADP | .283 AVG, 10 HR, 54 R, 51 RBI, 5 SB

I am still on the Moreno Train! He couldn’t really build on his 2023 breakout because while he did add a few points to his wRC+ total, he only played 97 games and saw his .284 AVG drop 18 points. Thumb and adductor strains in June and August, respectively, cost him about a month of time and contributed to his modest overall output. While it didn’t yield much in the way of his counting stats, it is worth noting that Moreno had a sharp improvement in plate skills, doubling his BB/K from 0.8, tops among catchers with at least 350 PA. A fully healthy season could see the 25-year-old backstop eclipse 400 PA for the first time while this projection likes him for a power surge, too. We did catch a glimpse of power production during their World Series run in 2023 as Moreno clubbed 4 HRs in 70 PA after hitting 7 in 380 during the regular season. The batting average is the key though, so even if he stays more in the 5-7 HR range, there is upside to chase with Moreno. In the last five full seasons (so 2019 added in to replace 2020) there have been just eight instances of a catcher hitting .280+ in at least 400 PA with William Contreras being the only guy to do it twice. I like Moreno to join that club in 2025.

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Many indicators are pointing to Adolis García’s power declining. If so, his fantasy value may be careening down a cliff. Read the rest of this entry »

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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We learned last year that Barrel% is still the most highly correlated Statcast metric with batter HR/FB rate, even more so than the newest metrics such as Blast Per Bat Contact and Fast Swing Rate. Of course, Barrel% does end up underrating extreme ground ball hitters and overrating extreme fly ball hitters, but because these hitters live in the extremes, they are the rare exceptions. To account for the fact that Barrel% is prone to being skewed by a hitter’s batted ball type distribution, let’s instead use a slightly better metric — Barrels per FB + LD percentage, or Barrels/(FB+LD).

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All around American neighborhoods lights are being taken down, candles put away, and trees placed out on curbs. Winter holidays are coming to a close and, dare I write it, playoff football is about to begin. But you are here because you have an Ottonue team to manage. Read the rest of this entry »