OOPSY peak projections for all minor and major league players are now available for FanGraphs Members.

I’ve been publishing current season OOPSY projections since 2025. They broadly fit in with the other FanGraphs systems in terms of methodology (and accuracy), though like all projection systems, they have their own methodological quirks. OOPSY makes use of its own aging curves, major league equivalencies, park factors, league scoring environment factors, regression, and recency weights to project players. For those interested in learning more, the OOPSY introduction, 2025 review, and recent Top 100 Prospects list provide a more detailed overview of my methodology.

Since 2022, OOPSY peak projections have been featured on Scout the Statline and, since 2024, they have also been featured on RotoGraphs, where I’ve used them to generate fantasy-oriented top prospect lists. Last month, after incorporating defense into the projections using our prospect team’s work on The Board, I published OOPSY’s first top prospect list with a real-life focus, ranking prospects by peak projected WAR. Read the rest of this entry »

This article unveils OOPSY’s first WAR-based Top 100 prospects list.

Broadly put, OOPSY mirrors the other projections systems at FanGraphs, but it uses its own ingredients, including its own aging curves, regression amounts, recency weights, major league equivalencies, and park factors. In terms of accuracy, it has held its own with the other projection systems, including when projecting rookies.

Since 2024, I have published OOPSY’s top prospect lists over at RotoGraphs with more of a fantasy focus, ranking pitchers by peak ERA and hitters by peak wRC+. In 2025, I began incorporating average fastball velocity into my prospect pitching projections, using data from The Board, as well as Stuff+, provided by Eno Sarris, where possible. Midway through 2025, I started accounting for EV90 in my prospect hitting projections, using data from Prospect Savant; I also include bat speed where data permits. And now that OOPSY accounts for defense as well as baserunning, I can rank prospects by projected WAR, to theoretically align more closely with the valuations used by major league organizations. Read the rest of this entry »

What sort of production can we expect from Munetaka Murakami in 2026? This article walks through my steps in creating an MLB projection for the Yakult Swallows superstar.

With one day to go before his negotiation window closed, Murakami and the Chicago White Sox agreed on a two-year, $34 million deal; you can read Michael Baumann’s thoughts on the signing here. Both the dollar amount and the length fall well short of expectations: Ben Clemens projected him to land a seven-year, $154 million contract, while our median crowdsourced contract estimate was for six years and $132 million. Prior to Murakami’s posting, Eric Longenhagen highlighted his 80-grade raw power, as well as his major contact issues, in an October international players update. Both Eric and Ben saw first base as Murakami’s likely defensive home, something the White Sox have already confirmed for the coming season. ZiPS projects him for a .237/.363/.454 triple-slash line and a 126 wRC+. My own projection system, OOPSY, does not yet project NPB players, but that’s something I’m aiming to change with this article. So what might OOPSY expect from Murakami?

The first step in creating a Murakami projection was to review the literature. Previous research from team scouts, analysts, and the creators of other projection system tends to rate NPB as close to Triple-A in terms of its quality of competition. This serves as a good reference point for developing my own quality of competition estimate. Read the rest of this entry »

The projection system OOPSY made its major league debut this year. So how did it do?

OOPSY’s methodology mirrors that of the other FanGraphs projection systems, with a few twists — most notably, the inclusion of bat speed for hitters and Stuff+ for pitchers. Projection systems are comprised of many different components, however, including aging curves, major league equivalencies to account for minor league and foreign league performance, recency weights, regression to the mean, league run environment, and park factors. There are many ways for projection systems to stand out or lag behind their peers beyond just the inclusion of a particular variable like bat speed. Projection systems are comprised of hundreds of small methodological decisions. Given the sum total of the decisions that went into OOPSY, did it hold its own in 2025 relative to its more established peers?

To review the projections, this article follows industry best practices as outlined by Tom Tango, MLBAM’s senior data architect. I have conducted this review process for pitchers before, as my pitching projections have been featured by Eno Sarris in The Athletic since 2023, but this was my first year publishing a full set of hitting projections. This review focuses on wOBA for hitters and wOBA against for pitchers (an alternative to ERA, further defined below). These metrics are typically the focus of projection system reviews, the most important hitting and pitching rate statistics for projection systems to get right from a “real-life” perspective. Both are catch-all rate statistics that measure, respectively, a player’s offensive and pitching value. The various component projections, e.g., K% and BB%, feed into these catch-all metrics. Read the rest of this entry »

As we’ve seen with the FanGraphs Depth Charts and ATC, averaging projections from multiple systems is a common approach for improved accuracy when forecasting performance for the current season. This article applies the same “wisdom of the experts” aggregation logic to combine peak projections from various systems — in this case, ZiPS, Clay Davenport’s projections, and OOPSY — to build a consensus top hitting prospects list.

Overview

Earlier this offseason, I published OOPSY’s top hitting prospects by peak projected major league wRC+. You can check out the article for a detailed explanation of the methodology, but the short version is that peak projected wRC+ is essentially a 2025 projection, except with extra aging added to it in order to forecast how good each prospect will be at their (late-20s) peak. Read the rest of this entry »

I have been publishing projections in some form or other since 2019, making painstaking improvements to my process along the way. To borrow an expression from Dan Szymborski, my projections have now reached a level of “non-craptitude” such that I am content — though no projector is ever truly content — to share them with you here at FanGraphs. This article introduces OOPSY, your friendly neighborhood projection system.

OOPSY aims to summarize all of the information you see on a player page — a whole slew of component statistics from different years, leagues, levels, and teams, compiled at different ages — in an attempt to make it easier to evaluate players. I have always found it difficult to account for all of this information in my head without the help of a projection system, and now I have one.

Like many popular projection systems, OOPSY takes MARCEL as a starting point, adding methodological innovations to account for additional complexity (MARCEL projects all rookies to be league average, for example). OOPSY uses its own approach to account for all of the usual factors captured by popular projection systems: league scoring environments, aging effects, major league equivalencies (with inspiration from Clay Davenport) to account for (minor and major) leagues and levels that boast differing quality of competition, park effects (both minor and major league), historical performance weighted by recency and, perhaps most importantly, regression to the mean, with statistics subject to more random variance regressed more heavily. Instead of regressing every player to the same mean as MARCEL does, OOPSY regresses players to different means based on their probability of making the majors, which is assumed to be a function of their age relative to level (based on historical data), with complex-level players regressed to a worse mean than Triple-A players, for example. Read the rest of this entry »