After just a few games this year, I decided to check in on batter maxEV. If a batter was already setting a new high only a couple of games into the season, I wondered if it was an early sign of a power and HR/FB rate surge over the rest of the year. So let’s now review those hitters who increased their maxEV the most at that point to see if it was indeed a hint of things to come. As a reminder, maxEV may have been recorded on a ground ball, which is far less exciting than one that comes on a fly ball. It doesn’t matter how hard a grounder is hit, as it cannot leave the park! A better look would have been to filter for only fly balls, so I’ll look at it that way next season.

Read the rest of this entry »

Today, I share the final review of my pre-season Pod Projections posts. This time, we shift to a starting pitcher, Zach Plesac, whose original writeup is here. Plesac did post a sub-4.00 ERA during his 2019 debut, but it wasn’t backed by his skills, as he handily outperformed his ugly 5.13 SIERA. During the short 2020, he enjoyed a true breakout as his strikeout rate surged thanks to pitch mix changes. Let’s see how he did for an encore and how it compared to the projections.

Read the rest of this entry »

Let’s continue reviewing the Pod Projections I shared early in the year. Today, I’ll review my Trent Grisham forecast. You can find the original writeup here. Grisham enjoyed somewhat of a fantasy breakout during the short 2020 season, as he went 10/10 over 252 plate appearances, putting him on a 20+/20+ pace over a full season. We fantasy owners salivate over that power/speed potential. Let’s see how he followed up and compare it to my projections and the rest of the forecasts.

Read the rest of this entry »

As you probably already know, I manually project player performance each and every year, and make the forecasts available on my Pod Projections page. It’s a seriously time-consuming task, but the manual process gives me some advantages versus a computer system, so I continue to create them. Early in the year, I share a couple of my Pod Projections, the individual forecasted metrics, and an explanation of the process I follow to arrive at each number. This year, the first projection I shared was that of Ha-seong Kim, who had just signed a four year contract with the Padres after spending seven seasons in the KBO (Korean Baseball Organization). Projecting veteran baseball players is challenging enough, so you can imagine the added layer of difficulty when working on a forecast for a player coming over from a foreign league. Let’s find out how Kim performed compared to my projection and the two that were published in early January.

Read the rest of this entry »

Yesterday, I reviewed my comparison of starting pitcher spring training strikeout rates to Steamer projected strikeout rates and the list of pitchers with potential strikeout rate upside this past season. Now, let’s review the list of potential walk rate improvers based on the gap between spring walk rate and Steamer projected walk rate. Was Spring the first hint at an improved walk rate than expected? Let’s find out.

Read the rest of this entry »

At the end of March, I shared a list of starting pitchers who posted the largest gaps between their Spring K% marks and their Steamer projected K% marks. A many years old study I conducted confirms that Spring strikeout rate does convey meaning, so while 99% of Spring stats should be ignored, strikeout rate isn’t one of them. Small sample sizes and uneven competition caveats still heavily apply, though. Let’s find out how these Spring strikeout rate surgers actually performed. How many, if any, beat their Steamer projected strikeout rates?

Read the rest of this entry »

Yesterday, I reviewed the list of eight starting pitchers my Pod Projections were more bullish on ERA than Steamer. Today, let’s now flip to the starting pitchers I was more bearish on, suggesting downside versus Steamer. Overall, the Steamer ERA projections were more pessimistic than mine, so the gaps between the upside pitchers were much larger than the gaps between these downside pitchers. That means this list is more of a crapshoot. Let’s check out the results.

Read the rest of this entry »

Last week, I reviewed my hitter Pod Projections vs Steamer projections comparisons. Let’s now move along to the starting pitchers and ERA. As a reminder from my original post:

Though Steamer is the best pitching projection system out there, it struggles on pitchers that have shown consistent BABIP and HR/FB rate suppression skills and deficiencies, as what usually works for the majority of pitchers — projecting a heavy dose of regression to the MLB mean — means it misses on those uncommon exceptions. I use Statcast’s xBABIP now for my projections, so I’m not afraid to forecast a mark that strays from the league average. However, I certainly still include some regression as we don’t always have enough batted balls in a pitcher’s history for that mark to stabilize.

Read the rest of this entry »

Yesterday, I compared my Pod Projections in the stolen base category to Steamer and reviewed the five hitters I forecasted for a meaningfully higher stolen base total. Today, let’s now review the hitters I projected for fewer stolen bases than Steamer over a 650 plate appearance pace. As a reminder, stolen bases were down this year, so theoretically it should have been easier to hit on more of the downside guys.

Read the rest of this entry »

Today, I continue my Pod Projections vs Steamer battle reviews, this time moving along to stolen bases. Similarly to the way I compared our home run forecasts, I calculated a PA/SB rate first and then extrapolated that projection over 650 plate appearances, so we’re only comparing stolen base projections and playing time forecasts don’t factor in. We’ll start with the stolen base upside guys. For some context, the league stole the fewer bases per 650 plate appearances since…1971! So hitting on the upside guys is going to be a lot tougher than hitting the downside guys.

Read the rest of this entry »