Author Archive: Mike Podhorzer

We know by now that spring training stats are almost completely meaningless. So stop looking at batting averages and ERAs and using those marks to drive draft day decisions! However, there are some metrics that do matter, pitcher strikeout and walk rates, which I discovered from a study I had conducted. So it follows that pitchers who posted significantly higher strikeout rates and/or significantly lower walk rates, and vice versa, than projected should get slight bumps (or the opposite) in their season projections.

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It’s bold predictions time! Last Thursday, I shared my bold hitter league leaders, and then yesterday my bold pitcher league leaders. While those picks should add some insight, they are more for fun given the loooooong odds of getting even one of them right. On the other hand, I expected to hit on several of my bold predictions, aiming for at least two to three correct calls. Let’s dive right in.

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On Thursday, I unveiled my bold hitter league leaders, which are all guaranteed to hit. Today, let’s jump over to the pitching side, where I’ll do the same for the throwers. Once again, I’ll use my Pod Projections to guide me toward players I’m more bullish on than the other projection systems. Unlike for hitters, I’ll only be sharing bold leaders in four categories. There will be no bold wins league leader named, because wins are silly and unpredictable.

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Every season, in addition to posting my standard bold predictions (which I’ll publish next week), I up the ante with my bold league leaders. If you thought nailing a bold prediction was tough, the bold league leaders are even more difficult! Just getting one right is worthy of celebration. Because these are bold, I automatically disqualify players I don’t personally believe would be considered bold or is already projected to finish top five in the category. So I challenge myself and it typically causes me to bat .000, though I actually have hit on a couple over the years. This is more for fun and dreaming of what could be, rather than any serious attempt at being right. Naturally, I use my Pod Projections to identify players with that 80th-90th percentile upside to vault to the top of the category mountains.

Today, I’ll start with the bold hitting league leaders in each of the five categories, split up by league. Next Monday I’ll move on to the pitchers.

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On Monday, I shared the names of eight pitchers whose Pod Projected ERA is significantly lower than Steamer. Today, let’s flip to the ERA downside names. Remember that in aggregate, Pod ERA projections are lower than Steamer, so the gap between ERA forecasts below are a lot smaller than on the upside list. Since it’s really relative projections and calculated dollar values that matter (we care how the projections compare to the player pool, not whether the pitcher is projected for a 3.00 ERA vs a 14.00 ERA), try to ignore the small degree Pod’s ERA is higher than Steamer and remember these are the largest outliers, so if put on the same ERA scale, the difference would be greater.

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For the ninth year, I had the honor of being part of the Tout Wars family of auctions and drafts this past Saturday. From winning the inaugural mixed league draft back in 2013, to moving into the AL auction in 2014 and competing there ever since, including a win in 2017, my history suggests that I would never make the mistake I just did that ruined my entire day. Before I share what happened that led to an embarrassing performance, let me remind you what this league is all about.

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This week, I finish up the Pod vs Steamer series that pits my Pod Projections against the Steamer projections. Today, we move on to pitchers, where I’ll compare the ERA forecasts from each of the systems and identify those pitchers I am projecting for significantly better ERA marks. 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.

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Yesterday, I compared my Pod Projections in the stolen base category to Steamer and identified five hitters I am forecasting for a meaningfully higher stolen base total. Today, let’s now look at the hitters I’m projecting for fewer stolen bases than Steamer. I’ll only highlight the fantasy relevant names as there are a number projected for limited playing time that aren’t worth discussing.

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Today, I continue my Pod Projections vs Steamer battle, this time moving along to stolen bases. Similarly to the way I compared our home run forecasts, I’m going to calculate a PA/SB rate first and then extrapolate that projection over 650 plate appearances so we’re only comparing stolen base projections and playing time forecasts don’t factor in.

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Yesterday, I compared my Pod Projections to the Steamer projections to identify the hitters with home run upside. I calculated each hitter’s AB/HR rate and then extrapolated it over 600 at-bats. At that point, I compared how many home runs each system is forecasting, given a 600 at-bat projection. Today, I’ll share the names of hitters Pod is projecting for significantly fewer home runs than Steamer.

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