Archive for Meta Analysis

With people already participating in 2020 drafts, I thought it was time to see where and if any positional tiers exist. I don’t believe in making up a tier where a dropoff doesn’t exist. I’m more looking for spots where for two or more rounds, a position should not even be in consideration to be drafted. Also, is there a point where the position just falls off and no one decent is left?

To set up the tiers, I used this 15-team Roto setup and our Depth Chart projections. I know everyone won’t agree with all the projections. I don’t, but they’ll provide a nice guideline for this discussion. It’s time to start with catchers.

Catcher

Tiers

• Tier 1. It’s four options and then wait.
• Tier 2. The rest of the options are evenly spaced until the end.

Read the rest of this entry »

Yesterday, I reviewed the hitters who had increased their FB% by at least 10% through May 4 of the season and noted how they had performed over the rest of the season. As a group, they held onto a little bit less than half of their gains from 2018. It goes to show that regression toward historical averages are a powerful force, but that batted ball profiles are more controllable and changes could indicate a real change in approach. Will the same results show up when reviewing the hitters who “departed” the fly ball revolution through early May? Let’s find out if these guys got their FB% marks back to where they settled in 2018 or if the early marks were the first sign of an altered batted ball profile.

Read the rest of this entry »

In Early May, I dove into the world of small sample size theater to discuss some of the statistical oddities that had occurred so far on the pitching side of the ledger. Let’s review how these pitchers performed the rest of the way in the metrics highlighted.

Read the rest of this entry »

While it might be better to focus on FAAB usage right before the season starts, I wanted to have an idea on how to focus my draft resources. Also, FAAB management was one of my major faults after I picked over my 2019 teams. It was an issue and I need to address it. Now is the time. I took the 50 players with the highest average FAAB bids in the 2019 NFBC Main Event ($1000 in FAAB) and found which players were the best and worse deals and did the best deals have similar actionable traits.

Note: One unintended side effect was that the minimum average value was $51, so all players with a bid of over $50.

To rank the player’s usefulness, I pair them up against each other and let my Twitter followers which of the two players were a better deal last year. While not ideal or the only method I could have used (I could create from value to EOS), it was the quickest and the rankings pass the idiot check (me, myself, and I).

Read the rest of this entry »

A couple of weeks ago, I devised a deserved barrel rate (where barrel rate is calculated as a percentage of batted ball events, or Barrel/BBE) based exclusively on a hitter’s average exit velocity (EV) and average launch angle (LA). To employ such a simple model, I made a broad but accurate assumption: the average hitter’s average EV (or LA) has a distribution of EVs (or LAs) centered around it, and this distribution does not differ dramatically from other hitters’ distributions.

In layman’s terms, the typical hitter’s average launch angle is his — he owns it, and it reflects his swing plane and mechanics — but he is no better than any other typical hitter in repeating his average launch angle. He, like everyone else, will likely vary from the mean by a certain margin of error. I make the same assumption of exit velocity as well. The two variables bear almost zero correlation to each other. In light of this assumption, the best thing a hitter can do is maximize his exit velocity and hopes it coincides with an optimal launch angle.

(Some folks have suggested I include the percentage of balls hit 95+ mph to refine deserved barrels. The notion intrigues me. However, to illustrate a point: if you have two hitters with identical average EVs, would you expect their distribution of EVs to be dramatically different? Probably not. The inclusion of hard-hit rate accepts as fact that one hitter might be better at hitting 95+ mph more frequently — which would also suggest he hits more softly more frequently as well, and with certainty. This doesn’t stand out to me as a repeatable, let alone necessarily desirable, trait.)

Read the rest of this entry »

The best part of small sample stats are the enjoyment we get from finding the weird and the extreme. This year at the beginning of May, I discussed a variety of players riding on one side of the bell curve. Let’s revisit these players and stats and find out how they performed the rest of the way.

Read the rest of this entry »

At the end of April, I calculated hitter xHR/FB rate using my equation to determine who had most underperformed at the time. I identified and discussed 11 of those hitters. Let’s see how many of these hitters actually did improve their HR/FB rates over the rest of the season and how close they came to their xHR/FB marks. Remember that xHR/FB isn’t meant to be predictive, but descriptive. The difference being that xHR/FB rate helps us determine what should have happened, rather than what will happen in the future. It attempts to strip out luck, like the majority of our expected/deserved metrics.

Read the rest of this entry »

I’m already tired of hearing everyone’s hot take on how Matheny is going to halt the Royals from stealing bases. I’m not sure they are wrong but the analysis, if there is any, is suspect. Most talking heads point to his Cardinals being second to last in stolen bases during his tenure (2012 to all of 2018). The Cardinals weren’t exactly full of speedsters during that time. Here are their top-10 hitters ranked by plate appearances.

Talk about “Wheelz”. Of these 10, Yadier Molina was second in steals. A catcher is second. This group would have problems stealing candy for a blind lady.
Read the rest of this entry »

Last week, I reviewed and discussed various hitting and pitching metrics and how they have trended over the past five seasons, from 2015 through 2019. After also discussing the early Statcast trends back in mid/late April, let’s take a look again with the full season now in the books.

Read the rest of this entry »

This year’s edition of the Fantasy Baseball Process by Tanner Bell and myself is finally completed and the e-book is now available for sale (the paper version will be available at Amazon in a few weeks). We added over 50 pages of new material (link to list), cut out some info we felt was not needed, and included the historic standings and Standing Gains Points formulas to help with planning the 2020 season.

Before I move along to some of the highlights, I’d like to thank Dylan Higgins for the editing, Jared Cross for the Steamer projections, Clay Link for the introduction, Rob Silver for giving it a read over, and especially Tanner for hours and hours spent grinding through all the new changes.
Read the rest of this entry »