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.
Every year, I pit my Pod Projections against the Steamer projections in various categories. Today I’m going to continue the annual smackdowns by calculating AB/HR rates and then extrapolating them over 600 at-bats. At that point, I’ll compare how many home runs each system is forecasting, given a 600 at-bat projection. I’ll start by sharing the names of hitters Pod is projecting for significantly more home runs than Steamer. Many of these players figure to be part-timers, so consider them sleepers in deeper leagues.
As part of my preseason prep, I watched a Kris Bubic start from last season. During it, Rex Hudler, who is never short on opinions, brought up an interesting point. The more pitches each batter sees, the quicker the batter becomes with the pitcher repertoire, and the more likely the batter gets a hit. At first, I thought someone else was speaking, but no, the concept warranted further investigation. It’s the same theory behind the times-through-the-order penalty but the new effect could be felt depending on how many pitches a pitcher throws per hitter and depth of arsenal for the pitcher. That idea started me down a wormhole that led to many questions and one subpar answer, but there seems to be at least one nugget of wisdom in Rex Hudler’s head.
First off, with less than a month before the season starts, it’s not an ideal time to start a study that could take weeks to iron out. I barely have enough time to report news, velocity readings, and draft my own teams. The following “answers” are not set in stone and there are so many more questions to investigate. I could either shelve the ideas for months or just make a snippet available and let others run with the ideas while I grind through the fantasy season. I’m giving others the chance to refine the ideas before I come back to them.
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Yesterday, I assembled a 14-player offense from the hitters who weren’t bought in NFBC auctions since Feb 1. Today, let’s flip over to the pitching side.
The inspiration for this study came from this blurb in a recent Mining the News.
In the past, the general rule is that a higher fastball velocity leads to more strikeouts. Once spin data became available, the link between more spin and strikeouts was quickly found.
And they can go in separate directions. After going way too deep down this rabbit hole, I came out with a simple formula.
Before diving in, a few items need to be cleared up. First, other factors besides velocity and spin can affect a fastball’s effectiveness such as pitch location, deceptiveness out of the hand, and how often the batter has seen the pitch. The key with this study is to have a simple formula to start evaluating fastball changes for the accessible stats.
Second, I tried to use Bauer Units (simply spin/velocity) to determine how a fastball performance would change. While there was some correlation between Bauer Units and pitch performance, it became unnecessary noise. Seperating out spin and velocity was a better approach. Bauer Units are more helpful with pitch design than pitch evaluation.
Now, to the analysis. I found all the pitchers from 2015 to 2020 who threw 200 four-seamer fastballs in matched seasons (n=1134). Sinkers were not included because the goal with them is to remove spin and get the ball down through the zone.
After looking at several possibilities, I ended up with the following three rules of thumb for determining a change in a fastball’s swinging-strike rate. The r-squares aren’t high for any of the values (~.1 to .2), but that’s expected with so many inputs into a fastball’s performance.
Note: Remember that spin and velocity are related, don’t combine the first two formulas. Use the third one.
Spin: 100 rpm change * 1.0% SwStr%
Velocity: 1 mph change * 0.9% SwStr%
Spin & Velocity: 1 mph change * 0.78% + 100 rpm * 0.5%
Going back to the Paddack example (+0.2 mph, -60 rpm, -2.0 SwStr%), here are the expected changes.
Spin only: (-60/100) *1.0% = -0.6%
Velocity only: 0.2 * 0.9% = +0.2%
Spin & Velocity: 0.2 * 0.78 + (-60/100) * 0.5% = -0.14%
The loss of spin outweighed the velocity loss but the entire decline in swinging-strike rate can’t be explained with just the two factors. Paddack and the team identified those other causes and hope to correct them.
That’s it for today. I believe Mining the News will be more important than going over a few more examples. The formula will be useful once the regular season rolls around and spin and velocity data are available on every pitch.
Last year, I debuted a series of posts using NFBC average auction values (AAV). It was a jolly good time, so I’m going to do it again this season. Once again, I’ll start by building a $14 offense. That’s right, 14 hitters, all just a buck. Isn’t that exciting?! I can only imagine the thrills that will be had choosing between players most fantasy owners have no desire to roster. But think of how amazing you $246 pitching staff would be!
Yesterday, I used my xHR/FB rate equation to identify and discuss the hitters whose actual HR/FB rates most underperformed. Today, let’s flip to the other end of the spectrum — those hitters whose actual HR/FB rates significantly exceeded their xHR/FB rates.
Finally, it’s time for the main event! After weeks discussing the history and research, correlations, the xHR/FB v4.0 equation itself, and various xHR/FB rate components, we now set our sights toward 2021. Today, I will identify and discuss a handful of fantasy relevant names that underperformed their xHR/FB rates most significantly. Remember that this doesn’t automatically mean we should be projecting a higher HR/FB rate this season. But perhaps rather than take the hitter’s actual HR/FB rate at face value, we should substitute our xHR/FB rate mark when reviewing his historical marks and making a 2021 projection.
Last week, I used my xHR/FB v4.0 equation to share the names of the hitters who either enjoyed a HR/FB rate surge from 2019 or posted a surprise mark in 2020 after not playing in 2019. The wrinkle is that these players all posted xHR/FB rates that validated the HR/FB rate spikes. Today, let’s discuss hitters on the opposite end of the spectrum — those that suffered a surprise decline in HR/FB rate that was confirmed as a legit falloff by xHR/FB rate.
Now that we’re through understanding the xHR/FB v4.0 equations and its components, let’s finally use it to evaluate past performance and help forecast 2021 performance. Today, I’ll share a list of names who enjoyed a breakout HR/FB rate in 2020 and their xHR/FB rate validated that surge (including surprising marks from players who didn’t play in 2019). Over small samples, luck plays a greater role, so knowing which spikes were real, based on the underlying skills displayed, is more important than ever when looking toward 2021.
