Injuries and demotions are a deep league owner’s best friends, as long as they don’t occur to your players. But really, it’s one of the few ways to improve your roster since trading seems like an impossibility in such leagues. Both players in this week’s waiver wire are the beneficiaries of these two events.
I am often asked what I use a hitter’s average fly ball and home run distance for. There are various ways the distance number could be used, such as identifying potential HR/FB rate surgers and decliners. This is a good method to uncover short-term breakouts and busts, but batted ball distance isn’t exactly a predictive statistic. It merely tells us what has already happened.
Heading into the season, Chris Archer was a pretty popular starting pitcher target in fantasy leagues. He was one of those non-sleeper sleepers, if such a term exists. You know, the guy everyone is hyping as a sleeper, which jacks up his price to “he has to enjoy a full season breakout just to break even” territory. It was easy to see why he was so appealing to many. He averaged 95 mph with his fastball, struck out a high rate of hitters in the minors and posted an impressive 3.22 ERA over more than half a season’s worth of starts with the Rays. But over his first nine starts of this year, things haven’t gone exactly the way his fantasy owners had hoped and expected. Read the rest of this entry »
Today’s waiver wire features a recent tradee and a middle infield speculation. As usual, these recommendations are geared to mono leaguers.
Yesterday, I shared which starting pitchers have suffered from the most rotten luck so far this season by comparing their ERAs to their SIERA marks. Naturally, today it’s time to look at the opposite side of the coin — those whose ERAs are not supporting by their underlying peripherals and have significantly higher SIERA marks. Let’s take a gander.
We have come a long way when it comes to evaluating a pitcher’s performance. No longer do we look at W/L record, hits allowed and other metrics that are greatly influenced by factors outside the pitcher’s control. We have learned to focus on the pitcher’s underlying skills. However, it remains very difficult to look past ERA for the majority of fantasy owners. This is somewhat understandable when looking at full year results, but when we’re still just a month and a half into the season with pitchers generally in the 50-60 innings range, ERA should be almost completely ignored. ERA estimators such as SIERA are much more predictive of rest of season performance. So with that in mind, here are your SIERA underperformers and acquisition target list. I have only included pitchers whose SIERA marks are below 4.00. If a pitcher is sporting a 7.00 ERA with a 4.70 SIERA, then sure he may be a bit unlucky, but he still stinks!
Over the last week, a trio of interesting pitchers joined their respective American League teams’ starting rotations. While two of the three were likely already owned in AL-Only leagues and almost have to blindly be slotted onto a fantasy team’s active roster, the bigger question is whether these guys are worth considering in shallower formats. Let’s dive in.
It’s another truly deep dive in this week’s waiver wire adventure. Since I’ve been asked several times, the deep league edition of the weekly waiver wire is typically meant for Mono league owners. Or perhaps if you play in a 20-team mixed league, you could use these recommendations as well.
Yesterday, I discussed eight hitters whose batted ball distances suggested an imminent power surge. Today I’ll take a look at the opposite side of the coin — those whose distances suggest their current HR/FB rates are unsustainable. As a reminder, this analysis assumes the distances are maintained. Obviously we’re still dealing with a relatively small sample size and these hitters are capable of boosting their batted ball distances to match their HR/FB rates.
There is just so much fun you can have analyzing the batted ball distance leaderboard. The possibilities are seemingly endless and we haven’t even scratched the surface of exactly how we could use this data. But since we know distance is highly correlated with HR/FB rate, we could look at the leaders and laggards to predict who might be in for a surge or decline in their respective HR/FB rates. The key, of course, is that the player maintains a similar batted ball distance. We’re still dealing with a relatively small sample size here, so these potential surgers will only surge if they keep hitting balls as far as they have on average.
