Last Thursday, I discussed the 10 hitters I identified in the preseason as potential 2017 BABIP surgers, due to xBABIP marks well above actual marks. Eight of the nine hitters that actually recorded an at-bat enjoyed a BABIP increase. Let’s see how the nine hitters I identified in the potential BABIP decliners list fared.
I have spent the past few years creating a family of stats that I’ve called xStats. These stats use Statcast batted ball metrics to analyze each player, which I then manipulate and export in a manner I hope is useful for fans and analysts.
Exit Velocity and launch angle data are good, and I include those, but they aren’t yet intuitive for more baseball fans so I have set forth to display my data in terms of numbers that are more relatable. Namely the standard slash line numbers. I have expected batting average, on base percentage, slugging percentage, batting average on balls in play, and weighted on base average. For pitchers I have bbFIP, which is an ERA scalar. Today, though I’m only going to be looking at batters.
These stats are available, but they don’t help much unless you know how well they are working. To that end, I have created the following table, which compares the regular, standard slash line to the xStats slash line. Read the rest of this entry »
Let’s stick with my xBABIP equation and discuss the group of 10 hitters I identified as potential 2017 BABIP surgers. These were the guys whose xBABIP marks were significantly above their actual BABIP marks.
Sometimes it’s obvious when a hitter’s BABIP is due for a rebound or regression, but that isn’t always the case. For the first time before this season, I decided to discuss some of the hitters whose BABIP marks looked relatively sustainable, but my new xBABIP equation disagreed. Let’s see how this group ended up doing.
Most fantasy owners expect drafted players to under and over perform some amount. When a player overperforms, the owner looks and feels great because they “knew” a breakout was coming. Owners hope they didn’t pick too many players on the other end of the spectrum. The underperformers are the ones who drag down a team and owners find as escape goats for a bad season. I’m going to start laying the groundwork to determine a hitter’s disappointment chances.
The first major step in finding a disappointing hitter is to define what is a disappointment. After owning too many fantasy teams over the years, I’ve had my share of disappointments (e.g. Brandon Webb in a 2009 first round) and feel they are just part of the game. This ambivalence doesn’t mean I shouldn’t know the breakout chances. Even small changes in the odds can make a major difference after rostering 20 or more players.
Justin Vibber has released his final 2017 dollar values by player. I’ve captured Ottoneu average player salaries as of the end of the season (prior to inflation and arbitration). Let’s combine the two to determine which hitters were the best bargains of 2017, and take a quick look at what might be in the crystal ball for 2018.
Last week I laid out my plans for combining prospect grades and “scoutable” traits to help project major league performance. Finally, I’m able to output projections with encouraging results. Just by using traits people can scout with their eyes, I created a set of projections which competes with Steamer projections. Additionally, it helps point to the traits people should look for in prospects.
Previously, I tried to use just the five traits prospects get graded on (Hit, Power, Speed, Field, and Arm) to come up with a player’s value. I found the Speed and Power grades useful but came to the following conclusion on the Hit grade:
Basically, the Hit tool is a useless component to determine hitter value as it’s currently being distributed.
The more I thought about the Hit tool, the more I concluded that it’s trying to evaluate too much information (examples of different Hit tool definitions).
For these projections, I matched up the traits hitters display with common stats. To start with, here are the core traits I decided to utilize:
Read the rest of this entry »
This time of year, I tend to look over leaderboards. My leaderboard perusing extends beyond MLB to the upper minors, NPB and KBO. It’s often a fruitless waste of time, but is it ever really a waste of time immersing yourself in something you enjoy (don’t answer that)? Sometimes players will stand out who I deem worthy of tracking in the offseason, and that’s the case with 26-year-old outfielder Oswaldo Arcia and Colin Moran. The former is well traveled and spent the entire 2017 season in the minors, and the latter is still rookie eligible and coming off of a career year with the stick. Read the rest of this entry »
Let’s finish the infield by reviewing my 2017 Pod’s Picks at second base and shortstop.
We start with my picks at each of the two positions, those players I was significantly more bullish than the RotoGraphs Consensus:
We continue our look back at my picks and pans, this time with the infield corners. Refresh your memory by checking out my original article.
