Review season continues and today I’ll recap the starting pitchers whose spring training strikeout rates were significantly above their Steamer projected K% marks. As a reminder, with the help of Matt Swartz, I found that spring strikeout rates do provide meaning with regards to regular season performance, something that Dan Rosenheck validated as true more recently.
So yesterday, I reviewed the results of my other HR/FB rate analysis based on ESPN Home Run Tracker data and recapped the players identified as having significant upside in 2015. My list didn’t perform too well, so let’s hope today’s goes better. I’ll be checking in on the guys who hit the most Just Enough home runs in 2014, which I deemed as having serious HR/FB rate downside in 2015.
Today I’ll review another article focused on identifying hitters with home run upside. On Monday, I recapped the names based on my xHR/FB rate equation, while today I will discuss the list I cobbled together using ESPN Home Run Tracker. What’s always interesting is when both methods disagree and I’m not always sure which to believe. I tend to side with the xHR/FB rate equation though just because the formula uses significantly more data points.
Yesterday, I reviewed my preseason target list of hitters whose xHR/FB rates suggested significant HR/FB rate upside in 2015. Today I’ll recap the opposite end, those hitters who my xHR/FB rate equation suggested had significant HR/FB rate downside. These were the guys who most outperformed their xHR/FB rates in 2014 and were at risk of a decline.
In a runaway win, the player with the most value from a single tool easily goes to Billy Hamilton and his 57 SB. He was completely useless otherwise for his fantasy owners with a .226 AVG (.274 OBP), 56 Runs, 28 RBI, and 4 HR in 114 games. Even with those horrible numbers, he ended up 88th (~7th rounder) in ESPN’s Player Rater ahead of Robinson Cano, Jason Kipnis, and Alex Rodriguez. Those 57 SB are gold though with teams now running less and less.
I told you last week that I made a lot of predictions, doled out a ton of preseason advice and published lists, lists and more lists filled with players to target or avoid based on that article’s theme. So you better believe that I’m going to review as many of those as I can to discover just how valuable that advice was! Today I review my target list heading into the season comprised of players I determined underachieved with respect to their HR/FB rates. That is, their actual HR/FB rates were significantly below their xHR/FB rates, based on the equation I developed.
As the season draws to a close, and the Red Sox try to push their record to .500, there’s plenty of blame to go around. The rotation didn’t end up working out, really, and there’s more than one high-priced acquisition that fizzled in his first year. But if you sort the leaderboards for the worst players in baseball this year, one name drifts to the top: Pablo Sandoval.
The easiest analysis is to say that he’s been better in the past and will be better again. And one-year defensive samples are certainly part of this story, so he could easily get back to being a decent defender and recover his value that way. That said, Sandoval has lost nearly forty points of adjusted offense, and that seems extreme. It’s even worse for second place on the losers list, as Victor Martinez has lost a whopping 90 points of weighted runs created plus from last season to this one.
Victor Martinez just showed us the worst single-season drop-off since free agency began.
Can we just pencil Martinez and Sandoval into their career numbers when they’re on the wrong side of this single-season dropoff leaderboard?
It’s been one heck of a year for rookie hitters. Though, if you’re in a CBS league and take a look at the season-to-date value rankings, you will only find one rookie that made the top 50, Kris Bryant. That’s no surprise though as it’s not often a rookie is that good that he’s able to break into elite fantasy territory in his first season. Of course, some hitters were just promoted too late in the season, like Carlos Correa, to build the counting stats necessary. So while the counting stats from this year’s rookie crop are what we generally expect, the power is not.
Where the defensive shift and batting average on balls in play (BABIP) intersect intrigues me, but I’ve had a hard time figuring out a way to quantify it without having some sort of access to shift data. Despite advances Major League Baseball has made in measuring and collection data, not all of this information is publicly available or easily accessible, even if you know someone who knows someone (this guy).
But I think I finally had some kind of breakthrough or epiphany or what-have-you. It would be a time-intensive approach — a problem for a lazy person (this guy) — but it would be worth it to, perhaps, chip away at the relatively enigmatic BABIP with only publicly available tools at our disposal.
More than four months ago, I posted an expected BABIP (xBABIP) equation that is not necessarily better than any other that exists but does use strictly publicly available data. Here, I expand.
Javier Baez has been getting quite a bit of acclaim since returning to the Cubs with his reduced strikeout rate. In 2014, Baez had a 42% strikeout rate (K%) in the majors and 2015 value of 22% K% looks to be a huge improvement. Don’t get too excited about this small sample of data as his swing strike rate (SwStr%) points to a strikeout rate which will likely increase quickly.
Historically, a hitter’s strikeout rate correlates almost identically to their swinging strike rate. The reason to use SwStr% instead of K% is because it stabilizes a bit faster. I took all hitters since 2002 who had at least 200 plate appearances in a season and found the linear correlation between their strikeout rate and swinging strike rate. The R-squared between the two values was .947 with the equation working out to:
K% = 2.25 * SwStr%
With this little bit of information, here is a look at Baez’s stats in the majors the past two seasons.
Season: SwStr%, Predicted K%, Actual K%
2014: 19.2%, 43.2%, 41.5%
2015: 18.5%, 41.6%, 22.2%
