Surprise! Park Switch Boosts Stephen Piscotty’s Value
Last week, the Athletics traded for Stephen Piscotty, alleviating a bit of the great depth in the Cardinals outfield. Piscotty is coming off a forgettable offensive performance, in which he dealt with injuries, a minor league demotion, and the terrible news that his mother was diagnosed with ALS. Typically, the knee-jerk reaction is a move to Oakland will likely hamper a hitter’s offensive results. But surprisingly, this appears to be one of those rare instances in which the park switch may actually provide a boost. Let’s dive in.
| Park Name | R | 1B | 2B | 3B | HR | GB | FB | LD | K | BB |
|---|---|---|---|---|---|---|---|---|---|---|
| Busch Stadium | 88 | 103 | 89 | 90 | 82 | 104 | 100 | 96 | 96 | 100 |
| Oakland Coliseum | 101 | 95 | 100 | 170 | 89 | 100 | 95 | 109 | 95 | 97 |
Woah, that’s a far friendly hitting environment in Oakland compared to St. Louis than I would have guessed. We see from the first column, run scoring, that Oakland was actually a tick better than neutral in 2017, while Busch drastically suppressed runs scored. That’s all well and good, but we’re more interested in finding out what’s driving the discrepancy and how that may affect Piscotty.
First, we dive into the hit types. Busch’s only advantage in in singles, which likely means it offers slightly better BABIP potential, but likely not by a significant degree. The better doubles and triples rates in Oakland (wowzers that triples factor is insane!) are probably not enough to make up for the much more frequent single.
We then move on to homers, the factor we probably care most about. Both suppress the long ball, which is something we all basically knew, but Busch did so more dramatically than Oakland this season. So while Piscotty will continue to be held back by his home park, it might not be to as great a degree. And guess what…Piscotty has underperformed his xHR/FB rates for three seasons running now. He actually owns a better HR/FB rate in away parks than at home, sitting at just 11% at Busch, but 13.9% in away parks. Just more proof that his home run power has been hampered.
Piscotty’s Brls/BBE and resulting xHR/FB rates have tumbled each season, but he’s still just entering his age 27 season and deserves a mulligan for all he dealt with this year. So I think he has real home run upside.
In terms of batted ball types, the most interesting to note is the line drive rate, in which Oakland inflate, while Busch deflates. That may be enough to offset the loss of singles from park factor and keep his BABIP in a similar range as if he hadn’t been traded. And he could certainly use the line drives and BABIP rebound, as both plummeted this season.
The strikeout and walk rates are close enough that we could just focus on his skills when forecasting his 2018 performance. He swung at significantly fewer pitches both inside and outside the zone this season, which led to a walk rate surge, and also managed to cut his SwStk%, though his strikeout rate didn’t budge because of the aforementioned fewer swings inside the zone. His Swing% actually remained just above the league average, which seemingly doesn’t match with a 13% walk rate. I’d bet on some serious regression there, but he should maintain some of that growth, giving up more than he keeps.
Overall, this is probably a good move for his value as the park switch is a positive and his playing time is likely to be more secure in a less crowded outfield. I’m definitely buying low here.
Mike Podhorzer is the founder of ProjectingX IQ, an advanced fantasy baseball analytics platform that transforms projection data and in-season performance signals into actionable intelligence. He is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year and three-time Tout Wars champion. He is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. Follow Mike on X@MikePodhorzer and contact him via email.
As people suggested in the comments on the Zack Cozart blurb, it might be helpful to at least mention whatever the 2016 (or earlier) park factors were, given that the 2017 numbers seem to be rather wonky
Maybe someone can help me on this ballpark factors stuff. How does a ballpark impact strikeout and walk rates? Or even something like batted ball types? Other than things like good hitters backdrops or changing approach to take advantage of the park and stuff like that, it seems like a lot of this ballpark factor stuff attributes things to the ballpark that has little or nothing to do with it. Obviously there are exceptions like Colorado and Arizona where the thin air could make a major impact on K’s and batted batted balls relative to other parks. Also, shouldn’t ballpark factors more or less be constant every single year short of moving fences in/out? It doesn’t really make sense for a ballpark to have major swings in value year to year, I guess unless the weather was different, but that isn’t necessarily a repeatable thing anyway.
Loving all the off-season content by the way, but was just curious if I’m missing something here.
I’m with Michael. Unless there are physical changes made to the ballpark, it seems logical that the effects should remain relatively constant from year to year. Are home team lineups taken into account?
Example, if Stanton, Judge and Gary Sanchez were in St Louis’ lineup last year, how much would that affect their ballpark effect?
Well-done park factors are generally calculated in such a way that the home team’s performances are compared to their away performances and the away teams performances are compared to their play in other parks. Weighting gets scaled so that the home team being good or bad at something doesn’t overly influence the overall calculation. Ther
Sorry, hit the enter key. Here’s a mathy explanation of how B-R does park factors and takes into account each team that played in the park: https://www.baseball-reference.com/about/parkadjust.shtml
Thanks sabrtooth! One thing I noticed about the equation in your link is that the AL and NL park factors are calculated separately to achieve a base score of 100. If I read that correctly, a base score in one league may represent higher numbers than the other league (DH effect?). So, comparing an NL park to an AL would require a synchronization adjustment. Is that right?
Right, the leagues are calculated separately because the AL always has more offense and NL always has higher strikeouts (mostly pitchers). I think it’s probably safe to compare park effects (as in, you don’t need further adjustment beyond what’s provided) since you’re looking at how much they influence a team vs. the average.
@DDD FYI — Park factors do not stabilize over one season. Not sure why Podhorzer is assuming they do. Even using three seasons of PF data is extremely noisy; you’d only want to predict future park discrepancies if the past differences are quite large.
Totally agree, but even the 3 years fluctuate on statcorner, so really not sure how best to do it
I think the cause may be pitchers responding to the ballpark’s dimensions by pitching more cautiously or aggressively.
The amount of foul territory can factor into the BB & K rates at a stadium. Oakland is notorious for having a lot of foul territory. A ball that goes into the stands for a strike in many stadiums becomes a foul out in Oakland… thus a lower strikeout and walk rate there.
Duh, that’s also a huge reason. Thanks for pointing that out, can’t believe I overlooked that.
Yea, good call. There definitely are a few things unique to ballparks/weather that can truly impact K’s and BB’s.
And thanks for the link sabrtooth. Still seems to me the numbers should be relatively the same year to year, and if they aren’t, it kinda shows that the method they are using bakes in more than just pure ballpark factors. Maybe that is just impossible to avoid, though.
The changing ball likely makes park factors wonkier on year-over-year changes than normal because if some parks had lots of warning track balls, many of those might now be homers. I would guess that the number that represents a meaningful sample for park factors, like defense, might just need more than a season of data to allow us more confidence in the park’s “true” factor.
Not to mention he gets to play in Houston and Texas for a few more series as well. But I guess that may be offset by missing out on playing in Milwaukee and Cincinnati
With hopes someone will patiently and generously help me – where do I find the park factors referenced here? Clearly these are different than in “guts” above. At baseball reference, all I can find is the methodology write-up referenced in these comments. Searches such as “park factors” or “park adjustments” were not fruitful.
hmmm, I could swear I included a “source” line in the table, but it’s not appearing.
http://www.statcorner.com/ParkReport.php
this actually includes all minor league parks too
troubling is that park factors found at different sites are sometimes wildly different. wish this stuff was easier and everyone agreed on the best method!
Single-season component park factors? Really? Year-to-year correlation is probably about 0.02.
Stephen Piscotty is a very intelligent player.
Oakland is close to home.
Everything seems to point to him being a perfect fit.
His mind was on mom,and not baseball,last season.