GPP Stacking with Park Factors
Good morning DailyRoto members! Welcome to another installment of “GPP Stacking with Park Factors.” If you are new to the segment, please be sure to catch up on the previous weeks by selecting “Park Factors” from the MLB Free dropdown menu.
If you’re just catching up for the first time, let me explain the experiment to you. Each week I stack lineups from different games using park factors and enter them into GPPs at DraftKings. A park factor is simply a relative rating of a ballparks aptitude for offense.
Since the second week of the season, I’ve been using a manipulation of the FanGraphs park factors by handedness as shown below.
For the entirety of the season, I’ve been focused on finding success in GPPs and most importantly trying to narrow down a successful lineup building strategy using stacks based on park factors.
However, this week, I decided to try my hand in some satellites. Satellites, if you don’t know, are small tournaments that payout a ticket to a larger game or qualifier. For this week, I was trying to turn my .25 into a ticket for the $3 GPP.
I’ve included the teams below:
While neither one of these teams cashed (although they were both close) I wanted to show them both for a few reasons.
- This is exactly what we have been expecting in terms of percentage owned in our lineup creation. The team with the bad ballpark (Citi Field in New York) was made up of a string of really low owned players that other DFS users simply passed over. This is what makes being contrarian in terms of park factor so dangerous (in a good way). If the Mets were able to muster up any more than six runs, this team would have skyrocketed up the leaderboard. Hopefully further down the road I can get another bad ballpark to hit.
- The pitching worked out. I know early on in this experiment I was hyper-focused on the offensive prowess of my lineups. However, the pitching makes up for a large portion of the points scored, and I’ve begun to focus more on it in the recent weeks which has helped up my scores a little bit. On this particular night, all of my pitching was fairly solid and contributed quite nicely to my overall team scores (35% and 29% respectively).
- One last point that doesn’t carry as much merit, but something that I’m proud of, is that I’m taking less 0’s. Obviously in baseball with the extreme variance from night to night, you can get away with having 0’s and still have a good team. But since I’ve focused a little bit more on using common sense in my pick making, instead of just blindly throwing players from a certain game into a park, I’ve been seeing a better all around approach. (I’m not taking notes on how few 0’s I’ve been getting, it’s just a trend that I’ve been noticing from night to night that speaks to a “solid” process).
Last week I challenged myself to stick to the aforementioned process, and I’m proud to say that it’s churning out decent results on a nightly basis.
I’ve updated the chart below with this week’s scores and the ROI numbers.
|Week||Worst Park Avg.||Best Park Avg.||Worst Park ROI.||Best Park ROI||Total|
|Year to Date||100.28||106.45||253.85||169.23||211.54|
**Note: I found a mathematical error in my calculations that I missed in the last two weeks ROI Calculations. It was a minor error in distinguishing a game for this experiment with one of the games in my own personal play. The updated totals are correct dating back to the April 25th numbers.
Thoughts on Logan’s GPP experiment? Want to start an experiment of your own?