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GPP Stacking w/ Park Factors

GPP Stacking with Park Factors

GPP Stacking with Park Factors
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GPP Stacking with Park Factors

Good morning DailyRoto members! It’s been one month since I began this seasonal experiment with park factor and 20 minutes since I last wrote the piece on this week’s trials (darn you computer problems!).

If you’re unfamiliar with the basis of the experiment, I’ll offer a brief explanation here. However, thanks to the newly added sub-category, you can easily look back on the other pieces by clicking “Park Factors” under the “MLB Free” section. I advise you do so.

Each week this season I have been experimenting with different GPP stacks on DraftKings based on park factors. Park factors are simply a relative ranking of major league ballparks based on their aptitude for offense. A ballpark with a high park factor is better for hitters, while one with a lower park factor benefits the pitchers.

The last three weeks I’ve been using a manipulated version of the FanGraphs park factors by handedness, which I’ve included below.

In recent weeks I’ve been focused on ownership levels and the extremes that accompany the park factors. But this week, I want to focus a little bit more on the “middling” effect that stacking can have, as well as diving into some potential goals for the upcoming months of the season.

This week – I got “middled.” I know that isn’t a word, but I’m using it in lieu of the bubbling term that is commonly referred to in DFS.

Below are two teams that I put in the quarter arcade during Wednesday’s late slate. I know that neither one of these teams was very good, but I’m really glad that this worked out the way it did for one important reason.




This performance reined me back in to notice that while park factors do have a significant influence on roster success and lineup creation, they are not the only basis of lineup construction.

In the recent weeks I’ve been focused on just shoveling players from the best parks into the lineup to help prove the point of the experiment, but I want to change that to mock a better lineup construction strategy.

Never do we want to just blindly pick players for our lineups, instead, we want to select the best players in the best situations to try and create the closest thing we can to an optimal lineup. This means that I’ll be paying closer attention to lineup spot, platoon advantages and price points much more than in the past. In other words, the core of the experiment (focusing primarily on park factors) won’t change; I’ll simply be adding a bit more common sense into the mix. It was good to begin the experiment with extreme rules, but now that we’re a month in it makes sense to adjust a bit and see if results can be improved.

For example, by taking this approach from now on when constructing these lineups, I’ll make sure that players like Daniel Descalso don’t find their way back in. (Yes, it happened, see above).

Another point that I have not touched on much in recent weeks is the pitching selection. I’ve been focused on selecting hitters from the best and worst parks, but haven’t taken into consideration much taking pitchers from the respective parks.

I’m going to begin doing so. By more closely selecting pitchers based on their park factor, we can begin to monitor the effects of ownership level, player and lineup performance. To do this, I’ll be following the same guidelines I set forth for hitters. I’ll be selecting the pitchers from the park with the worst park factor (best pitchers park) available, and also selecting pitchers from the best park factor (worst park for pitchers) to accompany their respective teams.

While I’ll be focusing on these two items more closely in the coming weeks, it does not mean that I’ll be abandoning the analysis on ownership levels and the success of the worst park or best park.

Overall, it was a successful week. Not so much in terms of ROI, but in terms of knowledge gained. Although it’s incredibly cliché, sometimes it is good to lose or be “middled” in this instance as it has led to the reflection of my lineup creation for the ongoing experiment.

I’ve included the updated ROI chart below.

Date Worst Park ROI Best Park ROI Total
Week 4 -100% 40% -30%
Season To Date 311.36% 262.86% 295.68%


Note: I noticed an error when recalculating the total in an earlier piece, this yearly reflection is correct.

After a month of the season, the Worst Park stacks are earning me a higher ROI. This will be an interesting trend to monitor of the course of the year. As I’ve discussed in previous pieces, the worst park stacks typically contain much lower ownership levels, allowing the lineup to separate itself from the crowd in the case of a high run total game. Therefore, while the best park stacks will most likely maintain a more consistent cash rate, the worst park lineups have the potential to earn a “big” finish when the chalk goes dry.

I’ll continue to monitor these situations more closely as the weeks unfold!

Thoughts on Logan’s stack strategy/experiment or want to share one of your own?


GPP Stacking w/ Park Factors