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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! Welcome to the fifth installment of GPP Stacking with Park Factors. Thanks to the newly minted subcategory, you can catch any of the first four installments by selecting “Park Factors” from the “MLB FREE” drop down menu.

If you are just checking in for the first time, welcome. Each week I stack teams on DraftKings using park factors, a relative rating scale of major league baseball parks and their aptitude for offense. As the season has progressed I’ve been experimenting with and following different trends that appear to be more or less successful.

As is customary, I’ve included the table with the park factors that I’ve been using for the last month. These park factors are a manipulation of the FanGraphs park factors by handedness and take into account both home run and non-home run actions.

Last week I talked about a few adjustments that I wanted to during the second month of the season. This week I set about putting those adjustments to work and monitoring the results.

One of the important adjustments was trying to use a little more “common sense.” Instead of just blindly shoveling players from the best or worst park into a lineup, I wanted to try and choose the “best” players from those parks. Notice that when I say best, that doesn’t actually mean the best overall player, but the players with a combination of a good matchup and price point.

Another thing that I wanted to work on was tailoring my pitching staffs to the respective best and worst parks and monitoring their performance as well as their ownership percentage.

So I did it.



These two teams are from Tuesday this week.

First I want to point out that I did excel at fitting in players that made “more sense.” Neither one of these teams has a weird outlier, or a player that just doesn’t fit in the lineup. Each player on these respective teams made sense for some logical and DFS-minded reason.

Secondly, I did in fact select pitchers to match the respective teams. In the team with the best park, I also picked pitchers from some of the best offensive parks. Note, that with the difficulty of fitting players and my attention on the hitters, I do have to cave in certain regards in order to fit players. It will never be a perfect lineup construction in terms of my rules and regulations from the first week of experiments, but I always do the best I can. As a result, this team ended up with Scott Feldman, a really cheap starting pitching option against a weak Texas offense, and Michael Pineda, who well, is Michael Pineda.

Pineda was one of the better pitchers on the slate, so I expected a decent ownership percentage and that was where he fell in line. Feldman though, as a mediocre pitcher in a good hitters ballpark carried an expected low ownership level, just 4.9%. Feldman was awful, but you can see where this might help us down the road. If we can separate ourselves from the field on a pitcher in a good hitters park who happens to dominate, we give ourselves a chance to carve out a big score and an unique lineup.

On the other team, I picked pitchers from bad offensive parks and combined them with the worst park on the slate. Again, as expected, their ownership levels were in the Pineda range most likely aided by not only the park but also by their names – Cashner and Strasburg.

While experimentally, this strategy didn’t yield any rewards this week – in fact there wasn’t a single cash in 3 nights of play this week (insert sad face emoji) – I’m glad these teams performed the way they did.

Much like last week was a week of reflection, the aforementioned teams bring us back down to Earth a little bit and helped me realize the volatility of stacking as a strategy.

Although stacking yields a really high upside, especially if a team explodes for a bunch of runs, it can also result in a lot of zeros. That was the case this week. There were zeros all over the place, even from the best players (I’m looking at you Mike Trout).

These teams were really bad and I expect that from time to time. It is not always going to be raining cash, but sometimes, we will hit, and it will be glorious.

I’ve updated the table below for the ROI for the week and the year.

Here is to a better next week!

Date Worst Park ROI Best Park ROI Total
Week 5 -100% -100% -100%
Season To Date 297.45% 246.38% 260.56%

GPP Stacking w/ Park Factors