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GPP Stacking w/ Park Factors MLB

GPP Stacking with Park Factors

GPP Stacking with Park Factors
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GPP Stacking with Park Factors

What’s up DailyRoto members? We made it! Baseball season is finally underway and is here for the long haul. I hope you have been following along with all the free and premium content being produced for MLB and have turned our hard work into greenbacks. Each week, I’ll be putting together this piece about Park Factors and discussing the changes that venue has on our lineup-making decisions every day in MLB  DFS.

I’ve discussed Park Factors a bit in many of the MLB previews, but in case you didn’t get a chance to read those (shame on you), I’ll give you a reminder. MLB DFS, unlike the other sports, is largely influenced by the venue in which the game is being played. Playing in a big spacious park versus a small bandbox inherently changes the value of not only the hitters playing, but the pitchers taking the mound. This isn’t some collegiate athletics “home court advantage” type thing, but the game literally changes from park to park.

Since the difference in park can have such a drastic change on our team (as shown below) we want to be informed of what parks will play best into our lineup creation, and which ones will lead us to crash and burn.

Therefore, the aim of this piece each week is to properly discuss that change and it’s impact in the last week and to show MLB DFS players just how important the ball park is when factoring who to put in your lineup every day.

Week 1

I’m not a mathematician, but I do know that a larger sample size is more indicative of what is to come and that relying on a few games is a bad idea in trying to predict an outcome. Therefore, I’ll be using the data from last year’s Park Factors provided by ESPN:

****(http://espn.go.com/mlb/stats/parkfactor/_/year/2014/sort/HRFactor)****

While the ESPN Park Factors are not the most sophisticated, they work. My aim isn’t to overcomplicate you with a bunch of math and numbers, when we all know that most likely, Coors Field is going to grade as the best park.

Using the ratings from the 2014 season, I put ballparks to the test in week one.

Each day I started by isolating the best and worst parks using the runs column of the ESPN Park Factor. Never is it the case that all of the parks will be used at once and there were no games in Colorado so I simply moved up or down accordingly to the next best stadium with a game being played.

For this week, the best hitting environment was Chase Field in Arizona, while the worst was Safeco Field in Seattle.

After identifying the best parks, I continued to stack lineups using players playing in the respective parks. However, on DraftKings, where I’m conducting the “experiment,” you are forced to take hitters from two different games, therefore I used one player from a different park in play, either the next best or next worse for the respective game.

Note: This final roster spot situation sort of gets sticky due to the slate of games, the weather, the salary cap and positional eligibility. Although I’m trying as best I can to be strict with the rules of the stacks, sometimes it is nearly impossible to find a player that fits from the next best available park.

As I noted above, it is quite difficult to quantify my results after a week where we haven’t seen every ballpark in play, but I still wanted to show the drastic differences that park can have on our results.

ParkFactor1

ParkFactor2The two teams above were placed in the same game on Tuesday, April 7th. As you can notice in the roster creation, the two teams are isolated by ballpark. The first, was made up of players at Safeco Field, the worst hitting environment as selected using the criteria I mentioned earlier.

Notice that I didn’t just pick a bunch of scrubs to try and get a bad score to show the drastic differences between the parks. In fact, there are multiple all-star players on both teams. Also note, it was a short slate, so my pitching options were limited and Mat Latos getting absolutely destroyed did help lower my score (Thanks Mat, it actually helped for the purpose of this exercise.)

The game in Safeco played out to 2-0 and didn’t really attract many players in terms of ownership percentage, with the exception of Mike Trout. That lineup placed 14,005 out 14,100, only 95 spots from the bottom.

The latter of the two lineups however was baller! Is that too cliché? The best hitting environment for the day was Chase Field. Using strictly players from Chase, with the help of Troy Tulowitzki in Milwaukee, was able to earn a 12th place finish, a difference of almost 14,000 spots in the standings.

Despite being in the best ballpark with the highest total set by Vegas, for some reason the players in that game were not targeted as highly as I expected. The low ownership combined with the great run scoring effort vaulted this team up the leaderboard, with the help of the absence of Mat Latos.

If you’ll notice, the ownership percentages between the two teams seem to be a bit backwards from what we’d expect. Four different players from the game in Safeco Field had a higher ownership percentage than those playing in Chase Field in a game that was predicted to score more runs. This will be an interesting trend to follow.

Why would more players roster those in a poor run scoring situation, as opposed to options in a park where we expect more runs?

The answer is, I don’t really know. Perhaps people gravitated to the big names available in Seattle as opposed to the lesser-known guys that filled the Giants and Diamondbacks roster. On most nights, I’d expect the ownership percentages between these two teams to be fairly different, with those in the better situation having a higher ownership percentage.

However, this might be a trend we are able to exploit. Because of the large ownership percentage that will be attached to those in great run scoring opportunities, we can separate ourselves from the field by using players from a lesser run-producing environment. This contrarian stack strategy is not one you’ll wish to go overboard using as more often than not, it’s going to play out as we expect. Nevertheless, the few times where teams do explode in a poor park, you’ll be the guy holding all the money. It will be interesting to see which stack produces a higher return on investment (ROI) over the long run. Will it be stacking the hitters with a higher probability of success in favorable ballparks or it will be landing bigger wins when the stack of low owned players in less favorable ballparks hits?

Overall, things shaped up pretty well for the first week, as evidenced by the chart below.

Date Worst Park ROI Best Park ROI Overall ROI
4/6 220% 100% 124%
4/7 -100% 2700% 1300%

 

A few notes on the above chart. Due to the mixed slates and the fact that I’m human, and currently still a busy college student, I was only able to successfully get two days worth of games in. I dropped the ball on this, but from now on, it’ll be an everyday thing (or close to it). Secondly, the ROI probably makes you think I’m a millionaire. Well, I’m not. To begin with, I’ve been putting these teams in either the .25 GPP or the $1 GPP. So the 2700% ROI was a $7 win on a .25 entry. A super awesome return for the investment, but yes, I wish I had put that in the $27 GPP.

These things happen, and the stakes will grow a little bit as the season goes on and I build a bigger bankroll. But expect the returns to deviate from the rather small sample size I’ve given you here.

Hopefully you can manage to get even a glimpse of the differences that the ballpark can have on the game being played and the amount of money going into our pocket. Next week I’ll have an even bigger sample size, and hopefully more money! Until then, I encourage you to incorporate these little things into your own game, and maybe experiment a little on your own.

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

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GPP Stacking w/ Park Factors MLB

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