A Daily Fantasy Baseball Strategy with Statistics
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It’s a guilty pleasure for me; I love watching Major League Baseball. When my favorite team of all time came one game close to the World Series last year, I was in ecstasy. They couldn’t quite pull it off, but the Brewers kept all of us entertained late into fall (see image above...heartbreaker!) Then we got into the drudgery that is Green Bay Packers football (but I won’t bore you with all that drama at this point.)
With that said, I love to play to DFS MLB games. I don’t play much in respect to dollars risked, but I do enjoy watching scores trickle through at night, even though the most at risk is typically a quarter. If I’m feeling really lucky, I might even throw a $1 at Draftkings, Fanduel, or Yahoo. It’s fun to see potential winnings go from $10 USD, to $100USD, back to zero. I know it’s not a consistently winning endeavor for me, but not all of us have the luxury to make money selling mystic knowledge, esoteric leanings, gnostic truths, or even 2 hour podcasts with monthly subscriptions. If I can make a few dollars throughout the course of a season, much like flipping comic books, I’m good with it. With that said…
I know, I know…money is a frowned upon thing in the realm of the alternate research, yet sadly, everyone for the most part a) needs it and b) seems to enjoy charging material for it. Hey I get it- it takes a lot of work to present material, and if you don’t have a full time job getting in the way of finding time to research things you love, you have get it somehow. Content is always about chasing the dollar, and brands need to be developed to chase even more dollars; it is simply the way the vast majority of the world works. Sadly, I’m still part of the vast majority too.
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OK, off the soap box as to why I play DFS sports. I want to begin a weekly column that utilizes a few things I love in life: MLB, data analysis, and possible schemes to earn cash. I know it’s not the same as talking about Billy Shears, or discussing blue UFOs, the occult, or even lost ancient cities. That’s the beauty of Steemit; write what you want to write about, and if people like it, they can choose to appreciate your work. If they don’t, they don’t have to provide feedback. But you can still continue to write about a subject matter you enjoy for one’s own happiness.
Who wants to be labeled anyway?
Thoughts for a New Strategy in MLB this Season
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I’d like to present a few pieces of data that I’ve compiled at this very early point of the 2019 MLB season. You know how DFS works at this point, I think; if not, I recommend the Google Machine for further assistance. Ultimately, the goal is to create a team of offensive and pitching players, for that day’s games, that generates the most amount of points possible based off of a preexisting scoring system. So, draft players who score a lot of points on any given day, and you’ll win cash. Seems easy enough, right? Well as all things relating to money, it is not.
Thoughts for This Week
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It’s not as easy as it seems as you can’t roster a full group of players of MLB studs. That means you can’t draft Mike Trout, Christian Yelich, Cody Bellinger, Khris Davis, etc. every day as you only have x-amount of dollars to spend on this fictitious roster. Those players cost a lot of “fake money”, so it is impossible to put a full roster together as you will run out of your budgeted “fake money” to draft a full team. That means you have to find value in other players; that means you have to get guys on the cheap that will yield big points. With that said, I’ve gone down the rabbit hole of MLB data in an attempt to correlate it to DFS results. This series of articles will go back to an old idea of KISS : Keep It Simple Stupid. Stupid, in this case, is me.
Let’s Evaluate Starting Pitching
I want to draft the Max Scherzers and Jacob deGroms every time they pitch, but that isn’t feasible as it will kill your budget. Therefore, I thought I’d look at this graph:
Starting pitchers have averaged, by start, 26.59 points / start against the lowly San Francisco Giants. In contrast, starters have averaged .16 points / start against those damn Minnesota Twins. The league average is currently 19 points / start.
My goal this week is look for affordable starters against SF, Detroit, Toronto, Cleveland, and Miami.
Let’s Evaluate Starting Offenses
The same could be said for staff pitching vs team offenses. This chart, for example shows:
Baltimore has given up the most points to opposing offenses at 109.50 pts / game; the Rays, however, have only given up 49.15 pts / game to opposing offenses. The league average is roughly 78.21 pts / game; therefore, I’ll concentrate on the teams that give up the most points. Remember, I’m KISSing it here. These teams current include Baltimore, Chicago White Sox, the Mets, Rangers, and those damn Cubs.
What Players To Look At
Now some interesting averages:
This highlights that the #1 - #4 hitters in a lineup average more points than hitters #5 - #9. I usually ignore anything in the NL 8 – 9 spot given pitcher hitters. Intuitively, there are more plate appearances for the top of the order, so this makes some sense. The AL does seem to yield more points until you look at the back end of the order…that’s curious. We’ll see if the trend holds up this year.
Strategy For This Week
With KISS in mind, I will focus each night’s lineup on:
- Starting pitchers facing the Giants, the Tigers, the Blue Jays, the Indians, and the Marlins
- Starting hitters facing the Orioles, the White Sox, the Mets, the Rangers, and those damn Cubbies
- Hitters in spots 1-4 in the AL, then NL
I’m also going to use Yahoo’s Daily Site to begin with, as I can apparently access that site on my Droid while at work; the others, not so much. Seriously, I work in a dungeon.
Will this yield results? I don’t know; it’s a system I’m starting with and plan to use consistently. There will be wins and losses, but over time, I’m curious how the system plays out. I’ll log my results and share with the world each week. At the very least, I will find this fun to analyze, write, and summarize. And if it doesn't work, well, don't listen to me!
Good luck tonight for those playing; if willing, I'd love to hear your ideas on the game.
An advanced analyse of the game through statistic value.
I cant wait to see how it plays out.
Thank you for sharing.
Thank you! I hope to keep it going and advance a few more kpi's out there for everyone to (hopefully) benefit from.
Hi sagesigma,
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well thank you so much, once again @curie - I'm happy those who read it appreciated the content!