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TL;DR – @radtaylor9’s thoughts mirror my own on the issue of quantifying edge and then applying it to bankroll management + utilize opponent tracking software / scrape opponent lineups to create a power ranking + a weighted recent results ranking to combat players now purchasing good tout plays + contest entering times for non-H2Hs.
Long Version
@rdtaylor9 – I think your post is closest to the correct answer and basically what I concluded using my past experience (+ the insights from @mlbmodel). It looks like @znmeb touched on something I tried in the past in his first response.The only thing other thing I tried doing to some success was scraping the lineups of all the contests I could (against the ToS so gets tricky/technical) and evaluating the strength of the plays chosen by each contestant and creating an opponent power ranking. This actually led me to finding an RG Top 10 TPOY player who was a negative EV player in cash games. I ended up scooping almost all of his 2013-2014 NFL H2Hs until he finally stopped posting them. However, after this one player stopped posting H2Hs, I noticed something with several players the more they played – their results drastically and rapidly improved. One would assume that a player would gradually get better at DFS with more experience, but there were several players that went from hugely losing players to winning players seemingly overnight. My hypothesis is that they started using a site like this one to get better projections / plays which made all my old data useless and any potential edge calculation “impossible”. It was this obvious finding that led me to my question of diminishing edge in the other thread.
The above, in conjunction with all the key pieces @rdtaylor9 already pointed out, IMO, is the only way to truly determine a H2H edge in any predictive manner and potentially apply it to bankroll management. Tools to track results are absolutely something every player should have, but they aren’t predictive and will lead you to overestimating your actual edge – especially in a static priced DFS environment.
Speaking of H2H, game selection also will determine this edge quantification process. IMO, if you’re playing 50/50s or double/triple/quadruple ups, if you do maintain an opponent tracking ranking through scraping, you’re best severed by joining contests that are as close to full as possible so you can identify the strength of the field. For instance, even in as low as $2 contests, you may sometimes find that the first 10 entrants in a 100 field 50/50 are condia, 1ucror, 00oreo00, etc. and you’re already playing at a far smaller edge than you would in a normal 50/50. I have actually seen from some of the data I scraped from some qualifiers that is almost the opposite – that if a respected “pro” puts in the max amount of entries early, you’ll see the participation from the other pros fall below their normal participation number and the edge is actually greater than normal qualifiers. A good example of this was 2015 MLB I believe when BeepImaJeep would put his entries in early and you’d see other “pros” put in a much smaller amount of lineups than normal. Only after he maxed out his live final seats did you see some of those same pros start entering their normal amount of lineups again.
Obviously, the sooner you join a contest, the more limited the opponent information is, the harder it is to even approximate field strength / edge.
It’s just so super complicated and so hard to actually quantify that it’s a really great thought experiment / learning exercise, but far more complicated than sports betting. Really great discussion though, this is quickly becoming a daily stop of mine.
@mlbmodel – Thanks for the reply again.
“I would question how quantifiable an Advantage Players’ edge ever was considering the pool sizes and vig.”
This is a fascinating statement because not only do I completely agree, but it does bring about the issue of proper bankroll management – i.e. if you can’t accurately quantify your edge, how does a DFS player properly manage his bankroll. Of course, there are simplistic rules which one could adhere to, but it seems on the surface that one could easily see their bankroll destroyed before properly adjusting (see – max obliterating condia before condia realized what was happening) because it seems quite difficult to properly quantify. It’s quite possible I’m an old man and too old to learn new tricks, but this all seems much easier in the sports betting realm. It really shows the several levels of depth/skill needed to be a super profitable DFS player in 2016.
Anyways, thanks for spending the time replying. AG’s always been good to me so I’ll try and pump some #content into the forums here to get this place rolling.
PS – Forum needs a “quote” option if you can turn that on.
@mlbmodel – Thanks for the very thoughtful reply, it is much appreciated.
I could not agree more with your assessment of the tools needed to be an AP in the present DFS landscape and that without those things an early DFS APer will do poorly now. I had two follow ups if that’s ok –
1) With the prevalence of sites like yours, do you feel like your quantifiable edge has decreased or have you been able to leverage your other skills (game theory, lineup construction, etc.) to maintain or even increase your edge? From a theorycrafting perspective, the reason why DFS always remained nothing more than a low hanging fruit / learning exercise for me (in addition to being an old man who unless I automate real-time breaking news consumption I have no desire to put in the requisite work for point #3 that you raised) is because with static pricing and the availability of ever increasing accurate sites like yours, one would think the edge diminishes. Throw in the almost to double to sports wagering vig and I wonder how more serious DFS players such as yourself feel about your future quantifiable edge.
2) Just to make sure I fully understand your EMH explanation, am I correct in concluding that the sharper the pricing gets on a site, the greater the edge for the DFS pro vs average user and, conversely, the softer the pricing is (cough DK), the more variance you will run across which actually decreases your edge. Are these statements correct?
Feel free not to spend too much time on replying to these questions – I really posted the first time to help with #content, but you raised some key pieces of information so now I’m interested.
@doreedo – Your post echoes where I’m at right now.
Obviously, I see the RG Field Reports and have a general idea of ownership percentages, but this is obviously an incomplete picture for numerous reasons (DK, late swap, contest price differences, contest type, etc.). Like you said though, I can instinctively get an idea, but this means I’m not getting an accurate, “precise” (with a certain confidence level) EV for each play. I guess continuing to work on my ownership projection model is a good use of my time.
Your line on “the trickiest situations are when you project a person to have a high point total, a high value, and a high projected ownership” is exactly the problem I ran into last year. My nature is to be contrarian so players like Doug Baldwin and his absurd TD rate to close the year, Devonta Freeman’s talent vs volume (weeks 3 and 4 specifically), etc were all players that fit your statement. Obviously, being severely underweight on those plays despite having point/cost projections due to what seemed like too high of ownership put me in the red for the season.
Thanks everyone for the confirmation bias I needed to keep working down this path.
