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  • JonBales

    RotoAcademy Lead Instructor

    • 501

      RG Overall Ranking

    • 2019 DraftKings FFWC Finalist

    • x3

      2015 DraftKings FFWC Finalist

    It’s that time of the year again – the time when no one is thinking about baseball but I am because I’ll be writing another daily fantasy baseball book.

    I have a bit of time before I actually begin writing, so I wanted to reach out to the community and see if there’s any specific topics you want me to cover or data you want me to collect. Obviously I’ll be doing all of the normal stuff, updated for 2016 (stacking data, cap allocation, park factors, etc), but feel free to throw out any specific ideas you’d like to see me research and write about.

  • bosoxfan0509

    • 2015 FAWBC Finalist

    @Jon Bales said...

    These are both great ideas. I think accurately predicting steals is one of the bigger potential edges in MLB right now.

    This is definitely something I’d like to improve on and would get me to buy the book on its own.

  • dpadilla

    I’ve been thinking since the offseason began, would it be profitable to stack the same bad offense, especially one with a favorable park (PHI) every night and lock in their cheap price and low ownership and just wait for them to have a good game?

  • JonBales

    RotoAcademy Lead Instructor

    • 501

      RG Overall Ranking

    • 2019 DraftKings FFWC Finalist

    • x3

      2015 DraftKings FFWC Finalist

    @dpadilla said...

    I’ve been thinking since the offseason began, would it be profitable to stack the same bad offense, especially one with a favorable park (PHI) every night and lock in their cheap price and low ownership and just wait for them to have a good game?

    I don’t think there’s any question this will be a profitable strategy with certain teams – it’s just a matter of how accurately you can identify those offenses.

  • toomeyy

    I would like to be able to identify weather patterns per ballpark ratio in regards to fantasy points. I know this is a major asset to identify early in the season. Another point I would like to come across while reading is the strength in bullpen and the effect it has on fantasy points per teams line up. I also read a comment that lead me to think of another topic: Managers contribution to a players fantasy points. For instance, is one manager more likely to make a substitution during a game? Specifically targeting pitchers whom get yanked or batters in the bottom half of the order whom are taken out due to a negative pitcher matchup. Obviously there is a lot of variance is this question however, we must narrow down variance while decision making while drafting.

  • DntSwtDaTcnq

    When can we expect this to be completed?

  • Putz

    @Jon Bales said...

    It’s that time of the year again – the time when no one is thinking about baseball but I am because I’ll be writing another daily fantasy baseball book.

    I have a bit of time before I actually begin writing, so I wanted to reach out to the community and see if there’s any specific topics you want me to cover or data you want me to collect. Obviously I’ll be doing all of the normal stuff, updated for 2016 (stacking data, cap allocation, park factors, etc), but feel free to throw out any specific ideas you’d like to see me research and write about.

    I’d like to see how you can use sentiment and trends from social media and from experts to pick players (sleepers and contrarian). A guy another site did this at small scale for football, but I would like to see it applied to MLB, at least for pitchers, but maybe even other positions.

  • rotoryan

    I think a chapter on umpire data and tendencies would be useful.

  • JonBales

    RotoAcademy Lead Instructor

    • 501

      RG Overall Ranking

    • 2019 DraftKings FFWC Finalist

    • x3

      2015 DraftKings FFWC Finalist

    @DntSwtDaTcnq said...

    When can we expect this to be completed?

    Maybe mid-to-late March? I wrote the last one in 13 days so I move quickly once I get motivated.

  • JonBales

    RotoAcademy Lead Instructor

    • 501

      RG Overall Ranking

    • 2019 DraftKings FFWC Finalist

    • x3

      2015 DraftKings FFWC Finalist

    @Putz said...

    I’d like to see how you can use sentiment and trends from social media and from experts to pick players (sleepers and contrarian). A guy another site did this at small scale for football, but I would like to see it applied to MLB, at least for pitchers, but maybe even other positions.

    I’ve looked into this a lot. It is a BIG project and so fragile. More of art than science for me at this point, but I’ll continue to dig.

  • JonBales

    RotoAcademy Lead Instructor

    • 501

      RG Overall Ranking

    • 2019 DraftKings FFWC Finalist

    • x3

      2015 DraftKings FFWC Finalist

    @rotoryan said...

    I think a chapter on umpire data and tendencies would be useful.

    This will be in the book.

  • AidenTheEnemy

    @Jon Bales said...

    I don’t think there’s any question this will be a profitable strategy with certain teams – it’s just a matter of how accurately you can identify those offenses.

    I’ve yet to play one slate of DFS baseball but plan to do so this year and I was thinking if that is a viable strategy. I’m loving this thread :)

  • Putz

    @Jon Bales said...

    I’ve looked into this a lot. It is a BIG project and so fragile. More of art than science for me at this point, but I’ll continue to dig.

    There is a science behind the wisdom of the crowd aspect of it.

  • TeamTwerk

    I’d like to read about the trend of fewer and fewer innings for starting pitchers and the impact on DFS strategy.

  • topspinner

    would like to see more analysis on hot and cold streaks as pertaining to both pitchers and batters. Read score casting and don’t buy what they are selling.

  • JonBales

    RotoAcademy Lead Instructor

    • 501

      RG Overall Ranking

    • 2019 DraftKings FFWC Finalist

    • x3

      2015 DraftKings FFWC Finalist

    @topspinner said...

    would like to see more analysis on hot and cold streaks as pertaining to both pitchers and batters.

    I’m working on this. I like that you separated the two because I do think we need to approach them differently – almost like trying to identify a hot and cold streak in NBA vs NHL

  • mlemming15

    I’d like to see how you attack the beginning of the MLB season vs the middle and end of the season. To be more specific, (please correct me if I’m wrong) while according to your books you’re a big GPP player. Do you play even more GPPs in the beginning of the year because there is a very limited sample to start the year, thus making it a bit easier to exploit initial hot hitters/pitchers at the start of the season before sites like FD and DK can catch up? Then do you pump the brakes a bit on GPPs after DFS sites have had a chance to adjust their pricing for these hot hitters/pitchers? Also, at what point in the season is the sample big enough for you to start using a player’s current season stats/splits along with the previous year and their career averages?

    I bought your last DFS MLB book and am currently subscribed to FantasyLabs. Great insight all around and would definitely recommend any and every DFS player to check them both out (hopefully this comment doesn’t get my post deleted). Hoping to one day make DFS more than just a hobby. Huge fan of all your work.

    Cheers,
    -mlemming15

  • smallANDflaccid

    @Putz said...

    There is a science behind the wisdom of the crowd aspect of it.

    It would involve getting access to the right data feeds (most of which are not free and not easy to pull free), historically as well as live (drinking from the firehose of Twitter is expensive and a very large amount of data).

    From there you need to build the right natural language processing tools to analyze the sentiment type you want to look for – you could likely leverage existing Python libraries for it.

    From there, historically map the names/phrases and their sentiment to their events – and decide if you map actual day to actual day, front lag them, or post lag them – or some combination.

    Then run that through machine learning processing – at the bare minimum for correlations, but something more complex (random forest, XGB, Vowpal Wabbit, or nn) to then get any predictive elements out of it… and then score them to see how accurate they are vs random noise.

    I know for a fact there are hedge funds doing the equivalent for trading stocks (look up what happens to Berkshire Hathaway trade volume when positive news about Anne Hathaway movies hits – as for a way to not do it – I have’t looked recently, but I assume whatever fund was f’ing that up fixed it or went survivor bias into the night).

    From experience, I would say this likely isn’t as helpful as you might think.

    The wisdom of the crowd is one thing, but there are also a lot of noisy idiots, so you need to figure out how to mask the noise.
    Much harder than you may think.

  • smallANDflaccid

    @smallANDflaccid said...

    Much harder than you may think.

    That said, I would love for it to show up in one of these books so a bunch of people spend months tilting at windmills instead of more constructive areas.

  • dncolonna

    Does anyone have total DK/FD points scored by each player last year? I can run all the calculations if necessary, but wanted to see if this was accessible in raw form.

    My thought is that a FP/AB stat would at least be interesting if not useful.

  • cdenny99

    @Jon Bales said...

    One thing I’ve wondered but don’t actually know is whether or not it’s worth stacking the bottom of the order on high-powered offenses or just going with a typical top-of-the-order stack on a less potent contrarian offense.

    I had a little success with this last year. I thought by taking the chalk pitcher, the leftover monetary constraint forced me into some unique stacking opportunities. There were days when the bottom of the order Blue Jays killed it.

  • KindGuy

    @smallANDflaccid said...

    It would involve getting access to the right data feeds (most of which are not free and not easy to pull free), historically as well as live (drinking from the firehose of Twitter is expensive and a very large amount of data).

    From there you need to build the right natural language processing tools to analyze the sentiment type you want to look for – you could likely leverage existing Python libraries for it.

    From there, historically map the names/phrases and their sentiment to their events – and decide if you map actual day to actual day, front lag them, or post lag them – or some combination.

    Then run that through machine learning processing – at the bare minimum for correlations, but something more complex (random forest, XGB, Vowpal Wabbit, or nn) to then get any predictive elements out of it… and then score them to see how accurate they are vs random noise.

    I know for a fact there are hedge funds doing the equivalent for trading stocks (look up what happens to Berkshire Hathaway trade volume when positive news about Anne Hathaway movies hits – as for a way to not do it – I have’t looked recently, but I assume whatever fund was f’ing that up fixed it or went survivor bias into the night).

    From experience, I would say this likely isn’t as helpful as you might think.

    The wisdom of the crowd is one thing, but there are also a lot of noisy idiots, so you need to figure out how to mask the noise.
    Much harder than you may think.

    Did this actually happen? And how the hell did you learn all this “programming” language, ie. Nerd talk? Lol, I’ve tried to get into programming but it is just too much… sigh

  • KindGuy

    @dncolonna said...

    Does anyone have total DK/FD points scored by each player last year? I can run all the calculations if necessary, but wanted to see if this was accessible in raw form.

    My thought is that a FP/AB stat would at least be interesting if not useful.

    I’ve thought about this as well since it sounds similar to fp/min in basketball. The thing is, since baseball is such a team sport, atleast in dfs don’t runs and rbi have to be highly dependent on team production?

  • smallANDflaccid

    @elementasrat said...

    Did this actually happen? And how the hell did you learn all this “programming” language, ie. Nerd talk? Lol, I’ve tried to get into programming but it is just too much… sigh

    Yes.
    (not sure this is the best article for it, but close enough http://www.theatlantic.com/technology/archive/2011/03/does-anne-hathaway-news-drive-berkshire-hathaways-stock/72661/ )

    And I was CTO in the finance world for 12 years, related to hedge funds, had my own consulting firm, a failed startup, and these days do architecture analysis for buyouts and machine learning / data science for fun.

  • dncolonna

    Without testing it my guess would be that fp/min in NBA is going to be a stronger predictor than fp/ab simply because all at bats in baseball are not created equal. For example, if a hitter comes up with two on and no one out he has a stronger chance of accumulating points then if he were to be leading off an inning.

  • NeRo9k

    How about the Hot-hand fallacy?! https://www.gsb.stanford.edu/insights/jeffrey-zwiebel-why-hot-hand-may-be-real-after-all

    Although this article is over a year old, I still found it interesting; and, this entire topic may be an appealing read in your book.

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