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Innovative Investing (Notes from June 3 – June 9, 2019) June 25, 2019

Posted by Anthony in Automation, cannabis, Digital, education, experience, finance, Founders, global, Leadership, medicine, NFL, questions, social, Strategy, training, Uncategorized, WomenInWork.
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The primary theme of the week seemed to be how data can get pooled together to determine a signal and how to learn to seek the best way we, as individuals or teams, can discern valuable content to motivate actions on that information. Data is plenty – it’s a matter of gathering, curation, analysis and testing before putting it into action. This is done by any number and types of companies nowadays – this is a source of advantage seeking that forward-thinking ones make, in my opinion.

Since my notes were more detailed, I’ll try to keep this brief. The wonder people below hailed from banks (First Republic Bank), funds like Emerson Collective and Womens VCFund, marketing company like BEN or LikeFolio and then David Epstein’s Range, Sinead O’Sullivan’s work on space or the data Rohan Kumar collects with Azure Data.

Create a hypothesis. Test the hypothesis. Put into action, or iterate. Rinse, repeat. Good luck!

  • Samir Kaji, (@samirkaji) MD @ First Republic Bank (20min VC 093)
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    • Leading private bank and wealth management, before at SVB
    • 1999 – “anyone with a pulse could get a job” but he was working selling vacuum cleaners at dept store
      • Was told by family to get a real job – applied to first business SVB, got resume in and interview immediately before starting
      • First couple years were tough – learned a lot, but was 2004 until companies had scaled and were getting bigger
    • First 10 years were tech companies, series A and B and venture debt – post 2009 Lehman / Bear, went to venture group at SVB for 4 years
      • Made the move with a few others from SVB to First Republic, now leading team in micro-VC and early-stage tech co’s
    • Says the micro-VC is more entrepreneurial & collegial compared to extended stage VC’s
      • First fund is that you can get traction for a second or third one, fees as pressure – most likely why many people come from some wealth
        • Writing large checks as GP, as well
      • 2-2.5% management fees initially vs 1 / 25 or 1/30 model
      • 1999 – 2002 distribution was 0.9x and you’d get 10x return (whoops) – very difficult for funds to get 2-3x for LPs
    • Barriers to entry much smaller for $20-25million as compared to $500mln – institutional, etc — he can go to family friends and high net worth
    • Seed over next 5 years: contraction in space (wrong), but said there isn’t enough returns for funds to max it
      • 1100 in the 2000 year and burst
      • Continued prominence of Angelist platforms, maybe an integral part of the ecosystem
      • Starting to see use of data (Mattermark, CBInsights, SignalFire) to more efficiently identify and action at this level
    • Favorite book is Phil Jackson’s – behavioral psychology, Give and Take is another one
    • Really respects the pioneers of the industry and first-time fund-raisers
      • Mike Maples, Michael Deering, Steve Anderson, Jeff Clavier when it wasn’t a thought
    • Habit – reading book or blog post for 20min in the morning before email
      • Disconnect from audio / video devices and reflect for an hour
      • 2 hours a day for family/friends and disconnecting, as well
    • Thomas Redpoint, Mark Suster, Brad Feld, Strictly VC, Ezra at Chicago Ventures
    • Knows awesome fundraisers but terrible at returning capital – didn’t mention any
  • Collectively Driving Change, Laurene Powell Jobs and Ben Horowitz (a16z 5/27/2019)
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    • LPJ – founder, president of Emerson Collective
    • Grew up in NJ – father passed away in a plane accident when she was 3 – 3 children.
      • Mom remarried so there were 6 of them. Wooded area of NJ.
      • Core values and dedication to education to get out of the area.
      • She went to Upenn – first student from her high school that went to Ivy League – ~20% went on to more schools
    • Addressing East Palo Alto school as a volunteer to help – 1st talk, 0 had taken SATs
      • What happens when you’re first to graduate high school? What’s it mean to the information from family?
      • What happens to be first to want to go to college, thrive&complete it?
        • To have the aspiration, can be a leader in the family – translator, get sucked into all problems
      • Started with 25 freshmen – would have to come with friends for responsibility mechanisms – for College Track
        • 3000 high school students, 1000 college, 550 grads
    • Collective of leaders, innovators – education inequities, access and need for enhanced/robust curriculum
    • 10 year time horizons – getting them together is scheduled with Monday all-staff meetings (3×3 matrix of videos)
      • 5 cities, sometimes philanthropic speakers or reports
      • Discussion of reading as you fall behind through third grade before switching to reading to learn – already behind
    • XQ as SuperSchool dream – 17 of 19 will open in August
    • Caring about impact and solving problems, not wealth increasing – wants access to policy or money and not taxes
      • Judged Giving Pledge for not wanting to be more philanthropic
      • Environmental, edtech portfolio, cancer / oncology investments, immigration incubator, new thinking to old problems
    • How do you know when you’re succeeding? Collecting data on everything they do.
      • Example: XQ – schools and districts, state of RI as switching to statewide competition
      • Chicago has good data for fatal/nonfatal deaths (I disagree)
    • Imperiled or important institutions like journalism and media need to be sustained, how many join?
      • Concentrating and following where IQ is migrating (hahaha – what a joke)
  • Data Infrastructure in the Cloud, Rohan Kumar at BUILD conference (Data Skeptic, 5/18/19)
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    • Corp VP of Eng of Azure Data Team at Microsoft – SQL and data services, open source, analytics, etc
    • Trends in data engineering in the cloud, serverless and hyperscale
      • ML and AI and enabling applications – shifting to edge vs cloud – analysts predict 70% will be on edge devices
      • Solutions and private edges – training in the cloud and deploy them on the edge applications
        • Data platform needs to be the right foundation
    • Highlight for him from conference: work they’ve done on relational databases in the cloud – as volumes grow, scalability challenges
      • Hyperscale for Azure and PostgreSQL, as well as MS SQL soon enough – system scales with needs (they’ve tested <= 100TB)
    • Acquired Citus Data, support scaling out the compute layer – strong team, great product, matches in Azure and open-source
    • Releasing serverless option for Azure database – costs designed to stay low and optimized
    • Analytics side: customers wanted to do real-time operational analytics – didn’t want to move them outside of their core product
      • How is data distributed and having compute be co-located with the data to gain Spark efficiency being nearest to node
      • Support Jupyter notebooks across all APIs to modernize to do more predictive analytics
      • Attempting to build out pipelines requires too much scripts, instead have Data Flows in Azure Data Factory – no-code and UI
      • Wrangling data visually and seeing if something can be recognized or learned to repeat across other columns/tables
    • Latency won’t be ideal if compute nodes occur nonlocal to the data changes – can’t do 50,000 nodes all at once
    • Excited for the future: Horizon 1 (next 8-12 months), Horizon 2 (~3 years), Horizon 3 (moonshots)
      • H2: Hardware trends, what do customers want? Pushing boundaries of AI and ML, healthcare, gaming, financial services, retail
  • Wide or Deep? David Epstein, author of Range (Invest like the Best, 5/28/19, ep. 133)
    • First book’s research lead him to get into specialization and finding kernel for next
      • Some countries: turning around national sports teams – why don’t we try other sports? Contrary to 10,000 hour rule.
      • SSAC – debating Gladwell – athletes have a sampling period instead of first gene – delay specialization
        • Used Tiger vs Roger – Roger had tried a ton of sports vs Tiger who was born and was playing golf
    • He was not good at predicting what people/public would attach themselves on to – 10,000 hour rule – race/gender as most talked (but weren’t)
      • 10,000 hour rule were based on 30 violinists in world famous music academy (restriction of range)
      • Height in American population vs points scored in NBA (positive correlation) but if you restrict height to NBA players, negative
    • Finnish cross country skier who has genetic mutation similar to Lance’s boosted
      • Sensitivity to pain and modification to your environment – also sudden cardiac arrest in athletes (what pushed his interests)
      • Book as opposition to Outliers and Talent Code – interpreted a lack of evidence as evidence of absence (genetics matter)
        • First year he read 10 journal articles a day and not writing – they were making conclusions they could not make based on their data
      • Differential responses to training – best talent were missed because we don’t know about training responses
    • Collection and exploration phase – competitive advantage for expansive search function to connect sources or topics
      • Has a statistician on retainer, essentially, to check models or surveys
      • Wanted to know what he was missing – “how come I broke the 800m women’s world record after 2 years of practice? – genetic difference”
        • Racing whippets – 40% had a genetic defect that gave them more muscle and oxygen
    • All of sports as a limited analogy (problem after Sports Gene; now, more tempered)
      • Robin Hogarth addressed “When do people get better with experience?” Don’t know rules, can try to deduce them but can’t know for sure.
      • Kind learning environment: feedback immediate, steps clear, information, goal ahead
      • Wicked learning environment: can’t see all information, don’t wait for others, feedback delayed/inaccurate
    • Study at Air Force on “Impact of Teacher Quality on Cadets”
      • Have to take 3 maths – calc I, II, III (20 kids randomized) – professors best at causing kids to do well (overperforming) systematically undermined their performance thereafter
        • 6th in performance and 7th in student evaluations was dead last in deep learning
        • Narrow curricula were better at the test that they had at the end would be negatively correlated with going forward in performance
      • Teachers that ignored what was on the test taught a broader curriculum (making connections vs procedures)
    • Learning hacks: Testing (wonderful – primed to test ahead of learning), Spacing (deliberate not-practicing, Spanish ex spread 4 hour twice, 8 hours), Mixed practice
      • Ease is bad – known time horizon for when you have forgotten again – interleaving and spacing mixed
    • Passion vs Grit (“Trouble with Too Much Grit” – Angela Duckworth’s research)
      • Duckworth did a study at West Point for East Barracks cadets – candidates score (test + leadership + athletic) was not good prediction of doing this (overall it was good)
        • Grit was a better predictor for making it through East Barracks – she questioned whether it had an independent aspect
        • Variance for grit was probably 1-6%, especially after “flattening” groups – looking at people that had a narrowly defined goal for short periods (cadets or spellers)
      • Cadets were scoring lower on grit at late 20s vs earlier – tried some things, learned others about what they want – grit is poorly constructed
        • Look holistically – if, then signatures (giant rave – introvert, small team – extroverts) right fit looks like grit – developmental trajectory as explosion matching spot
    • Choosing a match for a future them who they don’t know in a world they can’t comprehend – people that find good fits (in practice, not theory)
      • Paul Graham’s “Commencement Speech” that he wrote “Most will tell you to predict what you want in 20 years and march toward it.” (premature optimization)
        • Everything you know is constrained by our previous experiences – limited as a teenager – just expanding and learning as you go forward
    • Gameboy example – with so much specialized information that can be disseminated easier – can take from all types of domains and recombine them
      • System of parallel trenches – can be broader much easier now – hired people for Japanese and German translations
      • Japanese man profiled in his book – technology was changing faster than sun melts ice – didn’t get Tokyo interviews
        • When he got to Kyoto company making playing cards, he was a tinkerer who was maintaining machines – started to mess with them (arms)
        • Turned them into a toy, and it was Nintendo – cartoon-branded noodles (failed), and had toy development
          • Lateral thinking with withered technology – stuff that’s cheap, easily available – takes into other areas
            • Remote control, more features – wanted to democratize this and strips it down – LeftyRX only left-turns
        • Sees calculator from Sharp and Casio and thinks he can do a screen and handheld game – small games
          • Had issues with Newton’s rings so he found other small tech (credit cards embossed) to fix small pieces
      • What it lacked in color, graphics and durability (could dry it out, batteries would be fine, split it up, “app” developers because it was super easy to understand)
      • In areas that next steps were clear, specialists were much better – less clear, generalists were more impactful – depends on the specificity of the problem
        • 3M had a lot of areas for this, “Periodic Table of Technology” – post-it note came from reusable adhesive that had no use for
        • Only Chinese national woman to win Nobel – “Three No’s” (No post-grad, foreign research, membership in academy)
          • Interest in science, history – Chinese medicine for treatments of malaria – world’s most effective treatment from ancient text
  • Greg Isaacs, BEN (Branded Entertainment Network) (Wharton XM, Marketing)
    Print

    • Discussion of getting data from Netflix / Amazon / Hulu / tv to better match brands and advertising
      • Dirty data via a wharton grad who set up a survey style
      • Cohorts and demographics, along with psychographics
    • After getting data, attempting to approach Youtubers / social media influencers, tv spots and channels or shows to get their brands in front of the right people
      • More pointed, depending on what interests are for their cohorts
      • Creative storytelling as the change of cultural mind shift has increased
  • Understanding the Space Economy, Sinead O’Sullivan (@sineados1), entrepreneur fellow at HBS (HBR IdeaCast #684, 5/28/19)
    • Facebook, Amazon (3000), SpaceX (12,000) and other funding like Blue Origin / SpaceX / asteroid mining or travel
    • Global space economy as $1tn by 20 years – currently $325bn so it would need to 3x
      • Breaking apart space resources and otherwise – earth-focused (delivering or existing in space that helps earth)
        • Exploration or creating interplanetary existence
    • Running out of space in space for satellites – comparing to airplane docking / loading
      • $2500 per kg now to launch, used to be $50k / kg
    • Reliance had been on unilateral agreement for space policy – one tech startup launched a satellite that didn’t have permission (but no fall-out)
      • Food / grocery stores, wifi, phone, insurance pricing due to satellite data – reliance on services are increasing as the market increases
      • Thinks that we’re close to seeing the cheapest cost of launching – cites SpaceX, but won’t allow everyone to participate
    • Ultrahigh accuracy will require higher powered satellites – GPS, nonmilitary grade is ~0.5 m – thinks it will prevent autonomous vehicles solution
    • Ton of money going into asteroid mining but thinks it’s better for testing missions to Mars and figuring out the problems for future
      • Looking at Uber at start and say “people won’t get into a stranger’s car” or other cases as how we see the future – going to Mars, etc
    • Earth-focused space technology – 100+ launched satellite start-ups, micronano satellites, relay companies, downstream analytics
      • More touchpoints for everything in this manner
      • SpaceX will increase public and government intervention and within 50 years, maybe see a human launched there
  • Investing w Twitter Sentiment, Andy Swan (@andyswan), LikeFolio (Standard Deviations, 4/25/19)
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    • 1700+ tweets examined per minute in LikeFolio – discovering consumer behavior shifts before news
      • Direct partnership with Twitter to create massive database and how they’re talked about to look for mentions
      • Purchase intent, sentiment mentions – trends across product categories or brands
    • Example – Delta (as host is a loyalist) – making adjustments
      • Expectations are the relative part – comparison to the baselines (metrics compared to itself as baseline)
    • Put out a comprehensive report on Apple day after keynote event – September 14, 2018
      • Consumers were unimpressed with iPhone lineup – more price sensitive than maybe they’d considered
      • Apple Watch was the silver lining – stock / sales may struggle over 3-9 months (upgrade cycles)
    • WTW version of keynotes – NYE resolutions – subscribing early to drive revenues the rest of the way
      • Purchasing mentions were only up 30-40% compared to 5 or 7x weekly mentions (big difference)
    • Shelf-life and how to consider the sentiment data – lead time may be binary corp event (same store sales or year)
      • Couple months with Apple, for instance, but with Crocs – resurgence that persisted to current time
    • Set up keyword structure and brand database – “I’m eating an apple” as opposed to an Apple mention – human eyes to ‘label’
      • “Closed my 3 rings” – apple watch but sarcasm / spam that wasn’t caught (estimates at 2-3% of data)
      • If spam / sarcasm are consistent portions of the data, doesn’t really have an effect
    • Twitter Mood Predicts Stock Market – Bollen, Mao, Zeng (88% and 5-6% predictions) – fund closed up shortly
    • Advantage being better than analysts or pricing and codifying sentiment behavior compared to past quarters, data
      • Some consumer trends analyzed as true tipping point or actual movements
      • Public prediction before productizing their modeling – made 40 and were 38-2 (confidence as highest)
      • Investing as very specific, concentrated and holding ammo compared to trading with option spreads and has risk profile built
    • https://arxiv.org/pdf/1010.3003.pdf
    • Diversification as 20-25 stocks, doing it over time and with conviction can be done
    • Starting in Louisville for his fintech company, host in Alabama, for instance
      • Talent can be more difficult to seek out but the world is globally flattening via the internet
      • 70% lower overhead cost than being in SF, for instance – developers would anyhow be in Slack channels / not a big deal
      • Reduction in cost maintains greater control of company since they don’t have to take reduction of equity to gather more
    • Network effects don’t matter if you don’t have a great product or product-market-fit
    • Free association game
      • grapenuts: best cereal (Co’s been around for 100+ years, branding and $ spent and they can’t figure it out)
      • Fintech Future: individualization and customization
      • Victory: most important thing in life, achieved what you set out to do – setting goals and achieving these
      • Bourbon: pappie von winkle – collecting for dust on shelf 10 years ago and now going for $3000
  • Jonathan Abrams, co-founder Nuzzel news (Launch Pad)
    nuzzel

    • Landing hedgehog as the mascot – animal as cute, 99designs and surveying 50 friends – 25 men/women
    • Discussing how VC’s don’t have great advice, especially when general – too hard to be an expert in such a wide range
      • Finds it easier to be very context-driven and providing solutions or action-oriented questions to founders
      • Investing now easier with YC and Angelist, etc…
    • Timing and other mistakes he made – out of control, losing equity part early (but depends on where you are / what you need)
  • Etan Green, professor at Wharton (Wharton Moneyball)
    • Discussion on paper of how sharp money comes in at horse racing tracks
      • Difference between sites – fairground action compared to tracks, and specific to region (New Orleans, Minnesota, for instance)
      • Big sharp money comes in very late, pushing the underdog prices to higher values
        • More expensive to bet while at the track than the APIs enabling higher volume bets
        • Books at the track are incentivized to bring in as much $ as possible, so $0.20 on $1 vs $0.15 rebate on $0.20 for volume
    • Value and differences in how people will bet
  • Edith Dorsen, Women’s VCFund founder, MD (Wharton XM)
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    • Talking about their focus on first fund, approach
    • Opportunity for finding diverse founders, 25% of their fund had a woman founder
    • Starting a second fund
    • Had consumer tech, enterprise and not so much b2b, but trying to increase
      • Hard to say or give advice if one of their partners don’t have expertise in the domain
  • Sophie Lanfear, Silverback Films producer on Netflix “Our Planet” (Wharton XM)
    • Species that are dying, going extinct
    • What we can do about it
  • Aliza Sherman, Ellementa co-founder, CEO (Wharton XM)
    logo

    • Discussion of client talks when she made them aware of her cannabis endeavors
    • How friendly the community is
      • Then knocked the idea that ~30% was female to start before diving off a cliff
    • CBD to mask opioids – does it really do anything from a pain/treatment perspective, though?
      • Anti-chemo because of CBD – really?
    • Sounded too rehearsed – made it sound fake, not genuine
      • Passion/motivation/mission and kept repeating as the best advice she could give – painful
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Experimentation & Testing (Notes from March 25 – March 31, 2019) April 17, 2019

Posted by Anthony in Digital, experience, finance, global, Hiring, questions, social, training, TV, Uncategorized.
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I know, I know. It’s a bit of a cop out to use a Game of Thrones image on the back of the Season 8 premiere from Sunday. Sue me [please don’t]. And I’ll give credit to the image creator: Instagram @chartrdaily for the fun visualization. However, after listening to Pinnacle Sports’ Marco Blume, I couldn’t help after hearing deployment strategies for their prop bets on popular TV shows, such as who will be left on the Iron Throne or the ever popular “Who dies first?” props. They experiment, hypothesize, post a line with a limit (hedge risk) and let the market decide from there. And boom – we have the theme of the week!

Antoine Nussenbaum, of Felix Capital at the time, mentioned going from private equity to start-ups and venture funding where they had to decide between backing people or belief in the company. He got first-hand experience by starting a company with his wife, successfully gaining funding, and then exiting – only to fail with a different company that wasn’t scaling. How did he go through frameworks to decide on startups to fund or help?

Mark Suster gave his take on how he comes to investment funding – sales, technical skills and being aware of each. How did his entrepreneurship experience influence his framework for funding new start ups? Why is it that there is a sweet spot for amounts based on run rate? Experimenting, failing and adjusting.

Then I had listened to 2 data scientist / researchers in their discussions of NLP parts – what to test, what they assumed to be true, how to approach new methodology and testing this methodology. Is there a limit to the progression that can be made with NLP? Why might it be relevant to decide on testing state-of-the-art further? Then, ultimately, what’s the applications for how we can use that optimization to improve the current status quo?

I hope everyone checks out what may interest them – this was a fascinating and fun week. So much so, that I suggested to a few different students for them to check out different parts (granted, I do this often, but I was quite excited to share these ones).

Cheers!

  • Antoine Nussenbaum (@Nussenbaum), Principal and cofounder of Felix Capital (20min VC 084)
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    • Partner at Atlas Global prior, p/e fund that was part of GLG Partners
      • Working on digital early-stage, venture fund and helped startups bootstrap after missing the tech side
      • Miraki, Jellynote, Pave, Reedsy, and 31Dover as some of his best investments
      • Helped start Huckletree with his wife
        • Looked for investment of $80mln but got $120mln
    • Backing someone vs backing the company initially in early stage funds
    • Raised in Paris in international environment, lived in UK as well
    • Launched 2004 software-on-demand business with 2 friends “that was not scalable at all”
    • Did M&A in the UK after leaving software
    • Felix Capital at intersection of creativity + technology, lifestyle brands: ecommerce and media, enabling tech
      • Stages – flexible capital, but have made investments from $200k – $6mln, focus on Series A + B
      • Geographic – agnostic, as long as backing entrepreneurs
      • Advisory services and focused on helping their investment companies
    • More entrepreneurs that know the playbook and how they can build, grow and scale
      • Looking for more companies that can scale globally or expanding outside with proper funding
    • Using Triangle as an example – bathing suits on Instagram strategy and launching millions of product via digital
    • ProductHunt as a blog he gets lost in – 15 min of destruction
    • Lifestyle-related excitement: food side, better life, marketplaces
    • Hard Thing about Hard Things and Capital in the 21st Century – relationship of wealth and economic wealth
  • Mark Suster (@msuster), MP @ Upfront Ventures (20min VC 085)
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    • Was VP of PM at Salesforce.com before Upfront
    • Late 80s – had an interest in development as a student in college in the UK
      • Worked initially as a programmer at Anderson (Accenture) for 8 years
      • Entrepreneurship isn’t for everyone – better to start earlier, need to have a fundamental understanding of systems (coding)
        • Python, PHP, Ruby, JavaScript – not trying to become best developer – just knowing the systems
        • Sales experience would be second – telesales or customer support – ask CEO to do an hour a week of calls
    • Started 2 software companies – one in England and then Silicon Valley, selling both – backer brought him in to VC
      • Fred Wilson wasn’t an entrepreneur, but does give you the insight
    • Don’t get the sense of urgency with too long a time – 3 months vs 12 months
      • Too much capital creates laziness and shortcuts that lead to mistakes
      • 18 month run rate for capital – takes 3-4 months to raise (start with 6 months plus)
    • Wants to see early stage companies once a month, roughly.
    • $240mln fund – invest half into companies and reserve the other half for follow-ons
      • 3 year timeframe, $40mln with 5 partners – $8mln per partner
        • Series A, B rounds where each partner is doing 2-3 deals per year when avg is $3-5mln investment
    • On his blog, has the “11 Attributes of Entrepreneurs”
      • Best known post would be “Invest in Lines, not Dots” – x-axis as time, y-axis is performance (any given day, your dot)
        • Interactions create a line that matches a pattern and he can decide if he wants to do business
      • Not a big fan of deal days or investor days where you hype up a company because of this
    • 50 coffee meetings a year – once a week, if you meet 50 entrepreneurs a year, maybe you’ll become close with 5-10 of them
      • Single best introduction is from a portfolio company CEO for an investor
    • He knows and built software company – SaaS-space since he knows how to be helpful
      • Data and video tech industry (has 11 personal investments and 5 are video)
      • AgTech as an underappreciated industry so far – stays quiet until a few investments before hyping
    • Too much company, too much money and entrepreneurs clouding the market for everyone else
    • Book “Accidental Superpower”, how demographics and topology will drive the future and how areas grow
  • Marco Blume, Trading Director at Pinnacle Sports (DataFramed #54 2/18/19)
    pinnacle_logo

    • Got into data science by “sheer force”, building quant team out from Excel going to R
      • Efficiency was by orders of magnitude since R was better than Excel
      • Could do anything with risk management, trading, sports
    • Pricing GoT, hot dog eating contest, pope election and making the lines
      • Use pricing and market analytics to let the people set prices
    • Risk management in general – maximize probability and hedging risk
      • Does the bottom line change? Does it affect anything? Regulations.
    • NBA where all teams have played each other – have a good idea of strength of teams
      • Soccer or world cup – not as much certainty with teams not always playing each other
      • Start of season has a lot more volatility and responsiveness to bets because of uncertainty
        • By end of season, bookmarkers have the price and knowledge, so they’re likely to increase risk
      • Bayesian updating
    • Goals to improve models, open new betting options to clients
      • Low margin, high volume bookmaker – little bit with a lot of options
      • Book of Superforecasting – group of people who are better at forecasting
        • Pays them already at Pinnacle – consultants, betting and paying the price
    • Much bigger R shop than Python at Pinnacle, active in the R community
      • R becoming more of an interfacing language and production language (vs C# or other), can use R-keras or plumbr
      • Teaching dplyr, rmarkdown and ggplot cover 95% of their work outside of specialists
    • GoT as one of his favorite bets
  • Matthew Peters (@mattthemathman), Research Scientist at AI2 – ElMo (Data Skeptic 3/29/2019)
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    • Research for the common good, Seattle, WA research
    • Language understanding tasks – ELMo (embeddings from Language Models)
    • PhD in Applied Math at UW, climate modeling and large scale data analysis
      • Went to mortgage modeling, tech industry with ML and Prod dev in Seattle
    • Trying to solve with very little human-annotated data, technical articles or peer-reviewed
      • Very difficult, very expensive to annotate – can you do NLP to help?
    • Word2vec as method for text to run ML on text, context meanings of say, bank
    • ELMo as training on lots of unlabeled data
      • Given a partial language fragment, language modeling predicts what can come next
      • Forward direction or backward direction (end of context), neural network architecture
    • Research community may want to use ELMo, commercial use to improve models already in prod
      • Pre-trained models available and open source
    • In the paper, evaluated NLP models on 6 tasks – sentiment, Q&A, info extraction, co-reference resolution, NL inference
      • Got significant improvements on results from the prior state-of-the-art models
      • Character-based vs word approach
        • Single system should process as much text as possible (morphology of the word, for instance)
    • Paper over a year old now but Bert was put up on ArXiv to improve upon ELMo (transformer architecture for efficiency)
      • Scaled the model that could be trained by many X’s, quality is tied to the size / capacity
      • Language modeling loss changed, as well (word removed from middle of sentence and predict before/after)
      • Large Bert models have computational restrictions – how far can you get by scaling the model
  • Kyle and early Data Science Hiring Processes (Data Skeptic 12/28/18)
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    • Success isn’t correlated with ability to give good advice
    • Conversion funnel for businesses: website that sells t-shirts, for instance
      • Tons of ways to bring people into the door / website (ads, social media campaign, ad clicks)
      • Register an account or put into cart (what %, track it, a/b test and improve)
      • Cart to checkout process (how many ppl? Credit card entered, goes through, etc…)
    • Do any sites convert faster than others? Keep track, find out why / focus on continuing it
    • Steps for job hire: video chat / task / phone screens / on-site next / offer
    • Resume should be pdf (doc may not open nicely on Mac or otherwise) – include GitHub
    • SVM – should have margins or kernel trick on resume (otherwise, don’t include it)
      •  Ex: ARIMA (auto-regressive integrated moving average) – time series data

Luck versus Skill June 9, 2016

Posted by Anthony in experience, social.
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On my drive home from work late this evening, I was listening to Wharton Moneyball Business XM. They had Michael Mauboussin on, author of The Success Equation: Untangling Skill and Luck in Business, Sports, and InvestingThe Success Equation: Untangling Skill and Luck in Business, Sports, and Investing. Fascinating stuff, discussing the difference between skill and luck, primarily how difficult it is for the human mind to differentiate the two. Humans tend to not just accept something that is, and something that may happen needs a cause or something that created it. There should be a reason for any given occurrence.

In trading or investing, we call some of these events “black swan events”. Companies and markets attempt to assess the risk of these events. Since they’re usually rare and unpredictable, it’s a tough thing to assess risk of something you may not know the cause / effect of at the time it occurs, let alone prior. So we assume that our skill and history will provide the outcome – likely incorrectly, or just by luck we may be correct.

Michael went on to discuss how in business there is a market for possibly being lucky. Using a trader who performed well as an example – recent studies show that it’s increasingly about chance/luck than skill in trading performance. However, if a trader outperforms, they could request a raise or use their performance as the expected amount to move to another company. Depending on the due diligence and statistical/skill assessment of the firms, this creates a market for production by luck.

In reading moneyball and the increasing sports analytics movement, they measure this against regression to the mean in a number of + stats. But in general, pros have a higher skill vs others, and the standard deviation, if you will, of said skills is much smaller. Minor nuances represent the differences in ‘higher’ skill than ‘lower’ at a professional level. A great year by an average pro could result in regression toward the career average. If this is not the case, then that player has probably found an efficiency level that could be affected by actions on their part to reduce the level of variance in that element.

I found the paradox of skill and luck explained very well. Typically, we see the two as a continuum – where on one end luck would play a part such as a roulette table or coins. On the other side, skill – maybe boxing, running. However, it seems to be more array/matrix-like, in that as you increase skill, you increase the dependence on luck. Separation at the most-skilled level involves all kinds of luck.

One author described it using Ted Williams’ .406 batting average in 1941. He had tremendous skill, ahead of most players in the professional leagues. However, that year, he also exhibited a tremendous amount of luck, again more than most players. That combination can attribute to some of the most heralded sporting feats. Our acknowledgement of those streaks come without luck – and that the players were just that skilled. Skilled yes, but also incredibly lucky.

Michael continued to go on about the statistics of lacrosse, and its rules are pulled from hockey and basketball. He noted that Canadian players in college lacrosse are extra efficient. Citing rules of box lacrosse (played usually on a hockey rink, much smaller in comparison to the field as is typical), they aim for smaller goals and have less space to work with. When they get on the field, the added space and larger goal sets them up to be monsters in shooting efficiency. Numbers-wise, 5% of D1 lax players are Canadian. Yet, they make up ~20% of the goals. Additionally, there was a 5% arbitrage between shooting accuracy – overall average was about 28%, non-Canadians shot 27.8% and Canadians? – nearly 33% of shots turned into goals. Astounding.

Statistical notes such as these create fascinating opportunities for further studies and team options, not just in sports but also in business. Taking note and then taking advantage will be easier with the increased abundance in acquiring data, but how much can we direct to noise, and how much is actually signal?

 

 

Fiduciary Standards – ’bout time April 8, 2016

Posted by Anthony in finance, questions, social, Uncategorized.
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Well, it’s a start. The US has passed a law that is set to go into effect by, *drum roll, please!*, early-2018 for fiduciary (client’s best interest) standards for some 300,000 financial advisers who deal with retirement plans (401k’s, IRAs, Roths, etc…). So, come 2018, your adviser may now only legally act in your best interest and give objective advice on your options – you know, what lawyers and bank trust officers have had to do for decades.

Don’t hold your breath just yet, however. It seems that this will be fought, as appeals are thought to be in the works. Why would advisers want to act in your best interest if that doesn’t make them the most money?? Currently, advisers are only limited to giving advice on products/plans that fall into ‘appropriate age and risk-tolerance’.

So basically this means you cannot get straight sleazy sales – any adviser would have to produce all of the options and give an objective opinion on what would be the best – cost and plan-wise. How that will be determined is anyone’s guess since it’s their job to know all of the options. I do not expect someone that works and is an expert in their own field to also know about finances and everything that goes into them – which is all the more reason to make sure you vet the experience and practice of any adviser that you wish to go with.

The Time article that mentioned this article Fiduciary Standard approximated that it could save $17 billion for retirement investors. In a country that holds a dumb amount of debt in the form of student loans / credit card debt, this seems small (Trillions of debt vs billions saved), but it’s certainly not insignificant.

Something around 3-5% of people currently retired have more than $60k a year. I would hope that this action helps that poor statistic, and that in the near-future, with the amount of knowledge and technology available, that costs come down and everyone can either automate or receive the pointed help that they deserve.

Note: Licensed Life, Disability, LTC Insurance

Week 5 Quick Review & Week 6 Start October 14, 2015

Posted by Anthony in Daily fantasy football, DFS, Draftkings, experience, FanDuel, NFL, Stacks, Week 5, Week 6.
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So, I’m going to keep this brief. I got way too ballsy this past week. Too many tournaments, too much risk, and not enough lineups to increase variation. I got slammed with wrong choices in having Demaryius / Julio in almost all line-ups. I got hurt by Lacy’s lack of pass-catching (and too much emphasis on home success for GB) & JC’s injury. TE & D/ST picks were bad, as well. Charles Clay didn’t do anything & Bennett was targeted 11 times but caught only 4 in what played out as an underperform. Jaguars got shellacked and the Giants, for whatever reason, created minimal pressure Sunday night. Well – that’s a lot of bad in spots that statistically should have been consistently high-floor.

So, the good? Rivers & Bell saved my ass Monday night so I didn’t get blanked. I wasn’t on ABrown due to Vick’s lack of rapport with him, which was good in a fade-case. Allen Robinson performed with Blake Bortles (who I was on in a few leagues). Rivers had a few good games. Brady/Edelman worked out after the 4th qtr touchdown but Gronk disappointed for how expensive he was. Dion Lewis was productive. I was not on Devonta Freeman and he continued to have a nice game. I also faded a good Doug Martin spot (suspect weather, minimal passing potentially). I figured that I would have scored with a few lineups in the high 140’s but I didn’t. Only one lineup hit 165+ and cashed on DK.


Week 6
I will try to do better – buckled down and read through more of Jonathan Bales’ series Fantasy Football for Smart People. Staying consistent with bankroll management and being diligent with a process weekly will be vital in success. To compile the statistics, I’ll likely use a trial for DailyFantasyNerd or Fantasy Labs to ensure I have the data in one spot. I could create a page for myself, but that will be fine-tuning what I want to use consistently.

So, let’s start with defenses. I read through Bill Barnwell of Grantland’s NFL Statistical Temperature. Without looking at this week’s schedule, I pared down defenses that I would be interested in playing, depending on home/away, weather, opponent, in no particular order.

Denver, Arizona, GB, NYJ (w/out a D TD so far) are the top tier. TEN, DET, NE, CIN, SEA, CAR would be the next, likely. PHI, MIN and ATL have been making plays but could be inconsistent depending on game flow.

RB’s – Forte, Foster, TJ Yeldon look good so far. Ryan Mathews has been productive. Do we continue to ride Devonta?
WR’s – Hopkins is just gobbling up targets for ppr leagues. AJ Green could be interesting – I feel like Dalton alternates between his receivers/tight ends. Just focuses in on them. EIfert was last week so Green could be this week. We cannot forget about TASER, Bryan Mears’ new statistic – anticipatory of red zone regression for touchdowns (potentially). Golden Tate, Amari Cooper, Demaryius Thomas & Keenan Allen headline it. Of those, I’d think Cooper & Golden Tate are most likely to score (Denver needs to get NEAR the red zone first and Keenan may be left out since Antonio is back). Let’s flip a coin between Hurns/Robinson again or play them both – that’s worked before. Snead on a Thursday night could be a fade position, or minimal play because of the cheap cost still. We all THINK it will be high-scoring… but will it on a Thursday with Julio in pain? I’d like to think that it won’t be and be in better position Sunday.

TE’s – I’ll have some action with Gronk & Gates. Chargers will have to throw against GB. Eifert played incredibly on Sunday (thanks to that for my main season-long league). Barnidge is apparently a) a real-life football player and b) target monster. Charles Clay is near the top of TASER as well, but with Tyrod potentially out, I may want to avoid that Bills line-up altogether.

These are my initial thoughts – I’ll see today and tomorrow what I can put together and post going forward.

Good luck to the ALDS teams today in their Game 5’s!

Happy to say my Red Wings in NHL are an impressive 3-0-0 with a +7 differential to go with the Broncos 5-0. Keep it up!

Week 4 Results – DK Fail & FanDuel Success October 7, 2015

Posted by Anthony in Daily fantasy football, DFS, Draftkings, experience, FanDuel, gym, NFL, Stacks, week 4.
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Yup, that’s right! FanDuel – there wasn’t a contest that I didn’t place in. Mainly due to the fact that my lineups did very well in all of the 50/50’s, DoubleUps and free-rolls. However, on draftkings, where I played a majority of tournaments, I was very close to min. cash or not in the money at all.

For week 3, I was gone/without service all weekend, so the few lineups I had in, I minimally cashed/lost. 2 weeks in a row on DK that I have lost a few $ here and there (not to mention the fun runs I took at LoL Championships as well as Wed-Fri spread of MLB games – not wise when teams were already clinched or didn’t care).

  • Fanduel results:
    5 entries played – 4 wins – 2 ticket (survivor advance, barely)
    I don’t remember where I read it, but there was an analysis done on larger 50/50’s where, assuming you’re on average better than a majority of players, you have a greater success rate in larger pools. So far, that’s proved to be correct.

    • In $350k Double-Up (with nearly 75k entries), I placed 5842 with a score of 123.74 ($5 entry for $10 winnings).
      Dalton/Green stack with LMurray, Karlos, Demaryius, Amari, MBennett, MBryant/Falcons D stack (high score)
    • FPFC Qualifier Double-Up (152 of 521) with score of 111.94 ($10 entry for $20 winnings)
      Carr/Crabtree stack w/ Karlos, JCharles, JuJones (ouch), JaJones, Barnidge (savior), Hauschka/Seattle D stack
    • Excl Football Guys Contest didn’t go that well (777 of 3119) with 110.84 ($2 entry $2 win)
      same as FPFC except swapped JamesJones/Barnidge for Amari & Martellus
    • FBGFC Qualifier Double-Up tournament (32 of 1097) with 130.06 ($10 entry $20 win & ticket later in season)
      Cam, Forte, Karlos, Julio, Amari, Moncrief, Martellus, Josh Lambo (nice 14), Falcons D (yup)
    • $250k Sun NFL Survivor Tourney (42406 of 57471) with 88.54 ($5 entry, ticket won, advanced)
      Dalton/Green stack with Randle/LMurray(ouch comb 13), demaryius, stevieJ (ugh), ebron (injured), mcmanus/Denver D stack (decent scores)
    • FantasyPros $2000 contest for saved lineups – placed 65 of 951 for $20 credit on FD (devonta and andy dalton)
  • So I found that I like stacking kickers with the defenses so far. Relatively positive correlation and it has worked with a select few kickers/defense – good field position relations, typically.
  • Draftkings results:
    • As I mentioned, this didn’t go as well. Likely because I played in too many tournaments.
    • In the double-ups I played, I cashed in all 3. So for those keeping count, I was 6 for 6.
    • Same lineup for 3 double-ups score of 136.74 (62 of 217), (99 of 340), (423 of 1135) for 3x $1 entries, $2 each of winnings
      Carr/Crabtree stack, Gore, Karlos, Cobb, Deandre (2nd half!!), JuJones, Bennett (yup), Broncos D
    • I played all-day Sunday teams and had a ~112 score, while I played early Sunday match lineups and had those go for 147 and 144 for winners.
    • 2x $.25 Arcade & $2k First Down ($1 entry, $2 win) earlies for $.50 winnings on $.25 entries (144.66 score)
      Tyrod/Karlos/Clay/Bills D stack w/ Forte, AJ Green, T.Y., Deandre (savior), AllenRob
    • Had a ThursSun Line-up that did terribly (103.96) for $3 entry and 0 winnings
      Flacco/SmithSr/MaxxWill/Ravens stack, Leveon, Ivory (rb successes), ABrown, Rishard, Jarvis (wr duds)
    • Played $0.25 Quarter Arcade (Sun only) for $1 entry, $2 winnings (147.56 score)
      Rodgers, Devonta (great), Hyde, Keenan, Amari, JuJones, Fleener (good), Karlos, Raiders D
    • Daily Dollar ($50k) $1 entry for $2 win (136.74 score for 9220 of 52596)
      Carr/Crabtree stack, Gore, Karlos, Cobb, Deandre, JuJones, Bennett, Broncos D
    • 130.16 score didn’t work for $1 $150k First Down or $0.25 Arcade
      Rodgers/Packers, Devonta, Gore, Evans, JuJones, Moncrief, Olsen, Karlos
    • Another Flacco / Ravens stack for Thurs-Sun produced the 112, which didn’t go well in any of the 3 entries (-$5).
  • So we’ll see. Seems consistent with stacks where I’ll go 2 for 4 and hope that the stacks hit bigger. This week, they didn’t do as much because of the chalk not performing as well. The few good calls at TE and Devonta saved those stacks to at least cash.

We’ll see how the next week goes – I may end up trying more double-ups to at least build bankroll.

To next week!

Week 2 NFL DFS Recap September 23, 2015

Posted by Anthony in Daily fantasy football, DFS, experience, NFL, Week 2.
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I woke up this morning to news from the lineup optimizer on FantasyPros: “CONGRATS! You came in 14th out of 1,728 players in our DraftKings Lineup Contest. Claim your $20 DraftKings Prize: You’re all set. The $20 prize will be credited to the DraftKings use”

Great news! I only entered 2 lineups since I had forgot about it, and my Ravens stack did very well.

QB Flacco, RB Forsett, RB Lynch, FLEX Hyde (two weaknesses), WR A Brown, WR A Robinson, WR J Landry, TE Gillmore, D Ravens

I have really enjoyed fantasypros – found them last year before the season so I have a pro subscription for seasonal fantasy and have found their optimizer one of my favorites. Nearly all of the data is pooled from experts across the industry.
First, let’s speak of Fanduel, where I played 3 lineups in a total of 6 contests. I had 2 H2H (one was free, other for $2), 1 50/50 league at $1, 1 DoubleUp for $2, another for $10, and a $4k tourney for $1. I won both H2H and the $10 double up. Something like $16.00 in fees for a simple winnings of $23, +$7 (~38%). The TeamRankings Football Championship Week 2 was my best scoring lineup – 123.2, good for 83 of 535. Lineup was saved by Antonio Brown (32) and Larry Fitz (33.2), Jordan Matthews garbage time TD (17), and consistent scoring from Bailey (8) and Texans D (6), but dragged by Brees, Abdullah, Lynch, ASJ.
99.9 was enough to win a the $2 and free H2H’s, rolling with Carson Palmer (22.2), John Brown (7), L Miller (6.7), Lynch (7.7), Garcon (11.3), ODB (24.1), Eifert (12.9), Josh Brown (9) and Ravens D (-1). RB’s didn’t end up haunting me in the H2H, thankfully.
With DraftKings, I had a lineup go for 188.66, one for 180.36, one for 171.96, 129.9, 122, 89.36, 80.26, 115.9, or 126.3.
  • I played 5 H2H’s – one lost on Monday night. I went 1-1 in $5 H2H (net -$1), 1-2 in $1 H2H (net -$1.20).
  • I played 3 freerolls and went winless.
  • Where I did make my money was in $0.25 arcades, $1 and $3 tourneys.
    • I placed 1405 of 115k for $7 on a $1 entry (net $6) with 180.36
    • I played 5 quarter arcades (3-2) for 3 entries won (net $3.75) – 188.66, 180.36 and 171.96.
    • I played 2 $1 entry to $10k gpp and finished 149 and 151 of 11.5k entries (180.36 and 180.26) for (net $8)
  • I found great success with stacking Pitt (Big Ben, Deangelo, ABrown, Heath) as well as mixing in Woodhead, TWill, and the Cardinals D.
  • Flacco, Forsett (eh), Gillmore and Ravens D (eh) with receivers ABrown, Allen Robinson, and Jarvis. Marshawn was not a success but needed a big horse to anchor the RB spots.
  • I had Saints and Eagles stacks that did not perform very well. Luckily, they were good enough in h2h games, saved by other receiver plays.

Until next week, see you!

Thoughts of the Day – GenY finance, Daily fantasy ‘expert’-testing, other questions September 17, 2015

Posted by Anthony in Altucher, DFS, experience, finance, gym, PGA, questions, Scutify, social, training.
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Every day I receive a post from James Altucher @jaltucher – he’s an author of one of USA Today’s “12 Best Business Books of All-Time” Choose Yourself, which describes at length the power of one’s self, as well as a successful (and that does not mean he hasn’t failed) entrepreneur, hedge fund manager, asset manager, columnist, as well as podcast producer. His valuable insights, podcasts and publications enlighten us to choose yourself and your passions to create revenue streams aplenty. He simply asks a lot of questions of many people to see what has driven them, and in turn, learn for himself.

In my recent conversations, I have noticed that this is a skill that is falling out of favor very easily of many people – and more so, whether they’re just more of who I come into contact with, but Gen Y and Millennials. So, in light of my observations, I would like to go over what I observed/questioned today.

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Daily Fantasy NFL Week 1 Results September 15, 2015

Posted by Anthony in Daily fantasy football, DFS, NFL, Week 1.
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So, after 1 week I am in the black quite a bit, despite an overall losing record! Who doesn’t like those tournaments?!

Last year I was going more off of a feel for playing and breaking even. This year, I will look for tournament opportunities for overlay and pad with a better performance on h2h, triple ups or 50/50s. Fortunately, for overlays (where value is much higher per entry because the guaranteed money does not meet the number of entries), I stumbled upon a number of sites that aid in finding these. Namely, Super Lobby! which combines Draftkings and Fanduel (among others) to show overlay in tournaments. Incredibly useful for the people who are trying to be smart about entry fees.

For those that are curious how I did this past week, continue reading. Otherwise, I will post who I’m looking at and what I’m doing for Week 2 in the coming days! Again, if you’re looking to play or take a shot: Draftkings link! – Remember to search for a promo code on your favorite fantasy / insider website to valuable premium subscriptions (Rotogrinders, 4for4, fantasypros, teamrankings). Fanduel if you prefer .5 ppr and Kickers

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