Harvey

@roberto_boberto

PhD student looking into afl models follow for nothing but footy content

Sydney, New South Wales
Vrijeme pridruživanja: prosinac 2010.
204 fotografije ili videozapisa Fotografije i videozapisi

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  1. proslijedio/la je Tweet
    prije 3 sata

    The DNC trying to access the Iowa caucus results

    1 reply 18 proslijeđenih tweetova 113 korisnika označava da im se sviđa
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  2. proslijedio/la je Tweet
    28. sij

    From Football Newbies to NFL (data) Champions | A Winner’s Interview with The Zoo

    7 proslijeđenih tweetova 44 korisnika označavaju da im se sviđa
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  3. proslijedio/la je Tweet
    27. sij

    The coolest thing about predictions is we get distributions, and not just point estimates. Touchdown probabilities, first down probabilities, probability of getting stuffed. All accessible. Here are CDFs of two sample plays, as well as the running back view points

    6 proslijeđenih tweetova 57 korisnika označava da im se sviđa
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  4. proslijedio/la je Tweet
    25. sij

    Next pup up at Butler! Blue IV, who will replace retiring Blue III, was introduced on Friday and he is adorable 🐶

    85 replies 1.259 proslijeđenih tweetova 13.761 korisnik označava da mu se sviđa
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    25. sij

    #68 - 'Then What'

    20 replies 49 proslijeđenih tweetova 366 korisnika označava da im se sviđa
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    21. sij

    We've put together our own votes... Can anyone guess what we're trying to say? 🤔

    2 proslijeđena tweeta 8 korisnika označava da im se sviđa
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    24. sij

    Do running backs matter? What if I told you for the winning NFL data bowl competition that the running back didn't matter or wasn't taken into account? Wonder what think?

    21 proslijeđeni tweet 71 korisnik označava da mu se sviđa
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  8. proslijedio/la je Tweet
    24. sij

    Unreal precision. 🎯 dominated during on !

    41 reply 428 proslijeđenih tweetova 3.772 korisnika označavaju da im se sviđa
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  9. 24. sij

    sample sizes included 😍😍😍

    49ers EPA/play during regular season, win probability between 20% and 80%: Kittle + Juszczyk on field: 0.23 (n = 189) Juszczyk on, Kittle off: 0.10 (n = 69) Kittle on, Juszczyk off: 0.05 (n = 328) Both off: -0.01 (n = 92)
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    22. sij

    When we read graphs, we use a variety of cognitive shortcuts, leading to either better or biased interpretations. explains how cognitive science helps us build effective visualizations - and go to for a how-to guide

    4 proslijeđena tweeta 25 korisnika označava da im se sviđa
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  11. proslijedio/la je Tweet
    21. sij

    via Sharing data improves visibility, and makes the research process transparent.

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    20. sij

    My colleagues at and I have written a series of reports for on the impacts of extreme heat 🌡 and on summer sports, including the 🎾

    1 reply 8 proslijeđenih tweetova 12 korisnika označava da im se sviđa
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  13. 19. sij

    This keeps getting posted, wonder why its not getting filled?

    1 proslijeđeni tweet 11 korisnika označava da im se sviđa
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  14. proslijedio/la je Tweet
    17. sij

    The 2020 Fellowship application is now open! Grant recipients receive $5k to cover expenses over the course of a 6-week internship in sports tech. See below to learn more and apply ⬇️

    11 proslijeđenih tweetova 14 korisnika označava da im se sviđa
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  15. proslijedio/la je Tweet
    17. sij

    Some Jatz and onion dip $12 at a Sydney pub, reminding me once again to trust my instincts and never leave my house

    114 replies 121 proslijeđeni tweet 1.063 korisnika označavaju da im se sviđa
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  16. proslijedio/la je Tweet
    17. sij

    An Introduction to College Football Data with R and : - Loading college football pbp data in R - Data manipulation and creating stats - Foundations of Visualization - Clean summary tables with gt()

    14 replies 60 proslijeđenih tweetova 432 korisnika označavaju da im se sviđa
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  17. proslijedio/la je Tweet
    17. sij

    How should statisticians communicate uncertainty? ACEMS Chief Investigator from is this week's guest on the Stats & Stories podcast

    17 proslijeđenih tweetova 21 korisnik označava da mu se sviđa
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  18. 16. sij

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  19. proslijedio/la je Tweet
    16. sij

    Fantastic work by , with code that serves as a great tutorial for those who want to sharpen their skills and/or become more familiar with the infer package.

    We all know about score effects at 5v5, but what about 5v4? I hinted at this during my OTTHAC talk, but I’ve wanted to look into this for a while. So I used ’s adjustment method to examine potential score effects on the power play.
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