Jonah Rosner

@jonisportsci

I like exercise and data. Sometimes I try combining them.

Vrijeme pridruživanja: kolovoz 2019.
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  1. proslijedio/la je Tweet
    prije 17 sati

    Excited to announce on Thursday of this week, I'll be in Austin, TX to give a talk on baseball analytics / about in 's Sports Analytics class! If you haven't read his book, Sprawlball, you need to. This chart alone tells the tale of what it's about.

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  2. proslijedio/la je Tweet
    17. pro 2019.

    📢New paper from (Performance Coordinator at the Academy) et al. ⚽️Monitoring the Athlete Match Response: Can External Load Variables Predict Post-match Acute and Residual Fatigue in Soccer? 📃Full free text:

    1 reply 36 proslijeđenih tweetova 105 korisnika označava da im se sviđa
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  3. proslijedio/la je Tweet
    29. sij

    Statistical errors in medical research are surprisingly common. Register for our Feb 5 webinar with and learn simple ways to sleuth out statistical errors in medical literature. You don't have to be a programmer to be a stats detective!

    3 proslijeđena tweeta 15 korisnika označava da im se sviđa
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  4. proslijedio/la je Tweet
    1. velj

    Forget about multiple testing corrections. Actually, forget about hypothesis testing entirely.

    28 proslijeđenih tweetova 66 korisnika označava da im se sviđa
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  5. proslijedio/la je Tweet
    1. velj

    OH NO JERICHO 😱

    97 proslijeđenih tweetova 522 korisnika označavaju da im se sviđa
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  6. proslijedio/la je Tweet
    1. velj

    My P-values on Trial is out at Harvard Data Science Review. I've linked to it and a summary by the editor on my blog: Your comments are invited.

    10 proslijeđenih tweetova 21 korisnik označava da mu se sviđa
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  7. proslijedio/la je Tweet
    30. sij

    Anaerobic speed reserve (ASR) = important factor when programming supramaximal efforts (above vVo2 max) 🚀 Higher ASR = better able to tolerate high speed running & lower exercise load (player B) 📈 More info in the book & online course

    13 proslijeđenih tweetova 78 korisnika označava da im se sviđa
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  8. proslijedio/la je Tweet
    29. sij

    Several folks at have told me they are curious about the Tidyverse but they are a little overwhelmed because of all of the Tidyverse knowledge on display this week so I wrote a post called An Irresponsibly Brief Introduction to the Tidyverse:

    81 proslijeđeni tweet 327 korisnika označava da im se sviđa
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  9. proslijedio/la je Tweet
    29. sij

    Exercise-induced skeletal muscle growth. Hypertrophy or hyperplasia?

    1 reply 18 proslijeđenih tweetova 44 korisnika označavaju da im se sviđa
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  10. proslijedio/la je Tweet
    30. sij
    Odgovor korisnicima i sljedećem broju korisnika:

    ACWR makes no sense but all are ignoring the signals that something is wrong. The solution is that practitioners should do what they have done before. Using training principles and common sense. And no, ACWR has been sold as magic numbers but maybe you haven’t realised.

    2 proslijeđena tweeta 15 korisnika označava da im se sviđa
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  11. proslijedio/la je Tweet
    30. sij

    Acute:Chronic training load debunked NEVER VALIDATED yet appears in a Consensus! Why is this acceptable for science+evidence informed practitioners? Expect more on complexity in future: less 'Sweet Spot,' 'ACWR',' load management'

    5 replies 32 proslijeđena tweeta 105 korisnika označava da im se sviđa
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  12. proslijedio/la je Tweet
    29. sij

    Modeling Longitudinal Outcomes: A Contrast of Two Methods

    1 reply 6 proslijeđenih tweetova 12 korisnika označava da im se sviđa
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  13. proslijedio/la je Tweet
    28. sij

    Divergent roles of inflammation in skeletal muscle recovery from injury | Review

    8 proslijeđenih tweetova 38 korisnika označava da im se sviđa
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  14. proslijedio/la je Tweet
    29. sij

    "Extracting Player Tracking data from video using non-stationary cameras and a combination of computer vision techniques" is a research paper finalist at ! Looking forward to presenting my research and hopefully I will have a few cool updates to share too!

    Extracting Player Tracking data from video using non-stationary...
    Extracting Player Tracking data from video using non-stationary cameras and a combination of computer vision techniques
    11 replies 51 proslijeđeni tweet 390 korisnika označava da im se sviđa
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  15. proslijedio/la je Tweet
    17. sij

    Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs 📊

    4 proslijeđena tweeta 17 korisnika označava da im se sviđa
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  16. proslijedio/la je Tweet
    10. sij

    Our preliminary data shows that patellar tendon pathology likely develops in adolescence. Other literature supports this concept. Excess load disrupts normal tendon-bone maturation Data from a larger cohort early next year

    Please, please do not inject corticosteroids into teenagers’ patella tendons. Or at least warn them.
    7 replies 93 proslijeđena tweeta 286 korisnika označava da im se sviđa
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  17. proslijedio/la je Tweet
    31. sij 2019.

    ⁦⁩ keynote. Do you dislike someone? Give them a corticosteroid injection & they are 11 times more likely to have a recurrence of their tendinopathy than if you inject placebo. .

    10 replies 243 proslijeđena tweeta 412 korisnika označava da im se sviđa
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  18. proslijedio/la je Tweet
    28. sij

    An issue I see w/ Sports Sci is that people keep creating ratios & random values to describe training & suggest that some athletes did “a lot” while others did “a little”. Yet they haven’t linked these measures back to in game perf —the stuff that actually matters. Too much noise

    6 replies 14 proslijeđenih tweetova 112 korisnika označava da im se sviđa
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  19. proslijedio/la je Tweet
    28. sij

    Retweeting because I am really excited about this. I am willing to bet that 1) thinking about the next hypothesis you will test in machine readable terms will immediately improve what you are doing, and 2) better meta-data will make science massively more efficient.

    New preprint with where we make the case for machine readable hypothesis tests . We give a real-life example, argue this would improve the rigour and falsifiability of hypothesis tests, as well as facilitate the re-use of key info in articles.
    1 reply 11 proslijeđenih tweetova 72 korisnika označavaju da im se sviđa
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  20. proslijedio/la je Tweet
    27. sij

    The p-value debate has three related but distinct pieces - Bayesian/frequentist, dichotomization, and standard of evidence. Essentially, you can object to "p<0.025" for any/all of the p, the <, or the 0.025. The Bayesian and "standards" questions are too often confounded.

    ‘Appalling science’ and dichotomania: my response to Hardwicke and Ioannidis
    5 proslijeđenih tweetova 16 korisnika označava da im se sviđa
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