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Vivid last won the day on September 4

Vivid had the most liked content!

About Vivid

  • Rank
    Head Coach
  • Birthday 06/17/1992


  • Favorite Team
    Baylor Bears and Wisconsin Badgers

Coaching Information

  • Offense
  • Defense
  • Special Teams
  • Clock Mgmt
  • Discipline
  • Youth Mgmt
  • CFBHC Career
    UNLV Rebels (2021) Baylor Bears (2021-Present)
  • NFLHC Career
  • Achievements

Recent Profile Visitors

1,113 profile views
  1. Vivid

    IRL Football

    For sure dude! Lets see them!
  2. Vivid

    IRL Football

    got any pics playin hockey?
  3. Seriously don't understand this one. Not a single bit.
  4. Vivid

    IRL Football

    Thought it would be cool to post some pictures of the good ole' playing days if anyone wants to share some of their IRL football experiences. These are from the HS days way back in 2009.
  5. Obviously would’ve liked the W there but actually happy with how that went. Tried a different plan and made a difficult game very close. Gg @Kremit! Looking forward to a tough large dozen schedule.
  6. Vivid

    CFBHC v1.6b

    Unless Wisconsin State and Wisconsin Tech get any better he may just do it....
  7. Don't get me wrong they are talented and I thank you for recruiting them, but somehow I don't know how to get them to consistently show up on statsheets for me. Maybe they just miss you....
  8. Ik its Troy but its nice to see Powers get on the statsheet for once. Also the offense keeps momentum up for the in conference schedule.
  9. Working on two papers and two presentations currently. I can post links here when/if they get published. I will also put any presentations and such on my website I'll try to post some graphs later of the network.
  10. Thanks bud! unfortunately it will probably be just a one time thing. but if we have enough people that would want to do it again I could always do it again and we can see how things change over time and seasons.
  11. Hey All, As some of you might remember I am working on my PhD and asked for people to fill out a survey last year to help do some research. I used several different ways to analyze this data and will try to break down some of it below. If you want to know more let me know. I am making sweeping conclusions based on the collective results and it may not represent everyone here; however, I would like to hear your thoughts on what you think about it. I apologize if the below is phrased overly "scientific" its largely copied from a draft I am working on. In short, I used network correlation/regression models and exponential random graph models to analyze the patterns in depressive symptoms, social support, and speaking to others online about important life matters. 37 members filled out the entire survey. Members who responded to the survey were on average 24.76 years old. Members reported they spent, on average, 12.57 hours (SD=8.60) on the site per week. To measure depression a standardized scale was used called the PHQ. The average PHQ score was 6.92 (SD=5.86). By using the suggested score of 10 as a cut-point, 13.5% (5 members) of the sample would be listed as depressed or at-risk. Members reported significantly more support from their “in-real-life” network, when compared to the support they reported from their online network. When asked to nominate other members with whom they spoke to about important life matters, members reported an average of 6.11 other members. Using this network I was able to analyze network effects. Depressive symptoms, online support, and IRL support were not spatially autocorrelated. This means that these features were not clumped in certain areas of the network. Members were significantly more likely to reach out and speak to other members about important life matters if they reported more site hours, more depressive symptoms, and less IRL support. When a model was developed to predict depressive symptoms, IRL support decreased the severity of depressive symptoms;however, members who reported more online connections also reported more depressive symptoms on average. The model also showed a significant network effect. A significant network effect means the depressive symptoms present within a someone’s network were significantly associated with the depressive symptoms of that person. Too long; Didnt read summary: Thank you to everyone that did this. I am eternally grateful and this is really cool stuff. I think this site is a very unique place where people can reach out and create connections regardless of IRL. Depression is a very real thing and people shouldn't be hesitant to reach out to formal and informal ways of helping.
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