Rubbertuckie

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About Rubbertuckie

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  1. And to think, the Texans were ready to run fuller out of town.
  2. How much BC football and Philadelphia high school football do you watch? That's probably the reason.
  3. I use ipvanish app on fire stick. 10 bucks a month. Super easy to use.
  4. It's just makes it easy to get what you can get for free and legalish. I live out of market. (Wilmington, NC). I watch these games with my cable subscription and VPN. I connect to a server in Atlanta(so I have an Atlanta IP) then log in to my cable ap and I get the local fox channel.
  5. That's okay. I was writing that post and forgot to add it in the totals. I had to go back and edit. Sitting in my house waiting for Dorian to hit. Bored to death.
  6. Gotta add one more game to loss total. For that whole Superbowl debacle thing. 23-22
  7. Lol. I had a cat as a kid named that. Back before I was married I would tell drunk girls that was my name to start convo. Lol. My name is Scott really though.
  8. I think week 13 is the last week with byes. Not next week.
  9. How Our NFL Predictions Work Filed under Methodology See our latest predictions references Pro-Football-Reference.com Autocorrelation / Elo rating / Monte Carlo simulations / Regression to the mean the details FiveThirtyEight has an admitted fondness for the Elo rating — a simple system that judges teams or players based on head-to-head results — and we’ve used it to rate competitors in basketball, baseball, tennis and various other sports over the years. The sport we cut our teeth on, though, was professional football. Way back in 2014, we developed our NFL Elo ratings to forecast the outcome of every game. The nuts and bolts of that system are described below. Game predictions In essence, Elo assigns every team a power rating (the NFL average is around 1500). Those ratings are then used to generate win probabilities for games, based on the difference in quality between the two teams involved, plus the location of the matchup. After the game, each team’s rating changes based on the result, in relation to how unexpected the outcome was and the winning margin. This process is repeated for every game, from kickoff in September until the Super Bowl. For any game between two teams (A and with certain pregame Elo ratings, the odds of Team A winning are: Pr(A)=110−EloDiff400+1 ELODIFF is Team A’s rating minus Team B’s rating, plus or minus a home-field adjustment of 65 points, depending on who was at home. (There is no home-field adjustment for neutral-site games such as the Super Bowl1 or the NFL’s International Series.) Fun fact: If you want to compare Elo’s predictions with point spreads like the Vegas line, you can also divide ELODIFF by 25 to get the spread for the game. Once the game is over, the pregame ratings are adjusted up (for the winning team) and down (for the loser). We do this using a combination of factors: The K-factor. All Elo systems come with a special multiplier called K that regulates how quickly the ratings change in response to new information. A high K-factor tells Elo to be very sensitive to recent results, causing the ratings to jump around a lot based on each game’s outcome; a low K-factor makes Elo slow to change its opinion about teams, since every game carries comparatively little weight. In our NFL research, we found that the ideal K-factor for predicting future games is 20 — large enough that new results carry weight, but not so large that the ratings bounce around each week. The forecast delta. This is the difference between the binary result of the game (1 for a win, 0 for a loss, 0.5 for a tie) and the pregame win probability as predicted by Elo. Since Elo is fundamentally a system that adjusts its prior assumptions based on new information, the larger the gap between what actually happened and what it had predicted going into a game, the more it shifts each team’s pregame rating in response. Truly shocking outcomes are like a wake-up call for Elo: They indicate that its pregame expectations were probably quite wrong and thus in need of serious updating. The margin-of-victory multiplier. The two factors above would be sufficient if we were judging teams based only on wins and losses (and, yes, Donovan McNabb, sometimes ties). But we also want to be able to take into account how a team won — whether they dominated their opponents or simply squeaked past them. To that end, we created a multiplier that gives teams (ever-diminishing) credit for blowout wins by taking the natural logarithm of their point differential plus 1 point. MovMultiplier=ln(WinnerPointDiff+1)×2.2WinnerEloDiff×0.001+2.2 This factor also carries an additional adjustment for autocorrelation, which is the bane of all Elo systems that try to adjust for scoring margin. Technically speaking, autocorrelation is the tendency of a time series to be correlated with its past and future values. In football terms, that means the Elo ratings of good teams run the risk of being inflated because favorites not only win more often, but they also tend to put up larger margins in their wins than underdogs do in theirs. Since Elo gives more credit for larger wins, this means that top-rated teams could see their ratings swell disproportionately over time without an adjustment. To combat this, we scale down the margin-of-victory multiplier for teams that were bigger favorites going into the game.2 Multiply all of those factors together, and you have the total number of Elo points that should shift from the loser to the winner in a given game. (Elo is a closed system where every point gained by one team is a point lost by another.) Put another way: A team’s postgame Elo is simply its pregame Elo plus or minus the Elo shift implied by the game’s result — and in turn, that postgame Elo becomes the pregame Elo for a team’s next matchup. Circle of life. Elo does have its limitations, however. It doesn’t know about trades or injuries that happen midseason, so it can’t adjust its ratings in real time for the absence of an important player (such as a starting quarterback). Over time, it will theoretically detect such a change when a team’s performance drops because of the injury, but Elo is always playing catch-up in that department. Normally, any time you see a major disparity between Elo’s predicted spread and the Vegas line for a game, it will be because Elo has no means of adjusting for key changes to a roster and the bookmakers do. thats how they come up with it. Lol
  10. Apparently 538 thinks we have the 11th best chance to make the playoffs. I’m not sure about their formula but I do know they take SoS into account.
  11. New position. It's different than nickel. Ease him into it.
  12. I think they probably dont trust the over the top help. Also it's hard to cover anyone for 5 seconds.
  13. Honestly, I think poole would make a pretty darn good safety. I would work Oliver into nickel and start giving poole some safety work. You can get away as not being as fast as a ss.