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Titans to start Ryan Tannehill

Discussion in 'Miami Dolphins Forum' started by bbqpitlover, Oct 16, 2019.

  1. KeyFin

    KeyFin Well-Known Member

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    LOL, I so don't want to have any part of this RT conversation....but I think you're completely missing his point. A "streak" is a series of games; nothing more, nothing less. What he's saying is, if RT had a hot streak in 2016, had one briefly in 2018 then another one this year, he's saying that it's likely that he'll have a similar "streak" of good games in 2020 and beyond.

    Note- he's not saying RT will be an elite QB...he's saying that the past several years have shown a pattern where RT got hot for a number of games within the season. The word "streak" is meaningless here other than to represent consecutive games, and I think every 7+ year QB would have "streaks" to be found (hence, how they made it 7+ years in the league).

    Anyway, I posted all of that to offer this- what if we looked at five random QB's plus Tannehill of their best 3-game stretch of a season and their worst 3-game stretch...then averaged the two? Wouldn't that give a better indication of "streakiness" in a QB?

    LOL, yes, I just invented a word. Edit- crap, no I didn't =)
     
  2. resnor

    resnor Derp Sherpa

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    But cbrad, we know that the more times you do something, the harder it becomes to do. Like shooting a free throw. Making one is pretty easy. Making two in a row. Harder. 5 in a row? 100 in a row? So yes, streaks certainly matter. You aren't dealing with numbers on a page, you're dealing with human beings.
     
  3. cbrad

    cbrad . Club Member

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    No I think you've completely missed the point of the conversation KeyFin. Phins_to_Win asked a very good question in post #629 so you need to go back and read from there. He's not saying anything like what you're talking about. He was asking whether the probabilities calculated by the statistical test I used were biased upwards because they didn't consider when the streak started. It's a good question.. but the answer is equally important because it tells you that the assumptions of the statistical test make that question meaningless.

    What resnor just pointed out leads to the really important follow-up question to my response to Phins_to_Win: whether the implicit assumption of that statistical test – that "streaks" only exist in the minds of the human observer and not in reality – is accurate or not.

    Enter the hot hand fallacy. Psychologists demonstrated decades ago that humans were perceiving "streaks" in all kinds of sports when the data was actually consistent with randomness (started with basketball players making X number of consecutive baskets). A few researchers got a Nobel Prize for showing that, among other behavioral and decision making biases they discovered. Now, it turns out that in recent years there is research showing that a "hot hand" can exist in some cases. So what's intuitive to humans may have some merit. But it's still the case that humans find patterns in randomness, and to a 1st approximation (unless proven otherwise in a specific case) the assumption that "streaks" aren't real is a good starting point.

    So I wouldn't go looking for evidence of "streakiness" given the huge amount of research showing that in most cases it's more consistent with randomness. Or if one is to look for that evidence one shouldn't do so casually – the analysis has to hold up to scrutiny.

    Oh and resnor.. that statistical test calculates the probability of observing any set of ratings given other sets of ratings, so whether we call it a "streak" or not, as long as Tannehill did well X out of N times, the difficulty of that is taken into account.
     
    Last edited: Nov 29, 2019
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  4. The Guy

    The Guy Well-Known Member

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    But even this, while methodologically sound of course, illustrates the problem with the interpretation, in that there is nothing telling us definitively that the Titans' surrounding cast is any better than what Tannehill had with the Dolphins.

    If Tannehill's pattern of play continues, it'll be just as plausible that Tannehill was humbled by the Dolphins' giving up on him and has redoubled his efforts or improved his mentality such that he's a better player individually.

    Again the problem is that nobody is reliably measuring the effect of surrounding casts on quarterbacks' performances. Just because many people thought Tannehill would play better with a better surrounding cast doesn't mean he's actually getting that at the present time.

    You certainly can't use his better play, alone, as evidence of his having a better surrounding cast and then simply say, "see there -- I told ya that would happen." First you need to actually determine whether he has a better surrounding cast in some empirically reliable manner.
     
  5. Fin-O

    Fin-O Initiated Club Member

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    What can't be counted or measured however is the brain and how it reacts to a "streak" good or bad. It IS random but the human element can come in and if a guy has missed 4 free throws in a row?? I think the percentages actually go down that he makes it. Our brains can make things harder or easier or some people have the ability to perform on par. Depends on a plethora of variables. And frankly the mental make-up of 'X'.
     
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  6. cbrad

    cbrad . Club Member

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    Yeah it's important to point out that no matter how much data you obtain there is technically an infinite number of hypotheses still consistent with that data – any mathematician can fit an infinite number of distinct functions through any finite amount of data. So no matter what you do you can't definitively pin down the causes of what occurred.

    However, you still want to restrict your hypotheses to things that are measurable, like a change in surrounding cast. So while it's possible that "Tannehill redoubled his efforts" where's the evidence of this? At least you have evidence there was a change in surrounding cast. So I wouldn't put these hypotheses on the same level – filter out the ones for which you don't have evidence first.
     
  7. cbrad

    cbrad . Club Member

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    That's exactly what was being counted though.

    The initial studies looked at the conditional probability of making or missing a basket after X consecutive successes or X consecutive misses. Actually, to be technical, they sometimes conditioned on "X or more" consecutive successes or misses, and later studies pointed out that there are some subtle changes in the probability that those Nobel Prize winners weren't taking into account lol. Either way, that was precisely the condition researchers were looking at to show that the probability of making a basket or missing it after X consecutive successes or misses was essentially identical.. with some random variation (binomial probabilities).
     
  8. resnor

    resnor Derp Sherpa

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    You're arguing there are no streaks in statistics. You're saying they're just numbers, doesn't matter the order. I'm telling you it most certainly does. You're dealing with humans and their performance, and streaks are real. Anyone who's played any level of organized sports understands this. Playing high school basketball, for instance, some nights you just can't miss. You're "in the zone." Sometimes you can string that together over several games. Better players get "in the zone" more, and are better at stringing games together.

    I don't care what statistics and statisticians say about streaks.
     
  9. resnor

    resnor Derp Sherpa

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    Another example...let's say a guy is a 65% comp rate for the season. Then one game he throws for a 90% comp rate. He then follows up with 4 more games of 85, 87, 75, 92. That's 5 games well above his average. Now, it may not be abnormal, in that he might follows that up with 5 games of 65, 55, 58, 63, 61, which could lower him back to his average (fake numbers, don't think they actually average out to 65, but you get the point I'm making). Would still make that 5 games streak significant. Of course you'd need to look at what was going on, try to figure out why he was better those 5 games.
     
  10. cbrad

    cbrad . Club Member

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    Yeah like I said the evidence is more mixed today than decades ago. There is evidence that in some cases the "hot hand fallacy" itself was a fallacy, but in many cases it still holds. Anyway, your reaction to that was exactly the same reaction by professional athletes and coaches. Actually, that's the same kind of reaction Sabermetrics and most statistical analysis of sports that didn't fit with what coaches thought elicited. Over time though that resistance decreases as evidence mounts, and the statistics get incorporated by professional coaches.

    NBA and MLB are hugely into analytics now, and NFL is slowly following suit. So I think it's better to just take the point of view that not everything that fits with intuition based on experience is actually correct. But that's up to the individual. Trend is clear though.
     
  11. resnor

    resnor Derp Sherpa

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    I can tell you that right now I play basketball with the kids I work with. I won't be hitting any shots. Then I'll hit a bunch in a row. I'm telling you, it's a different feeling. When you're on, you KNOW it's going in. You can feel it. If you haven't played sports, then you can't understand what I'm saying.

    Accepting some metrics, like with moneyball, is not what I'm talking about. Convincing managers to play averages and hire athletes based on specific metrics was extremely controversial...and frankly, I don't think it will work in football as well as it has in baseball. The season is too short.
     
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  12. cbrad

    cbrad . Club Member

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    IF you can identify some external condition that independently separates those first 5 games from the next 5 (independent of the ratings themselves!), then yes you can analyze different parts of the season separately. What you don't want to do is to invent conditions based on the data itself to support a desired hypothesis. That's called cherry picking.

    As far as I'm concerned, the "conditions" here are performance by season and by team: easiest ones to justify looking at for every QB.
     
    Last edited: Nov 29, 2019
  13. Fin-O

    Fin-O Initiated Club Member

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    I see what you are saying, I just feel like it’s a case by case, person by person situation.

    Everyone gets butterflies in their stomach, not everyone handles it the same.

    One of those things that are always fun to discuss. Math/emotions/positive energy/negative energy etc etc etc
     
  14. resnor

    resnor Derp Sherpa

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    And maybe you can't find anything. Because it's simply an athlete being in the zone.
     
  15. Phins_to_Win

    Phins_to_Win Well-Known Member

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    I think we are looking at 2 different points here now. I'm assuming you are looking at a accumulative point structure? while I'm looking at the games themselves as a stand alone data point.

    Here is what I'm trying to say. If you go by the most basic model of tier for a QB game you end up with Good, average, bad.

    If you have 5 starts you have a TON of possible outcomes: good, bad, average, average, average or good, good, good, good, good, and so on. Order/streak was never actually part of my argument, it was just a tool to show variances in a model. I'm good not talking about streaks going forward.

    What I am saying and still standing by, is that Tannehill even using this basic equation that honestly robs him of his great games, there is no way that you can say that good, good, avg, good, good equals 25% probability. Especially if you believe he is an average QB. The most likely outcomes for an average QB would be the dead center of the probabilities, while this one is clearly near the top end of the likely outcomes.

    I know you are great with stats man, but there is just no way that number is right for the above scenario.
     
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  16. Fin D

    Fin D Sigh Club Member

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    Wait..... now the argument is that if a guys first game with a new team coming off the bench is not good, then he light's up the next 5 games, there's no statistical importance to that.....and that argument is somehow a positive for purely stat based view?
     
  17. Phins_to_Win

    Phins_to_Win Well-Known Member

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    Not the same thing. You are picking from the entirety of the NFL and all of history, while clearly my entire argument was only applicable IF Tannehill did it. You essentially just took the "there is Life somewhere in space" and tried to compare it apples to apples with my "there is life on this planet".

    Even if we wanted to look at it as if it was comparable do you believe that Dalton's year was strictly a random series of events, and had nothing to do with any other influences in the NFL? My guess is if you dig deep enough you would find a couple things that were different that year. If not, then you found the guy that actually won the lottery that I was talking about. That still doesn't make a strong case that this is what happened to Tannehill.
     
  18. cbrad

    cbrad . Club Member

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    Well.. the first thing to note is that Tannehill's ratings aren't near the top end of likely outcomes when looking at HIS game-by-game ratings. When adjusted to 2019, Tannehill's average rating is 91.62 (league average is 91) while the standard deviation in game-by-game ratings is 26.76. And that standard deviation isn't abnormal either. For Tom Brady it's 27.94 and for Russell Wilson it's 28.01.

    Tannehill's average rating in 2019 is currently 111.4 which is 0.739 standard deviations above his overall mean (z-score = 0.739). And a z-score of 0.739 corresponds to top 23rd percentile for HIS game-by-game ratings (not distribution of year-end passer ratings). That's a pretty rough way of getting an intuition for how "likely" performing at that level is (for smaller sample sizes). So intuitively 25.57% is about right.


    Maybe second thing to note is that you have to consider all possible permutations of those "good", "average" and "bad" to calculate the probability. Let's just simplify this and test your intuition. Let's suppose we flip a fair coin 6 times. What do you think is the probability you get 4 heads and 2 tails? Here's the thing: you can't just imagine something like HHTHTH. You have to imagine all possible permutations of 4 heads and 2 tails, and people tend to not be able to do that correctly.

    Answers (X heads out of 6 flips for a fair coin):
    0 heads = 1.56%
    1 head = 9.38%
    2 heads = 23.44%
    3 heads = 31.25%
    4 heads = 23.44%
    5 heads = 9.38%
    6 heads = 1.56%

    Is that similar to what you expected? In my experience people tend to think 5 heads is WAY lower than 9.38%, which would jibe with your intuition. Of course in your case we'd have to look at 3 possibilities, but there's no need to look at such artificial examples when we know the mean and standard deviation of Tannehill's ratings. The probabilities make sense.
     
    Last edited: Nov 29, 2019
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  19. Phins_to_Win

    Phins_to_Win Well-Known Member

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    Actually I like to play craps, so yeah that's not far off from my expectation. 3 heads being the strongest and you get weaker as you make your way out from it. ITs the same with the number 7 in craps.
     
  20. cbrad

    cbrad . Club Member

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    To be technical, he didn't have a 5 game streak. He had one below average game, then two very good ones, then one below average game, then two very good ones. So there's no "5 game streak" here anyway, which in many ways supports the assumption of the statistical test.
    https://www.pro-football-reference.com/players/T/TannRy00.htm
     
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  21. BrazForPhins

    BrazForPhins From south

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    This may be the biggest thread ever
     
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  22. Phins_to_Win

    Phins_to_Win Well-Known Member

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    Why would you use standard deviation as the mark of good or bad? That doesn't make any sense. That value has a purpose but determining what a good game is not it. 120.1, 109.8, 133.9, and 155.8 will be considered a GOOD game by any reasonable metric (3 of them are amazing).

    So my original statement of 4 good games and 1 average game stands, so does the formula I provided.

    average, average, average, average,average would be the dead middle of the probability triangle.

    followed by average,average,average, average good and average, average, average, average bad
    followed by average, average,average, good, good and average, average, average, bad, bad and average, average, average, good, bad

    As you can see we still have a long way to go to get to good, good, good, good, average, and just like the graph above that you posted, the percentages start to plummet once you get away from the center. The only way to reduce that effect would be to place Tannehill at a higher starting level player, which changes the whole argument.
     
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  23. cbrad

    cbrad . Club Member

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    I'm not using standard deviation as a mark of "good" or "bad". There is no "good" or "bad" per se here because this is a continuous scale. The mean and standard deviation specify (approximately) the distribution you're randomly picking individual game ratings from. So if the average of a small set of ratings you randomly select is top 23rd percentile, then that's a quick and dirty way of getting an intuition for what a true statistical test will say about the probability that small set of ratings comes from a QB with that distribution.

    You have to get off this "good", "average", "bad" categorization because there's none in the ratings themselves. That kind of categorization is what I was talking about with ordinal ratings previously. We don't have ordinal ratings, we have a continuous scale. So you want to know the type of distribution those ratings come from and that's (approximately) specified by mean and standard deviation.

    Anyway, the probability not only makes total sense it's actually correct.
     
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  24. cbrad

    cbrad . Club Member

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    You haven't seen the ones in Club. I rarely post there anymore (and also rarely go there nowadays) but it's just a few gigantic threads lol. I stopped posting regularly after some unbelievably long threads about Kyler Murray being 1 inch too short or something like that. Went on forever!! Turned me off quite a bit, though I'll probably post there again someday.
     
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  25. Phins_to_Win

    Phins_to_Win Well-Known Member

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    Okay so now there is no such thing as good, average, and bad. What your describing doesn't make sense, WE CLEARLY use good bad and average to describe QBs in the league, I have seen you use the term Average several times, but now all of a sudden, there is no such thing as a good or bad outing for the QB?

    You are trying to eliminate good/bad because it ruins your 25% stance. If you admit to something that is clearly evident to ALL SPORTS fans your argument is ruined. There is such a thing as a good/bad outing by the QB, and there is literally no discussion on this board that an argument has been made that it doesn't. You can argue that the QB rating doesn't always capture the good/bad game, but as you like to point out it will average out.

    So if I said that Ryan Tannehill was going to have 5 games that fall into the 80-100 range.
    Then I say he is going to have 4 games that fall into that 80-100 range and 1 that falls into the above 100 range.
    Then I say he is going to have 4 games that falls into the 80-100 range and 1 that falls into the sub 80 range.
    I then say he is going to have 3 games that fall into the 80-100 range, and 2 games that fall into the greater then 100 range.
    I then say that he is going to have 3 games that fall into the 80-100 range and 2 games fall into the sub 80 range.

    I just gave you 5 scenarios that are more likely to happen (based on average) than what Ryan has actually done, but all could have easily happened in the time structure given. You are going to honestly tell me that given the above examples that Ryan's output falls at 25%?

    The only way you can possibly say that is if you are using such a large generic umbrella of performance that it literally renders itself useless in any discussion.
     
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  26. cbrad

    cbrad . Club Member

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    Unbelievable. Sorry dude you just don't understand the concept of randomly choosing from a continuous distribution. I mean.. I don't mind if people don't understand basic math, but how you don't even attempt to try and learn some elementary statistics based on what I wrote – just google this stuff!! – before you accuse me of doing something shady I won't understand. I'd NEVER do that myself.

    But so be it. Believe what you want. Anyway.. the probabilities I listed are correct as anyone can verify themselves (data are all publicly available and I've explained precisely what I did). You go ahead and believe ANOVA is shady and wrong.
     
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  27. Fin D

    Fin D Sigh Club Member

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    Forget Thill. The premise in and of itself makes no sense, which is why I didn't;t mention Thill.
     
  28. AGuyNamedAlex

    AGuyNamedAlex Well-Known Member

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    1) We would need to look at the specific strengths/weaknesses of the QB in question.

    2) I think that calls back to #1
     
  29. Fin D

    Fin D Sigh Club Member

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    The problem with stats in general with sports is they are just a way to categorize results. They are not all that helpful in finding the reasons for the results. The best they can do is point us to what is and isn't likely reasons. But unlikely things happen quite frequently, especially in a situation like pro football and the virtually infinitesimal variables there are. Randomly choosing from a continuous distribution, in these kinds of cases, literally removes variables. Weather, newness to a team/system/staff, postseason chances, etc. all factor in to a given performance. By just randomly selecting numbers out of that, removes the context with which those numbers were created. I feel like you believe that's ok, because you have a long history of basing much of your approach on assuming all variables outside of the QB, come out in the wash. Your approach only works if all WRs, TEs, Olines, coaching staffs, opposing defenses are virtually identical. Even with this randomly choosing from a continuous distribution, you are literally saying, the ONLY variable that matters, is the only I'm accounting for, and that is individuals QB performance regardless of literally anything else, because every QB faces the exact same variables.
     
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  30. Phins_to_Win

    Phins_to_Win Well-Known Member

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    So what is the percentage that in a 5 game stretch Tannehill has 4 games above league average and 1 below. I think we can agree that this would be worlds easier to do then what he actually did.
     
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  31. The Guy

    The Guy Well-Known Member

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    True, and that's quite an accomplishment, given that what we're talking about is at its root only math.

    Unlikely things don't happen frequently. They happen infrequently. That's why they're unlikely.

    What happens frequently is a bias people experience that makes them believe their preferred result will happen, despite its low likelihood. The quarterback of my team will be the Drew Brees/Steve Young, the guy who started his career not so great and then changed teams and did very well.
     
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  32. cbrad

    cbrad . Club Member

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    Yes this is correct.

    No they happen infrequently.

    OK.. this is a misunderstanding. The passer rating is the effect, not the cause. So NO causal variables are being removed as long as you are randomly choosing from the exact same distribution as the actual distribution. Thus, the only source of uncertainty is the degree to which the set of observed ratings is representative of the actual distribution. But at this point at least no variables are being removed.
     
  33. The Guy

    The Guy Well-Known Member

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    Dalton's performance in 2015 could be entirely the result of non-random events (enhancements in his surrounding cast, better coaching, etc.) that are nonetheless unlikely to be replicated. His performance throughout the rest of his career is a testament to that.

    Again, the point isn't that surroundings don't contribute to quarterbacks' performances. The point is whether the surroundings necessary to make the quarterback perform at the necessary level 1) are likely to be assembled, and 2) can be sustained.

    I might be able to make a really good high school quarterback perform well enough in the NFL if I can assemble the best 10 other offensive players in the history of the league. But then of course the issue becomes that it's impossible to assemble those other players.
     
  34. cbrad

    cbrad . Club Member

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    That depends on which range of ratings "above average" and "average" refer to. Post #658 is the key. When "above average" is ONLY at 0.739 standard deviations above the mean (i.e., nowhere near the top end of likely outcomes) you'll get a pretty high probability.
     
  35. Phins_to_Win

    Phins_to_Win Well-Known Member

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    No I'm talking exact average. which is 91 or 92 I think? With Tannehill being average my guess is this is practically the flip of the coin scenario that you referenced before.
     
  36. The Guy

    The Guy Well-Known Member

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    The issue for that fellow is that he isn't sufficiently appreciating the history of variation involved, which makes the z-score of the recent performance less than a standard deviation from the mean. He appears to be assuming that Tannehill's recent performance is well beyond what would be expected given the history of (what he thinks is smaller) variation in his performance.
     
  37. cbrad

    cbrad . Club Member

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    OK we can define "average" as exact average which right now in 2019 is 91. So how do you want to define "above average"? The probability you seek totally hinges on that.
     
  38. Phins_to_Win

    Phins_to_Win Well-Known Member

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    So are you saying that an Average QB isn't equally likely to have a below avg passer rating then above?
     
  39. Phins_to_Win

    Phins_to_Win Well-Known Member

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    91.0001 and up
     
  40. The Guy

    The Guy Well-Known Member

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    Here's one of the issues here with regard to quarterbacks and surrounding casts.

    Take the following two quarterbacks and their season passer ratings:

    QB 1
    80.4
    87.4
    88.8
    83.5
    106.2
    91.8
    86.6
    89.6
    79.2

    QB 2
    100
    101.2
    95
    110.1
    92.6
    95.4
    110.9
    112.1

    Those are for Andy Dalton and Russell Wilson.

    So what we see here is that Dalton can perform at Wilson's level for a season, perhaps if he has the necessary surroundings. The fact that he's performed at that level for only one of his nine seasons in the league begs the question of how likely those surroundings are to be assembled and sustained.

    Likewise, Ryan Tannehill may be able to play at Russell Wilson's level if he has the requisite surroundings. But if we look up in two or three years and find that his 2019 season was merely what Andy Dalton's 2015 season was (a single season of elevated play that wasn't sustained), what will that tell us?

    In my opinion it'll tell us that Tannehill can play at a high level, but only if he has the kinds of surroundings that 1) aren't likely to be assembled in the first place, and 2) can't be sustained.

    And if so, then what good will it be to be a quarterback who is dependent on surroundings that aren't likely to be assembled or sustained? Of course you'd rather have the Russell Wilson, the guy whose performance varies at a level much higher, who isn't so dependent on his surroundings.
     

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