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Ryan Tannehill Analytics Profile Video.
- Thread starter Jerrysanders
- Start date
Don't hold your breath lol
"Elite qbs produce"
Drew Brees? Last 5 years? Why has there been no mention of this? He's an 'elite qb' why haven't the saints been a top level team as of late?
That's the best example there is.
Also...There is only one single "elite" qb in the NFL right now that came out of the shoot producing and that's Rogers. They all took roughly 5-7 years before they consistently put up numbers that they do. Compare RT to ANY playoff QB over the first 4 years of their career and will be right there if not ahead. And NOT ONE of them had to deal with anything close to what he has.
It's beyond ridiculous how we still continue to deal with these agenda driven threads.
This is the purest statement from any post on the subject. A players football iq, speed of the game as well as the "why" something happened all comes from game film. Analytics does nothing in that regard. Watch some tape on RT and you will see all you need to..... if you know what the hell you're seeing unfold. The kid has it between the ears and the arm to compete with ANY qb in the league. That tape don't lie.
Archie Manning was probably the 2nd best Manning but he was mired in mediocrity for the Saints. In his prime on a contending team, he well could have been lights out.
Conversely, QBs like Trent Dilfer and Doug Williams.. and maybe even Jim Plunkett has a .500 winning record, a 52% career completion percentage and 34 more career ints than TDs yet he because of the team he played on, he's been to 10 playoff games.
Sure QBs make a difference, but as Brees and Rivers presently underscore, the team on which they play is more of a determinant of them being succesful.
Don't forget about Phillip Rivers who many believe to be a first ballot hall of famer (pretty much the definition of elite). Why haven't the chargers made any noise if elite qb talent elavates others to win games?
Edit: Vaark beat me to it
Just going to put this right here. The article is geared towards scouting but the principles remain true for subjects like the OP is tossing out in this thread.
https://www.ndtscouting.com/notes/crabbs-scouting-tip-disassociating-play-results-from-scouting/
Lies, damned lies, and statistics.
https://www.ndtscouting.com/notes/crabbs-scouting-tip-disassociating-play-results-from-scouting/
Lies, damned lies, and statistics.
.....
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The hell...
This is not how f'ing analytics works!
Data analysis only provides clues or red flags that signal to the researchers that further investigation (other sources of data, observation, process review, etc) is needed. Analytics should NEVER be used to formulate a conclusion. The only way that analytics can lead to a direct conclusion is if the concerned parties already have a pre-conceived agenda. This is where analytics fails, and where those who, like the "source" and the OP, fail at their understanding of the data.
I review data all day. I have one key source of data that I mine every day that tells me people's performance based on a percentage. However, once I see a low performance percentage, I have combine that data with other data, I have to observe the individual, research machines for faults, review the processes being utilized for inconsistencies, and interview his boss for feedback on the individual. Then and ONLY then, can I make a conclusion on the individual.
If I instead rely on only one piece of analytical data, good people would lose there jobs, even when there might be multiple sources that say otherwise.
This "source" and the OP have cherry picked a single source of data which has fit a pre-conceived assumption/agenda, and hid behind their agenda in the name of “analytics." They have both failed miserably to understand what analytics truly is, and the dangers of doing so. By ignoring the basic tenants of analysis and claiming otherwise, they have exposed that they are, in fact, driving a pre-established agenda.
And to make matters worse... We are now discussing "elite" - a subjective term. So, we are using faulty analytics to prove/disprove a subjective categorization?
Da-fuq guys?
Da
Fuq
(edited for typos)
This is not how f'ing analytics works!
Data analysis only provides clues or red flags that signal to the researchers that further investigation (other sources of data, observation, process review, etc) is needed. Analytics should NEVER be used to formulate a conclusion. The only way that analytics can lead to a direct conclusion is if the concerned parties already have a pre-conceived agenda. This is where analytics fails, and where those who, like the "source" and the OP, fail at their understanding of the data.
I review data all day. I have one key source of data that I mine every day that tells me people's performance based on a percentage. However, once I see a low performance percentage, I have combine that data with other data, I have to observe the individual, research machines for faults, review the processes being utilized for inconsistencies, and interview his boss for feedback on the individual. Then and ONLY then, can I make a conclusion on the individual.
If I instead rely on only one piece of analytical data, good people would lose there jobs, even when there might be multiple sources that say otherwise.
This "source" and the OP have cherry picked a single source of data which has fit a pre-conceived assumption/agenda, and hid behind their agenda in the name of “analytics." They have both failed miserably to understand what analytics truly is, and the dangers of doing so. By ignoring the basic tenants of analysis and claiming otherwise, they have exposed that they are, in fact, driving a pre-established agenda.
And to make matters worse... We are now discussing "elite" - a subjective term. So, we are using faulty analytics to prove/disprove a subjective categorization?
Da-fuq guys?
Da
Fuq
(edited for typos)
Last edited:
Why have I thought about this guy for the first time in forever ??
My question to the OP is what are your intentions here? Always posting anti Tannehill stuff. Do you think you are going to sway votes in favor of your dislike for the guy simply because you bombard the board with anti Tannehill material? What you are doing is crushing your reputation on this message board and, I don't know if you're familiar with message boards, that doesn't usually end well for said person. They either just disappear or often get so consumed with wanting to prove their once sided point that they break forum rules and are suspended. Why don't you try posting some pro Miami Dolphins material for once?
This is the second most true thing i've read in this thread.
OK Jerry considering you are all about those stats, here are 4 QBs, and how they looked in their 5th year, compared to Tannehill. Tell me would you have taken any of those other 4 QB after seeing how they looked in their 5th year?
..............G...GS...Comp...Att...Pct......Yds.... Avg...TD...Int.....Rate
QB #1-16...16....281.....469...59.9...3,301...7.0.....17...15......80.1
QB #2-16...16....350.....541...64.7...4,434...8.2.....30....7.......103.2
QB #3- 16...16...288.....474...60.8...3,692...7.8.....28...14.......92.6
QB #4- 16...16...392....591...66.3...4,200...7.1......27...19........88.8
>Ryan- 13...13...261....389...67.1...2,995...7.7......19...12........93.5
>Ryan-16...16...321....478..67.1....3683...7.7.......23...13........93.5
That up there is Ryan's 13 game averaged out stats in 16 games, but considering Ryan was getting very hot at that point before going down, I think I can say those 16 game stats up there are actually very conservative, considering what he might have had.
hoops
Tua time!
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Actually it never becomes irrelevant. The "large" sample size of which you're suggesting would be a playing career of around 200 years. That's when all of those things could fully normalize. But when we're talking about taking an NFL career of 4 years and trying to elongate that through a collegiate career of 2 years and not collecting the data to elongate it through the high school years like the Author assumes is needed for his analysis ... it is the very definition of not enough data. This is the classic "too small" sample size. Clearly 4 years is not going to show any normalization of variances in crucial factors such as offensive line protection, presence of a run game, and average time to throw. It's like comparing 1st and 10 performance with 3rd and long performance ... it's apples to oranges.