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5 hours ago, Sugashane said:

I've argued against him and still prefer Trask to him. I went over one game and posted it here where his "pinpoint accuracy" seemed really in question - especially for some of the advantages he has compared to about any other QB last year. 

I don't see a huge separation between the two. I'd be happy with either. 

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7 hours ago, Heinz D. said:

I don't see a huge separation between the two. I'd be happy with either. 

Agreed.  I just think Jones was in a massively more advantageous position and Trask stil performed at the same level. 

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Just throwing this out here. Per SIS rookie book.  I may have underestimated Jones.  And Trask as well to some degree.
 

The evaluation of quarterback accuracy has drastically improved in recent years, as it has transitioned away from using simple completion percentages. Completion percentage had its usefulness in the past as it was the best measure of accuracy available. But with advancements in charting, tracking, and publicly available data, more advanced measures of quarterback accuracy have been created with different levels of complexity. At SIS, our measures of accuracy range from the relatively straightforward Catchable% and On-Target%, to our more advanced model-based measures, which include Predicted Completion% (pComp) and Expected On-Target% (xOn-Target). Catchable% and On-Target% SIS Video Scouts chart the accuracy of every throw on two main levels beyond whether the pass was completed or not:

• Whether the pass was Catchable
• Whether the pass was On-Target (off-target throws are classified as overthrown, underthrown, in-front, or behind the receiver, but that’s a discussion for another day)


A catchable pass is fairly straightforward in that it is put in a spot where the receiver has an opportunity to catch it, whether a significant adjustment is needed or not. An on-target pass, on the other hand, is the highest level of accuracy we chart. An “on-target” pass is one in which the receiver is able to make the catch with minimal to no adjustment. While they measure a similar concept, by having multiple levels of accuracy, we are better able to understand what truly happened on a given play.

These levels of accuracy allow us to differentiate between throws that appear the same under completion percentage, but potentially have very different context behind them. A few examples of this include:

• An overthrown uncatchable incompletion versus a pass that is thrown well enough but the receiver couldn’t reel in.
• An off-target throw that is completed only because the receiver made a highlight reel catch versus an on-target throw that hits the receiver in stride.


A look at the leaders in On-Target% among current draft eligible quarterbacks over the past two seasons shows two quarterbacks above the rest and that some higher profile prospects rank lower than you might expect.

On-Target% Leaderboard (2019-2020) for 2021 Draft Class

[img]https://i.imgur.com/DH2pjGY.png[/img]DH2pjGY.png


The fact that Alabama’s Mac Jones ranks highest among the draft class in On-Target% over the past two seasons is somewhat unsurprising. Although he filled in due to injury for part of 2019, most of his career attempts came this past season when he broke the NCAA completion percentage record. He also is tied for the highest Accuracy grade given by our scouting staff within this year’s Football Rookie Handbook.

Feleipe Franks joins Jones as the only two quarterbacks in the class to top an 80% On-Target%. Franks enjoyed a solid senior season at Arkansas after transferring from Florida. Comparing his 2019 and 2020 numbers gives a prime example for why Completion% can be misleading:

2019:   71 Atts, 76.1 Comp%, 80.1 On-Target%
2020: 238 Atts, 68.5 Comp%, 82.1 On-Target%

Even though it was on only 71 attempts, his 2019 Completion% shows a quarterback with top-tier accuracy who had a drastic drop-off in 2020. But, his On-Target% shows a quarterback with consistent raw accuracy numbers who actually improved in 2020. The biggest reasons for the discrepancy in his Completion% were an increase in dropped passes (his receivers dropped a single pass in 2019 and 12 in 2020) and a decrease in his receivers’ ability to haul in off-target but still catchable throws (they caught 18% of these throws in 2019, but only 8% in 2020).

Justin Fields’ low ranking in On-Target% is a bit surprising considering that he is a highly rated prospect—he is the second ranked quarterback by our scouting staff. Fields’ low ranking is representative of a problem with raw accuracy metrics that even measuring On-Target% instead of Completion% does not account for: the difficulty of each throw.

When comparing the average depth of target (ADoT) of each quarterback, it becomes apparent that Fields was asked to make more difficult throws than most of the players listed, including Jones and Franks. Fields’ ADoT of 10.8 was much higher than Franks’ 7.0 and Jones’ 8.2. To account for this, we can adjust Completion% and On-Target% by the difficulty of each throw and measure accuracy on a more level playing field.

pComp and xOn-Target

Over the past few seasons there has been an increase in the use of more advanced measures of quarterback accuracy than using just raw numbers. These are led by expected completion percentage models, which attempt to account for the context surrounding each throw. SIS’s model, Predicted Completion Percentage (pComp), factors in charting data to account for route type, coverage scheme, distance of throw, and pressure on the quarterback to determine how likely each pass was to be completed.

We can use pComp and each player’s actual Completion% to calculate pComp +/-, or how much better or worse a player’s actual completion percentage was than what was predicted. This is a similar metric to the NFL’s NextGen Stats based Completion Percentage Over Expectation (CPOE). In fact our NFL version of pComp +/- has a correlation of 0.9 to CPOE, without needing the tracking data. This comes in handy when applying the metric to the college game.

pComp +/- helps add context that raw numbers are unable to show. It is also useful as a way to compare not only quarterbacks, but receivers and defensive backs as well. However, it falls prey to the same limitation as Completion% when attempting to solely evaluate quarterback accuracy. Since it uses completions as its basis, pComp-and other expected completion models such as the NFL’s NextGen CPOE-attributes the entire outcome of the pass to a single player.

In an attempt to more accurately measure only a quarterback’s contribution, SIS created an Expected On-Target% Model (xOn-Target). This model uses our on-target accuracy classification as described above as the basis, instead of completions.

As with expected completion models, throw depth is a main component of Expected On-Target Rate. While throw depth can explain a large part of a throw’s difficulty, it is only one of many inputs into our model. Inputs also include the throw’s horizontal location, whether the quarterback was pressured, whether the quarterback was moving, the throw’s trajectory, route type, coverage type, stadium roof type, and other factors SIS collects.

Also similar to pComp, we can use each quarterback’s xOn-Target% and his actual On-Target% to calculate how far above or below expected he performed or xOn-Target +/-.

Now let’s take a closer look at how SIS’s pComp and xOn-Target models affect the accuracy leaderboards for the 2021 Draft Class.

pComp +/- Leaderboard (2019-2020) for 2021 Draft

C3cIJnO.png

 

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40 minutes ago, JAF-N72EX said:

Just throwing this out here. Per SIS rookie book.  I may have underestimated Jones.  And Trask as well to some degree.
 

The evaluation of quarterback accuracy has drastically improved in recent years, as it has transitioned away from using simple completion percentages. Completion percentage had its usefulness in the past as it was the best measure of accuracy available. But with advancements in charting, tracking, and publicly available data, more advanced measures of quarterback accuracy have been created with different levels of complexity. At SIS, our measures of accuracy range from the relatively straightforward Catchable% and On-Target%, to our more advanced model-based measures, which include Predicted Completion% (pComp) and Expected On-Target% (xOn-Target). Catchable% and On-Target% SIS Video Scouts chart the accuracy of every throw on two main levels beyond whether the pass was completed or not:

• Whether the pass was Catchable
• Whether the pass was On-Target (off-target throws are classified as overthrown, underthrown, in-front, or behind the receiver, but that’s a discussion for another day)


A catchable pass is fairly straightforward in that it is put in a spot where the receiver has an opportunity to catch it, whether a significant adjustment is needed or not. An on-target pass, on the other hand, is the highest level of accuracy we chart. An “on-target” pass is one in which the receiver is able to make the catch with minimal to no adjustment. While they measure a similar concept, by having multiple levels of accuracy, we are better able to understand what truly happened on a given play.

These levels of accuracy allow us to differentiate between throws that appear the same under completion percentage, but potentially have very different context behind them. A few examples of this include:

• An overthrown uncatchable incompletion versus a pass that is thrown well enough but the receiver couldn’t reel in.
• An off-target throw that is completed only because the receiver made a highlight reel catch versus an on-target throw that hits the receiver in stride.


A look at the leaders in On-Target% among current draft eligible quarterbacks over the past two seasons shows two quarterbacks above the rest and that some higher profile prospects rank lower than you might expect.

On-Target% Leaderboard (2019-2020) for 2021 Draft Class

[img]https://i.imgur.com/DH2pjGY.png[/img]DH2pjGY.png


The fact that Alabama’s Mac Jones ranks highest among the draft class in On-Target% over the past two seasons is somewhat unsurprising. Although he filled in due to injury for part of 2019, most of his career attempts came this past season when he broke the NCAA completion percentage record. He also is tied for the highest Accuracy grade given by our scouting staff within this year’s Football Rookie Handbook.

Feleipe Franks joins Jones as the only two quarterbacks in the class to top an 80% On-Target%. Franks enjoyed a solid senior season at Arkansas after transferring from Florida. Comparing his 2019 and 2020 numbers gives a prime example for why Completion% can be misleading:

2019:   71 Atts, 76.1 Comp%, 80.1 On-Target%
2020: 238 Atts, 68.5 Comp%, 82.1 On-Target%

Even though it was on only 71 attempts, his 2019 Completion% shows a quarterback with top-tier accuracy who had a drastic drop-off in 2020. But, his On-Target% shows a quarterback with consistent raw accuracy numbers who actually improved in 2020. The biggest reasons for the discrepancy in his Completion% were an increase in dropped passes (his receivers dropped a single pass in 2019 and 12 in 2020) and a decrease in his receivers’ ability to haul in off-target but still catchable throws (they caught 18% of these throws in 2019, but only 8% in 2020).

Justin Fields’ low ranking in On-Target% is a bit surprising considering that he is a highly rated prospect—he is the second ranked quarterback by our scouting staff. Fields’ low ranking is representative of a problem with raw accuracy metrics that even measuring On-Target% instead of Completion% does not account for: the difficulty of each throw.

When comparing the average depth of target (ADoT) of each quarterback, it becomes apparent that Fields was asked to make more difficult throws than most of the players listed, including Jones and Franks. Fields’ ADoT of 10.8 was much higher than Franks’ 7.0 and Jones’ 8.2. To account for this, we can adjust Completion% and On-Target% by the difficulty of each throw and measure accuracy on a more level playing field.

pComp and xOn-Target

Over the past few seasons there has been an increase in the use of more advanced measures of quarterback accuracy than using just raw numbers. These are led by expected completion percentage models, which attempt to account for the context surrounding each throw. SIS’s model, Predicted Completion Percentage (pComp), factors in charting data to account for route type, coverage scheme, distance of throw, and pressure on the quarterback to determine how likely each pass was to be completed.

We can use pComp and each player’s actual Completion% to calculate pComp +/-, or how much better or worse a player’s actual completion percentage was than what was predicted. This is a similar metric to the NFL’s NextGen Stats based Completion Percentage Over Expectation (CPOE). In fact our NFL version of pComp +/- has a correlation of 0.9 to CPOE, without needing the tracking data. This comes in handy when applying the metric to the college game.

pComp +/- helps add context that raw numbers are unable to show. It is also useful as a way to compare not only quarterbacks, but receivers and defensive backs as well. However, it falls prey to the same limitation as Completion% when attempting to solely evaluate quarterback accuracy. Since it uses completions as its basis, pComp-and other expected completion models such as the NFL’s NextGen CPOE-attributes the entire outcome of the pass to a single player.

In an attempt to more accurately measure only a quarterback’s contribution, SIS created an Expected On-Target% Model (xOn-Target). This model uses our on-target accuracy classification as described above as the basis, instead of completions.

As with expected completion models, throw depth is a main component of Expected On-Target Rate. While throw depth can explain a large part of a throw’s difficulty, it is only one of many inputs into our model. Inputs also include the throw’s horizontal location, whether the quarterback was pressured, whether the quarterback was moving, the throw’s trajectory, route type, coverage type, stadium roof type, and other factors SIS collects.

Also similar to pComp, we can use each quarterback’s xOn-Target% and his actual On-Target% to calculate how far above or below expected he performed or xOn-Target +/-.

Now let’s take a closer look at how SIS’s pComp and xOn-Target models affect the accuracy leaderboards for the 2021 Draft Class.

pComp +/- Leaderboard (2019-2020) for 2021 Draft

C3cIJnO.png

 

The is an article I think ESPN that Jones stats for this past season were incredible, Wilson’s were also. I’ll see if I can find it. 
 

https://www.espn.com/nfl/draft2021/insider/story/_/id/31113560/2021-nfl-draft-qb-class-do-stats-match-tape-trevor-lawrence-justin-fields-zach-wilson-others
 

Didn’t realize it’s an ESPN+ article, for those that have it can read through it. 

Edited by blkwdw13
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On 3/27/2021 at 3:25 AM, blkwdw13 said:

The is an article I think ESPN that Jones stats for this past season were incredible, Wilson’s were also. I’ll see if I can find it. 
 

https://www.espn.com/nfl/draft2021/insider/story/_/id/31113560/2021-nfl-draft-qb-class-do-stats-match-tape-trevor-lawrence-justin-fields-zach-wilson-others
 

Didn’t realize it’s an ESPN+ article, for those that have it can read through it. 

Yeah, I read this the other day.  My favorite part about it was this below.  It summed up how I felt about it while reading this because the college field is much different from the NFL. 

That said, I do read these tidbits and when I go back and watch these games, with articles like this in mind, I do start to see how the numbers match the eye test.

Hitting the out route (11-20 yards downfield, outside the numbers) over past 13 starts

Parolin: Who cares about this? Well consider a 15-yard out route. A QB in the middle of the field is 80 feet from the sideline, meaning a "15-yard out" throw actually travels over 30 yards into what can be a very tight window. Rainbow-launch angles won't work with the sideline, and underthrows invite catastrophe. (Not all of these are traditional out routes, but this sample can give us a glimpse of how each prospect performed on throws they will need to make at the next level.)

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