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Draft analytics: SPARQ, SEMTEX, Force, Slaytics, etc.

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  • Draft analytics: SPARQ, SEMTEX, Force, Slaytics, etc.

    Introducing SEMTEX, a new way to look at college production

    By Ethan Young/FanragSports

    Iíve been pretty cavalier about my skepticism with using college production for NFL evaluation purposes in the past. But I believe in being informed and personally testing everything, even methodologies I donít buy into. So I started to look into production so I could see exactly how effective or ineffective it truly could be on a macro scale.

    I started at the quarterback position. And Iíll be honest, I thought that I would find that college production was not predictive for signal callers. I could not have been more off-base. When optimized, college production can be very predictive at quarterback.

    Before you jump around and yell about ďbox score scoutingĒ ó believe me, thatís the last thing I buy into ó keep in mind that I am a tape evaluator first and foremost. Using film to identify translatable traits is vital to the NFL evaluation process and is always the first thing that should be looked at when analyzing a player. And when I say production is predictive, I donít mean your typical passing yards and touchdowns. In fact, typical raw box score numbers have minuscule correlations to NFL success since 1999:

    MAVPY stands for Modified Approximate Value Per Year. You can read about it and the MAVEM system here.

    Situationally Enhanced Metrics

    So, if these box-score numbers are essentially worthless, what do I mean by ďproduction can predictive at QB when optimized?Ē I am referring to Situationally Enhanced Metrics rather than raw statistics, meant to isolate player performance by accounting for external factors outside a playerís control. Put simply, these metrics are created by adding additional numerical context to position-specific rate statistics, to provide a clearer picture of how a player performs in their respective environment. Here are the enhancements applied to our QB rate stats:

    Accounting for level of competition: To account for the level of competition differences among players, all of the rate stats used are given a conference-based adjuster.

    Accounting for era: To account for the period in time in which a prospect played, a year-based adjustment is applied to all the quarterback rate stats used in the dataset.

    Accounting for surrounding talent: To account for supporting cast, the draft capital spent on a prospectís college teammates is added up and factored into the dataset. For current prospects, a consensus big board is used to determine projected draft position.

    Accounting for pace and translatable schemes: This adjustment requires a bit more explanation. Quantifying the translatability of a college offense to the NFL is difficult to nail down. To remove the subjectivity and time output associated with manually tagging each teamís scheme in every season, we can use an offenseís era-adjusted plays per game instead. No solution will be perfect for tackling this, but using adjusted plays per game makes sense from a methodological perspective (because gimmicky offenses run more plays). And over a large dataset, it works quite well as a macro solution and captures many of the elements we are looking for to account for.

    After applying these adjustments to our positional rate statistics, Situationally Enhanced Metric Testing for Efficiency and eXplosion (SEMTEX) is conducted. SEMTEXís testing parameters are inspired by machine learning protocols, and when ran over this enhanced production data set, indicate quarterbacks with the highest and lowest chances of NFL success based on past data. These protocols are trained by the MAVPY values discussed above, as well as Draft Capital Adjusted Returns (DCAR) for players since 1999. If you arenít familiar, DCAR is a MAVPY-based metric that evaluates a player relative to where they were selected in the draft.

    SEMTEX separates Football Bowl Subdivision (FBS) passers into three different groups based purely on their college enhanced production metrics. SEMTEX is limited to FBS prospects, as inconsistent stat keeping at the lower levels makes it hard to not only find accurate historical stats, but to also apply sufficient situational enhancements to said stats as well. We will call these three groups the Gold, Silver, and Bronze buckets for now. The premise is that members of the Gold group are the most likely to succeed in the NFL, while Bronze passers have the lowest likelihood of becoming starters at the next level.

    SEMTEX Gold QBs

    Here is every Gold passer drafted since 1999, sorted by their NFL MAVPY output. Remember that MAVPY is not a part of SEMTEX formulation, but rather a way to evaluate the success of Gold, Silver, and Bronze buckets quantitatively after the fact. MAVPY is generated in the pros, and SEMTEX is meant to be applied to college QBs before they are drafted:

    Italics Indicate Position Conversion

    If you arenít familiar with MAVPY, in very general terms, a MAVPY of around 4 is NFL starter level, while a MAVPY of 8 is around Pro Bowl level. Keep in mind that MAVPY is an average of a playerís outputs over his career. Obviously this hurts players like Tyrod Taylor, who were backups for multiple seasons.

    For readability and relevancy purposes, this list is all the Gold quarterbacks who have been drafted. In total, there have been 131 college quarterbacks in the Gold SEMTEX category since 1999, and when we evaluate success and starter rates for each group later on, these UDFAs will be included. Notable Gold UDFAs include Case Keenum, Billy Volek and Kellen Moore.

    Iíve noticed that a good chunk of the failures in the Gold group fall below the lower bound QB threshold in Slaytics. As much sense as it makes to filter out lower bound passers who donít have the baseline physical traits to be NFL starting QBs, I plan to keep Slaytics and SEMTEX as separate standalones, and wonít intertwine them here.

    The main reason for this is SEMTEX doesnít cover other positions at the moment, and we are a ways out from knowing if it will work at other spots yet. If we did blend the systems together, though, David Carr, Brian Brohm, Garrett Grayson, Greg McElroy, Case Keenum (UDFA), Kellen Moore (UDFA) and Graham Harrell (UDFA) would be removed from the Gold list. So obviously, there is some credence to the idea that removing these players would be valuable, and itís something I will be monitoring as an evaluator.

    The position converts will be included in all these lists and listed in italics. The gray area between position conversions can be muddied at times, so to keep it simple, we will just keep everyone in. Isaiah Stanback and Pat White are the main Gold examples, as they were college QBs even though Stanback was drafted as a WR and White had more than four times more carries than he had throws in the NFL. White was intended to be a QB, though, so he straddles the line enough to where he isnít in italics.

    SEMTEX Silver QBs

    Here is every Silver passer selected since the 1999 NFL Draft, also sorted by MAVPY:

    Italics Indicate Position Conversion

    I like to call this the ďgunslingerĒ group due to the type of passers it attracts. Stylistically, these are largely volume passers. The Silver group has a few Pro Bowl-caliber passers, but with this being the intermediate group, traits like mental make-up and supreme arm talent become vital for prospects in this group to succeed as volume types. Winston, Stafford, Palmer, and Cutler fall into the elite-level arms and body types category, with cannons and frames to match.

    On the other hand, passers like Bulger, Hasselbeck, and Cousins proved their intellectual acuity with pro style concepts in college, then paired up with innovative offensive minds like Mike Martz, Mike Holmgren, and Jay Gruden in the NFL to fully weaponize that ability. While these types of passers likely wonít win you a Super Bowl by themselves, these play caller/quarterback duos are a great way to build an identity for teams struggling to find one. Obviously, it takes the right offensive coordinator to pull off building a unique scheme that showcase a passerís strengths while hiding their flaws like in those cases, though.

    If you donít evaluate a Silver passer as clearly fitting one of those buckets, or lack the offensive system to build around the latter, I would recommend avoiding those prospects. Looking at you, Jimmy Clausen.

    Since 1999, there have been 112 college passers in the Silver group. Notable UDFAs include Vernon Adams, Brett Smith, Timmy Chang, Brian Hoyer, Cleo Lemon, and Anthony Wright.

    Obviously, Antwaan Randle-El was a starter-level player at WR, not QB. Braxton Miller is following this path as well. They are both italicized to indicate their position conversion.

    SEMTEX Bronze QBs

    Here is every Bronze passer drafted since 1999:

    Italics Indicate Position Conversion

    Since 1999, there have been 865 college QBs in the Bronze category. Notable UDFAs include Chase Daniel, Matt Moore, Connor Halliday, Taylor Heinicke, Seth Doege, Matt McGloin, Caleb Hanie, Tim Hasselbeck, Chris Leak and Juice Williams.

    Of the drafted Bronze prospects that have a MAVPY above zero, 10 of them are italicized position conversions. After factoring out these position converts, only three passers have a MAVPY above starter level out of the 865 quarterbacks in the Bronze group since 1999.

    One of them is Josh Freeman, a first-rounder who didnít even make it through his rookie contract in Tampa Bay. The main reason he is over starter level is due to how quickly he left the league after a couple of successful seasons early on in his career. He did not outperform his draft capital.

    The second one is David Garrard, who continues to spit in the face of predictive thresholds. In case you donít know, Garrard is the only starter-level QB under the lower bound Slaytic threshold since 1999, and continues to be a special exception to most rules.

    The last and by far biggest outlier of the Bronze group is Ryan Tannehill.

    Why did he slip through the cracks?

    Looking into the context of his college background may help answer that. Tannehill actually played WR his first two and a half seasons at Texas A&M, getting only one full year to start at quarterback for the Aggies. It is not like Tannehill was starting from scratch and had never played the position before, though. He played quarterback as well as safety in high school. But the lack of reps for two years not only may have set his long-term development behind, but this break likely negatively impacted his stats as well.

    Now these arenít excuses. Tannehill is certainly a big miss, and his success removes us from being closer to absolution, but his unique circumstances may have contributed to his placement in the bottom grouping.

    Machine Learning Protocols

    Now, before we quantifiably evaluate our threshold groups with MAVEM valuation metrics, letís circle back to something we briefly talked about at the beginning: machine learning. Or more specifically, the potential for overfit models in machine learning.

    Overfitting is an issue that can occur when machine learning protocols match their training dataset too well and make random noise from the dataset part of the model rather than just translatable data. This is an exaggerated example, but this would be like if a parameter was established that all QBs with the last name Leinart should be avoided. This would be a problem when Matt Leinart Jr. shows up in 7 years.

    Now, this couldnít happen with the machine learning protocols used here, as they are all heavily supervised for this purpose. Additionally, as mentioned before, SEMTEX parameters are inspired by machine learning protocols, not completely generated by them. This distinction means I have to methodically buy into a parameter being viable when replicated with future draft classes for it to be included. This isnít just a casually trained macro set, and I believe it takes a ďfootball personĒ to fully construct and understand how to implement this strategy to full capacity.

    The standard way to evaluate if a model is overfit is to separate a dataset into a training portion and a testing portion. As discussed, overfit issues can occur if the training portion makes up the whole dataset. This isnít possible with Slaytics though, as measurable data doesnít go far back enough to split. That is why Slaytic thresholds are so strictly supervised and have to make sense in a football capacity to be implemented.

    The whole idea for using machine learning to inspire rather than generate came from this issue. This isnít so much of an issue with production though, as the base statistics go much further back than athletic testing. We are still taking the same precautions we do with Slaytics in SEMTEX, as we want these results to be as accurate as possible going forward, but we can apply the trained tests from our post-1999 results to older data as statistics go much further back.

    To fully accomplish evaluating this new time period, the MAVEM system had to be extended further back, as it went to 1999 originally as well. So, MAVEM now supports analysis all the way back to 1983. Now, letís look at the SEMTEX results for passers in this older era:

    SEMTEX Gold QBs (Pre-1999)

    Notable Gold UDFAs in this testing era include Steve Young, Jeff Garcia and Mike Elkins.

    SEMTEX Silver QBs (Pre-1999)

    The Silver UDFA group includes Jake Delhomme, Mike Gundy and Darrell Bevell.

    SEMTEX Bronze QBs (Pre-1999)

    Damon Huard, Chad Hutchinson and Kirk Herbstreit were the most notable undrafted Bronze passers from the pre-1999 group.


    As you can see, SEMTEX translated to our testing group very well. From 1983 to 1998, only one drafted Bronze passer turned into a starter-level QB. And that one starter wasnít any of the six first-round, five second-round, seven third-round, or nine fourth-round Bronze passers selected over that span, but rather eighth-rounder Elvis Grbac, a journeyman who managed to stay above the starter baseline after a starting a few years in Kansas City, partially because he never came back and played after being displaced.

    Of the six first-rounders in this Bronze testing group, the biggest wastes of Draft Capital were both drafted in 1993: Rick Mirer (second overall pick) and Todd Blackledge (seventh overall pick).

    Looking at the player list for this Silver group, I believe the volume passer moniker still fits with the pre-1999ers. Top-level arm talent or elite structure paired with quick mental processing is needed for success in this group. Dan Marino certainly fits the bill, having one of the best arms ever, while the middle-tier guys showcase the latter portion.

    The biggest wastes of draft capital among the Silver group were Ryan Leaf (second overall pick), Kelly Stouffer (sixth overall pick), and Andre Ware (seventh overall pick)

    The Gold group has its fair share of busts as well, though. Heath Shuler (third overall pick) and Dan Klingler (sixth overall pick) lead the way in wasted draft capital, and even from our original post-1998 training group, there are misses like Blaine Gabbert and Joey Harrington as well. That said, just from analyzing the player lists, it is clear that if you want a starter- or Pro Bowl-level quarterback, you should avoid drafting Bronze passers and target Gold ones.

    Using the MAVEM framework, we can back this up and quantify how each group has performed on a macro scale across the testing/trained eras, giving us a full look among drafted FBS quarterbacks since 1983:

    Starter Percentage: The percentage of players in each group above starter level NFL MAVPY output.

    Success Rate: The percentage of players in each group that have a positive DCAR, meaning they have outperformed their draft-capital investment.

    Impact Score: The average of the top five MAVPY outputs in each grouping. These are meant to showcase the upside of prospects in these groupings. In very general terms, a MAVPY of around 4 is NFL starter level, while a MAVPY of 8 is around Pro Bowl level. Because we are using MAVPY, which is scaled horizontally in a cross-positional manner as well as vertically to match surplus values given in NFL contracts, impact scores can be compared this way as well.

    For reference on what these numbers mean in context, the starter percentage among all drafted QBs is 17.78 percent, the average success rate for a drafted QB is 21.39 percent, and the average NFL MAVPY output of a drafted QB is 1.69.

    As you can see, the Gold group wins in every category. The Silver groupís performance is interesting because it has a higher starter percentage than success rate, which is not normal. That means, in general terms, that players in this group are overdrafted. I would imagine that the supreme arm talent half of the ďvolume passerĒ bucket discussed earlier causes that.

    The Impact Scores are pretty telling. The Gold impact score is extremely impressive, and scrolling through the player lists showcases why. The upside on the Bronze QBs is extremely low, however, as the best case impact score scenarios in that group come in barely above starter level, and that is with Tannehillís odd path bringing that score up.

    Other tidbits:
    • 96.1 percent of starter-level quarterbacks drafted since 1983 have been Gold or Silver passers.
    • There has never been an Bronze 1st Team All-Pro QB in the SEMTEX era.
    • The Starter Percentage for drafted Gold QBís (34.38%) is nearly double the Starter Percentage among all drafted QBís (17.78%)
    • Correction: Mark Rypien (Bronze) is the only eligible non-Gold QB to win a Super Bowl as a starter. Bernie Kosar (Silver), Elvis Grbac (Bronze) and Brock Osweiler (Bronze) were on Super Bowl-winning teams as backups, behind Gold quarterbacks Steve Young, Troy Aikman and Peyton Manning. Super Bowls are obviously team achievements, but interesting to note.

  • #2
    Which QBs in the 2017 NFL Draft are most likely to succeed?

    Ethan Young/FanragSports
    To keep the research aspect of SEMTEX as objective as possible, I will talk about my opinions on the 2017 passers and what this means to me as an evaluator on Twitter. But remember: 96.1 percent of starter-level quarterbacks since 1983 have been Gold or Silver passers. Every Super Bowl-winning starting QB drafted since 1983 has been a Gold passer. There has never been an All-Pro Bronze QB in the SEMTEX era.

    Here are the 2017 SEMTEX results:

    Even if you think one of the 2017 Bronze passers is going to hit, the ones that have before havenít even been worth a first-round pick. The Impact Score for the Bronze group comes right between the draft-capital value of the 32nd and 33rd pick, according to the MAVEM Draft Chart, so keep that in mind with bigger-name Bronze prospects like Deshaun Watson and Davis Webb.

    SEMTEX is not an end-all, be-all system, but rather a new piece in the ever important quarterback-evaluation puzzle, highlighting which QBs have the highest and lowest chances of outperforming their draft slot, and becoming starters based on their enhanced production.

    As we discussed, just because you are in the Gold bucket doesnít mean you will be good NFL QB. But to be successful at the next level, you clearly need to be in the Gold group or in the Silver one with top level arm talent or mental processing skills to pair with it, or the deck is stacked against you. And when paired with other parts of the NFL draft process, such as evaluating a playerís translatable traits on tape, determining scheme fit, checking to see if prospects have baseline physical size and athleticism measurables as we put them through interviews and medical checks to eliminate undesirable Gold prospects, SEMTEX should become even more effective in application.

    Even just applying Slaytics results to the Gold group to root out passers that donít have baseline macro measurables, the corresponding starter percentage and success rate both jump up another 4 percent each.

    As mentioned before, raw production on its own is not valuable in player evaluation. But, there is clearly something to utilizing Situationally Enhanced Metrics for predictive purposes. The whole point of this framework was to discover what aspects of ďproductionĒ have been actually historically important, and to see just how effective those aspects are on a macro scale. Itís not about using production to create a Big Board, but rather eliminating prospects who donít have the baseline production to be successful and highlighting ones with the best chances.

    Being able to grasp and contextualize the relative importance of information available to you is obviously valuable in determining its usage, and that is what research like this does. In the future, I may analyze other positions as well, as even if they donít work as well as this, knowing how important production is at each position is valuable intel.

    I certainly didnít expect anything close to this level of effectiveness from something production-based coming in, but the results over a 34-year period speak for themselves. Itís clear that enhanced production can be a powerful predictive tool at QB, and NFL teams curious about optimizing production, or looking to increase their odds of hitting at the quarterback position in the draft, should consider SEMTEX as a vehicle for doing so.


    • #3
      What are Slaytics?
      Created by Ethan Young
      Slaytics are analytic composites designed to quantify a NFL prospects measurables. This started with SPARQ. While the original SPARQ formula has never been released, I was able to recreate something very close to it through a series of regression models. Then, I changed the equation to use drills performed at the NFL Combine, instead of inputs like the Kneeling Power Ball Toss and the Beep Test. The total list of inputs are the 40 Yard Dash, 10 Yard Split, Bench Press,Short Shuttle, 3*Cone, Vertical Jump, and Broad Jump. Iíve always loved SPARQ, but felt like something was missing. And that is how I came up with tFREAK.

      While SPARQ takes many factors into account, it doesn't measure any size or length measurables other than weight. Size and length are obviously important for football players, and yet they are ignored in SPARQ. Take​ ​ DeForest Buckner for example. Just by looking at him, you can tellhe is a freak. But he only tested in the 55th percentile for SPARQ score among defensive lineman,so clearly there is a disconnect somewhere. Players like Buckner are why I created True FREAK Rating (or tFREAK for short), aptly named to capture the "freaky" players that SPARQ misses. Think of it as SPARQ for size and length rather than athleticism. It takes height, weight, arm length, and hand size into account to develop a size and length composite score. Anyway, back to Buckner. He ranks in the 94th percentile of tFREAK among defensive linemen since 1999, which really paints a better picture of his traits. That is just a quick example of how tFREAK effectively captures the subset of lengthy players SPARQ can miss on. The NFL covets length, and we can't ignore it in our analysis. But like SPARQ, while tFREAK is helpful on it's own, it doesn't fully capture all the measurables. So that's why I made a third formula, combining SPARQ and tFREAK.

      I named this third formula Size, Length and Athleticism score, or SLA score for short. Thisall*encompassing composite seeks to solve the issues with tFREAK and SPARQ by bringing them together. SPARQ tends to rate small explosive players very highly, and tFREAK rates long,lumbering players higher than we want. SLA on the other hand, gives us the players with the most desirable measurable sets. And while it's not perfect, I think these size, length, and athleticism analytics (I call the whole data set Slaytics) are the best way to quantify a football player's measurables.

      How should Slaytics be used?

      Ideally, Slaytics should be implemented as part of the player evaluation process. They currently serve four main functions in this process:

      1. Identifying​ ​ Red Flags​ : Players with a very low success rate based on their measurables and past data (ďRedĒ Players under PST).

      2. Identifying Potential​ : Quantifying ďceilingĒ, and showcasing which players have tools in line with elite players (ďBlueĒ Players over TLP).

      3. Comparing Players: ​ Looking at Slaytic splits to identify how closely related players measurable sets are.

      4. Deciding between Players in Same Tier: ​ When looking at players with similar grades, the one with better measurables often has more success.

      Slaytics can be very effective when implemented in draft strategy, but they are not a big board and should be judged as such. Obviously factors like film,production, age, injury history, behavior, and scheme fit all play a role in the evaluation process. Slaytics serve as another filter to tell us how a prospect stacks up compared to past data.

      Results Summary

      ● First, I take the scores for each prospect and rank them by positional SLA percentiles (Pages 1*38).
      ● Next, I apply my Positional Slaytic Thresholds to each group. These thresholds can use SPARQ, tFREAK, SLA scores, and even raw drill results.Each position has different thresholds. Blue players are above the upper bound (elite level, called Top Level Players), green players are in suitable range, and red players are below the lower bound. If a player isnít listed here,that means either a threshold doesnít exist at that position, or it is someone I don't have data on (Pages 39*42).
      ● Then I use the Return on Draft Capital Chart to measure the effectiveness of the thresholds (Pages 43*48).
      ● After that, I look at this yearís prospects, and whether they hit the PST or TLP requirements. (Pages 49*53).
      ● Finally, I close by analyzing all this data.

      To simplify this, we are quantifying each prospects measurables,identifying which prospects have a very low or very high chance of success based on these measurables and past data, and then proving that past data right with RODC values.

      Return on Draft Capital

      RODC starts with Approximate Value. AV is a universal and unbiased way to quantify theimpact of players. I use it to examine players in relation to the PST's. If you want to learn more about AV, check out Pro Football Reference. I divide WAV (weighted AV) by career length to level out the playing field, since we donít want career length to effect these values.Averaging WAV per year also allows us to accurately quantify younger players earlier on in their career.

      RODC serves as a way to measure a player's value relative to where he was picked. ​ ​ Bysubtracting a player's expected WAVPY (based on the average return of where they were drafted) from their actual WAVPY, you get a quantifiable return on draft capital. So if you are the 13th pick, you should be near the 13th best WAVPY of an average class, not the average of the 13th pick. In the case of David Carr, the equation looks like:

      4.00 (Actual WAVPY) * 11.09 (Expected WAVPY for the 1st Pick) = *7.09 (Return on Draft Capital)

      RODC scores between *0.5 and 0.5 are near an acceptable return. Scores over and under that are where you can start calling players good or bad picks. Some positions have different expected WAV scales, to account for AV devaluing certain positions.

      RODC is meant to look at players on a macro scale, rather than nailing each individual player exactly. It provides a very good, although not perfect, view of if a player was worth being selected where he was. We can use these values to evaluate the effectiveness of thresholds like PST and TLP.

      Success Rate Summary

      Here are the success rates of TLP and PST for each position. Remember, we define success as having a positive Return on Draft Capital. Some positions like Fullback, Center,and Nose Tackle arenít included, due to a lack of effective thresholds. Impact Rating issimply taking the average WAVPY of the players with a positive RODC at each position, so higher numbers are better.
      Lower bounds donít exist at RB, and upper bounds donít exist at QB, as you can see here.

      LB is by far the least effective of the included positions, due to the instinctual nature of the position. But these markers still provides a lot of value. Take a look at the full TLP list below,and youíll see many of the best linebackers in the league on the list. And while there is a lot clutter there as well, that is where things like tape evaluation are crucial to determine which players to target.

      TE has a very small sample, but Jimmy Graham, Rob Gronkowski, Greg Olsen lead the pack. Two of the ďfailuresĒ were Vernon Davis and Kellen Winslow, due to being drafted so highly. I expect that success rate to jump as more TEís come in the league. The others are more stable.


      A bit of a SPARQ and Slaytics primer, from a paper on 2016 draft class.


      • #4
        2017 Force Players

        Justis Mosqueda
        Force Players is a combine metric study I have been running for pass-rushers for years. You can find most of the numbers I’m going to cite, updated through the 2016 draft class, on Playmaker Mentality.

        Here’s the gist of it:
        • The athletic backgrounds of pass-rushing prospects matters a lot. The problem is, many don’t realize that combine numbers need to be adjusted for density when talking about line of scrimmage defenders. While 10-yard splits are more important than 40-yard dashes, I still have yet to see a defensive lineman run 10 yards straight into the backfield untouched and make a play. When adding density into the equation, these numbers essentially turn into body explosion and body control through contact, which is exactly what you’re looking for in edge defenders and one-gap defenders in general.
        • There are three types of categories for pass-rushers: Force Players (elite athletes), Mid Tiers (near elite athletes whose 10 splits/short shuttles don’t totally add up) and non-Force Players (non-elite athletes).
        • First- and second-round Force Players were 8.21 times more likely to be retained by their original team than non-Force Players by their sixth season in the NFL (2005-2011.) I will update these numbers sometime in the offseason for the 2012 class.
        • First- and second-round non-Force Players were 12.69 times more likely to be out of the league by their slated sixth season in the NFL than Force Players (2005-2011.) I will update these numbers sometime in the offseason for the 2012 class.
        • A third-round Force Player, on average, is equal to a first-round non-Force Player in terms of the player’s averaged three best sack totals in his career. When you take into account of the draft value of first-round picks relative to third-round picks, that’s very interesting. Here is the 2017 update for those numbers. See for yourself.
        • Using Force Players/Mid Tiers/non-Force Players, it’s fairly easy to pick who is and isn’t going to be a successful pass-rusher at the NFL, based on their production as a 23-year-old. These thresholds lead me to labeling players as “Prodigy” pass-rushers, on top of their athletic background.
        These numbers are why it was easy to see why a Danielle Hunter or Frank Clark were going to be steals for their price point in the draft. It’s why Vic Beasley should have been drafted before Dante Fowler in the 2015 NFL draft.

        If you’re going to draft C-gap defenders, at least in the first two rounds, they should almost exclusively be Force Players. Those players, even just at their peaks, record twice as many sacks as their non-Force Player counterparts. When the going rate of a sack is roughly $2 million per, and pass-rushers are now clearly only second to quarterbacks in the pecking order of NFL positions, this form of risk analysis is important. This should not be used as a metric to push players up a board (draft a second-round film grade pass-rusher in the top-10 because of his metrics,) but it should be used to downgrade non-Force Players, if their major value comes as a C-gap pass-rusher. History plays out there.

        I will say, there are two major molds of players who tend to break the Force Players mold:
        1. The injured player without a clean run. Aldon Smith was coming off of a broken leg during his draft cycle. Justin Tuck was coming off of a torn ACL during his draft cycle. It’s no surprise that those are two of the four best non-Force Players drafted in over a decade.
        2. The player with a strong inside presence. Michael Bennett wasn’t drafted, but he wasn’t a great athlete on paper, either. He wins inside, though. Joey Bosa didn’t have a great 40-yard time, but he also plays inside and has various moves on film to cross the face of a tackle instead of trying to bend around the edge every play. Even Leonard Floyd based his game off of inside stunts with his long Gumby frame. Don’t count out players who can kick inside to defensive tackle or who have inside counters (swims, spins.) Again, this should be used for edge-bender types. Those labeled as athletes.
        First-round splits 2005-2014:

        Second-round splits 2005-2014:

        Third-round splits 2005-2014:

        Fourth-round splits 2005-2014:

        You should notice some things by looking at those charts.
        1. Athletic pass-rushers on average blow non-athletic pass-rushers out of the water in terms of their peak production. Their retention rate is even better. Athleticism equals production equals longevity.
        2. It’s not as easy to find pass-rushers outside of the top 20 picks in the draft as some fans would assume. There’s a reason why insane contract are being thrown around and defensive ends are making about $2 million per sack in that no man’s land range of contracts like Andre Branch’s. Taking a pass-rusher in the late first round or second round is similar to the market of quarterback. Those two positions are essentially the same in terms of value.
        3. It’s harder and harder to find Force Player and Mid Tier athletes the deeper you get into a draft. Athletes go early. Some of the Day 2 selections who slipped through the cracks (Frank Clark, Justin Houston and Randy Gregory) were only there because of their off-field issues, too.
        Draft athletes. Try to get them early.

        I don’t disclose the formula, but I do tell the public who these “Force Player” and “Mid Tier” pass-rushers are going to be before the draft. Here’s how I calculate their numbers:
        • Use the numbers listed on the NFL’s official combine sheet when possible.
        • If a player did not participate at the combine, I will use their pro day numbers. If they have combine numbers, I’m going to use those. The only time I didn’t follow this rule was when Randy Gregory’s weight and numbers fluctuated greatly during his draft process. With his off-field record, and lack of on-field play, it’s hard to say if I was right or wrong for doing so. Gregory would have been a non-Force Player based on his density-adjusted combine performance. Pro day numbers will come from NFL Draft Scout/CBS Sports.
        I’m a big believer in that the NFL draft is really only four or five rounds, with the back end of Day 3 being filled with players who are mostly free agent level talents. That’s one reason why I only study the first four rounds of the draft for this Force Players study.

        With that in mind, there are X players with at least a “7th-FA” grade on NFL Draft Scout this season who were Force Player or Mid Tier athletes as projected NFL pass-rushers. Here’s the list:

        1 Myles Garrett, Texas A&M

        Force Player or Mid Tier

        NFL Draft Scout Projection: first-round grade

        Myles Garrett has been an all-star his entire life. He was one of the highest recruited players in his 2014 graduating class. From the Under Armour All-American game on, Garrett started separating himself from this pack of pass-rushers. Only one other pass-rusher in FBS history, 2017 Tennessee prospect Derek Barnett, recorded more sacks in the first three years of his college football career than Garrett did. He has athleticism, production, an NFL defensive end frame, the pedigree of a top-end player and he improved greatly against run. Against UCLA at the start of the 2016 season, you see how far Garrett has come as a run defender and counter player. Garrett’s testing is incomplete, but his elite jumps automatically kick him into Force Player/Mid Tier category. Unfortunately, he never ran the short shuttles, so we’re never going to get the answer to which category he’s in. The only player with this background in my database is former San Diego Charger star Shawne Merriman. As a player, Garrett is similar to a young Jason Pierre-Paul.

        2 Solomon Thomas, Stanford

        Force Player

        NFL Draft Scout Projection: first-round grade

        Myles Garrett is mocked as the consensus number one overall pick in the 2017 draft. The second player drafted in most mocks is Solomon Thomas. As a freshman with Stanford, Thomas played in a four-man rotation on a 3-4 defense. He was playing nose tackle reps, and a lot of them, as a young player with an edge defender’s body. He’s not going to play inside, at least on “on pace” downs, in the NFL. Thomas athletically has a profile similar to Justin Houston and Chris Long. He has great burst off the line of scrimmage and a swim move, but he’s going to need to refine himself as a 4-3 defensive end or 3-4 rush linebacker.

        37 Tyus Bowser, Houston

        Force Player

        NFL Draft Scout Projection: first/second-round grade

        Tyus Bowser might be one of the more interesting projections in this draft class. He never really played as a pass-rusher at Houston. He had great production, on a per game basis, in his limited 2016 season, but he was mostly used as an overhang defender. Really, he was playing a similar role to Darron Lee, formerly of Ohio State and now with the New York Jets, where he’d line up in the box if there was a heavy offensive formation, but split out as a slot player if offensive players were detached.

        42 T.J. Watt, Wisconsin

        Force Player

        NFL Draft Scout Projection: second-round grade

        I’m not a big fan of T.J. Watt’s game. I understand that he’s the brother of J.J. Watt and he makes a lot of effort sacks, but I still believe that as a pure pass-rusher, his teammate Vince Biegel is more talented. Watt’s best future, in my opinion, would be as a 4-3 outside linebacker who can sometimes drop down to the line of scrimmage in a two-point stance or line up as a defensive end in nickel situations. Another good fit would be in a man-blitz heavy defense, like the New York Jets’ or the Arizona Cardinals’, where he can flash in limited reps as a rush or drop linebacker. The element of surprise is where Watt is going to win at the next level. If he just lines up and rushes every down, players are going to key on his athletic timing and make him win with effort, a losing plan in the NFL.

        64 Jordan Willis, Kansas State

        Force Player

        NFL Draft Scout Projection: second-round grade

        I can’t figure out how Jordan Willis tested as well as he did. Willis on film looks like a more athletically underdeveloped version of Nick Perry or Olivier Vernon, but he tested like Vic Beasley. On the field, Willis looked stiff enough to run himself out of plays after initial penetration, a specialty of Perry and Vernon. My prediction is that Willis will be one of those players who records a lot of pressures on a chart, but in reality his penetration is going to mean less than most other pass-rushers with his sack numbers.

        74 Derek Rivers, Youngstown State

        Mid Tier

        NFL Draft Scout Projection: second/third-round grade

        Derek Rivers owns Youngstown State’s career sack record. He plays with his hands above the eyes, a great sign of an all-around player, better than just about anyone in this draft class. His bench numbers align with his impact as a run defender, again playing with his hands above his eyes. As a pass-rusher, he can play either defensive end or outside linebacker at the next level. When he went head-to-head with FBS talent over his last three great years of production, he didn’t get slowed down. When he was at the Senior Bowl, he was the most consistent pass-rusher in one-on-one drills in Mobile, and he had a great game. He’s passed about every test that you can imagine an FCS prospect going through, but he’s still going to be drafted at a discount because of where he played college football.

        103 Trey Hendrickson, Florida Atlantic

        Force Player

        NFL Draft Scout Projection: third-round grade

        Trey Hendrickson looks like a pass-rusher drafted in the 100s. He was a bit of an opportunist at Florida Atlantic, going up against low-quality offensive tackles, and I never really saw the type of bend that would make him a top-100 pick. He apparently did well in practices at the East-West Shrine Game (he wasn’t a Senior Bowl invite or injury substitute,) but I thought that based off of the practice steams and game day looks, Deatrich Wise of Arkansas was on another level compared to Hendrickson. He’s probably someone who you can mold to get onto your two-deep, though, and he will be able to contribute on special teams.

        188 Hunter Dimick, Utah

        Force Player

        NFL Draft Scout Projection: fifth/sixth-round grade

        Hunter Dimick has been a known prospect out west for a while, but for whatever reason he wasn’t invited to the combine. His pro day numbers got him here, the only player we can say that of in this draft class. He has a bit of a stocky body, but you see his athleticism at his burst off the line of scrimmage, when he turns the corner and when he’s avoiding cut blocks.

        The majority of difference-making pass-rushers in the NFL have the same rare athletic backgrounds as those players listed above. Without knowing which players will go in which rounds, it’s hard to make sack total projections for the players, but if they’re drafted in the first four rounds, expect them to fit in with this graphic: