by Nathan Forster
In 2008, Football Outsiders introduced Speed Score, a metric that projected the likelihood of success for running back prospects available in the NFL draft. Speed Score found a correlation between NFL success and the prospect's score on the 40-yard dash after adjusting for the prospect's weight. In short, players who tested as big and fast at the NFL combine ultimately found more success than players who tested as small and slow. Speed Score has had a number of hits, projecting success for players like Chris Johnson (who was considered a huge reach as a first-round pick at the time) and third-rounder turned All-Pro Jamaal Charles.
However, Speed Score also yielded some counterintuitive results that ultimately proved to be incorrect. For example, Speed Score gave high marks to Arizona's Chris Henry, who dazzled by running a 4.40-second 40-yard dash at 230 pounds. Henry, however, was otherwise unimpressive. In his best college season, Henry recorded just 581 rushing yards and he averaged only 3.3 yards per carry -- exceptionally poor for a running back headed to the NFL. Henry was severely overdrafted by the Tennessee Titans in the second round, and his career ended with just 122 rushing yards.
Over the past few months, we have worked to expand Speed Score to include more than NFL combine results, with a new system called BackCAST. We are still developing the new model, and have some kinks to work out, but we wanted to share with you our preliminary findings -- and what they suggest about the prospects available in this year's NFL draft.
The current database contains the 279 Division I halfbacks drafted in the period from 1998-2014 (2015 was excluded because those prospects have not had enough NFL experience to meaningfully evaluate). For obvious reasons, we included only players rostered as halfbacks for at least one full college season.
Right now, BackCAST is based on projecting each player's total NFL rushing and receiving yards over his first five years in the NFL. However, one change we may make in the future is to switch from measuring success by yards to measuring success by DYAR. Such a switch might be meaningful because total yards may make certain "compilers" seem successful simply because they received a lot of carries early in their careers. For example, Trent Richardson has managed to amass nearly 3,000 total yards to date, which is more than the "average" drafted running back. In reality, however, Richardson is an epic flop whose aggregate numbers were entirely a function of the Browns and Colts essentially banging their heads against the same wall by continuing to feed Richardson the football.
The first two factors in BackCAST are the familiar ones: BackCAST preserves Speed Score's use of 40-yard dash time and weight. In terms of success in the NFL, 14.5 pounds is roughly equivalent to 0.1 seconds in the 40-yard dash (so a 214.5-pound player with a 4.5-second 40-yard dash has the same prospects of success as a 200-pound player with a 4.4 40-yard dash, all else being equal). We also looked at other combine metrics (especially the 3-cone drill) to see if they also correlated to success. However, 40-yard dash was by far the strongest indicator of success of all the combine metrics, and none of the others were independently significant.
The first new factor in BackCAST is a metric called yards over expected per game (YOE/G). YOE/G is a sophisticated way to look at college yards per attempt. Basically, YOE/G creates a baseline for the strength of each college team's running game and compares the performance of the prospect against that baseline. More specifically, YOE/G compares the player's yards per attempt during his entire college career to the yards per attempt of all other teammates to record rushing attempts during the player's career, as well as the year before the player started at the college. We use the year before in case the prospect so dominated his team's carries that the other players on his team did not provide a large enough sample size for a meaningful comparison.
For example, LaDainian Tomlinson, recorded 5.7 yards per attempt for the Texas Christian Horned Frogs. That stat, in isolation, is not particularly spectacular for a collegiate running back. However, his numbers are extraordinary when placed in context. Texas Christian's running game was in poor shape. During the period from 1996-2000, TCU players not named "LaDainian Tomlinson" carried the ball 1,078 times for 2,146 yards -- a cover-your-eyes awful 1.99 yards per attempt. For the Horned Frogs, the difference between Tomlinson and anyone else to run with the football was just over 3.7 yards per carry. Tomlinson carried the ball 943 times, and thus produced 3,510 yards (3.7 * 943) over the numbers expected from "other" Texas Christian players. Because Tomlinson took 45 games to reach that number, his YOE/G is 78.0 (3,510 / 45). Tomlinson's 78.0 YOE/G is the best of any prospect in BackCAST's database.
The aforementioned Chris Henry is an illustrative counter-example to Tomlinson. Henry averaged only 3.3 yards per attempt in college, which was only slightly better than the 3.0 yards per attempt averaged by his teammates (nearly every drafted running back averages more yards per attempt than his teammates). The difference is a paltry 0.4 yards per attempt (after rounding), and Henry only had 269 college attempts. The result is a weak YOE/G of 2.8 -- the worst number recorded by any player in our dataset who was drafted in the first or second round.
The beauty of YOE/G is that it can also identify players whose numbers are buoyed by a strong supporting cast or a run-friendly system. For example, YOE/G would have suggested caution on Trent Richardson (although he did score decently on other BackCAST metrics). Richardson recorded 5.8 yards per attempt, which is slightly better than Tomlinson. However, Richardson did not play for the early aughts Horned Frogs; he played for an absolutely dominant incarnation of the Crimson Tide. In fact, the Crimson Tide was not much better running the ball with Richardson than without him (5.1 yards per attempt).
The next new factor is the prospect's peak percentage of team rushing attempts. Specifically, BackCAST measures the prospect's share of his college team's rushing attempts in each of his college seasons, and picks the highest one. For example, if a junior running back had 5 percent, 25 percent, and 65 percent of his team's rushing attempts in his three college seasons, BackCAST would use the 65 percent.
This measure recognizes that nobody knows the quality of a college running back better than his coaches, and that most coaches are smart enough to feed the ball to a player who is a true NFL-level talent. BackCAST recognizes that sometimes circumstances will dictate that a player may not receive as many attempts as his talent would suggest -- such as slow development, injuries, suspensions, or early mistakes in assessment by the coaching staff -- but for at least one season, he should dominate the backfield.
One objection to this metric might be that it downgrades a running back who shares his backfield with another elite NFL talent. However, this appears to be a feature, not a bug. The small sample size of first- and second-round running back prospects who entered the draft with a college teammate who was also a first- or second-round pick is filled with disappointments:
|Teammate RBs Drafted in First or Second Rounds, 1998-2014|
However, this is one factor that we will be scrutinizing more closely as we are developing BackCAST. There is a semi-strong relationship between peak attempts and YOE/G, and the two metrics might in fact be measuring the same thing. So it is possible that one of these factors may be dropped, or that one or both could be reconfigured. However, it is clear that the final version of BackCAST will include one or more metrics that factor in efficiency and usage.
BackCAST's final new factor is each prospect's receiving yards per game over his college career. Receiving yards per game does not correlate at all to the prospect's rushing yards in the NFL, but, as intuition would suggest, it does correlate strongly to NFL receiving yards.
BackCAST is expressed in terms of the percentage that the running back is projected to over-perform or under-perform the average running back prospect. For example, a player who has a +50% BackCAST score is expected to be 1.5 times as productive as the average drafted running back. Conversely, a player with a BackCAST score of -50% is expected to be only half as productive as the average drafted running back. Below is a table of the top 25 BackCAST scores from 1998-2015:
|Top 25 BackCAST Scores, 1998-2015|
|T.J. Duckett||2002||1||18||Michigan St.||+155.1%|
|Steven Jackson||2004||1||24||Oregon St.||+124.6%|
|Luke Staley||2002||7||214||Brigham Young||+119.6%|
|Michael Turner||2004||5||154||Northern Illinois||+114.6%|
|Edgerrin James||1999||1||4||Miami (FL)||+112.6%|
|Garrett Wolfe||2007||3||93||Northern Illinois||+112.0%|
|Chris Johnson||2008||1||24||East Carolina||+90.5%|
|Jerome Harrison||2006||5||145||Washington St.||+88.4%|
|Amos Zereoue||1999||3||95||West Virginia||+87.0%|
|Charles Sims||2014||3||69||West Virginia||+85.9%|
BackCAST also includes an output to measure the type of running back the prospect is likely to become -- whether the player is likely to be a ground-and-pound two-down back, a player who catches passes out of the backfield more often than he takes handoffs, or something in between.
Coming up with a metric to predict the prospect's likely usage patterns required a somewhat convoluted process. First, we took the historical NFL data and measured, for each prospect, the number of receiving yards under or over the amount that would be expected based on the player's NFL rushing yards. For example, take Alfred Morris. Morris has more receiving yards than the average drafted running back. However, nobody would fairly call Morris a receiving back, because he has an extremely low number of receiving yards for a running back who has seen the field as much as he has during his career. Once we had a measure of how many receiving yards the player gained relative to the number of rushing yards gained, we had a more accurate measure who in the past turned out to be a "receiving" back in the NFL and who was more of a ground and pounder.
Second, we transformed the metric into a Z-score. For lack of a better term, we call this the prospect's "RecIndex." It turns out that there are a few running backs who were almost exclusively pass catchers, so the median running back actually has a slightly negative RecIndex.
Finally, we ran a regression to predict which players are likely to have a high (receiving back) or low (rushing-focused back) RecIndex. The two factors that are significant in predicting RecIndex are receiving yards per game in college and weight, as smaller players are more likely to be receiving backs.
Here are the top running backs available in this year's draft according to BackCAST:
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Derrick Henry, Alabama
BackCAST Score: +63.1%
I was a bit surprised to see Derrick Henry top the inaugural version of BackCAST, given the recent strength of the Alabama backfield. However, Henry has several points in his favor, and he is a little better than Trent Richardson as a prospect in most metrics that BackCAST cares about. Henry's YOE/G is approximately 50 percent better than Richardson's. Henry was also more of a focal point in the offense, as 61.5 percent of Alabama's rushing attempts went to Henry. Henry is a bit slower than Richardson, but he more than makes up for the difference by being larger. Also, unlike Richardson, he is more appropriately valued as a late first- or early second-round pick, rather than a top-five prospect. The one big downside to Henry is that his chances to shine as a receiver coming out of the backfield are low, so if it turns out that he is not effective on the ground, he is likely to be a useless player.
The fact that Henry is our top back, however, also shows the weakness of this class according to BackCAST. (This is an interesting counterpoint to using just Speed Score, in which this year's class was particularly impressive.) Last year's top back, Todd Gurley, had a +85.0% BackCAST score.
Ezekiel Elliott, Ohio State
BackCAST Score: +46.2%
Ezekiel Elliott is a nice prospect who is probably overrated as a high first-round pick. On the positive side, Elliott has a nice size/speed combination -- he recorded a 4.45-second 40-yard dash at 225 pounds. Elliott also had a great 6.69 yards per attempt for the Ohio State Buckeyes. However, Ohio State's offense was highly prolific during Elliott's stay, and it averaged 5.61 yards per attempt on non-Elliott runs.
Interestingly Elliott's projection also suffers because he only carried the ball on 51 percent of Ohio State's rushing attempts during his junior year, which was his best season. However, Ohio State gave few carries to running backs other than Elliott. Rather, Elliott lost carries to Ohio State's quarterbacks. We actually looked at re-calculating peak rushing percentage by subtracting out quarterback runs to see if it made the model stronger. However, it turns out that subtracting quarterback runs actually makes this factor (and the whole model) much weaker. It's interesting to think about why this might be so. It could be that running backs compete with quarterbacks for rushing attempts in the same way they compete with other running backs, or that rushing in a backfield with a running quarterback could cause the running back's numbers to be overstated in a way not captured by other metrics.
(Also, the rushing totals used here are based on official college stats and thus include sacks as runs, not passes; at some point, depending on how far back we can get clean data, we plan on analyzing these percentages with sacks removed from team rushing totals.)
Given his higher draft projection, Elliott is still probably the best bet to be the best back of this class. However, he lacks the statistical indicators that have been harbingers of elite running backs in the past. Especially given the relatively low value of the running back position in the modern NFL, a team picking in the top ten might be well advised to go in a different direction with its pick.
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Devontae Booker, Utah
BackCAST Score: +23.4%
Devontae Booker should be one of the best values in this draft at the running back position. He has a 27.5 YOE/G, which is the higher than all other running backs invited to the NFL combine this year. He was also a highly productive receiver out of the backfield, averaging 27 receiving yards per game for the Utah Utes. So if Booker does not work out as a traditional running back, he might at least be useful as a third-down back.
The biggest knock on Booker is speed. Booker actually has not run the 40-yard dash in pre-draft workouts. However, scouts believe that Booker's 40-yard dash would be in the mid to high 4.5s, and BackCAST downgrades him accordingly. (BackCAST presently uses the projected 40-yard dash times from NFLDraftScout.com for those prospects who do not run the 40-yard dash in pre-draft workouts).
C.J. Prosise, Notre Dame
BackCAST Score: +18.2%
C.J. Prosise was a playmaker at Notre Dame. Prosise averaged 6.9 yards per attempt during his career, even though Notre Dame otherwise averaged only 4.6 yards per run.
If anything, BackCAST underrates Prosise because of the somewhat unusual circumstances of his college career. Prosise was rostered as a wide receiver his first two years and he was not pushed into action at running back until Tarean Folston suffered a season-ending injury against Texas. After that point, Prosise dazzled until suffering his own injury late in the season, which limited his carries on the year.
Prosise has such a small sample size, it could be that his strong play was simply a mirage. BackCAST is conservative on his prospects, although perhaps overly so. However, Prosise is generally considered a mid-round prospect and is well worth that price.
The following table provides the BackCAST and RecIndex numbers for all of the halfback prospects invited to this year's NFL combine.
|2016 RB Prospects by BackCAST Score|
|Ezekiel Elliott||Ohio State||4.45||225||18.3||51.0%||12.8||+46.2%||-0.04|
|C.J. Prosise||Notre Dame||4.43||220||12.0||32.8%||28.0||+18.2%||+0.64|
|Kenneth Dixon||Louisiana Tech||4.56||212||27.1||51.6%||20.6||+16.6%||+0.40|
|DeAndre Washington||Texas Tech||4.46||198||22.1||53.0%||22.3||+14.2%||+0.58|
|Tyler Ervin||San Jose State||4.36||178||24.5||55.4%||17.0||+8.6%||+0.52|
|Wendell Smallwood||West Virginia||4.42||202||18.3||38.3%||16.3||-2.9%||+0.30|
|Tra Carson||Texas A&M||None||202||0.6||47.9%||6.6||-40.4%||-0.11|
|Brandon Wilds||South Carolina||4.54||202||3.7||29.1%||13.2||-77.2%||+0.17|
|Shad Thornton||N.C. State||4.75||202||17.5||32.7%||14.0||-100.0%||+0.20|