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One of the biggest influences to fantasy football performances is a touchdown. There is an entire channel built upon showing red zone possessions and goal line opportunities. For running backs, few top performers can survive for long without strong goal line volume and a hearty conversion rate. Here is a look at the historical data and the top regression candidates for 2017:
Sample Size: 363 running backs from 2006-2015
Criteria: 3+ carries inside the five-yard-line in both the test season and the following season
THE DATA
Overall, these 363 criteria-fitting running backs averaged a 38% conversion rate, ranging from peak aggregate seasons of 42% down to 34%.
| Conversion Rate | Total | Regressed | Regression Rate | AVG |
| 100% | 3 | 3 | 100% | -70% |
| 70% | 6 | 5 | 83% | -28% |
| 60% | 27 | 27 | 100% | -25% |
| 50% | 38 | 34 | 89% | -15% |
| <30% | 47 | 40 | 85% | 14% |
| <20% | 14 | 12 | 86% | 18% |
| <10% | 11 | 10 | 91% | 29% |
The first observation is even 10% above or below the recent average is a strong regression rate. In 2016 on the high side, 9-of-11 regressed downward in the 50% or higher subset by an average of -15%. The top qualified conversion rate was Spencer Ware at 71% in 2015 and he sharply came down to 27% last year. Conversely, 12-of-14 from the 30% or lower conversion group in 2015 regressed upward by an average of 21%. Notable risers included Tevin Coleman (20% to 55%), LeSean McCoy (17% to 45%) and Jay Ajayi (0% to 56%).
2017 running backs: MOVING down
Let's start with the high conversion rate backs from 2016:
70-75% Conversion Rates
Tevin Coleman, Derrick Henry, Mike Gillislee
Coleman is the name to note here as more of a satellite back than interior grinder. Henry is bound to see more volume at the goal line than his four carries in 2016 once DeMarco Murray cedes the starting job to Henry. Gillislee saw a hearty 10 carries despite being behind LeSean McCoy. Now in New England, Gillislee has upside of 15 or more goal line carries. As a point of reference, LeGarrette Blount saw a whopping 30 carries inside the five-yard-line in 2016, more than any other running back in the NFL by a margin of 20%. Look for a drop in conversion rate for Gillislee, which could be offset by a rise in volume.
60-70% Conversion Rates
Lamar Miller, LeVeon Bell, Ty Montgomery
Miller hit the high-water mark of his career in 2016 with a 63% conversion rate. His last regression-worthy season was in 2014 where a previous 20% mark boosted to 43% and added a handful of touchdowns as a result. With D'Onta Foreman a legitimate threat to short-yardage carries and goal line opportunities, Miller's usage near the goal line (and Foreman's drafting) has been necessary considering Alfred Blue's 0-for-8 conversation profile near the goal line over the past two seasons. LeVeon Bell has operated near the NFL average over his career with his goal line volume in decline the past two seasons (just 11 overall carries) - a far cry from Bell's 19 goal line totes in 2013 alone. Ty Montgomery faces added competition on the Packers depth chart with the drafting of three running backs in the 2017 class, including interior grinder Jamaal Williams. Green Bay has averaged a strong 46% conversion rate as a team since 2010, higher than the NFL average.
50-60% Conversion Rates
Jonathan Stewart, Jay Ajayi, Ezekiel Elliott, Latavius Murray, Jordan Howard, Todd Gurley
Stewart as been an average or lower conversion back over the past six seasons. His 16 carries in 2016 also marked a career high. Stewart's lone appeal in 2017 will center around his goal line opportunities and overall volume. With Christian McCaffrey and Curtis Samuel added as moveable chess pieces near the goal line, Stewart is a strong regression candidate in volume and conversion rate. Ajayi's nine carries ranked outside the top-25 among running backs, speaking to Miami's offensive struggles overall to finish drives despite Ajayi's strong conversion rate. Ajayi is an ideal candidate to increase volume and lower his conversion rate in 2017.
Latavius Murray stands out on the above list with 31% conversion rate for his career and enjoying 53% in 2016. With Dalvin Cook in the fold for the Vikings, Murray will rely on touchdowns to prop up his value. With a questionable offensive line, Murray will have an uphill task to stay above the NFL average for 2017.
Todd Gurley is the most insulated of the list with a career 58% conversion rate despite non-ideal surroundings with the Rams. Gurley barely qualified with an exact 50% rate, one of the exceptions after hitting the regression list with 67% as a rookie.
2017 running backs: MOVING up
Now, let's hit the backs mostly likely to see a boost in conversion rate in 2017:
0% Conversion Rate
Lacy is on a positive track after passing his initial weigh-in checkpoints this offseason for Seattle. Lacy's conversion rate has fallen every year since 2013, both in volume and rate, near the goal line. With a 49% career conversion rate, Lacy is a strong regression candidate with the fresh opportunity and shutout on goal line carries a year ago.
20-30% Conversion Rates
Devontae Booker, Spencer Ware, Mark Ingram, Frank Gore
Ware whiplashed from the top conversion rate in 2015 (71%) to just 27% a year ago on 11 carries, now landing on the positive regression list. Ware's 11 carries inside the five-yard-line were the most by a Kansas City back since 2013. Mark Ingram's 34% career conversion rate is forgettable compared to the NFL average, hitting 50% in a season just once and more than 10 carries near the goal line in one season as well. Enter Adrian Peterson and Ingram's short-range opportunities are likely to sag below even his middling 19 cracks over the last two seasons. Frank Gore, like Ingram, has simmered around the NFL average for his career. Gore's last three years have been below his career average at 22%. Robert Turbin has been mixed in more near the goal line as a result - seeing 10 carries, equal to Gore's mark - in 2016. Expect more of the same as Gore continues his twilight period in 2017 with Turbin mixed in at the goal line and Gore seeing 1-to-3 more touchdowns as a best case scenario with positive regression.
