Matt Waldman shares samples of the after-contact data he has begun tracking in 2021 and what he hopes the information will provide in the future for his evaluation of running backs. 

Note: You can find this after contact data in the 2021 and 2022 Rookie Scouting Portfolio pre-draft publications, now available for download.  

After studying Saquon Barkley and Nick Chubb, it became clear to me that there had to be a better way to track Yards After Contact:

Yards After Contact is a great statistic in theory. It’s the sum of yardage gained after contact from a defender.

This sounds alright until you think about the way we track the data. If you track the data based on the literal definition of the stat’s name, then it’s the sum of all yardage gained after any form of contact from a defender.

It means if a cornerback slaps his hand across the hamstring of a running back turning the corner on a sweep and the running back gains 75 yards untouched afterward, the runner earned 75 yards after contact. This is a display of speed, but not of power. In other cases, the location of the slap—say, to the ankle—may require balance from the runner to maintain his stride.

Depending on who tracks the data, the scenario above earns the same value as a running back who drops his pads into the chest of a middle linebacker, runs the defender over, and then drags the tackler another four yards before pulling free and running away from the rest of the defense for 75 additional yards. In terms of which back executed the more powerful run, it’s no contest.

However, the data doesn’t provide this context. At best, it gives a location of where the defense initiates the contact.

I created a charting matrix for runners, separating contact into three different contact types:

• Reaches: When a tackler can only get one or both hands on the ball carrier and attempt to latch onto the runner’s body. This is the least stable form of a tackle attempt and is often broken. However, a well-placed reach to the lower leg or a reach from a defender much larger can often bring a runner to the ground.
• Wraps: When a tackler can get both arms around a body part and hang on through the tackle. This is a more stable form of tackling than reaches, but it’s often lacking a collision to reshape the position of the runner. As a result, a lack of collision can give the runner a greater chance of working through the wrap.
• Hits: When a tackler initiates contact with a collision. Many hits can knock a ball carrier to the ground because the angle, timing, and force of the hit can reshape the runner too dramatically for the runner to adjust. However, many hits are only slightly more effective than wraps because they have force but lack the stability of the wrap to limit a runner’s movement.

I also broke down Hits based on the angle of the collision (direct or indirect) as well as the sources of the contact (linemen, linebackers, and defensive backs). When you consider the position of the player, the type of tackle attempt, and the angle of the contact, it becomes clearer which combinations are more difficult to overcome than others.

The goal of filtering this information in greater detail is to generate a more valuable set of after-contact metrics that help analysts understand the various ways that runners solve problems–and potentially the schemes that are the best fits fo these runners.

With only two years of tracking, there’s not enough data to present anything meaningful about the position overall and I’m still considering some changes to what I track. Still, the information provides some basic validation to things we already know:

• The larger the defender, the greater the difficulty of breaking a tackle.
• The difficulty of each tackle type is based on the percentage of tackles broken is in ascending order of reaches, wraps, and hits.
• Broken tackles

For the sake of sharing a taste, let’s take a look at the top 20 performers against total wraps for the past two years. I chose wraps because it’s the tackle type that I believe (at least for now) serves as the most meaningful baseline for what teams reasonably expect from a running back to break if he truly has starter-caliber power.

This data is confined to each class in their respective publications, but I’m combining the classes for this post. The average percentage for Wraps Broken from the 107-player sample is 6 percent. Again, it’s a small sample size has a lot of variation but it’s fun to see range of performance among the top 20.

Class	First	Last	Wraps	Broken	Pct
2022	Bam	Knight	48	8	17%
2022	Dameon	Pierce	37	6	16%
2022	Kennedy	Brooks	110	16	15%
2022	Tyler	Nevens	47	6	13%
2021	Javonte	Williams	209	25	12%
2022	Brian	Robinson	95	10	11%
2022	Stephen	Carr	39	4	10%
2022	ZaQuandre	White	41	4	10%
2022	Tyler	Allgeier	53	5	9%
2022	Kevin	Harris	71	6	8%
2022	Jerome	Ford	61	5	8%
2021	Stevie	Brown	63	5	8%
2022	DeMontre	Tuggle	38	3	8%
2022	Kenneth	Walker	102	8	8%
2021	Travis	Etienne	90	7	8%
2021	Michael	Carter	90	7	8%
2022	Charles	Williams	65	5	8%
2021	Trey	Sermon	184	14	8%
2021	Kenneth	Gainwell	79	6	8%
2021	Jaret	Patterson	135	10	7%

The players with the highest sample sizes were Javonte Williams, Trey Sermon, Jaret Patterson, Kennedy Brooks, and Brian Robinson. Brooks has the highest percentage broken of the five. Brooks and Williams performed at twice the average rate.

Dameon Pierce, Bam Knight, Tyler Nevens, Stephen Carr, Zaquandre White, Tyler Allgier, and DeMontre Tuggle had samples sizes well-blow the average number of tracked wraps (64). Even so, Pierce, Knight, Nevens, Carr, Allgeier, and White demonstrated after-contact skills that project well to the NFL despite the lack of data.

Although I’m not showing the tables here, these were the backs from the list that had the most broken tackles versus linebackers who wrapped them.

1. Javonte Williams (22%)
2. Pierce (20%)
3. Brooks (18%)
4. Sermon (13%)
5. Kevin Harris (12%)
6. Robinson (12%)
7. Knight (12%)
8. Nevens (10%)
9. Ford (10%)
10. Walker (9%)

The most interesting back on this list to me is Kennedy Brooks because he’s the smallest and one of the least impressive athletically (although still well within the baseline data for a high-end contributor). Brooks also played in an athletic conference.

From the same original list, here are the top 10 against wraps from defensive backs:

1. Knight (27%)
2. White (23%)
3. Javonte Williams (21%)
4. Carr (20%)
5. Kenneth Gainwell (19%)
6. Charles Williams (18%)
7. Allgeier (18%)
8. Nevens (17%)
9. Four-way tie at 16%: Robinson, Brooks, Ford, and Pierce.

You can see there are some smaller backs (Gainwell, and Charles Williams) that made the list, which makes sense because scatbacks earn more opportunities in space and encounter more defensive backs. Of course, it’s also worth noting that backs who performed well against defensive linemen earned more use between the tackles.

And the top 10 against defensive linemen:

1. Knight (14%)
2. Tuggle (14%)
3. Nevens (13%)
4. White (13%)
5. Javonte Williams (12%)
6. Pierce (10%)
7. Robinson 10%)
8. Tre Sermon (9%)
9. Three-way tie at 8%: Charles Williams, Allgeier, and Carr.

Based on all three tables, the backs that have the most positive data are Javonte Williams, Brian Robinson, Trey Sermon, Kennedy Brooks, Zonovan Knight, and Tyler Nevens. The first five played in conferences with a lot of opponents who were NFL-caliber athletes. All five have the baseline athletic ability to start in the NFL.

As I collect more data over the next 8-10 years and pair it with athletic metrics, there will be even more entertaining ideas to tease from a larger sample.  For now, it’s fascinating to see who performed the best against each level of the defense. The RSP will continue tracking and see where this leads.

For the most in-depth analysis of offensive skill players available (QB, RB, WR, and TE), download the 2022 Rookie Scouting Portfolio for $21.95. 

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If you’re a fantasy owner and interested in purchasing past publications for $9.95 each, the 2012-2020 RSPs also have a Post-Draft Add-on that’s included at no additional charge.  

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