Using Market Share of Targets to Discern CFB DFS WR Value
Everyone knows that there are certain teams that are dependable for elite passing numbers and loads of Fantasy potential. Today, I want to take a look at what schools have been the best at passing in the past three seasons and how those teams used their receivers in 2014. It’s one thing to identify high volume/high quality passing offenses, but if we can pinpoint exactly who benefits the most from a team’s passing identity, we increase our probabilities of benefiting in DFS from these aerial attacks.
I broke down the top passing offenses from 2012-2014 into teams that did all of the following: threw for 10,000 yards, had a completion percentage of 60% or better and threw it 1,300 times or more. This picked out a combination of high volume passing attacks that also met certain levels of efficiency.
There were 10 teams over the past three seasons that fit this query:
For the most part, these 10 teams are still relevant in the passing game. Only three of the top 10 in terms of passing yardage from 2012 to 2014 were outside of the top 15 this past season: Oregon State (42nd), Fresno State (50th), and San Jose State (52nd). Washington State is a clear outlier, leading these 10 teams in attempts, completion percentage and yards per game. Chris Pacheco highlighted this offense last week.
Now that we’ve identified the high volume, quality passing attacks, let’s see how each team distributed their targets last season (2014). We’ll use a statistic called Market Share which represents the percentage of opportunities (in this case targets), an individual player has received from their team total.
Immediately we see the benefits of Market Share. While Washington State has a high powered attack that should be targeted in DFS, it’s a bit more difficult to figure out exactly who will benefit. Meanwhile, in West Virginia it’s pretty clear; no team that fit our passing query had a higher market share of targets go to their WR1 and their WR2.
In hopes of gleaning ever more actionable information, let’s look at the same chart and highlight the returning players in 2015. Note that the WR# is based on 2014.
Here’s an example of why this is useful. West Virginia’s third and fourth wide receivers (Jordan Thompson and Daikiel Shorts) are returning, but their top two wide receivers (more on them below) are not. Given the huge market share of targets among West Virginia’s WR1 and WR2 last season, Thompson and Shorts have a chance to be incredible early season values if they slide into those roles.
The Top Receivers
The top receiver when it came to target percentage from these 10 teams was Kevin White (West Virginia at 30.6%). The Mountaineers ranked seventh in the past three seasons for passing attempts (1,435) and were concentrated in their attack. White’s 69% catch percentage and 9.2 yards per target (best out of the top 12 targeted receivers) were very good, so his opportunities were not wasted. What I found interesting was that the top five receivers in target percentage had such a huge gap between them and the remaining wide receivers (top five at 28% or better while sixth best was 23.2%): Kevin White, Tommy Shuler (29.9%), Josh Harper (29.9), Justin Hardy (28.2%) and Tyler Winston (28.1%).
The sixth receiver on this list, Victor Bolden, comes in at 23.2% of Oregon State’s targets. This is almost 10 percentage points more than the second highest receiver on OSU, Jordan Villamin (13.6%). These two both return in 2015, and could push for an even higher number with 31.1% of the team’s targets leaving, including their third and fourth highest receivers from last season.
The Top Combinations
The highest receiver combo of the top ten teams was West Virginia’s Kevin White and Mario Alford. The two combined for 52.3% of the team’s targets (270). The next best combination is East Carolina and their top two combining for 47.1% of passes thrown to Justin Hardy and Isaiah Jones. Even though WVU threw it more frequently on a percentage basis to White and Alford, Hardy and Jones combined for 16 more targets (286 total) since East Carolina had a higher volume attack.
The next combinations were as follows: San Jose State’s Tyler Winston and Hansell Wilson (42.5%), Fresno State’s Josh Harper and Aaron Peck (42.4%), Marshall’s Tommy Shuler and Deon-Tay McManus (41.4%), Baylor’s Antwan Goodley and KD Cannon (40.4%), and Texas Tech’s Jakeem Grant and Bradley Marquez (39.9%),
As you can see, West Virginia and East Carolina had the most concentrated attacks. There is a 9.8 percentage point difference between WVU and SJSU, while the difference between ECU and SJSU is just 4.6 percentage points. In terms of targets, that number is 87 more targets to White/Alford and 103 more targets to Hardy/Jones.
It’s no guarantee that the third receiver in each team’s offense will cost you an average to a below average amount on the different DFS sites, but it typically is the case. Let’s take a look at these top 10 teams and see which ones target that third receiver most frequently.
Baylor’s third receiver was targeted at 17.6% of the time, which is the most of any of the top 10 teams. A 17.6% target rate is 14th amongst these 10 teams. While I do believe that injuries caused this number to be a little higher in this scenario, it’s Baylor’s offensive style to use more than two receivers on a consistent basis that makes me feel this number can be repeated in 2015.
The next best team in terms of third receiver target rate is Texas A&M. Ricky Seals-Jones was TAMU’s third receiver this past season and his 13.7% target rate is a realistic rate. The Kevin Sumlin led 2014 offense didn’t see many receivers miss games to injuries in 2014, so that’s no issue here and they’re returning most of their depth. We’d expect Texas A&M to spread it out again this season.
1 – West Virginia and East Carolina were the top two combinations in 2014, and with three of those four receivers now in the NFL, it’s time for someone replace them. Shorts and Thompson are slated to play the same roles as White and Alford did last season for WVU. Isaiah Jones is the clear-cut number one receiver headed into 2015 for ECU. I discussed both Shorts and Jones in my five unknown high usage players article just a few weeks ago. These target numbers back up the potential both of these players have this season.
2 – The usage of Baylor receivers found in this study is alarming. They spread the ball around more than any other team when it comes to their top three. Just one of those top WRs departed: Antwan Goodley. Jay Lee is the top candidate to fill in as third receiver, but guys like Levi Norwood and Ishmael Zamora are pushing him to be that third or fourth guy. From 2011 to 2013, Baylor has been a team with one superstar receiver followed by two others with very good numbers. Injuries may have been the reason for the change in 2014, but I believe it was the explosiveness of many players that led to a more balanced passing attack. The 2014 Cotton Bowl featured Kendal Briles as the offensive coordinator for the first time. In that game, he threw it 52 times for 607 yards, so the passing game should remain the bread and butter for this offense. Generally speaking, there may be an opportunity to play price points and bet on the cheap receiving offenses from high volume passing attacks that seem to spread the ball around (Baylor, Washington State, etc.).
3 – San Jose State is a team that with Joe Gray under center has the potential for great fantasy numbers. Because of what we saw with their top receiver target numbers, Tyler Winston will benefit from Gray starting. He started in only seven games last season, but threw 44-plus times in six of them and for 300-plus yards in five of them. If you pro-rate those seven games to a full 12-game season, we’re looking at 526 passing attempts for Gray, and 147 targets for Winston. In those seven games, Winston caught 44 passes for just 393 yards and two touchdowns. It’s the lack of yards per catch during that stretch that is alarming, but his freshman totals of 58/858/5 with David Fales at quarterback show off the kind of potential he possesses.
4 – Victor Bolden of Oregon State has the most potential of everyone discussed today and has the best chance of becoming a superstar performer in 2015. His contextual factors mixed with statistics give him the potential for elite PPR numbers. His 67.3% catch rate and 7.5 yards per target numbers show us that he runs his routes close to the line of scrimmage. Contextually, it’s hard not to love Bolden with a talented freshman quarterback (Seth Collins) and a defense that is going to give up points in bunches, forcing Oregon State into passing situations. Bolden is going to be the safety blanket for Collins, giving him some of the highest upside of all the receivers discussed today.
5 – The Red Raiders were the only team of the top 10 we’ve discussed that had just two players in double-digit percentiles when it came to target rate. And not only were they the only team to do so, they did it with their top two targets coming in at 39.9%, which ranks them seventh best in 1-2 combos. Quite simply, Texas Tech is an offense that wants to mainly target their top two receivers before spreading it around quite a bit (second highest percentage of targets categorized as other). We’ll see Jakeem Grant come back in 2015, but Bradley Marquez has graduated. Replacing him should be Reginald Davis, the third target in this offense in 2014 (9.0%). The lesser-known factor of Davis will give us a value play for at least the first few weeks.
6 – It’s important to realize that just because Washington State doesn’t have drastic stats pointing to only one receiver being targeted, the fact that they throw it 700+ times is going to make each of the top three receivers on this team playable in fantasy. Washington State is the only team in the country that had a third receiver with 100+ targets last season. Their third receiver, River Cracraft, was only targeted 13.5% of the time, but still ended up with 101 targets in nine games, or over 11 targets per game. Washington State is one of those offenses that makes up for quality with quantity, and we need to account for that when putting together teams.