Anthony schwartz weight8/11/2023 ![]() ![]() ![]() Jordan Addison, Zay Flowers, and Jayden ReedĬluster 2 Description: Intermediate depth of target, predominantly plays out wide, smallerĪDOT: 11.2 Slot%: 27.2% Wide%: 72.1% Height: 70.7 Weight: 184.5ĭede Westbrook, Henry Ruggs III, John Ross, James Proche, Anthony Miller, Amon-Ra St. Brown, Van Jefferson, Deebo Samuel, David Bell, Juwann Winfree, Drake London, KeeSean Johnson. Osborn, Trey Quinn, Laviska Shenault Jr., A.J. Hill, Christian Kirk, Amari Rodgers, Parris Campbell, Kadarius Toney, Devin Duvernay, Khalil Shakir, Freddie Swain, Kyle Philips, Justin Jefferson, Velus Jones Jr.Ĭluster 6 Description: Short-intermediate depth of target, predominantly plays out wide, above average sizeĪDOT: 10.2 Slot%: 27% Wide%: 72.2% Height: 73.2 Weight: 211ĪrDarius Stewart, N’Keal Harry, Corey Davis, Brandon Aiyuk, JuJu Smith-Schuster, Bryan Edwards, Zay Jones, Donovan Peoples-Jones, D.J. Jaxon Smith-Njigba and Kayshon BoutteĬluster 7 Description: Short depth of target, predominantly plays in the slot, about average sizeĪDOT: 8.6 Slot%: 87.6% Wide%: 10% Height: 71.5 Weight: 199.7Ĭurtis Samuel, K.J. The data used featured 153 wide receivers from 2017 to 2022 including the fourteen 2023 prospects mentioned in this article. I’ll be doing that throughout the draft process in a series of articles. The clustering is just sorting out the different metaphorical fruits, not telling you which fruits are the best. The goal is to figure out what type of wide receiver a prospect is, then use film and analytics to look at the players within the type to figure out what matters. I want to be clear that the clusters themselves have limited inherent use since they only show similar players in those 5 metrics. The math did its thing turning the data into 9 unique clusters, or groups, of shared characteristics within each cluster that we’ll see below. ![]() The 5 variables used to determine a prospect's collegiate “role” were Average Depth of Target (ADOT), Height, Weight, Percentage of Snaps in the Slot, Percentage of Snaps out Wide. Just know it’s math stuff that groups data into K numbers of clusters. I won’t bore you by explaining what K-Means clustering is. So, how does one go about separating and sorting players to get a clearer evaluation? Enter clustering. These two facts make evaluating wide receivers much tougher since you’re comparing apples to oranges (a 6’5” 220 X receiver to a 5”10” 160 slot receiver) in a market where everyone has their own favorite fruit (NFL teams unique preferences from the WR position). Whichever time you believe, it's obvious Schwartz has world-class speed.The wide receiver position is notorious for its vast amount of flavors and varying usage across different offenses in the league. Different requiters record slightly different speeds from a 4.29 to a 4.34-second 40-yard dash, but the Auburn receiver's fastest record time is 4.27 seconds, according to track and field site. "Honestly, I think so," Schwartz told 247Sports. After a game against Texas A&M where Schwartz used his speed to sprint for a 57-yard touchdown run, Schwartz was questioned if he could beat Deion Sanders in his prime. How does that compare to the 2020 NFL drafts's fastest player, Henery Ruggs? Ruggs won the state title after clocking a 10.58-sec 100-meter dash compare that to Schwartz's sizzling time, and Ruggs is left in the dust. He also ran at the IAAF U20 World Championships in Finland in July 2018 and took the silver medal in 100 meters (10.22) and a gold medal in the 4x100-meter relay (38.88) for Team USA. As a high school player in 2018, Schwartz enjoyed the title of the Gatorade National Boys Track and Field Athlete of the Year following his impressive win of Florida's Class 2A 100-meter dash (10.07w) and 200 meters (20.41w) both were state records. Compared to 2020 first-round pick Henry Ruggs. ![]()
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