Before the New England Patriots and Atlanta Falcons took the field at NRG Stadium in Houston on Sunday, February 5th, 2017, it took years of drafting, free agent signings, and general team-building to prepare those teams to be the last two standing. Now, on the day that the 2017 NFL Draft begins, we take a look at how the Patriots and Falcons were built for last year’s championship game, including how the draft plays a role in their specific approaches and what we can learn from the ratings of EA SPORTS Madden NFL 17.
With the 2017 NFL Combine just now in the rear-view mirror, it seems as good a time as any to take a look back at last year’s “Underwear Olympics” and see how the top performers experienced their rookie season in the pros; at least, according to Madden NFL 17.
For the sake of this article, we’re again working with the previous work done with the 2016 NFL Draft class here at Video Game Numbers; the primary comparison being the OVR ratings of players at the launch of the game/beginning of the season and that same rating at the end of the season.
Did success at the 2016 NFL Combine in Indianapolis translate into rookie stardom? Let’s take a look at the numbers…
As we continue our ongoing review of the 2016 NFL Draft here at Video Game Numbers, today we take a more individual look at some of the rookies who were drafted. Once again, we’re working on research done at the Madden Player Ratings Database, where we’ve taken the 253 players drafted last season and recorded their OVR rating at the launch of Madden NFL 17 and compared it to their OVR rating after Super Bowl LI and the end of the season.
Without further ado, a closer look at some individual numbers…
The NFL Draft is one of those intensely confusing contradictions. On the one hand, we have college players with 3-4 seasons worth of data points in all the statistical measurables, plus more measurables on top of that once they participate in the Combine, ready to get selected by one of the 32 teams hoping to find–if not their star of the future–at least somebody who can fill in at a position of need. Yet, in spite of those data points, these players are human beings; entirely unpredictable.
How else do you explain 21 of the 32 first-round picks from the 2012 Draft not being on active rosters? That’s over 65% of the top round of picks completely out of the NFL in under 5 years!
That said, if we make it incredibly simple, we can just use the (not at all debated or controversial) player ratings from Madden NFL 17 to evaluate which teams had the best experience with last year’s draft and which ones will be hoping this year goes better. And so begins the first of a series of articles breaking down what those ratings have to say about the 2016 NFL Draft!
Greetings reader, and welcome to Video Game Numbers.
For as long as I can remember, I’ve been obsessed with statistics when it comes to sports. Over the last ten years or so, that obsession found purpose with my participation in the EA SPORTS Game Changers program, an experience that continues to this day. Whether analyzing team ratings to identify the toughest conferences in NCAA Football or considering the individual ratings in Madden NFL to find the best fit for a scheme or position, I’ve spent more time in spreadsheets and menu screens that I feel comfortable admitting.
The most established expression of my interest in statistics and storytelling to this point happened with Stat Box Stories, which still maintains the complete archive of Bowl Blitz Invitational posts where myself and other gamers in the community played through all of the bowl games using NCAA Football to compare how closely (or not) those results compared to the real-life games. With Stat Box Stories, the focus was to consider the story that could be told as a result of the statistics in each game.
With Video Game Numbers, the interest in statistics continues but an expansion into simulation provides a new wrinkle and focus to the project. Instead of telling a story in reaction to the experience of playing a video game, more thought and consideration will be given to how the numbers that identify the competency of athletes in the virtual arena are determined. In conjunction with that, the process of re-evaluating those decisions over time will be considered as well. Finally, the application of those statistics and ratings to simulation will be evaluated, establishing not just a record of success (or failure) but an understanding of what influences the simulation results in the first place.