I'm curious about how y'all are selecting, prioritizing and labelling metrics. My first reaction to glancing at screenshots, such as the Ryan Tannenhill example, how do you define mental or physical? How open will y'all be at sharing the different datapoints that go into each category?
I love the Bloomberg Terminal metaphor to describe the job Edge Up services, but I also feel like this is an attempt to turn Madden Ratings into real-time stats and I'm skeptical about the efficacy of this approach (see this FiveThirtyEight feature on Madden for context: http://fivethirtyeight.com/featu...)
That said, I've backed the Kickstarter and I'm pumped to check out the app this season.
@noahchestnut We are building a rich signal landscape around the NFL. We have broken the general landscape of possible data points into variables that affect the mental, physical, or situational dimension of a player. The physical dimension is relatively straightforward and informs the player's capability to play (including injury and competitive status). The mental dimension is a bit more nebulous, but there are signals such as a player's social activity that can start to set a baseline and be tracked to a player's on-field performance. We will be very open with what signals are being integrated into each dimension. The main focus is to present all of this data in a visually rich and consumable way. As such, we are focused on the best user experience first.
To your comments about turning Madden Ratings into real-time stats, I would actually turn it around a bit. We are building a platform that will allow us to get closer to a data driven, evidence based derivation of player performance with each game. Further, as we continue to bring in signals that capture more than just statistical performance we gain a complete picture of what is happening around the game and work on exploring actual correlation with onfield performance. Very much like the technical stock analysis uses a rich signal landscape to search for alpha (https://en.wikipedia.org/wiki/Al...) we can leverage signal processing approaches to give our users an edge.
Looking forward to your thoughts and thanks for signing up to be part of our founding user team!
Backed the Kickstarter and excited to use the Edge Up app this season. @ilyatabakh - if you want to compare this to technical stock analysis, can you use data from last season and do a historical analysis of the recommendations/insights you provide vs. the actual results?
I'm hoping this can be used as a hedge against me losing (first time ever last season!) and having to take the SATs again.
@kunalslab We are focused on cleaning up the data space and segmenting this complicated space to be accessible first. The noise floor is very high when there are only 17 weeks in the regular season. To deal with this, we are adding signals besides on-field statistics for a more complete picture. To your point, we can use our own baseline to make sure we are monotonically moving in the right direction. Thanks for joining the founding user team!
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