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Week 5 Insights

This week our hope was to better understand some of the reasons for attrition through the special forces pipeline and try to see where we can provide value added, if possible. We found where a lot of the attrition is coming from. Of last year’s class of 1200 who applied, only around 500 made it all the way through. SWIC’s CG wanted a graduation rate of around 800. He began an initiative called Performance Integrative Training (PIT), as a way for soldier who don’t pass a module the first time to get help to achieve learning goals. PIT is based on HDP models for cognitive performance. This demonstrates that there is optimism in HDP’s training methods and there will be buy in for a data driven solution.




Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologist

Other Beneficiaries

  1. Instructors (Cadre)
  2. Trainees
  3. SWIC Commander
  4. Performance Integrated Training (PIT)




Week 5 Title Contact Key Takeaways Interviewer
Major Chuck Schumacher Operations Officer, SWEG charles.schumacher@socom.mil Performance Integrative Training (PIT), SWIC Commander’s initiative to get trainees through failed modules. Based on HDP work.

Class size of 50-60, 50% success rate.

JC Crenshaw SOFCCC Course Manager john.crenshaw@socom.mil Right now there are 11 instructors.

Only 33% of enlistees and 40% of Officers make it through assessment and selection.

Highest attrition from physical/mental assessment and culminating exercise.

Col. Joe Blanton Program Executive Officer, SOF Support Activity joseph.blanton@duke.edu   acquisition team works with communication team to determine what devices can be put on the network. Necessary step for any new equipment we’d be bringing them. AJ
Captain Oscar Gonzalez HDP Research Psychologist oscar.gonzalez@usuhs.edu Concerns arising over scope. Are we still trying to solve the problem that was posed?

Is the data we are collecting useful for providing an MVP?

Ian Ankney Lead CPC
CPCs are not involved in assessment selection. The new facility may be up to six years away.

Cadre operate fairly idiosyncratically. Since they don’t all measure the same things this leads to confusion. Soldiers are often told they are being measured one way when it is actually something else is being measured.

Michael Jelen H4D Course Advisor michaeljelen@gmail.com Rather than a central server, utilizing edge computing may be a better and lest costly solution. This would involve getting the biometric devices to sync to something the soldiers could wear ideally. TL
Major Amar Mohamadou SWEG Executive Officer mohamadou.amar@socom.mil Each SWIC dropout costs the army $30,000-$35,000.

Recruit Class was 1200 this year.

PIT saved $1 million this year.

Lieutenant Adam M. Beauregard Lieutenant, Navy adambeauregard@gmail.com We should think about what kind of data to collect,

determine what format the solution should be based on.

Focus on really understanding quantitative metrics for green beret training

What insights do we want the instructors to be able to easily obtain from the data?

Focus on wearable tech.

Major Ben Spain Major, Air Force benjamin.spain@gmai There is a price threshold: below the price point, commander can make purchasing decisions for software. Above that point then need to go through the bidding process.

There is an entire unit working on the bidding process, (highly complex).

Lieutenant Colonel Ormond Brendan SWEG Deputy Commander, Language Group brendan.ormond@socom.mil How do we deal with measuring intangibles like leadership?

Problems with shrinking recruiting class, both witin the army for SWIC and in the general US population.

Any solution is cost prohibitive, not only in procurement but also in time spent on implementing and maintaining any data system.




  1. Attrition is concentrated around Physical/psychological assessment, small unit tactics and the culminating exercise. Cost to the Army for relocation of trainees is around $30k.
  2. Without a way to measure some of the intangible elements in selection, like leadership, it will be difficult to get buy in for a data driven solution.
  3. The idea of a central server for data is becoming less feasible. A better solution might involve wearable tech that syncs to the biometric devices via Bluetooth.


  1. Now that we have a picture of where the data collection process is breaking down, as well as a general picture of where soldiers are experiencing difficulty in the pipeline, we need to begin developing a prototype. To get more insight about this, we need to find models of effective data management that we think, given SWEG’s constraints can be applicable to our problem.

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