Soma Intelligence
43 articles
- How to connect Soma Analytics to Claude
- Projects: a customised layer over Soma Intelligence
- Finding the tasks that produce inconsistent results squad-wide
- Finding the tasks with the most within-session variability
- Trending minute-on-minute variability over six months
- Checking whether variability rises before the mean drops
- Measuring how much an athlete swings between sessions
- Comparing an athlete's best and worst session
- Spotting consistency gains when the means have not moved
- Finding the demand an athlete is least consistent on
- Ranking athletes from most to least consistent
- Finding which athletes improved their consistency most
- Trending squad variability in one line graph
- Finding the squad's average breakdown minute over 12 months
- Checking whether poor sessions cluster on certain days or times
- Checking whether swings are demand-specific or spread evenly
- Flagging sessions outside an athlete's normal range
- Finding an athlete's biggest strength in a plan
- Finding an athlete's biggest weakness in a plan
- Reviewing mean and minute-on-minute data for every task
- Asking what you might have missed in a plan
- Building a 12-month athlete report
- Building an HTML report on demand improvement
- Setting training times around each athlete's peak window
- Finding the minute an athlete breaks down across all tasks
- Finding an athlete's breakdown minute in the current plan
- Comparing which tasks an athlete holds longest
- Finding an athlete's weakest cognitive demand
- Finding an athlete's strongest cognitive demand
- Checking which demands improved across a plan
- Choosing the demand to target in the next plan
- Finding an athlete's best and worst tasks
- Ranking every task for one athlete this year
- Ranking every task across the whole squad
- Seeing which tasks improved most and least in a plan
- Tracking minute-on-minute reaction time over six months
- Trending squad PVT-B data month by month
- Trending average reaction time in one line graph
- Trending average variation across all tasks
- Finding the time of day an athlete performs best
- Finding an athlete's best time of day across recent plans
- Listing every athlete with their best time of day
- Checking whether within-session consistency is tightening