Composition-Output Models

Individual performance prediction is useful. Team output prediction is transformative. A team's output is not the sum of individual performances — it is the product of how individual profiles interact. A team of five individually high performers with clashing profiles will underperform a team of five moderate performers with complementary profiles. The interaction effect overwhelms the individual capability. Composition-output models capture this interaction and make team-level prediction possible.

1. Delivery Speed Prediction Delivery speed correlates with D-aggregate and inversely with C-aggregate. High-D teams ship fast. High-C teams ship thoroughly. The ratio of D-to-C in the team predicts the speed-quality tradeoff the team will naturally make. A 70:80 D:C ratio produces fast, high-quality delivery. A 40:90 ratio produces exceptional quality at the cost of speed. Know the ratio, predict the tradeoff, staff accordingly.
2. Innovation Capacity Prediction Innovation correlates with I-presence and profile diversity. Teams with high-I members AND high profile diversity generate more novel solutions because different perspectives create creative collisions. Homogeneous teams — even homogeneous high-I teams — converge on similar ideas. Diversity in this context is not demographic — it is dimensional.
3. Reliability Prediction Reliability correlates with S-aggregate and C-aggregate. Teams with high S+C averages deliver consistently. Teams with low S+C averages deliver brilliantly or terribly, with minimal middle ground. If you need predictable output, staff for steadiness and conscientiousness. If you can tolerate variance for occasional brilliance, staff for dominance and influence.