How to lead an overfitting team

In machine learning, "overfitting" is a term that describes a model which performs exceptionally well on its training data but fails to generalize to new, unseen data. The model becomes too tailored to the specifics of the training data, capturing noise rather than underlying patterns. A team can become "overfitting" when they seem to be functional for their current tasks but they stop improving. Here are the signs of an overfitting team. 1. Complacency about the Status Quo: The team can handle current workloads, but they don’t perform well. The team members do complain a lot, just always without actions. 2. Resistance to Change: Any alteration in processes, technology, or objectives meet with resistance. The team has become so acclimatized to its routines that they believe every routine is there for a reason and they have no power or desire to change anything. 3. Lack of Innovation: Being too entrenched in the present means the team is less inclined to look forward, be creative, or innovate. As a result, opportunities for growth and improvement are missed. A good indicator of an overfitting software team is they write very little code and they launch very few projects, they are so busy keeping things running. Every day feels the same, monotonous and boring. Drawing inspiration from machine learning, where regularization techniques are used to prevent overfitting, we can apply similar strategies to "de-overfit" teams. 1. **Introduce Noise:** Just as noise in data can help a model generalize, introducing minor, controlled disruptions in a team's routine can encourage change and adaptability. This might mean rotating team members across projects, introducing new tools, do a game day or changing team dynamics. 2. **Cross-validation:** In machine learning, we use separate data sets for training and validation. For teams, this can translate into getting feedback from different departments or external sources. This external perspective and collaboration can identify unseen inefficiencies or suggest improvements. 3. **Learning Rate:** Just as we adjust a model's learning rate to ensure it doesn't become too narrowly tailored, teams need to adjust their pace with sprint intervals and smart sprint goals. Meaningful project (design and coding) work should be balanced with manual operations to keep the lights on in sprint planning. 4. **Weight Regularization** In algorithms, L1/L2 regularization can prevent certain weights from becoming too large. In a team context, this means teams should be discouraged from becoming too dependent on specific members. Share the knowledge, codify the special skills. Drop out the super heros that have become single points of failures. If you feel a team is doing the same old stuff every day, maybe the team is overfitting. Borrowing regularization strategies from machine learning, we can help the team become innovative and exciting again.