Codermetrics.org

We’re at the start of another major league baseball season.  Hope springs eternal for every team, of course some to a greater or lesser extent.  Here’s a simple question:  why are some fans more hopeful or confident of their team’s chance of success, while others are less hopeful or downright pessimistic?  The answer has to do with a simple concept we can call Variance, and it’s a useful concept to consider in the analysis of any team, including software teams.

The concept of Variance is tied directly to track record and predictability.  An individual (or team) with a consistent track record as measured for a specific area or skill over time, and for whom factors have not changed significantly, can be categorized as Low Variance.  A Low Variance individual or team, therefore, is more predictable, meaning that their consistent track record is more likely to be repeated in the “near-term” future.  Alternatively, an individual (or team) with an inconsistent or insufficient track record, or for whom critical factors related to performance have changed significantly, can be categorized as High Variance.  A High Variance individual or team is less predictable, meaning that there is a greater likelihood that they will do much better or much worse than might be guessed from what was seen before.  In other words, High Variance means that there is a much wider range of outcomes, good or bad, that have a higher probability to occur.

A simple way to categorize whether an individual or team is High or Low Variance would be to assign subjective percentages to how likely they are to perform much better, similar to, or much worse than before.  For example, a Low Variance individual or team might be projected as:

• 15% Likely to Perform Significantly Better Than Recent History
• 70% Likely to Perform Similar To Recent History
• 15% Likely to Perform Significantly Worse Than Recent History

Alternatively, a High Variance individual or team might be projected as something like this:

• 30% Likely to Perform Significantly Better Than Recent History
• 40% Likely to Perform Similar To Recent History
• 30% Likely to Perform Significantly Worse Than Recent History

In the Low Variance example, the total probability of variance from recent history is placed at 30%, while in the High Variance example the variance probability is 60%.  By definition, High Variance has a higher chance of “upside” but a higher chance of “downside” too.

So what does all this have to do with baseball or software teams?  The answer is that while having more High Variance individuals may make you feel more hopeful about your team’s upside (hello Royals and Astros fans) the probability of multiple “risks” paying off is low.  Having more Low Variance individuals (hello Yankees and Red Sox fans) is a much better prescription for repeatable success.

To illustrate, suppose there are two teams with twelve individuals each.  These teams are made up of veterans with consistent track records, some high performing and some medium performing, and “unproven” team members who don’t have a significant track record and who lack backgrounds that would make you highly confident in success.  Let’s call the first team “Sky’s-The-Limit” because they have a lot of unproven individuals who they are hoping will be a big success.  Let’s call the second team “Steady-As-She-Goes” because they have a lot of medium performing veterans.  The team make-ups are:

• Sky’s-The-Limit team members:  1 high performing, , 2 medium performing, 9 high potential
• Steady-As-She-Goes team members:  1 high performing, 8 medium performing, 3 high potential

The question is:  which team is likely to have better results?

Assume that 1-in-3 of the High Variance individuals becomes a high performer, 1-in-3 becomes a medium performer, and 1-in-3 becomes a low performer.  Then the results would be:

• Sky’s-The-Limit results:  4 high performing, 5 medium performing, 3 low performing
• Steady-As-She-Goes results:  2 high performing, 9 medium performing, 1 low performing

One view of these results would be that “the two teams perform exactly the same because the high-performing individuals make up for the low-performing ones.”  But the reality on many teams is that the problems and issues related to low-performing individuals is disproportionate.  It’s the “weak links in the fence” theory, essentially that teams with the greater number of low performers are dragged down by those individuals.  In this view, the Steady-As-She-Goes team is stronger, in that it has a significantly smaller percentage of low performers (8% vs. 25%).

To the extent you can abide and succeed with a certain number of low performers, maybe because you make up for it in numbers or in enough known high performers, then having a bunch of High Variance individuals who could end up as low performers might not be a concern for you.  Maybe it could even be a known part of your team-building and success-building strategy, like the venture capitalist strategy of betting on a bunch of long-shots in hopes that one or a few will pay off big.  But, in general, look out for pinning your team’s hopes on too many High Variance individuals.  A set of steady performers are an important part of most successful teams, while teams that take too many personnel risks usually don’t get positive results.

To read how some High Variance players might factor into the chances of winning for your favorite baseball team, you can check out Jonah Keri’s article on the subject at Grantland.com.  If you do have a rooting interest, here’s hoping that your baseball team has a great season.