Codermetrics

Understanding the Limits

The use of metrics and statistical analysis is a practice, a methodology, useful to find patterns, examine assumptions, and increase understanding of the past in hopes that you can improve the future. It is not a science, and clearly the explanatory capability of metrics is abstract and imperfect. Practically speaking, the metrics you gather will never provide a complete picture of all that coders do, or of the complex team dynamics and all the elements that lead to success.

While baseball’s WHIP statistic (walks plus hits per inning pitched) may help identify what makes pitchers effective, it cannot fully describe what made Sandy Koufax unique and special. No stats can fully measure his ability to focus and his desire to compete. And while victories and team stats are clear indicators of winning teams, those and individual stats cannot fully explain why the 1975 Cincinnati Reds stand out in fans’ minds as the greatest team of their era—because of their personalities and charisma.

This shouldn’t discourage you or make you believe that metrics aren’t useful, simply because there is so much they don’t capture. Instead you should be encouraged to use metrics for what they can provide, while accepting their limitations. You can continue on in the pursuit of better metrics in the happy knowledge that perfect understanding will never be achieved, and there will always be more you can measure and learn.