There are four types of data interpretations that can be undertaken. They are descriptive, diagnostic, predictive, and prescriptive.1) Descriptive- this analysis takes a look at what happened in the past. Game day summaries are one example. There are numerous examples of post event analysis.2) Diagnostic- this examines why something happened. It is important to identify that there is a significant disconnect at times between causation and correlation. Sometimes there is a coincidence rather than causation. After game summaries often look at where a team did poorly or where an opposing team did great. For example, can you prove that bringing a top flight prospect to a team will result in wins? How long would it take to have a winning formula?3) Predictive- this examines what might happen in the future. Every year there are attempts to predict which team will come in first place and they try to examine what will happen based on off season trade or signing/retirement of players. We often see this where people think they will pick the next NCAA champion basketball team during March Madness.4) Prescriptive- this examines what to do next. Every sport team is looking for an edge and sometimes they make the right decisions (such as Moneyball with the A’s or Rays) and sometimes the results are not as strong (such as the Yankees spending so much and not winning the World Series in years).Find examples of each one of these approaches in the real world. It can be collegiate or professional sport and you want enough detail so we can see whether or not there is any correlation between the data analyzed and the results.
