The longer you gather and keep metrics, generally speaking, the more useful they become. Analyzing metrics is a process of pattern recognition, which means finding repetitive patterns that provide insight. While a single set of metrics taken from a single time period might reveal interesting information, and possibly allow you to form interesting hypotheses, it takes multiple metrics over multiple periods of time to improve your theories, or to convert your theories to knowledge.
While you are looking for patterns, it is important to realize that not all patterns are simplistic. You should be careful to not just focus on the obvious, because the patterns and explanations found in combinations of metrics may far exceed the power and usefulness of individual metrics. For this reason again, it is useful to have metrics gathered at discrete intervals over longer periods of time, and to have a good variety of metrics to examine together.
Although voluminous, for thought-provoking material on complex pattern recognition and the richness of patterns, it is worth reviewing Stephen Wolfram’s A New Kind of Science. His studies focus on computer models and the natural world, but his point that what appears chaotic or extremely complex may actually be based on discoverable patterns is directly applicable to statistical analysis in any field.
In baseball, one of the most widely used and powerful statistics is OPS, which stands for On Base Percentage plus Slugging ...