Interval detection is only the first part of the job. The next part is figuring out whether all the work you're putting in is helping the performance abilities that can make the crucial difference to your success as a competitive cyclist. KOM Informatics can not only tell you if you're getting stronger, it can tell you why you're getting stronger. Can your analytics application do that?
KOM Informatics has one report called the Workload/PR Correlation report that can do exactly that. To run a Workload/PR Correlation Report first run a Personal Records (PR) report. This shows your best efforts in 16 different "time boxes". Just provide how many PR's you're interested in viewing and a date range.
Remember all those intervals you've been doing? Here's where KOM Informatics tells you if all that work has been paying off. The Personal Records/Workload Correlation report provides information on the strength and direction of the linear relationship between your training and your performance. It can provide insights into which aspects of your training are having the biggest effect on the performance abilities that can make a critical difference to your success as a competitive cyclist. The training side utilizes the kilojoules recorded for the Interval Zone Durations for each ride, along with the Non-Interval kilojoules for each ride. You specify a workoad window each time you run the report. A workload window is a block of training days which are defined by the days from the PR to the workload end (looking back in time), and the duration of the workload in days. The report uses Personal Records wattages on the performance side. The example below is looking at a workload window of 7 days looking back in time from the day of the PR. It's looking at the 5:00 - 8:01 minute time box. It found a statistically significant correlation between KJ expended in Z7 (neuromuscular) training and personal record wattage. In general, as long as the workloads involved are not small (your legs should be able to answer this question) where statistically significant correlations like this occur, there is a high probability that the workload category had a significant role in attaining a PR.