Prognostic times (PTs, or Gold Standards) are widely used in rowing communities for various purposes, such as: 1) Defining training intensity; 2) Evaluation of performance in various boat types; 3) Selection of rowers and crews; 4) Benchmarking and setting targets for short and long-term rower`s development; 5) Motivation for rowers and coaches to achieve the highest standards. Usually, PTs are developed at National Rowing Federations (NRFs). Our Google search reveals only two open sources (Table 1) from Australia (1) and Canada (2), which means that most of NFRs keep their PTs in secret. All published NRF`s prognostic times are anonymous and methods of their development were not disclosed, so it is likely that they are subjective, which creates the following problems: it is not possible to discuss PTs to make them more accurate and eliminate discrepancies between them, which may lead to errors in crews selection. Usually, NRFs PTs are set close to or higher than World Best Times (WBTs), which are unrealistic to achieve without very fast weather conditions. In an attempt to find an objective method of PT calculation, two different methods could be used: 1. Statistical method using trends of speed of the winners of World regattas. Advantages: Relatively simple and obvious, does not require biomechanical measurements. Disadvantages: Low reliability due to the effect of random weather conditions. 2. Biomechanical modelling using rowing power and drag factors in various boat types. Advantages: Ability to model rowing speed for various rower`s power/weight ratios and various weather conditions. Disadvantages: Requires additional complex measurements, uncertainty of the real power production in a race (usually, only erg scores are available) and the effect of weather. Here is a description of both methods to make them available for others to produce their own objective PTs (say, for juniors, U23 and other rower`s categories), as well as our BioRow PTs-2024 for elite rowers. Analysis of speed trends with method 1 consists of two steps: 1) Data filtering, 2) Selection of the most adequate trend type.
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