Traditional planning and periodization of training have been considered as one of the key elements when it comes to managing fatigue and maximizing both general and specific sports performance. However, these traditional concepts of periodization rely on the idea of designing an optimal training plan while assuming the stability of non-training factors. One of the most frequent logistical issues often seen in sports are missed sessions, both randomly (MCAR) and with a certain pattern (MNAR). Since these non-training factors considerably decrease the optimality of one periodization strategy, the objectives of this study were to assess the performance of different loading strategies for training load control and to explore what impact MCAR events had on the performance of these loading strategies. We simulated 19 different loading strategies. Each loading strategy was applied for 150 days, under normal conditions (no missing sessions), as well under MCAR conditions. The following MCAR conditions were simulated: (1) no missing days (missing 0 in 10), (2) missing 1 in 10, (3) missing 2 in 10, (4) missing 4 in 10, and (5) missing 6 in 10 days. Each scenario for every loading strategy was simulated 100 times. The performance was evaluated using an exponential moving average (EMA) approach to get acute training load and simple moving average (SMA) to get chronic training load (rolling 28 days), as well as acute to chronic workload ratio (ACWR) for every day in simulations. Moreover, between qualities standard deviation (btwnQualitiesSD) and ratio metric were introduced to estimate variance in training load between qualities and to determine the emphasis of a certain training quality compared total training load receptively. The novel 'don't break the chain' loading strategies demonstrated the most robust behavior under simulated MCAR conditions by the least drop in the total training load.