A Venn Diagram model has been proposed to assist athletes with self-monitoring daily changes in fluid intake by combining three of the simplest hydration markers: weight, urine color, and thirst (WUT). No study to date has examined relationships between WUT markers and urine hydration indices using a new, recently validated lavatory urine color (LUC) chart. Furthermore, this is the first study to investigate an adaptation of the WUT model for identifying low vs. high urine concentration, which is useful for athletes to determine whether they are drinking enough water on a daily basis. For five consecutive days, n=19 participants collected a first-morning urine sample before assessing body weight and thirst at home. The urine sample was later scored by each participant at the testing site after 3 mL was extracted to measure urine specific gravity (USG). Participants could score thirst as yes (1) or no (0), and urine color as darker than (1) or similar to/lighter than (0) when comparing their sample to the reference color on the LUC chart. The researchers calculated body weight change (%) from a predetermined baseline to score body weight as >-0.5% change (1) or <-0.5% change (0). Combined outcomes for the three assessments were assigned a score of 0, 1, 2, or 3 and categorized in the Venn Diagram. Scores of 0 or 1 suggest euhydration (USG <1.020) and scores of 2 or 3 suggest underhydration (USG >1.020). Median USG was 1.021 (ranging 1.003-1.035). WUT outcomes for all cases were: 5% (score 3), 33% (score 2), 53% (score 1), and 9% (score 0). WUT score 3 had optimal accuracy (100%) and WUT score 2 had fair accuracy (67%) for identifying a high urine concentration, but only 38% of cases were scored in this way. Based on the assumption that scores 2+3 should have USG >1.020 and scores 0+1 should have USG <1.020, the total accuracy of the WUT model to correctly classify urine concentration was 60%. The results indicated that athletes can use this approach to identify high urine concentration by monitoring simple hydration markers, but misclassifications may occur up to 33%.