This research article introduces an algebraic correction method to improve the accuracy of probabilistic assessments of clone overlaps in DNA fingerprint mapping. The Sulston score, a commonly used metric, is known to systematically overestimate match probabilities, leading to potential errors in overlap detection. The authors propose a correction method that applies a power-law equation to the Sulston score, significantly improving its accuracy without increasing computational complexity. The method is based on empirical data and involves transforming the Sulston score using a power-law formula to better approximate the exact probability distribution of clone overlaps. This approach results in a much more accurate estimate of the true probability of overlap, as demonstrated by numerical comparisons. The corrected score is shown to be more reliable in identifying true overlaps and reducing false positives, especially in traditional agarose fingerprint mapping. The study also discusses the limitations of the method when applied to newer technologies like capillary electrophoresis, where the parameter ranges differ significantly. While the correction method is effective for typical fingerprint mapping conditions, its applicability to other technologies requires further investigation. The authors conclude that the corrected Sulston score provides a more accurate probabilistic description of clone overlaps, which is crucial for tasks such as clone ordering and other mapping applications. The method is straightforward to implement and offers a significant improvement over the original Sulston score, making it a valuable tool in DNA fingerprint mapping.This research article introduces an algebraic correction method to improve the accuracy of probabilistic assessments of clone overlaps in DNA fingerprint mapping. The Sulston score, a commonly used metric, is known to systematically overestimate match probabilities, leading to potential errors in overlap detection. The authors propose a correction method that applies a power-law equation to the Sulston score, significantly improving its accuracy without increasing computational complexity. The method is based on empirical data and involves transforming the Sulston score using a power-law formula to better approximate the exact probability distribution of clone overlaps. This approach results in a much more accurate estimate of the true probability of overlap, as demonstrated by numerical comparisons. The corrected score is shown to be more reliable in identifying true overlaps and reducing false positives, especially in traditional agarose fingerprint mapping. The study also discusses the limitations of the method when applied to newer technologies like capillary electrophoresis, where the parameter ranges differ significantly. While the correction method is effective for typical fingerprint mapping conditions, its applicability to other technologies requires further investigation. The authors conclude that the corrected Sulston score provides a more accurate probabilistic description of clone overlaps, which is crucial for tasks such as clone ordering and other mapping applications. The method is straightforward to implement and offers a significant improvement over the original Sulston score, making it a valuable tool in DNA fingerprint mapping.