Purpose Overnight admission following anterior cruciate ligament reconstruction has implications on clinical outcomes as well as cost beneit, yet there are few validated risk calculators for reliable identiication of appropriate candidates. The purpose of this study is to develop and validate a machine learning algorithm that can efectively identify patients requiring admission following elective anterior cruciate ligament (ACL) reconstruction. Methods A retrospective review of a national surgical outcomes database was performed to identify patients who underwent elective ACL reconstruction from 2006 to 2018. Patients admitted overnight postoperatively were identiied as those with length of stay of 1 or more days. Models were generated using random forest (RF), extreme gradient boosting (XGBoost), linear discriminant classiier (LDA), and adaptive boosting algorithms (AdaBoost), and an additional model was produced as a weighted ensemble of the four inal algorithms. Results Overall, of the 4,709 patients included, 531 patients (11.3%) required at least one overnight stay following ACL reconstruction. The factors determined most important for identiication of candidates for inpatient admission were operative time, anesthesia type, age, gender, and BMI. Smoking history, history of COPD, and history of coagulopathy were identiied as less important variables. The following factors supported overnight admission: operative time > 200 min, age < 35.8 or > 53.5 years, male gender, BMI < 25 or > 31.2 kg/m 2 , positive smoking history, history of COPD and the presence of preoperative coagulopathy. The ensemble model achieved the best performance based on discrimination assessed via internal validation (AUC = 0.76), calibration, and decision curve analysis. The model was integrated into a web-based open-access application able to provide both predictions and explanations. Conclusion Modiiable risk factors identiied by the model such as increased BMI, operative time, anesthesia type, and comorbidities can help clinicians optimize preoperative status to prevent costs associated with unnecessary admissions. If externally validated in independent populations, this algorithm could use these inputs to guide preoperative screening and risk stratiication to identify patients requiring overnight admission for observation following ACL reconstruction. Level of evidence IV.