Using data on 103 recent P4 college football hires, we built a statistical model for predicting a coach's success at their new school. For each hire, we collected data about their background and experiences, the previous success as a head coach or coordinator and their success since hiring. Over 50 variables on these factors were recorded though we used 29 of these in building our predictive model. Our measure of success is based upon Bill Connelly's SP+ team ratings relative to the performance on the same metric of the school in the 15 year prior to their selection as head coach. Using a cross-validated regularized linear regression, we obtain a predictive model for coaching success. Among the important factors for predicting a successful hire are having been a previous college head coach, leaving a job as an Offensive Coordinator, age and quality of the hiring school's team in the previous 15 years. While we do find these factors are important for the prediction of a successful coaching hire, the trends here are weak. With 66\% accuracy, the model does identify coaching hires that will outperform team performance in the 15 years before the hire. However, no combination of these factors leads to high predictability of identifying a successful coaching hire. All of the data and code for this paper are available in a Github repository.

翻译：基于103项近期Power 4大学美式足球教练聘用数据，我们构建了一个用于预测教练在新学校成功率的统计模型。针对每项聘用，我们收集了其背景经历、此前担任主教练或协调员的成功记录，以及受聘后的表现数据。虽然记录了超过50个相关变量，但在构建预测模型时实际使用了其中29个。我们采用比尔·康奈利的SP+球队评分体系作为成功度量标准，该评分以教练受聘前15年内学校在同一指标上的表现为基准。通过交叉验证的正则化线性回归方法，我们获得了教练成功率的预测模型。影响成功聘用的关键因素包括：曾担任大学主教练的经历、离开进攻协调员职位的背景、年龄以及聘用学校球队过去15年的质量水平。尽管这些因素对预测成功聘用具有重要性，但其关联趋势较弱。该模型以66%的准确率识别出能够超越球队聘用前15年表现的教练人选，然而没有任何因素组合能实现高精度的成功聘用预测。本文所有数据与代码均已发布于Github存储库。