Doing exercises regularly and scientifically can bring better health for people and improve sports performance for athletes. Many investigations have carried on to build necessary models that utilize data collected from people to predict sports performance. Nevertheless, most researches use data directly related to the moment when exercises happen to build prediction models, even though more data related to people’s daily activities also impact on sports performance. Thanks to lifelogging, we can now have more data reports not only on people’s exercises but also on people’s daily activities (both mental and physical aspects). Unfortunately, finding out which data attributes correlate to the changing of sports performance and leveraging these correlated attributes to build a precise prediction model is not a trivial problem. In this paper, we introduce the solution that utilized the predictive power score of lifelog data attributes during a long time to predict an athlete who trained for a sporting event. We evaluate our solution using the dataset and evaluation metric given by imageCLEFlifelog task 2: sports performance lifelog.