This paper describes the risk of injury to the rider in a crash using a statistical model based on real-world accident data. We analyzed the road traffic accidents data in Los Angeles and Hanover. Logistic regression modeling technique was used to clarify the relationship among probabilities of minor, serious, fatal injury risk to the rider, and the influence of risk factors in accidents involving opposing vehicle contact point, motorcycle contact point, opposing vehicle speed, motorcycle speed, relative heading angle of impact, and helmet use. The odds ratio, which was adjusted for risk factors simultaneously, was estimated by using the developed technique, and was compared with the effects of risk factors individually. The results showed that there was a statistically significant relationship between minor and serious injuries and opposing vehicle speed, motorcycle speed and opposing vehicle contact point. Meanwhile, fatal injuries were correlated with opposing vehicle speed, motorcycle speed and helmet use. We found that the effects of such risk factors differed among the risks of minor, serious and fatal injuries of motorcyclists. The results suggest that this method may be valid for analyzing accident data.