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The growing deployment of unhardened embedded devices in critical systems demands the monitoring of hardware ageing as part of predictive maintenance. In this paper, we study degradation on a large deployment of 298 naturally aged FPGAs operating in the European XFEL particle accelerator. We base our statistical analyses on 280 days of in-field measurements and find a generalized and continuous degradation of the switching frequency across all devices with a median value of 0.064%. The large scale of this study allows us to localize areas of the deployed FPGAs that are highly impacted by degradation. Moreover, by training machine learning models on the collected data, we are able to forecast future trends of frequency degradation with horizons of 60 days and relative errors as little as 0.002% over an evaluation period of 100 days.