Bringing IoT to Sports Analytics


Mahanth Gowda, Ashutosh Dhekne, Sheng Shen, and Romit Roy Choudhury, University of Illinois at Urbana–Champaign; Xue Yang, Lei Yang, Suresh Golwalkar, and Alexander Essanian, Intel


This paper explores the possibility of bringing IoT to sports analytics, particularly to the game of Cricket. We develop solutions to track a ball’s 3D trajectory and spin with inexpensive sensors and radios embedded in the ball. Unique challenges arise rendering existing localization and motion tracking solutions inadequate. Our system, iBall, mitigates these problems by fusing disparate sources of partial information – wireless, inertial sensing, and motion models – into a non-linear error minimization framework. Measured against a mm-level ground truth, the median ball location error is at 8cm while rotational error remains below 12 even at the end of the flight. The results do not rely on any calibration or training, hence we expect the core techniques to extend to other sports like baseball, with some domain-specific modifications.

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@inproceedings {201462,
author = {Mahanth Gowda and Ashutosh Dhekne and Sheng Shen and Romit Roy Choudhury and Lei Yang and Suresh Golwalkar and Alexander Essanian},
title = {Bringing {IoT} to Sports Analytics},
booktitle = {14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17)},
year = {2017},
isbn = {978-1-931971-37-9},
address = {Boston, MA},
pages = {499--513},
url = {},
publisher = {USENIX Association},
month = mar

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