@inproceedings {26630,
	title = {Predicting Peek Readiness-to-Train of Soccer Players Using Long Short-Term Memory Recurrent Neural Networks},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = {IEEE},
	abstract = {We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern machine- learning methods have the potential to further fuel this process by deriving useful insights that are not easily observable in the raw data streams.This paper tackles the problem of deriving peaks in soccer players{\textquoteright} ability to perform from subjective self-reported wellness data collected using the PMSys system. For this, we train a long short-term memory recurrent neural network model using data from two professional Norwegian soccer teams. We show that our model can predict performance peaks in most scenarios with a precision and recall of at least 90\%. Equipped with such insight, coaches and trainers can better plan individual and team training sessions, and perhaps avoid over training and injuries.},
	doi = {10.1109/CBMI.2019.8877406},
	author = {Wiik, Theodor and H{\r a}vard D {Johansen} and Svein Arne {Pettersen} and Baptista, Ivan and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26629,
	title = {Real-time Analysis of Physical Performance Parameters in Elite Soccer},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = { IEEE},
	abstract = {Technology is having vast impact on the sports in- dustry, and in particular soccer. All over the world, soccer teams are adapting digital information systems to quantify performance metrics. The goal is to assess strengths and weaknesses of indi- vidual players, training regimes, and play strategies; to improve performance and win games. However, most existing methods rely on post-game analytic. This limits coaches to review games in retrospect without any means to do changes during sessions. In collaboration with an elite soccer club, we have developed Metrix which is a computerized toolkit for coaches to perform real- time monitoring and analysis of the players{\textquoteright} performance. Using sensor technology to track movement, performance parameters are instantly available to coaches through a mobile phone client. Metrix provides coaches with a toolkit to individualize training load to different playing positions on the field, or to the player himself. Our results show that Metrix is able to quantify player performance and propagate it to coaches in real-time during a match or practice, i.e., latency is below 100 ms on the field. In our initial user evaluation, the coaches express that this is a valuable asset in day-to-day work.},
	doi = {10.1109/CBMI.2019.8877422},
	author = {Andreassen, Kim and Johansen, Dag and Johansen, H{\r a}vard and Baptista, Ivan and Svein Arne {Pettersen} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {Pettersen2014a,
	title = {Soccer Video and Player Position Dataset},
	journal = {Proceedings of the 5th ACM Multimedia Systems Conference on - MMSys {\textquoteright}14},
	year = {2014},
	pages = {18{\textendash}23},
	publisher = {ACM Press},
	address = {New York, New York, USA},
	abstract = {Abstract This paper presents a dataset of body-sensor traces and corresponding videos from several professional soccer games captured in late 2013 at the Alfheim Stadium in Troms{\o}, Norway. Player data, including field position , heading, and speed are sampled at 20Hz ... $\backslash$n},
	isbn = {9781450327053},
	doi = {10.1145/2557642.2563677},
	url = {http://dl.acm.org/citation.cfm?doid=2557642.2563677},
	author = {Svein Arne {Pettersen} and Halvorsen, P{\r a}l and Johansen, H{\r a}vard and Berg-Johansen, Vegard and Vamsidhar Reddy {Gaddam} and Mortensen, Asgeir and Langseth, Ragnar and Griwodz, Carsten and H{\r a}kon Kvale {Stensland}}
}
@inproceedings {mmsys.2014-02-18.0527842670,
	title = {Soccer Video and Player Position Dataset},
	journal = {Proceedings of the 5th annual ACM conference on Multimedia Systems (MMSYS)},
	year = {2014},
	publisher = {ACM},
	type = {Conference},
	abstract = {This paper presents a dataset of body-sensor traces and corresponding videos from several professional soccer games captured in late 2013 at the Alfheim Stadium in Troms{\o}, Norway.Player data, including field position, heading, and speed are sampled at 20 Hz using the highly accurate ZXY Sport Tracking system. Additional per-player statistics, like total distance covered and distance covered in different speed classes, are also included with a 1 Hz sampling rate. The provided videos are in high-definition and captured using two stationary camera arrays positioned at an elevated position above the tribune area close to the center of the field. The camera array is configured to cover the entire soccer field, and each camera can be used individually or as a stitched panorama video. This combination of body-sensor data and videos enables computer-vision algorithms for feature extraction, object tracking, background subtraction, and similar, to be tested against the ground truth contained in the sensor traces.},
	author = {Svein Arne {Pettersen} and Johansen, Dag and Johansen, H{\r a}vard and Berg-Johansen, Vegard and Vamsidhar Reddy {Gaddam} and Mortensen, Asgeir and Langseth, Ragnar and Griwodz, Carsten and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l}
}
@inproceedings {Simula.simula.2150,
	title = {Combining Video and Player Telemetry for Evidence-Based Decisions in Soccer},
	journal = {Proceedings of the International Congress on Sports Science Research and Technology Support (icSPORTS)},
	year = {2013},
	month = {September},
	type = {Conference},
	abstract = {Technology is changing how soccer clubs train and interact with their supporters. Systems that provide acquisition and visualization of low-level player telemetry, like distance covered and speed, are already being widely adopted. A key observation is that such data when correlated with actual in-game video footage is a powerful tool for evidence-based decisions. As data volume and complexity grow, efficient tools for auto- mated high-precision retrieval become essential. This paper describes the unique combination of a radio-based sensor platform and several custom video retrieval systems in operational use at Troms{\o} Idrettslag (TIL), a Norwegian premier league soccer club. The systems have been developed using an experimental computer- science method where several prototypes were built and deployed for evaluation in close collaboration with the intended users. Although our method of computer-system prototyping has not yielded commercial quality products, it has enabled us to construct several novel systems combining low-level player telemetry with video retrieval, annotations, and user-centric security.},
	author = {Johansen, H{\r a}vard and Svein Arne {Pettersen} and Halvorsen, P{\r a}l and Johansen, Dag},
	editor = {Jo{\~a}o Barreiros {Correia}}
}
