Finn Marsland is graduating as a Doctor of Philosophy at the University of Canberra’s graduation ceremony in October 2019 (link).
The title of Finn’s thesis is Macro-kinematic performance analysis in cross-country skiing competition using micro-sensors. I am delighted that Finn has completed this remarkable thesis. From the outset, I saw his work as a great example of praxis in his combination of practice as a coach and a profound theoretical understanding of emerging micro-sensors developed with Colin Mackintosh at the Australian Institute of Sport. Dale Chapman was unable to attend the meeting too but he became a key member of Finn’s support team and who brought enormous knowledge of winter sports to the research team.
Finn’s research “lays the ground-work for future research and practical applications, which could include daily training monitoring, course profiling, evaluation of sub-technique efficiency, and similar algorithm development for the Freestyle technique”.
I met Finn in 2009 when I started my tenure at the University of Canberra. Our meeting was with Gordon Waddington and Judith Anson in the Physiology Canteen at the Australian Institute of Sport. It was a very important meeting that combined Gordon and Judith, champions of Finn’s work and Finn in the applied context of the Institute. Colin Mackintosh, pivotal in Finn’s use of micro-sensors, was unable to be at the meeting but he and Finn had met previously to prepare for the meeting.
Thereafter, Finn became one of Sport’s first PhD students. For me, it was an opportunity to explore Finn’s praxis ideas as a coach researcher. At that time he was Cross Country Skiing Program Director and Coach of Ski and Snowboard Australia.
Finn’s thesis contains four peer reviewed, published papers and one yet-to-be published papers.
The abstract of Finn’s thesis is:
Performance analysis in cross-country skiing is constrained by the variability of environmental conditions and terrain, and complicated by frequent changing between sub- techniques during competition. Snow conditions and skiing speed change constantly from day to day and often during the day, and competition courses vary in the length, gradient and distribution of hills from venue to venue. The aim of this body of work was to develop a new performance analysis method, using a single micro-sensor, to continuously detect skiing sub- techniques and quantify the associated kinematic properties that describe a skier’s performance during training and competition. Of particular interest was the relative use of each sub-technique, together with velocity, cycle rate and cycle length characteristics collectively defined as cross-country skiing macro-kinematics. Over five studies this thesis explores proof of concept through detection of different sub-techniques, develops an algorithm for the quantification of macro-kinematic parameters during training, demonstrates the use of a refined algorithm to investigate performance demands and macro-kinematic variability over an entire competition, compares macro-kinematics between different types of event, and finally examines the implication for coaches arising from analysis throughout rounds of a sprint event.
The first study (Chapter 3) (link) in this research showed how the cycles of sub-techniques of both classical and freestyle technique could be identified using a single micro-sensor unit, containing an accelerometer, gyroscope and GPS sensors, mounted on the upper back. Data was collected from eight skiers (six male and two female), of which four were World Cup medallists, skiing at moderate velocity. Distinct movement patterns for four freestyle and three classical cyclical sub-techniques were clearly identified, while at the same time individual characteristics could be observed.
The second study (Chapter 4) (link) quantified macro-kinematics collected continuously from seven skiers (four female and three male) during an on-snow training session in the classical technique. Algorithms were developed to identify double poling (DP), diagonal striding (DS), kick-double poling (KDP), tucking (Tuck), and turning (Turn) sub-techniques, and technique duration, cycle rates (CR), and cycle counts were compared to video-derived data to assess detection accuracy. There was good reliability between micro-sensor and video calculated cycle rates for DP, DS, and KDP, while mean time spent performing each sub-technique was under-reported. Incorrect Turn detection was a major factor in technique cycle misclassification.
The third study (Chapter 5) (link) used an algorithm with improved Turn detection to measure macro-kinematics of eight male skiers continuously during a 10 km classical Distance competition. Accuracy of sub-technique classification was further enhanced using manual reclassification. DP was the predominant cyclical sub-technique utilised (43 ± 5% of total distance), followed by DS (16 ± 4%) and KDP (5 ± 4%), with the non-propulsive Tuck technique accounting for 24 ± 4% of the course. Large within-athlete variances in cycle length (CL) and CR occurred, particularly for DS (CV% = 25 ± 2% and CV% = 15 ± 2%, respectively). For all sub-techniques the mean CR on both laps and for the slower and faster skiers were similar. Overall velocity and mean DP-CL were significantly higher on Lap 1, with no significant change in KDP-CL or DS-CL between laps. Distinct individual velocity thresholds for transitions between sub-techniques were observed.
In the fourth study (Chapter 6) (link) macro-kinematics were compared between six female skiers competing in Sprint and Distance competitions in similar conditions on consecutive days, over a 1.0 km section of track using terrain common to both competitions to eliminate the influence of course topography. Mean race velocity, cyclical sub-technique velocities, and CR were higher during the Sprint race, while Tuck and Turn velocities were similar. Velocities with KDP and DS were higher in the Sprint (KDP +12%, DS +23%) due to faster CR (KDP +8%, DS +11%) and longer CL (KDP +5%, DS +10%), while the DP velocity was higher (+8%) with faster CR (+16%) despite a shorter CL (-9%). During the Sprint the percentage of total distance covered using DP was greater (+15%), with less use of Tuck (-19%). Across all events and rounds, DP was the most used sub-technique in terms of distance, followed by Tuck, DS, Turn and KDP. KDP was employed relatively little, and during the Sprint by only half the participants.
The final case study (Chapter 7) focused on the insight coaches could gain from examining variations in individual macro-kinematics for six female skiers across three rounds of a classic Sprint competition. Individual macro-kinematic variations were influenced by personal strengths and preferences, pacing strategies, and by interactions with other skiers in the head- to-head rounds. Potential coaching implications include using a range of CR and CL during training, modifying these parameters during training to work on weaknesses, and altering macro-kinematic race strategies depending on the course terrain, event round and on other skiers’ tactics.
In conclusion this thesis outlines the development of a new cross-country skiing analysis method that uses a single micro-sensor and a unique algorithm to effectively measure macro- kinematic parameters continuously during training and competition. This tool could be used by researchers, coaches and athletes to better understand training and competition demands and enhance performance. This research lays the ground-work for future research and practical applications, which could include daily training monitoring, course profiling, evaluation of sub-technique efficiency, and similar algorithm development for the Freestyle technique.
I am looking forward to doffing my cap to Finn in October when he officially becomes Dr Finn Marsland after a decade of seminal research. His upgrade seminar was in 2012 (link).
Finn Marsland (ResearchGate)
Images from Finn’s Thesis Chapters3, 5 and 7) (CC BY 4.0)
Finn Marsland (Clyde Street, CC BY 4.0)