Audet, Jonathan

Benghanem, Abdelghani

Lussier-Desbiens, Alexis

English

Purdue E-Pubs

ISEA 2022 – The Engineering of Sport 14, Purdue University, 6-10 June 2022 Corresponding author email: alexis.lussier.desbiens@usherbrooke.ca  1  An Objective Way to Categorise Alpine Skis  Jonathan Audet1, Abdelghani Benghanem1 and Alexis Lussier-Desbiens1,2 1 Université de Sherbrooke, 3000 bd de l’Université, Sherbrooke, QC J1K 2R1, Canada; 2 Sooth Selector inc., 1234 William St Quebec City QC G1S 4E9, Canada;       Skiers are faced with an ever-growing number of alpine skis [1]. To simplify the shopping experience, skis are often divided into categories based on their intended usage. However, there is no agreed standard that allows ski manufacturers, shops and skiers to categorise alpine skis. The aim of this study is thus to investigate how alpine skis are currently classified, and if simple classification rules can be established. To do so, classification models will be build using the ski’s physical properties from Sooth Ski’s database, as input, and Evo categorisation (target values), as output. Sooth Ski, the partner of the project, is specialized in the measurements of key ski properties and assembled over the years a database containing more than 3,000 skis [2]. For this project, the raw geometrical and stiffnesses measurements [3] were expressed as 43 attributes. Evo is a well-known sport equipment retailer selling alpine skis across USA/Canada since 2001 [4]. Evo ski categories include carving, all-mountain, park & pipe, alpine touring, powder and big mountain. Each ski is tagged by Evo with up to 3 of these tags. As of June 2021, 327 unisex and women ski models were available on Evo, of which 141 of them were measured by SoothSki (a total of 18 ski brands are represented).      The classification model retained for this application is a predictive modelling approach called decision tree learning [5]. It allows, based on measured ski physical properties, to predict if a ski is part of a particular Evo category or not (the null hypothesis). To determine the splitting rules of the decision tree, the algorithm uses dedicated optimization techniques. To avoid overfitting, classification trees are kept simple by setting a minimum number of skis per leaf and a 10 folds cross-validation is used. As an example of the results, the decision tree for all-mountain category is shown in Fig.1. The resulting decision tree retained only four attributes among all 43 possible attributes: the waist of the ski, the surface to weight ratio (S/W), the tail to tip height ratio and the bending stiffness of the ski tip. In addition, the rules used inside the tree are very realistic. According to these rules, most all-mountain skis are under 107mm wide, not extremely light (i.e., S/W < 1.27) and tail/tip height ratio not too high. Light skis must also have tips that are stiff in bending to be classified as all mountain skis. Although these rules use only four attributes, the model can classify correctly 96.5 % of the all-mountain skis. At first glance, some skis seem to be misclassified in the input dataset (e.g., the women version of a ski with exactly the same measured properties as the unisex version is classified a different category).   ISEA 2022 – The Engineering of Sport 14, Purdue University, 6-10 June 2022 Corresponding author email: alexis.lussier.desbiens@usherbrooke.ca  2        Table 1 presents the correct prediction rate for each of the six Evo categories and the number of attributes used in each tree. The trees can correctly predict the ski’s category based on less than 6 attributes, but the small number of skis in the park & pipe category may imply an over-fit.  Table 1: Correct prediction rate for each ski category Category Number of skis with this tag Number of attributes used Correct prediction rate Carving 4 Not enough skis Not enough skis All-Mountain 108 4 96.5 % Park & Pipe 11 2 99.2 % Alpine Touring 56 5 95.7 % Powder 28 2 98.9 % Big mountain 56 6 92.2 %       In conclusion, physical measurements are shown to differentiate skis according to Evo categories with high prediction rates and easy-to-interpret decision criteria. Most of the splitting rules are related to geometry and mass specifications. Bending and torsional stiffnesses might be more relevant for predicting the ability level of a ski. Further work will use categories from a number of sources to create standardized classification rules and avoid misclassification in the input dataset.   1. Truong J et al., Historical Trends in Alpine Ski Design: How Skis Have Evolved Over the Past Century, Proceedings, Vol. 49, 2020 2. SoothSki. Available online: https://soothski.com/ (accessed on 1 Sep. 2021). 3. Truong J et al., A Method for Measuring the Bending and Torsional Stiffness Distributions of Alpine Skis, Procedia Engineering, Vol. 147, pp 394-400, 2016. 4. Evo. Available online: https://www.evo.com/en-ca/about/all-things-evo (accessed on 1 Sep. 2021). 5. Suthaharan S, Chapter 10: Decision Tree Learning, Machine Learning Models and Algorithms for Big Data Classification, 1rst Edition, Springer, Boston, MA, pp 237-269. Fig. 1: All-Mountain decision tree model 

A Simple and Objective Way to Categorise Alpine Skis

https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1030&amp;context=resec-isea

A Simple and Objective Way to Categorise Alpine Skis

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