The characteristic analysis of winners and losers in curling: Focused on shot type, shot accuracy, blank end and average score SungGeon Park 1 & Soowo

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1 The characteristic analysis of winners and losers in curling: Focused on shot type, shot accuracy, blank end and average score SungGeon Park 1 & Soowon Lee 2 * 1 Program of Software Convergence, Soongsil University & 2 School of Software, Soongsil University [Purpose] [Methods] [Results] [Conclusion] Key words:

2 Table 1. Types of curling operations and shots used at the Sochi 2014 Winter Olympics and the related literature Shot type Sochi Olympics Literature Draw Take-out Draw, Guard, Freeze Front, Takeout, Hit & Roll, Clearing, Raise, Promotion Takeout, Double Takeout, Wick/Soft Peeling Draw, Front, Guard, Raise, Wick/Soft Peeling, Freeze Takeout, Hit & Roll, Clearing, Promotion Takeout, Double Takeout Through Through Through Fig. 1. An example of selecting the last stone according to scoring in curling game

4 Fig. 2. Entity-Relationship Diagram for curling match information Table 2. Characteristics of participating countries in Sochi Winter Olympic curling games Men s Team Rank Game W L W Tie-bre aker Mean Age Women s Team Rank Game W l CAN CAN GBR Win 30.2 SWE SWE GBR CHN Loss 28.2 SUI NOR JPN DEN DEN RUS CHN SUI KOR USA RUS GER USA W Tie-br eaker Mean Age

6 Table 3. The descriptive statistics for shot types by position of the winners and the losers Operation Draw Take-out Shot type Draw Guard Freeze Front Takeout Hit & Roll Clearing Raise Promotion takeout Double takeout Wick & Sick Through Total Winner Loser Lead Second Third Skip Total Lead Second Third Skip Total 638 (33.1%) 0 (5.2%) 0 (0.0%) 262 (13.6%) 345 (1.9%) 19 (9.3%) 148 (.%) 64 (3.3%) 44 (2.3%) 122 (6.3%) 19 (1.0%) 9 (0.5%) 1, (28.5%) 131 (6.8%) 2 11 (6.1%) 414 (21.5%) 20 (.%) 195 (.1%) 61 (3.2%) 64 (3.3%) 1 (9.2%) (0.4%) 4 (0.2%) 1,930 6 (31.9%) 128 (6.6%) 4 (0.2%) 9 (5.%) 385 (20.0%) 216 (11.2%) 149 (.%) 6 (3.9%) 63 (3.3%) 166 (8.6%) 6 (0.3%) 11 (0.6%) 1, (34.8%) 2 (5.4%) 1 4 (8.1%) 369 (19.4%) 162 (8.5%) (8.3%) 2 (3.8%) 65 (3.4%) 142 (.5%) (0.4%) (0.5%) 1,902 2,465 (32.2%) 461 (6.0%) 642 (8.3%) 1,513 (19.%) 64 (9.9%) 649 (8.4%) 23 (3.6%) 236 (3.1%) 60 (.9%) 39 (0.5%) 34 (0.4%), (36.%) 135 (.0%) (11.9%) 340 (1.6%) 16 (9.1%) 112 (5.8%) 48 (2.5%) 42 (2.2%) 6 (5.5%) 14 (0.%) 18 (0.9%) 1, (30.4%) 122 (6.3%) 2 5 (5.4%) 426 (22.1%) 18 (9.%) 20 (.%) 69 (3.6%) 49 (2.5%) 164 (8.5%) 9 (0.5%) 4 (0.2%) 1, (29.6%) 0 (.8%) (6.0%) 381 (19.%) 222 (11.5%) (9.1%) 8 (4.0%) 48 (2.5%) 163 (8.4%) 13 (0.%) 11 (0.6%) 1,930 6 (32.2%) 111 (5.8%) (.6%) 36 (19.%) 182 (9.5%) 143 (.5%) 0 (3.%) 52 (2.%) 136 (.1%) 8 (0.4%) 13 (0.%) 1,909 2,481 (32.2%) 518 (6.%) (8.5%) 1,523 (19.8%) 6 (.0%) 63 (8.3%) 265 (3.4%) 191 (2.5%) 569 (.4%) 44 (0.6%) 46 (0.6%),699 Table 4. The result of shot types chi-square test by the position of the winner and the loser Lead Second Third Skip Total p-value p-value p-value p-value p-value * Table 5. The result of shot types chi-square test by the winner and the loser Shot type p-value Shot type p-value Shot type p-value Draw * Takeout Promotion Takeout Guard Hit & Roll Double Takeout Freeze Clearing.91.04* Wick & Soft Front * Raise Through

7 Table 6. The average shot accuracy by position of the winners and the losers Operation Shot type Winner Loser Lead Second Third Skip Mean Lead Second Third Skip Mean Draw Draw Guard Freeze Front Takeout Hit & Roll Take-out Clearing Raise Promotion takeout Double takeout Wick & Sick Through Total Mean Table. The result of chi-square test for the average shot accuracy by the winners and the losers Operation Draw Take-out Draw Guard Freeze Front Takeout Hit & Roll Clearing Raise Promotion takeout Double takeout Wick & Sick Through p 0.49* * *

8 Table 8. Blank end numbers by the winners and the losers partition End Total Game Number Mean 1st With hammer Winner Loser Sum 2 (.9) (16.3) 42 (16.0) (4.1) 4 (3.3) (3.8) 8 (4.) 8 (.6) (5.) 9 (5.3) 3 (2.2) 11 (4.2) 11 (6.5) 4 (3.3) 14 (5.3) 8 (4.) (8.) 16 (6.1) (5.9) (.9) 20 (.6) 12 (.1) 3 (3.3) (5.) (8.8) 6 (5.4) 20 (.6) 63 (3.1) 3 (39.1) 99 (3.8) (0) 9 (0) 262 (0) st Without hammer Winner Loser Sum (.5) 2 (16.4) 42 (16.0) 3 (4.1) 6 (3.6) (3.8) (8.2) (4.2) (5.8) 2 (3.1) 8 (4.8) 11 (4.2) 3 (4.1) (6.1) 14 (.2) 8 (.2) 9 (5.5) 16 (6.1) (.3) (6.1) 20 (.6) 3 (3.1) 12 (.3) (5.) 5 (6.2) 14 (8.5) 20 (.6) 36 (38.1) 62 (3.6) 99 (3.8) 92 (0) 165 (0) 262 (0)

9 Table 9. The result of the average score per end by the winner and the loser With hammer Without hammer partition End Winner (A) Loser (B) (A)-(B) Accumulated Winner (C) Loser (D) (C)-(D) Accumulated Total

11 Baek, J, C.(2003). Comparative analysis of culling operations team leveled the match. Miganhaeng master's thesis. Suwon University Graduate School of Education. Bae K., Park D. H., Kim D. H. & Shin H. (2016). Markov Decision Process for Curling Strategies. Journal of the Korean Institute of Industrial Engineers, 24(1), Cho G. J. & Baek J. C. (2003). A Comparative of the Strategy in Curling Game Based on Individual Team's Level. Korea sport research, 20(1), Heo, J, U.(2008). Man Shot in the distribution of Position Stars curling game. Sports Science Institute, first-class game, field leaders apply research report. Kostuk K. J., Willoughby K. A., & Saedt. A. P. (2001). Modelling curling as a Markov process. European Journal of Operation Research, 133, Kim C. H. & Yoon H. K. (2008). The Analysis on The World Curling Games for the Improvement in Performance Ability. The Korea Journal of Sports Science,1(2), 4-5. Kim C. H. (2009). The Analysis on Result of Junior Curling games. The Korea Journal of Sports Science, 18(1), Kim T. W. & Chae J. S. (2016). Analysis of Women s Curling Performance, Digital Media DB Construction and Artificial Neural Networks. Korean Journal of Sport Science, 2(2), Kostuk K. J., Willoughby K. A. & Saedt A. (2001). Modelling curling as a Markov process. European Journal of Operational Research, 133, Lee J. H. (2005) and 2005 Comparative Analysis on Curling Competitive Strategy. Master Thesis. Suwon University Graduate School of Education. Park G. I. (2000). An analysis on the game content of curling athlete. Korean National University of Physical Education The Graduate School of Community Sports Science, Master Thesis. Park S. G. & Lee S. W. (2013). The Curling Analysis Based on the Possession of the Last Stone per End. Procedia Engineering, 60,

12 Park S. G., Hwang B. K., Lee H. S., & Yun S. M. (2013). A Play Content Analyses of 2012 World Championship Games by the Four Selected National Wheelchair Curling Teams. Korean Journal of Adapted Physical Activity, 21(2), -26. Park S. G., Yoon H. K., & Lee S. W. (2016a). The Ranking prediction of Women Curling Games based on Performance Indicators in Curling. The Korea Journal of Sports Science, 25(2), 9-1. Park S. G., Yoon H. K. & Lee S. W. (2016b). Performance Analysis of Women's Curling games in 2014 Sochi Winter Olympics. The Korea Journal of Sports Science, 25(2), Robert P. S., Osama K. S., & Hsinchun C. (20). Sports Data Mining. London: Springer. Throp. C. (2013). Curling (Winter Sports). Chicago: Raintree. Willoughby, K. A. & Kostuk K. J. (2004). Preferred Scenarios in the Sport of Curling. Interfaces, 34(2), Willoughby, K. A. & Kostuk, K. J. (2005). An Analysis of a Strategic Decision in the Sport of Curling. Decision Analysis, 2(1), Yang J. B & Lee M. S. (2013). International Curling Games Analysis for 2018 PyeongChang Winter Olympic Using Logistic Regression Analysis. The Korea Journal of Sports Science, 22(3),

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