## 4 Discussions and Conclusions  
This study explored PIH games' winning and losing factors using a data mining-based decision tree analysis targeting the PIH games in international competitions organized by WPIH. The 66 games of the nations participating in four international competitions for the last two years were selected as study subjects, and game records were collected to achieve the study purpose. The descriptive statistics, Mann-Whitney U test, and data mining-based decision tree analysis were performed for data processing. This study analyzed the following upon the decision tree analysis: (1) winning and losing factors, excluding total goals occurring in each game; (2) winning and losing factors, excluding each period's goal-related factors; and (3) winning and losing factors, excluding total goals and shot-related factors. The statistical significance level was set to 1, and the IBM SPSS 24.0 version was used. The total decision tree according to para ice hockey's winning and losing factor analysis result is shown in Figure 5.  
Figure 4: Winning and losing factors exploration result, excluding goal and shot-related variables. The conclusions drawn through this study are as follows: first, the result of differences in the verification of game records depending on winning and losing, total goals, total saves (SVS), total shots on goal (SOG), total power play goals (PPG), total shorthanded goals (SHG), and total FO winning percentage (FO%) showed significant differences among the total game records. Regarding winning teams, 5.79 goals, 24.05 SOG, 57.79% face-off winning percentage, 7.62 SVS, 0.24 PPG, and 0.39 penalty-killing goals (PKGs) on average per game were recorded. Concerning the losing teams, 0.89 goals, 8.52 SOG, 42.21% face-off winning percentage, 18.26 SVS, 0.82 PPG, and 0.05 PKG were recorded and showed significant differences. When looking at game records by period, significant differences were revealed in the goals, SOG, and PPG except in the third period, and SHG and SVS except in the second period. The winning teams showed the highest goals and SOG in the first, second, and third periods, respectively, and the losing teams showed the reverse order. Regarding the goal in the penalty-killing situation in which the same team players lacked, the winning and losing teams recorded 0.39 and 0.05, respectively, and significant differences were shown. This results from the winning teams' average scoring being high, with goal-scoring power in unfavorable situations being high, not to mention favorable situations. The winning teams recorded more goals and SOG in the order of the first, second, and third periods, but the losing teams showed the reverse order. From the result, the winning teams scored the most SOG and goals in the first period. The losing teams recorded the most SOG and goals in the third period. When looking at total periods, the SOG and SVS, excluding goals, showed higher values in the third period. Through this, it was judged that the probability of winning became higher if shots and scoring were attempted intensively in the early part of a game, regarding game operation tactics. As for the face-off winning percentage, the winning teams showed 57.79%, but the losing teams showed 42.21%. As the mean face-off winning percentage was 50.0%, it was judged that the probability of winning can be elevated if the face-off winning percentage was secured over a certain level. Concerning SVS, the winning teams recorded 7.62 SVS, and the losing teams showed 18.26 SVS on average per game. Because the mean SVS per game was 12.94, it was judged that the probability of winning can be elevated by lowering the saving rate and reducing opportunities to shoot. As a result of this analysis, there is a tendency to lead the pace of an advantageous game only when goals are scored in the early stages of the game.