The first of 4 nodes has it has on condition “Homegoalscoredavegame <=0.88”, total 117 samples, where is 50.4, that is 59 “Team1scoredornot=1” percentage samples. The second of 4 nodes has >0.88 and condition “Homegoalscoredavegame it has on total 293 samples, where Homegoalscoredavegame <=1.24”, is 69.3, that is 203 samples. “Team1scoredornot=1” percentage The third of 4 nodes has >1.24 and condition “Homegoalscoredavegame Homegoalscoredavegame it has on total 490 samples, where <=1.71”, is 77.1, that is 378 samples. “Team1scoredornot=1” percentage The forth of 4 nodes has it has on condition “Homegoalscoredavegame >1.71”, total 412 samples, where is 87.6, that is 361 “Team1scoredornot=1” percentage samples. The same logic is applied for the rest nodes. For example, we want to estimate if a home team will score or not. say that Let’s home team scores 2 goals per home game on average. So, by following tree model, we find out that the percentage of occurring 0 (the team does not score) is 12.4 and the percentage of occurring 1 (the team scores) is 87.6. And the P-value=0.000, which is quite good. Thus, we say that the team will score with around 87.6% of probability. Classification Tree for Away Team (Team 2) Model Summary Growing Method CHAID Dependent Variable Team2Scoredbinary Team1, Team2, Bet1, BetX, Bet2, Team1Cost, Team2Cost, Team1Rank, Team2Rank, Team1Pointsplayedgamesintheseason, Specifications Team2Pointsplayedgamesintheseason, Independent Variables Homegoalscoredavegame, Homegoalconcededavegame, Awaygoalsscoredavegame, Awaygoalsconcededavegame
The first of 4 nodes has condition “Homegoalscoredavegame <=0.88”, it has on total 117 samples, where “Team1scoredornot=1” percentage is 50.4, that is 59 samples. The second of 4 nodes has condition “Homegoalscoredavegame >0.88 and Homegoalscoredavegame <=1.24”, it has on total 293 samples, where “Team1scoredornot=1” percentage is 69.3, that is 203 samples. The third of 4 nodes has condition “Homegoalscoredavegame >1.24 and Homegoalscoredavegame <=1.71”, it has on total 490 samples, where “Team1scoredornot=1” percentage is 77.1, that is 378 samples. The forth of 4 nodes has condition “Homegoalscoredavegame >1.71”, it has on total 412 samples, where “Team1scoredornot=1” percentage is 87.6, that is 361 samples. The same logic is applied for the rest nodes. For example, we want to estimate if a home team will score or not. Let’s say that home team scores 2 goals per home game on average. So, by following tree model, we find out that the percentage of occurring 0 (the team does not score) is 12.4 and the percentage of occurring 1 (the team scores) is 87.6. And the P-value=0.000, which is quite good. Thus, we say that the team will score with around 87.6% of probability. Classification Tree for Away Team (Team 2) Model Summary Specifications Growing Method CHAID Dependent Variable Team2Scoredbinary Independent Variables Team1, Team2, Bet1, BetX, Bet2, Team1Cost, Team2Cost, Team1Rank, Team2Rank, Team1Pointsplayedgamesintheseason, Team2Pointsplayedgamesintheseason, Homegoalscoredavegame, Homegoalconcededavegame, Awaygoalsscoredavegame, Awaygoalsconcededavegame