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![]() For example, if a team’s star player were to suffer a season-ending injury near the start of the playoffs, this would not affect the team’s statistics greatly, but this incident can potentially cause the team to drastically underperform relative to their predicted performance. While these statistics do hold a degree of predictive power, they lack any information about individual player data or any other specific data. One of the major assumptions we made for our premise was that regular season team statistics are strong predictors of a team’s playoff success. Taking into account all of these considerations, the combination of variables we deemed the best for our model was Win% and Offensive Efficient Field Goal% (OEFG%). We ruled this result to be a bit unreasonable, so we decided to change our variables. Although the Mavericks lacked in the other statistics, their Offensive Rating statistic essentially “overpowered” the rest of the variables, and they were projected to be the most probable winner of the Western Conference Finals. ![]() When we applied the regression on our playoff teams, the model generated extremely high win probabilities for the Dallas Mavericks, as they have the highest Offensive Rating in the league by far. For example, when we included Offensive Rating as one of the variables, the model placed a heavy weight on it. This was either due to outliers among our 2020 playoff teams, or simply due to lack of predictive power in our selected variables. Although some combinations met our previous two criteria, since we only used regular season statistics to project playoff win probabilities, some of the results were questionable. ![]() Another factor that influenced our variable selection was the reasonability of the final results. ![]()
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