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|Title:||Profitability of predicting the results of Major League Baseball games|
|Subject(s):||Major League Baseball
Statistical documentation in sports is important for outcome predictions. In baseball, statistics such as home runs, first base hits, and walks per game are insightful for determining which team will likely win a game. I have created a Python program that takes statistics like these to predict how many points a team will score in their next game, using the XGBoost algorithm. The result tells the predicted winner of any game. With these results, I created an interactive Tableau dashboard to visualize the profitability of this predictive program. This visualization shows the total number of times a team was predicted to win, along with the number of times this prediction was correct. Using these results and the moneyline multiplier, the amount a given bet will be multiplied by if correct, profitability for a team can be calculated. The visualization shows this profitability for the teams in the MLB when using this predictive algorithm. The view can be filtered on league divisions, profit, and the correctness ratio for teams. With this, it is possible to determine which teams the algorithm predicts correctly in such a way that betting on those teams is profitable.
Please view the dashboard in Fullscreen.
|Rights Information:||Copyright 2019 Suleman Bazai|
|Date Available in IDEALS:||2020-03-25|