Using Machine Learning to Predict NBA Games (Student Presentation, Group 22)

Key Takeaways

hello everybody i am almond bragg and today i'll be talking about my project so i use machine learning to predict mba basketball game outcomes so first just an overview of what i'll be talking about today i'll be going through my introductions last motivation for the project the data preparation i did the algorithm algorithm and model selection and the final model performance estimates along with the conclusion so now onto the introduction and motivation um the first thing um just the nba really is just such a large business making billions and billions of dollars every year along with the sports betting that goes into the nba making more more more money as well making billions and billions of dollars on that um this really was one motivation for the project but the main motivation was just on my love for the game of basketball and this project allowing me to merge both basketball and machine learning together to create just a very cool project that i could be very happy with um another thing is really sports analytics in general allows you to test a lot of different models that you can use in many different fields not just in sports analytics and the overall goal of this model is to create a predictor that can create win versus loss in the nba games so for data preparation i looked at two data sets um for this um the first data set i obtained from kaggle it was about 15 000 rows and 123 columns um this data set really was a lot of unneeded data it was it was really just mainly used to obtain um the match ups from specific games the date of the games and then the outcome of the games the rest of the data was data from the in-game statistics which really can't be used to help predict the game since it was what happened during the game um so for the second data set that i got i also obtained from kegel and it was about 30 000 rows and 39 columns so this was the more useful the two data sets and what i what i really used um to get my features so this was a data set comprised of the standings from every day of the nba season from 2012 to 2018. it contained where specific teams ranked how many games they had won verse loss either overall or just at home or just away along with how many points on average they scored versus how many points they gave up um along also like with their current winning versus losing streak so all of these um came in handy as as different features so for um the combined data set it was about 7 000 rows and 20 columns so how i combined these was by taking the team abbreviation along with the game date and merging the two data sets together and then this allowed me to have the the whole data set both data sets together and then from there i just took the relevant statistics from the second data set to apply to that specific game um i took the categorical variables such as the team location homeverse away and change that to numeric zero or one so zero for away one for home and then win verse loss or team result which would end up being my class label i change that to to a numeric of zero zero for a loss one for a win another thing i changed all the records to a percentage so home away in total i changed them all to percentages of how many game like the percentage of games they won um next for the team and opponents they i made sure that they all had the same features so they all had their their current streaks their current rank their current point differential going into that game so overall this data had 19 different feature variables and one class label which was team results win versus loss zero or one and then all the statistics were what the team had achieved coming into the game the training set that i created was um from the 2012 to 2016-17 season um and then i wanted to test it on the most recent season which was the 2017-2018 season for algorithm and model selection i used a nested cross validation to compare four different algorithms for the inner loop i used a stratified k-fold of two and for the outer loop a stratified k-fold of five and then i looked at four different algorithms k nearest neighbors decision tree random forest and gradient boosting so for k nearest neighbors i implemented it using the psychic learn i used the ball and tree method and standard scaling for it and then the parameter search i had was for um the number of neighbors 1 through 10 and 15 and 20 and then the distances manhattan and euclidean and then from this i got an outer cross validation accuracy of 60.1 percent plus or minus 0.87 for the decision tree classifier um i use this i used um the psychic learn to implement this as well on the parameter search of this i for max that i searched for max depth um 1 through 5 10 15 20 and none and then for the uh criteria i used was beginning and entropy and then from this i got an outer cross validation accuracy of 57.04 plus or minus 1.07 um then i used a random forest classifier i implemented this again through psychic learn um and the parameter i used for this was just number of estimators 10 100 500 1000 and 10 000 and from this i got an outer cross validation accuracy of 64.7 percent plus or minus 0.92 and then the last one i used was a gradient boosting classifier and i again implemented this using psychic learn the parameters i used were learning rate 0.1.5 and 1. number of estimators 10 100 500 1 000 10 000 and max depth of 1 through 10 the outer confidence the outer cross validation accuracy i got from this was 65.76 percent plus or minus 1.5 so for algorithm selection looking at these um i would i took the gradient boosting because it had the best um cross-validation accuracy even though the variance was a little bit high i still thought this was the best algorithm to go with so from this i looked at the hyper hyper parameters so to select for the hyper parameters i use the grid source cross validation um doing this i looked for the parameters of learning rate number of estimators and max depth from this i got the best cross validation accuracy to be 66.09 percent and then the best parameters were a learning rate of 0.1 on the number of estimators at 10 and the max depth of 2. so for the performance estimate of this model i ran an out-of-bag bootstrap with 200 splits and from this i got a mean accuracy of 65.84 percent and a confidence interval a 95 confidence interval of 64.2 0.12 to 67.65 and then after i did this i wanted to evaluate it on the test set which again was the most recent season of the 2017 to 2018 season i got a test accuracy here of 65.89 percent with a 95 confidence interval of 63 to 69. overall it performed it performed pretty well the only thing i would say is the variance was a little high on the test set but again this is mainly due to the fact that my data set is not super super large overall i think the model did perform pretty well at around 65 percent accuracy through all the tests um this was really what i was shooting for so i'm i was pretty happy with this um further investigation i really would look to add maybe more variables for like in-game statistics like um per game rebounds per game assist per game steals average pace stuff like that coming into the games to help predict um also creating a model that would choose whether to take the over under of the gambling spread i think would be really cool um this could be really useful because these spreads are pretty tough to predict and then lastly looking just at which statistics are like really most support uh most important in increasing win percentage just so teams could like look at that and analyze the data and decide how they want to go about improving their team that is all i have thank you guys for listening this project was really fun i'm i'm glad that i could do it thank you