Better Betting with Time?

Jordan Waters
Posted on Oct 25, 2016

Sports betting is an estimated 95 billion dollar industry in America.  What information does the public use when they place bets, and how accurate are those decisions?. Is the collective public better at making predictions than an individual? When looked at as a whole, do betting patterns follow any trends? These questions led me to create a data set that was a combination of NCAA football game outcomes and the historical betting records for those games.

My goal was to explore the associations between money lines generated by casinos and the general public's prediction on the game to see how accurate that number was at predicting the final outcome of the game. The money line is a great metric for measuring public sentiment because instead of just saying "win" or "lose" it scales with the amount of confidence in the decision. I don't want this post to be about how the betting betting works, but basically, the more bets that are placed on a team to win, the further below zero the money line is. Typically the money lines on each team in a match are the same, but one is negative ( i.e. 1000 and -1000).

All money lines for week 1 2015

This visualization shows the money line for each team playing in week one of the 2015 season. The fill for each bar represents the actual outcome of winning (blue) or losing (red). Notice as the confidence in winner or loser increases, the number of incorrect predictions decreases.

My hypothesis was that as time progressed, betters would gain more insight to the conditions they are wagering on, there by becoming more accurate with their predictions.

 

My first comparison by week did not support this hypothesis.

allweeks

This is every money line plotted for each week of the season. The teams order is preserved along the x-axis. I had hoped that as time progressed, the number of incorrect and confident predictions would decrease, however it seemed like the opposite was true.

To try to explain the decrease in accuracy, I thought that by filtering out non-conference games, I would see less confidence and more incorrect predictions earlier in the season.

week1normal

    Attempting to clean up more lopsided games, I immediately disproved my hypothesis. Using only games from week 1 between what should be more evenly matched opponents showed that all confident wagers were correct, and any incorrect predictions were made with relatively low confidence. As seen below, prediction accuracy dropped dramatically over season.

week13normal

While there are innumerable variables at play here, it is still surprising to see how little the public actually learns as more information is gathered throughout the season.

In attempt to find a trend in weekly betting, I plotted the overall distribution of confidence by week. The only finding gleaned here is that after week 15, the confidence in predictions decreased. This is most likely due to the fact that these teams are matched at the end of the year and are designed to be as competitive as possible.

weekdistro

 

To further develop this study, I plan to add more seasons and other variables to this data set to study all of the situations where confidence was high and incorrect.

About Author

Jordan Waters

Jordan Waters

After receiving as B.S. in microbiology from Auburn University, I was hired by the Centers for Disease Control in Atlanta, Ga. There, I designed DNA-based detection and diagnostic tools to decrease public health costs and response times ....
View all posts by Jordan Waters >

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp