Data Analysis on Sports Betting
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
The purpose of this project/app is to look into and analyze the data trends that exist in sports betting. Betting is a popular investment that takes place on a frequent basis in the world of sports. It makes the game more interesting, and can help bring popularity to sports that are less marketable in the world.
I did my project based on this because I was curious to see how accurate or inaccurate betting trends can be in the sports world, I wondered if there was a particular key component to how betting worked, and if that affected where people would place there money and end up benefiting from it, or regretting it entirely.
I decided to look at one of my favorite sports for the interest of this project/app: Tennis.
For your information:
-Data can be found through kaggle and the betting website used.
-You can go to the APP here.
-The Github used to code the data here.
-You can follow me through my Linkedin here.
For the project the data I acquired includes:
- Tournament data for all Grand Slams for men singles from 2000-2019
- Match times
- Total points won
- Service points won
- Return points won
- Break points saved
- Break points faced
- Total sets played
- Winning Player
- Losing Player
- Round
- Date of Match
- Average minutes per game
- Rank of player
- Birth date of player
- Country origin of player
- I also collected tournament betting data for all Grand Slams for men singles from 2003-2019
- The data was not fully there to stretch back to 2000, so 2000-2002 is not among the data used.
- The betting data was from Bet365, one of the more popular sports betting sites.
Things to keep in mind regarding the data:
-The app is still a work in progress, for now it shows the average statistical relations between Grand Slams and each round they correlate to.
For example: The average winning betting odds for players in the First round of the United States Open, compared to say the average losing betting odds for players in the Semifinals of the French open. Each of these categories are split up into tabs to compare the trends accordingly.
Further data analysis:
-Along with the betting trends, I also wanted to take a deeper nose dive to observe the variance between players and how experience plays a role in the level of consistency and success.
My logic there is that every player learns at their own pace, and the more they learn the more they will improve. So it would be helpful for coaches to understand the flow on how much experience (such as matches played) is needed in order to start generating consistency on a more frequent basis. This would not only generate more success for the player, but also increase the popularity of the sport because they are more successful, causing market value of the sport to go up. This type of study can easily be applied to other sports as well, not just Tennis.
Future Work on Data
-Future work will be to look at the lengths in which matches go per round and per tournament, my thoughts here are: do longer more rigorous matches help improve consistency of play for a player? Does it increase their odds (betting) of winning in the next round? The next tournament? Does it affect them negatively? Does a 6 hour match increase popularity for the sport, or does it lower it? These are just some of the questions I have at the moment. The list will surely increase based on how much more constructive this app becomes.
Conclusions:
-I also plan to look into further tournaments outside of grand slams to see how different those tournaments are compared to that of the grand slam level (grand slam tournaments are much longer, and matches take twice as much time) there is definitely more analysis to look at there.
-Once I have all the data and analysis needed to answer all my questions so far, I plan to move beyond just tennis and examine other sports with popular betting trends as well. Such sports include: NFL, NBA, MLB, etc.
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