NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship πŸ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release πŸŽ‰
Free Lesson
Intro to Data Science New Release πŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See πŸ”₯
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular πŸ”₯ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New πŸŽ‰ Generative AI for Finance New πŸŽ‰ Generative AI for Marketing New πŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular πŸ”₯ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular πŸ”₯ Data Science R: Machine Learning Designing and Implementing Production MLOps New πŸŽ‰ Natural Language Processing for Production (NLP) New πŸŽ‰
Find Inspiration
Get Course Recommendation Must Try πŸ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release πŸŽ‰
Free Lessons
Intro to Data Science New Release πŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See πŸ”₯
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > R > Validating Strokes-Gained Method for Measuring PGA Tour Player Success

Validating Strokes-Gained Method for Measuring PGA Tour Player Success

Ben Townson
Posted on Aug 22, 2016

Background

In 2016, the PGA Tour, the leading professional golf tour in the world, began using an upgraded version of the Strokes-Gained methodology pioneered by Mark broadie as the primary statistic used to describe player success on tour.  The strokes-gained statistic is calculated by comparing the improvement in a player’s position for each shot, compared to a baseline average improvement of the average PGA tour player from the same position.  While a simplified version of the metric was used for several years, statistics are now being collected for 4 specific areas of the course: off the tee, approaching the green, around the green, and putting.  By keeping a cumulative score in each area for each golfer, one can measure success in various parts of the game, or by summing across statistics, one can expect to measure the success of a golfer in a specific round vs the average golfer.  The full details of the calculation can be found here: http://everyshotcounts.com/.  More color about the metrics and the adoption of them by the PGA tour can be found here: http://www.pgatour.com/news/2016/05/31/strokes-gained-defined.html.

The resulting metrics provide a uniform way of explaining the success of a tour player vs the field despite the differences in the types of shots taken or the player’s position on the course.  Yet, for decades, the golf community has relied upon statistics separate from scoring to explain success in the sport.  For instance, rather than talking about how many strokes have been gained off the tee, the golf community relied upon descriptors such as driving distance and driving accuracy to measure performance in this area of the game..  These statistics can offer more description of the actual shots taken, while the strokes-gained metric captures how much each shot improved the golfers relative position to the field without describing β€œwhy” or β€œhow” that shot was played.

Because of this, as strokes-gained has been adopted and used increasingly as the metric of choice by the Tour, there has been backlash from the fanbase about the transition.  Many feel that the traditional statistics are just as sufficient as strokes-gained in describing a golfer’s success, and provide qualitatively a better description of a Tour players game.  So, do they have a point?

Using data from www.pgatour.com/stats, we can test whether strokes-gained can better explain success than traditional statistics.  Using data from 2009 through 2015, we can try to build predictive models using traditional stats and these new stats and compare how well they perform at predicting a golfer’s success in 2016.  β€œSuccess” on Tour can be measured by the number of Fedex Cup Points (FCPs) accumulated during the year.  As we will be transforming the data to allow us to predict a normally distributed variable (the raw Fedex Cup Point distribution is not normally distributed), we will not be able to directly predict the number of FCPs accumulated, but will be able to predict the rank compared to the field and compare how well the models pcan predict a players standing.

Code used to analyze the data in R can be found below.

The Models

We built and optimized two multiple linear regression models in R, predicting Box-Cox transformed Fedex Cup Points per tournament.  The best-fit model using the old statistics called for inclusion of many traditional statistics: driving distance, driving accuracy, greens in regulation, sand saves, proximity to hole around the green, putts made distance, and putts per round.  However, when evaluating the model, there was evidence of multiple collinearity amongst several of the variables.  By dropping the driving accuracy and putts made distance, we were able to produce a well-fit model with an adjusted R-Squared of 0.4977.  Qualitatively, this model makes sense, as it uses data from the four major areas of the game, which correspond with the four areas of the game which are captured by the strokes-gained methodology: driving (off the tee), greens in regulation (approaching the green), proximity to hole around the green (around the green), and putts per round (putting).

In the strokes gained model, we attempted to predict the same Box-Cox transformed Fedex Cup Points data, using the four strokes-gained metrics.  The best-fit model relies upon each metric with similar weighting and confidence for each, and upon inspection we see that there is no multiple collinearity.  The model produces an R-Squared of 0.7548.

The evidence suggests that the strokes-gained model is a better predictor of success, as defined by relative Fedex Cup Points gained per round played, than the traditional model.  However, the ultimate test is in predictability of results in the current year, 2016.  Again using data from www.pgatour.com/stats, we are able to capture statistics for Tour players in 2016, and can predict their ranking in the average Fedex Cup Points gained per tournament.

The Conclusion

We tested each of the predictions by comparing the model fit to the actual standings in 2016.  Applying the same Box-Cox transformation on the average Fedex Cup Points earned in 2016 provided a target for the model.  When comparing the errors to the actual results vs the fit from the models, we found that the mean error squared for the strokes gained model is 0.38 with a standard deviation of 0.94, while the standard model returns a mean error squared of 0.75 and standard deviation of 0.98.  It appears that the Strokes-Gained model is a better predictor of results in 2016 and we can conclude that the better fit model is in fact strokes gained.  The PGA Tour has wisely decided to use this metric to describe a golfer’s success.

The Code

About Author

Ben Townson

Ben Townson graduated from the New York City Data Science Academy 12-week Data Science Bootcamp on September 23. At NYCDSA he has mastered machine learning and data analysis techniques, complementing more than ten years spent in the finance...
View all posts by Ben Townson >

Leave a Comment

Cancel reply

You must be logged in to post a comment.

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans 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 boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis 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 seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    Β© 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application