- This data science project will attempt to predict whether or not a player will make the cut, based on previous weeks performance, coupled with season averages(drive distance, fairway percentage, putting average).
-
Is it possible to predict 'making the cut' or not at a PGA event?
-
Making the cut is important for players and sponsors alike.
- A typical PGA event is 4 rounds of golf. Thursday - Sunday
- 'The top 70 players (including ties) make the cut. Any player in 71st place or worse (after Friday's round) is cut.' - pga.com
-
From a players perspective, making the cut is important for many reasons.
- If a player misses the weekend, they do not get paid.
- Also, prized FEDEX cup points are missed. ('The FedEx Cup is the central professional golf competition that compares player performance across part of the PGA Tour season in a League and Playoffs structure.')
- For continued poor performance, their tour eligibilty could be in jeopardy.
-
From a sponsors perspective, making the cut equates to 'eyes on product'.
- Players making the weekend are televised more. Sunday draws the largest viewership of the tournament days.
- 2-4 million viewers for non-majors
- 5 million for majors
- 10 million for 'The Masters'
- Players making the weekend are televised more. Sunday draws the largest viewership of the tournament days.
Variable | Description |
---|---|
date | Date of the event |
course | Name of the golf course |
tournament name | Name of the tournament |
made_cut | Indicator of whether the player made the cut or not |
sg_putt_prev | Strokes gained/lost putting in previous events |
sg_arg_prev | Strokes gained/lost around-the-green in previous events |
sg_app_prev | Strokes gained/lost approach in previous events |
sg_ott_prev | Strokes gained/lost off-the-tee in previous events |
sg_t2g_prev | Strokes gained/lost tee-to-green in previous events |
sg_total_prev | Total strokes gained/lost in previous events |
driving_avg | Distance of the player's drives in yards |
fairways_hit | Percentage of fairways hit by the player |
putting_avg | Average number of putts per hole |
- Acquire a dataset that peaks a personal interest - Golf
- Prepare/clean the data for exploration
- Split data into train, validate and test
- Explore the data for significant features and answer questions we have about the data set
- Perform statistical tests that follow assumptive guidelines
- number of observations > 30
- test for equal variance and ensure 'equal_var' is properly set based on Levene's test
- normal distribution (using the central limit theorm for this study)
- Allow the data to guide us.
- Perform statistical tests that follow assumptive guidelines
- Model on scaled train,validate data sets
- Run model for accuracy on test dataset to see if there is predictive power
- Predict whether or not a player will make the cut at a PGA event
- Run model for accuracy on test dataset to see if there is predictive power
- Make recommendations and formulate next steps to continue the project
- Do strokes gained putting in the previous week affect whether or not someone will make the cut this week?
- Do strokes gained around green in the previous week affect whether or not someone will make the cut this week?
- Do strokes gained approach in the previous week affect whether or not someone will make the cut this week?
- Do strokes gained off-the-tee in the previous week affect whether or not someone will make the cut this week?
- Do strokes gained tee-to-green in the previous week affect whether or not someone will make the cut this week?
- Do strokes gained total in the previous week affect whether or not someone will make the cut this week?
- Does driving average affect whether or not someone will make the cut?
- Does fairways hit percentage affect whether or not someone will make the cut?
- Does putting average affect whether or not someone will make the cut?
- Players that make the cut gain .25 strokes on the field in 'strokes gained approach'
- 'Approach measures a players shots after the initial drive, so it highlights which golfers have excelled in those crucial second and third shots.'
- Second and third shot (accuracy) is a premium stat when trying to make the cut
- Players who make the cut gain .50 strokes on the field in 'strokes gained tee-to-green'
- 'Tee-to-Green is perhaps the best metric of them all. It’s essentially Total Strokes Gained — which is the sum of off-the-tee, approach, around-the-green'
- This stat covers complete game. Tee ball accuracy, iron play, and chipping around the green.
- If players are lagging in this stat, it may uncover weakness in their game and provide insight for focused practice
- 'Tee-to-Green is perhaps the best metric of them all. It’s essentially Total Strokes Gained — which is the sum of off-the-tee, approach, around-the-green'
- Driving average/Putting average
- Those that make the cut are 2 yards longer on average (296 vs. 294)
- Those that make the cut average 1.76 putts per hole vs. 1.77 of those that do not
- Golf is a game of inches and on any given Friday, the total game is required to make the cut
- And.... maybe a little luck
- Golf is a game of inches and on any given Friday, the total game is required to make the cut
- Aquire data from 'https://zenodo.org/record/5235684'
- save to your working directory and read into a notebook using pd.read_csv(name_of_file)
- Prep the data for exploration:
- using function 'prep_pga()' in the prepare.py file included in the Github repository
- Split data
- using function 'split_data()' in the preare.py file included in the Github repository
- data is split into train validate and test datasets using the 60,20,20 method.
- Explore the data using visualizations and stats tests.
- Using the best features run classification models on the train and validate to find the best model.
- Logistic Regression was the best/most consistent performing model.
- Read over the outputs and form a conclusion and summarize with recommendations/nextsteps.
- Completed the data analysis pipeline (Acquire, Prepare, Explore, Modeling)... twice.
- Selected features (strokes gained approach, off-the-tee, tee-to-green, total) were statistically significant, especially when using 2-week averages in the second iteration.
- Ran scaled data with selected features through four predictive models:
- In iteration 1, logistic regression performed the best with 61% accuracy on unseen test data.
- In iteration 2, the decision tree outperformed other models with 73% accuracy, surpassing the baseline and the iteration 1 model by 17% and 12%, respectively.
- Use the iteration 2 model as a guide for predicting next week's performance with 73% accuracy based on recent performance averages.
- Focus on improving the following factors to increase chances of making the cut:
- Strokes gained approach
- Strokes gained off-the-tee
- Strokes gained tee-to-green
- Tee ball accuracy and iron accuracy are crucial for minimizing strokes lost to the field.
- Caveat: It is impossible to predict with 100% certainty due to unquantifiable variables.
- Further investigation is required for a more robust/powerful predictive model:
- Incorporate weather attributes through clustering.
- Include course slope and length in the analysis.
- Explore player demographics (age, height, weight).
- Develop specific models based on course-specific requirements.