Gross Bombs: NFL Props for Week 16
Josh Gross
Over the course of the PGA season, I’d like to do a few deep dives into the world of statistics in golf. There are plenty of stats to choose from and, quite frankly, it’s hard to wade through it all. In the end, people focus on the granular data because it’s available but I’m not sure how useful it is across all applications. I’ve found the most success over the past three years, using what I call my Power Ranking Score. It’s a single number that attempts to predict how a golfer’s skill is relative to an average PGA Tour golfer on an average course.
How PRK is Calculated
PRK is a weighted average of a golfer’s scoring across different timeframes. It’s great to be able to look through different components of scoring separately with counting stats like greens in regulation or with strokes gained data, but at the end of the day, we’re not trying to predict that. We’re trying to predict how a golfer will score. There’s a lot of noise that comes along with all of those other stats and sometimes the simplest answer is the correct one.
Raw PRK is one way that I measure this which is basically an expected SG: Total number. An elite number would be somewhere in the -2 range. For example, Jon Rahm was right at -1.95 heading into this week’s tournament. An average golfer would be right at 0 and anything above 0 means that I’d expect that golfer to lose strokes to the field with their average play.
PRK Score is what goes into the volatility report data table. It converts that raw number into a field relative scale from 0-100 for a particular tournament.
A Deeper Dive on Winners
Last week, we looked at winners and other finish positions briefly, but I wanted to look a little closer at winners so that we could see how the Power Ranking performs in that category. Obviously, there’s only one winner per week and there’s plenty of randomness that goes into who wins a golf tournament.
Since the start of 2018, I have 176 tournaments in my database.
| Power Ranking Score Group | Events Won (Percent of Total Winners) | Sample Size (% of Population) |
| 0 – 50 | 25 (14.2%) | 38.6% |
| 50 – 60 | 15 (8.5%) | 22.4% |
| 60 – 70 | 36 (20.4%) | 14.5% |
| 70 – 80 | 39 (22.1%) | 12.7% |
| 80 – 90 | 36 (19.8%) | 7.9% |
| 90 – 100 | 25 (14%) | 3.9% |
As you can see, we get a bump from hitting that 60-plus mark. That’s the crossover point where the percentage of the population goes below the percentage of winners in our sample. Basically, I’m never going to play someone with a sub-60 PRK score or place an outright on them. When we move up the scale there, we can see that the relative number of winners compared to the sample size of the population goes up dramatically once we hit 80+. When you see those opportunities in the betting market where the PRK Score is showing someone in the mid-80s, but they’re priced around guys in the 60s, you should be strongly considering an outright or another bet.
