The Rabbit Hole Rundown: 10 Data Points to Build Into Your Valspar Championship Model

Building a model for the first time can feel overwhelming. There are hundreds of filters, dozens of stat categories, and it is easy to spiral into analysis paralysis before you ever place a bet.

The good news is that the Valspar Championship is actually a great tournament to start with. The course has very clear demands, the data points that matter are well established, and the Rabbit Hole on Betsperts Golf gives you everything you need in one place. Here is a straightforward ten-point framework, broken into three tiers depending on how deep you want to go.

Add a few pieces of data or mash together as many as you’d like.  You can always scrap it and start over; we do NOT charge by the model.  Make as many as you’d like.  Not a member?  Get 25% off ANY plan using promo code BSG25

The Basics — Start Here

These are the foundational stats. If you only have time for a quick model, these three alone will put you ahead of most casual bettors.

1. SG: Approach. This is the most important number at Innisbrook, full stop. The Copperhead Course ranks as one of the toughest tracks on Tour to hit greens in regulation, and more than half of all approach shots come from beyond 175 yards. The last six winners here averaged sixth in the field on approach for the week. Pull this over the past three months and the past two years, and weight it more heavily than anything else in your model.  Choose a time frame that you think is most meaningful.

View: Strokes Gained  |  Column: SG: APP  | Filters: None

2. SG: Tee-to-Green Before you get into the granular stuff, tee-to-green gives you a clean overall picture of a player’s ball-striking health. At a course this demanding from tee to green, you want players who have been consistently positive in this category over recent months. It is a good sanity check on everything else you are about to look at.

View: Strokes Gained  |  Column: SG: T2G | Filters: None

3. SG: Putting on Poa Trivialis or Bent Greens. The greens at Copperhead are overseeded with Poa Trivialis this time of year, not Bermuda. That distinction matters. A player who putts beautifully on Bermuda may not transfer that form here. Filter your putting stats to Poa Trivialis or Bent surfaces specifically, and you will get a much more accurate read on who is likely to roll it well this week.

View: Strokes Gained  |  Column: SG:P  | Filters: Green Surface – Poa Trivialis

Getting Granular — One Layer Deeper

Once your foundation is in place, these three filters start to separate the fits from the frauds in this specific course.

4. SG: OTT on Less-Than-Driver and Mixed Courses. Overall driving stats are misleading at Copperhead because almost nobody is hitting the driver. Guys are hitting three-woods, hybrids, and long irons off the tee on more than 46% of holes. You need to filter your off-the-tee data specifically to less-than-driver and mixed club courses, so you are measuring accuracy and positioning with those clubs rather than raw driving distance. A player who is elite with a driver but erratic with a three-wood is a liability here, not an asset.

View: Strokes Gained  |  Column: SG: OTT  | Filters: OTT Club – Less than Driver and  Mixed

5. Approach Play on Courses With Difficult or Tough-to-Hit Greens. Not all approach stats are created equal. Gaining strokes on approach at a wide-open, soft course with big greens is a very different skill than doing it at a tight, firm track with small greens and tricky pin positions. Filter your approach data to courses where GIR is difficult. Copperhead sits at 57% GIR for the week historically, so you want players who have proven they can find greens under those conditions, not just when the course is set up easily.

View: Strokes Gained  |  Column: SG:APP  | Filters: GIR Accuracy – Difficult

6. Sanderson Farms Performance — Country Club of Jackson. This one surprises people, but it has shown up in the data consistently over multiple years. Players who perform well at the Sanderson Farms tournament tend to translate that form to Copperhead. The connection likely comes from similar long approach shot demands and below-average fairway hit percentages at both venues. Pull SG: Total from the past three to four years at the Country Club of Jackson and use it as a lighter-weighted supporting filter. It is especially useful for identifying mid-range and longer-shot value plays who might not pop at the top of the basic stat categories.

View: Strokes Gained  |  Column: SG: TOT  | Filters: None  |  Choose Country Club of Jackson under Courses and widen the Time Frame to at least 3 years

7. Recent Form — Average Finishing Position Set the timeframe to the past two to three months with a minimum of three starts. You want players who have been showing up on leaderboards consistently, not guys riding one hot week from six months ago. This one is simple and quick to pull and it keeps you from rostering players who are quietly trending in the wrong direction.

View: Finish Position  |  Column: Avg Finish  | Filters: None

The Deep Diver

These are the filters that can take a little more time to set up, but can genuinely separate your model from the simplified versions.  Not stuff you want to weigh heavily, but still worth looking at.  They also may take some adjusting to get right. The biggest pratfall can be the fact that you are limiting the sample size and may be looking at a very small amount of data.

Be sure to adjust the time frame and minimum rounds as needed.  Not sure how?  Get into the Discord and ask some questions.  We have a channel devoted to the Rabbit Hole!

8. Par-3 Efficiency from 200 to 225 Yards. There are five par-3s at Copperhead, and four of them exceed 200 yards. This is the only course on Tour where both par-3 and par-5 performance impact the top-five leaderboard more than par-4 performance. Filtering specifically to par-3 efficiency in the 200 to 225 yard range gives you a direct read on who handles the holes that most people are not specifically researching. Players who simply avoid bogeys on this set of holes gain a massive advantage over the field.

View: Par 3 Efficiency  |  Column: SG: Par 3 201-225  | Filters: None

9. Proximity from 176 to 200 Yards, Inside 15 Feet. This is the most common approach distance at Copperhead and one that over-indexes significantly compared to Tour averages. Pulling proximity data specifically from this yardage range and filtering to shots finishing inside 15 feet tells you which players are not just finding the green but actually setting up realistic birdie chances from the distances they will face most often this week. Past winners have regularly appeared near the top of this filter when you run it back historically.

View: Approach Scoring Opps  |  Column: 176-200 Scoring Opps i15%  | Filters: None

10. Scoring Conditions Filter — Difficult to Very Difficult, Long Rough Set your conditions filter to difficult or very difficult scoring environments with long rough length. Copperhead consistently plays over par and ranks among the toughest annual scoring environments on Tour. Players who have shown they can grind out good results when conditions are hard, greens are tough to hit, and rough is penal are a completely different profile than players who pile up birdies on soft, easy setups. This filter is the final layer that helps confirm whether the players rising to the top of your model are genuinely built for what Copperhead demands or just stat compilers on forgiving courses.

View: Strokes Gained  |  Column: SG: TOT  | Filters: Scoring Conditions – Difficult and Very Difficult.  Rough Length -Long  |  This is another filtering that may require a bigger Time Frame to give you a helpful sample size

The beauty of the Rabbit Hole is that none of this requires advanced knowledge of analytics. You are simply pulling filters that reflect what the course actually asks of players and then seeing whose recent history lines up with those demands. Start with the basics, layer in the granular filters, and, if you have time, run the deep-dive numbers, too. By the time you are done, you will have a model that is grounded in real course-specific data rather than just going with the names you recognize on the odds board.