Wisconsin Basketball vs. Rutgers: Analytics Game Prediction

If you missed my latest Wisconsin Badgers Basketball Predictions, find them here:

Northwestern | Penn State | Indiana | Minnesota | Michigan St. | Nebraska | Purdue | Michigan

Man, there’s not much to say about the effort we saw from Wisconsin Basketball on Wednesday Night. Especially in the face of my prediction, where I told many of you to keep your hand away from the panic button. Puts some egg on my face, but I’d still urge you to not write this Badger team off quite yet.

The loss of Kamari McGee and Steven Crowl’s struggles have been the primary drivers of our underperformance over the last week and a half. Which makes sense because, we’ve gotten a lot better on the defensive end from an analytics standpoint. Not enough to get the job done, but considerably better from this point in January. That’s a good sign!

Where the impact has really been felt is in our offensive efficiency. Again this is where I point to Steven Crowl’s struggles having a really pointed impact on our offense. He and Chucky have done a remarkable job at creating for others when they control the basketball. And they’ve both been the leading assist makers this entire year. You see this impact really clearly when looking at Steven Crowl’s Offensive “Impact” and how that correlates with Wisconsin Basketball’s Offensive Efficiency:

What this chart shows is how strongly Steven Crowl’s Assist Rate in relation to his USG Rate combined with his Scoring Efficiency (True Shooting “TS” %) correlates with the Wisconsin Badgers overall offensive efficiency. And it’s extremely strong, showing that he’s a crucial piece to the Wisconsin Offense.

To hammer this home let’s look at the same chart with AJ Storr, who some may consider to be the “missing” piece or most valuable piece to our offense. I’d balk at that statement:

Zero correlation and a much lower offensive “Impact” then Big Steve. It turns out that being multifaceted as an offensive player in both scoring & setting up your teammates by drawing massive attention from help defenders is actually really important. That was something Steven Crowl up these last few games had exhibited tremendously.

He and Greg Gard must figure out a way to fight these aggressive double teams better. It starts with him playing wider and more physical, but I also feel our off-ball movements when we enter into the post need to change as well. We don’t typically send players off the strong side or ball side as cutters, but maybe we need to. With that dive cut, it could force help from the double, and we rotate a guy behind our cutter for an easier passing outlet.

There are multiple options, but seeing the impact this is having, it has to be the highest priority for Gard & Co. to at least figure out A SOLUTION to get this offense back on track.

Doing nothing simply isn’t an option.

With that, let’s dive into the Prediction Model for Saturday’s matchup vs. Rutgers!!!

Wisconsin Basketball Prediction Model

First, let’s start off with what comprises my Prediction Model (See Below):

Pace:

Gard Your Fickell’s Model | 68 Projected Possesions

KenPom Model | 66 Projected Possesions

Torvik T-Rank | ???

Haslam Metrics | 65 Projected Possions = (Projected Points / “Should” OEFF)

Height:

Wisconsin Basketball has the Height advantage over Rutgers, with an Average Height of 78.39 vs. 77.37 (inches). This does influence my DREB% Model but is only a part of the equation. Combining the overall Height advantage along with the other Metrics listed in the Model (3PAR, FTAR, 2P%, 3P%, and OPP OREB%), I can try to reasonably project DREB%.

DREB%:

Based on all the combined metrics in the DREB% Model, Wisconsin basketball is projected to lose the DREB% advantage vs. Rutgers, with an expectation of UW grabbing only 77.3% of their Defensive Rebound opportunities in Sunday’s Matchup vs. an expectation that Rutgers grabs 76.2% of their Defensive Rebound opportunities.

DREB% is a huge component in Stop Factor, which is essentially my own Defensive Efficiency Metric. I’m still working on adjusting my DREB% model to account for past performance and see if a team can have outsized influence over the model metrics. Still debating over the HOW to do this:

• Do I look at overall performance vs. the Model and regress over that?
• Or regress over each factor and adjust each factor in the model?

This will probably take a few weeks to test out! But for now, we’ll stick with the current model.

Stop_Factor:

Wisconsin basketball gets a huge boost in Stop Factor, coming in at a projected 2.15 Stop Factor to Rutgers’s 1.7. For those unfamiliar with Stop Factor, it’s looking at a Team’s ability to be efficient on Defense.

That can come in various ways:

• Low eFG% given up to your opponent
• Forcing a lot of turnovers
• Collecting a high % of Defensive Rebounds available.

Score Prediction & Model Table:

Final Thoughts on Wisconsin Basketball vs Rutgers

The Wisconsin Badgers desperately needs to get a win, otherwise they are in serious jeopardy of severely underperforming their capabilities this season. They are a really good team, and they have all of the pieces to make a deep run in the NCAA Tournament. But if they can’t regain their mojo and continue letting games slip through their fingers, they’re going to lose a ton of seeding value and probably a quick ticket home in March.

Again, I don’t foresee that being the case because this team is too good and has all the pieces. I touched on it earlier, but I think a large catalyst to get them out of this funk will be the return of Kamari McGee back into the rotation. He adds a critical role as backup PG, giving Chucky more rest while maintaining offensive efficiency. He also adds a similar level of defensive disruption in short stints as well.

Get Kamari back and Big Steve his mojo back, and things will get turned around in a hurry for the Wisconsin Badgers. Let’s hope they take that first step on Saturday!

On Wisconsin!

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