IUMBB Preview: Wisconsin

A quick preview of Indiana Men's Basketball's next matchup against the Wisconsin Badgers

Indiana infamously enters tonight’s matchup against Wisconsin with a 27-year losing streak in Madison. The Hoosiers’ last road win against Wisconsin was January 25, 1998, when Andrae Patterson led the way with 23 points and AJ Guyton added 13.

That doesn’t mean Indiana hasn’t or can’t beat Wisconsin. Mike Woodson teams have actually found some success against Wisconsin, going 2-3 against the Badgers after 11 seasons (2010-2021) of going 3-16 against Wisconsin. The Hoosiers are also coming off a win against the Badgers. They just need to do so in Madison.

Something that would have helped Indiana on this endeavor would have been securing a road win at Purdue, which was absolutely within reach Friday night. Indiana actually moved up to No. 54 in KenPom.com’s rankings and its Tournament odds increased to 8.8%, per Bart Torvik, following the loss. But it’s a punctuation mark at the end of three very winnable games — 79-70 at Northwestern, 79-78 vs. Maryland, and 81-76 at Purdue — Indiana allowed to slip through its fingers, and responding to that in February to construct an at-large resume for the Tournament just seems unlikely.

For Tuesday night, though, Wisconsin currently sits at No. 21 in the AP Top-25 Poll and No. 15 in KenPom’s rankings. The Badgers average 81.5 points per game, which ranks in the 91st percentile nationally. There isn’t a bad loss on its resume — Michigan, Marquette, Illinois, UCLA, and Maryland — but it has loss two of its last four, notably falling short of its normalized efficiency in its last two games, scoring just 68 and 75 points in a loss at Maryland and a win at Northwestern, respectively.

This Wisconsin team isn’t infallible, and Indiana has been competitive, which makes KenPom’s 82% chance of a Wisconsin win seem a bit extreme, especially after Indiana had just a 13% chance to beat Purdue. History favors those odds, though.

Observations

Indiana @ Wisconsin:

• Indiana’s biggest defensive challenge against Wisconsin will be guarding the perimeter. Not only is Wisconsin in the 83rd percentile in 3-point percentage (36.3%), but it also takes threes at the 20th-highest rate nationally. All three of the Badgers’ primary guards (John Tonje, Max Klesmit, John Blackwell) average more than four attempted threes per game. Wisconsin is a great shot-making team, including at the free throw line, where it leads the nation with an 84.6% percentage.

• Turnovers are an interesting aspect to this game, as Wisconsin doesn’t turn the ball over often (just 10.3 times per game) but also doesn’t force many turnovers either. In Indiana’s last five games, the Hoosiers have lost the turnover battle, 64-47. Who will this favor: Indiana’s tendency to turn the ball over or Wisconsin’s struggle to force turnovers? If Indiana carries over its 20-turnover performance from Purdue, it could get ugly in Madison.

• Wisconsin has some size inside — 7-foot center Steven Crowl, 6-11 forward Nolan Winter, and 6-7 forward Carter Gilmore at the four and five — and are fairly even analytically in term of rebounding. Indiana needs to pull that within its own favor.

• John Tonje is having a career-best season after playing at Colorado State for four seasons, spending a short-lived season at Missouri, and then finding a home in Wisconsin. He’s averaging a career-high 18.6 points per game and is making 47% of his 11 attempted shots per game. His shot is as dangerous of a threat Indiana will face this season.

• Wisconsin has a trio of heat check contributors — forward Nolan Winter (22 minutes per game), guard Kamari McGee (22 minutes per game), and forward Carter Gilmore (17 minutes per game) — who are all ranked in the top-100 nationally in KenPom’s Offensive Rating. For reference, the only qualifying player on Indiana’s roster to be in the top-100 is Luke Goode. It doesn’t matter who the Badgers have on the floor; it can put up some points, which is why KenPom ranks them 7th in offensive efficiency.

• Indiana needs to continue to get scoring from its wings, Mackenzie Mgbako and Luke Goode. Against Purdue, Indiana started strong offensively because its wings started strong, scoring 21 of IU’s first 23 points. Mgbako has attempted the most shots on the team, which is what Indiana needs now, as he’s averaged 20.3 points on 58% shooting in his last three games. Goode has hit double digits in six of his last eight games on 56% shooting from three. That dimension, combined with Oumar Ballo down low, is very difficult to defend, and Wisconsin only has one player — 6-7 Carter Gilmore — with the size to defend Mgbako and Goode, but he has the worst defensive rating on the roster.

Key

• PPP = Points per possession — PPP is a reliable measure of efficiency on either side of the floor and is calculated as it’s named.

• Tempo = Possessions per 40 minutes played — Tempo conveys how quickly a team plays on offense or how its defense is able to slow tempo

• Effective FG% = FGs made + 0.5*3P FGs made / FGs attempted — Effective field goal percentage is much like field goal percentage but with a weight applied to threes to somewhat account for range

• Points from FTs, Points from 2, Points from 3 = Total points from each range / Total points scored — This simply shows how many points are coming from each range for each team.

• 3-pt Rate = 3P FGs attempted / FGs attempted — This metric answers the question: What percentage of a team’s attempted shots are from three? Even shorter yet: How often does a team shoot threes?

• 3-pt% = 3P FGs made / 3P FGs attempted — This is the traditional three-point percentage metric.

• Layup/Dunk% = Layup/Dunk FGs made / Layup/Dunk FGs attempted — A traditional field goal percentage for layups and dunks; How well does a team convert its closest shot attempts?

• Free Throw Rate = FTs attempted / FGs attempted — Very similar to 3-pt Rate, free throw rate seeks to convey how often a team shoots free throws, which can show how well a team draws fouls.

• Assist Rate = Assists / FGs attempted — Assist rate conveys how well a team moves the ball, particularly as it relates to creating shots.

• Turnover Rate = Turnovers / Possessions — Turnover Rate simply shows which proportion of possessions end in turnovers for a specific team.

• Block Rate = Blocks / Opponent FGs attempted — How many shots does a team block?

• Off. Rebound% = Offensive Rebounds / missed FGs — How often is a team gathering its own missed shots?

How to Read the Chart

Radar charts are super common in sports analytics, and if you’ve played sports video games, you’ve probably seen them before (typically in comparing the attributes of teams or players).

Each chart is intended to compare each side of the floor (IU’s offense vs. Opponent’s defense; IU’s defense vs. Opponent’s offense), and Indiana will always be red, while its opponent will always be black. The colored fill is intended to quickly show where an advantage/disadvantage is felt within each statistic.

For each stat, each team’s percentile within Division I college basketball is conveyed, which means the measure is in a pool of 300+ programs.

Some percentiles are fairly straightforward, such as 3-pt%; if an offense has a higher 3-pt% percentile, that means they make more of their threes than most D-I programs. But if a defense has a lower percentile within the same stat, it means it allows a better percentage of threes to be converted. Therefore, if there is a big gap between the two, then whoever makes a good amount of threes could have an advantage.

Some other stats are not as straightforward, such as Turnover Rate; if an offense has a high percentile within Turnover Rate, that means it doesn’t turn the ball over often, whereas if a defense has a high percentile in Turnover Rate, it forces more turnovers. Or Tempo: If an offense has a high percentile in Tempo, then it runs fast, but if a defense has a high percentile in Tempo, it allows a fast game.

The idea is that if there is a gap between the two values within each stat, the color that fills that gap has the advantage in that dimension.

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