IUMBB Preview: Maryland
A quick preview of Indiana Men's Basketball's next matchup against the Maryland Terrapins
Dropping the game at Northwestern was tough. The Hoosiers absolutely had an opportunity to gather another Quad 1 win and head into a tough stretch of the schedule with consecutive road victories, but now Indiana falls to 2-6 in Quad 1 opportunities.
The good news for Indiana is that its only losses have come against Quad 1 teams, which can’t be said for most other teams. The bad news is that Indiana is 2-6 in Quad 1 games, and it’s next five opponents — including Maryland today — are Quad 1 chances. Only three remaining opponents are considered Quad 2, with Ohio State teetering on the edge to become the fourth in Indiana’s last game. Outside of quadrant talk, Indiana’s remaining opponents (save for Washington, the worst team in the conference) are in the KenPom top-45; Indiana has fallen to 62nd. KenPom only projects Indiana to win two more games, none of them Quad 1.
Indiana needs to out-perform that projection to make the Tournament, and in most games outside of the head-scratching blowouts, there are flashes of really great basketball that keep me thinking there’s something still left to this team. This is where they need to show it most, beginning with Maryland.
But it certainly won’t be easy.
Maryland is coming off a 21-point win at Illinois but also dropped a close game at Northwestern a week ago. The Terps are hungry to build their resume too, as they only have two Quad 1 wins (2-4), with those two wins coming at Illinois on Thursday and at home against Ohio State on Dec. 4. The resume isn’t perfect, but the team is strong.
Observations
Indiana vs. Maryland:
Maryland’s defense is very strong. In nearly every metric, the Terps are strong, as KenPom ranks them 18th nationally on defense. SportsReference has their Defensive Rating at 7th nationally. Most of the scoring done against Maryland is in the post, but Indiana has to get up some threes, which it has done more often since mid-December, just to varying degrees of success.
Offensively, Maryland does most of its damage inside, and while it doesn’t usually attempt many threes (20+ attempts in just 3 of its last 8 games), it shoots 35.8% (88th nationally). After Indiana allowed Northwestern to hit its season-high (by far) with 13 threes, it wouldn’t be surprising if the Terps got up a few more threes to open up space down low for their two bigs.
Derik Queen (who Indiana fans are familiar with) and Oumar Ballo will be an interesting battle in the post, but Maryland is one of the few teams out there running an efficient two-big lineup, with Julian Reese at the 4 as a top-50 rebounder on both sides and averaging nearly 14 points per game. How Indiana counters that will be interesting as well.
Former Hoosier Jordan Geronimo is still on this Maryland team and plays about 9 minutes per game.
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.