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Study Hall: Auburn 89, Mizzou 56

A no show on The Plains.

study hall 2022

We probably could have seen this coming. Or at least a version of this. Mizzou is a good basketball team built around a high efficiency offense and not much else. There are nights when the offense doesn’t click, and without anything to fall back on, things get away from this team quickly. On Saturday they played in an intense road game and found a way to come away with an improbable win against the top 10 ranked Tennessee Volunteers. With DeAndre Gholston burying a 35-footer at the buzzer, the celebration was on for Mizzou.

Auburn had the opposite experience. They’ve been in a rut, running the gamut of difficult games in SEC play, losing five of their last six games with the only win being over Georgia at home. The losses were all to teams towards the top of the standings in the SEC, and to West Virginia in Morgantown. They played everyone tough, but couldn’t come away with the win. It has to be grating for a team to be that close. Their Saturday was capped by a disappointing loss to their main rival Alabama.

Auburn needed a win, badly. Mizzou looked fat and happy to get theirs in Knoxville and go back to Columbia 1-1 after a tough road trip.

With all those factors at play, Mizzou stunk. Auburn was good. This game wasn’t close from the tip.

There’s a term for a team like Mizzou and it’s High Variance. When they’re good, they’re really good. They can beat anyone, including a Tennessee team shooting the ball well at home. When they’re bad, they’re really bad. The pitfall for the Tigers is there is no backup. There’s no plan B. It’s shoot well or lose big. Plan B didn’t look so good last night.

Team Stats

study hall 2023 auburn

I don’t know how much time I’ll spend on any of these numbers individually because it was just a huge blowout in every way. So here are just a few things which stood out.

  • When I say there’s no plan B: you can survive poor offense when you can defend and get stops. Mizzou does not do these things. When they are able to force misses, their opponents are able to get offensive rebounds to extend possessions. It’s just a bad mix for a team with any volatility on offense. So -10.3 in expected rebounds, and allowing 55% eFG is going to let a pretty mediocre offensive team hit 1.20 PPP.
  • The fun part of playing Auburn is they like to play fast: so they give you chances. But the things Mizzou has been good at, not turning the ball over and getting their own takeaways... well only one of those things happened at all last night.

Maybe just avoid playing teams from Alabama from here on out. Just 8 of 50 from three versus Auburn and Alabama. The efficiency was better against Alabama because at least they didn’t turn the ball over. But Mizzou’s 21.6% TO rate was only worst against Kansas, UCF, Mississippi State, and Lindenwood. They beat Lindenwood because they were just better than Lindenwood, and they beat UCF because they shot 52% from 3FG.

Player Stats

Your Trifecta: Kobe Brown, Sean East II, D’Moi Hodge

study hall 2023 auburn

On the season: Kobe Brown 40, D’Moi Hodge 36, Sean East II 18, Nick Honor 18, Noah Carter 17, DeAndre Gholston 15, Tre Gomillion 5, Isiaih Mosley 5, Mohamed Diarra 3

Congrats to Kobe Brown on his GameScore win by not being awful, basically. He only attempted 7 shots, but got 5 rebounds and 5 assists. D’Moi Hodge came in second with a similar output.

Sean East led the team in scoring but also had 4 turnovers.

Meanwhile DeAndre Gholston was aggressive, and was able to draw fouls, but he also had four turnovers. And then Mohamed Diarra had 4 turnovers. Those three players accounted for 75% of the team’s turnovers on 39 possessions, which means only 27 of those 39 possessions were scoring possession. And 24 points on 27 possessions is just 0.88 points per possession... which isn’t good. Factoring in the turnovers and it drops to 0.615. If over half of your possessions are going to guys with a 30% turnover rate, scoring just 0.6 points per possession, it might be a tough night.

study hall 2023 auburn

Maybe Kaleb Brown should have taken more threes!

But those are just a lot of red numbers in the ORtg and TORate% categories. Kobe Brown had a 44% floor rate, Aidan Shaw was pretty good but did most of his damage in the second half.

Noah Carter had a rough night, too.

So the Missouri resume and deep predictive metric dives gets even more dicey. The Tigers have a great resume if you ignore the per possession numbers. Being 4-7 in Quad 1 games is actually respectable, especially because they still have no losses outside of Q1. The Strength of Record is excellent. But the metrics are hard to ignore, because it points to a team with an extreme variance, as I mentioned above.

Mizzou’s 33-point loss at Auburn goes with 28- and 21-point losses to Kansas and Alabama at home. They also have an 18-point loss to Texas A&M and an 11-point loss to Mississippi State. If you just cut all those numbers in half, you’re probably in the mid-to high 30s in the Predictive Metrics and probably in NET as well. But Missouri just keeps dropping these awful results around good performances.

team sheet 20230214

As I said on the pod, Mizzou will feel a lot more comfortable if they can take down Texas A&M at home on Saturday. That’s easier said than done. The Aggies are 10-2 in league play, but their two losses are to middle of the pack SEC teams on the road. Hey, Mizzou is a middle of the pack team and A&M will be on the road! (Editor’s note: Don’t forget, it’s the Rally For Rhyan game, and Mizzou has historically done very well in these.)

Mizzou bought their tournament resume some buffer room with the Tennessee win. It was, as Matt Watkins said on the pod, one of the three best wins you could accomplish this season (at the time) because it was on the road at the 3rd best team in the NET rankings. But having a no-show on The Plains certainly feels like giving back a bit of the goodwill from Saturday. With five regular season games remaining, Mizzou has a realistic expectation to win all five. But four of those games are single possession games in win expectancy. Virtual toss ups.

All that matters is that the good Offense Tigers show up and they have a chance.

True Shooting Percentage (TS%): Quite simply, this calculates a player’s shooting percentage while taking into account 2FG%, 3FG%, and FT%. The formula is Total Points / 2 * (FGA + (0.475+FTA)). The 0.475 is a Free Throw modifier. KenPomeroy and other College Basketball sites typically use 0.475, while the NBA typically uses 0.44. That’s basically what TS% is. A measure of scoring efficiency based on the number of points scored over the number of possessions in which they attempted to score, more here.

Effective Field Goal Percentage (eFG%): This is similar to TS%, but takes 3-point shooting more into account. The formula is FGM + (0.5 * 3PM) / FGA

So think of TS% as scoring efficiency, and eFG% as shooting efficiency, more here.

Expected Offensive Rebounds: Measured based upon the average rebounds a college basketball team gets on both the defensive and offensive end. This takes the overall number of missed shots (or shots available to be rebounded) and divides them by the number of offensive rebounds and compares them with the statistical average.

AdjGS: A take-off of the Game Score metric (definition here) accepted by a lot of basketball stat nerds. It takes points, assists, rebounds (offensive & defensive), steals, blocks, turnovers and fouls into account to determine an individual’s “score” for a given game. The “adjustment” in Adjusted Game Score is simply matching the total game scores to the total points scored in the game, thereby redistributing the game’s points scored to those who had the biggest impact on the game itself, instead of just how many balls a player put through a basket.

%Min: This is easy, it’s the percentage of minutes a player played which were available to them. That would be 40 minutes, or 45 if the game goes to overtime.

Usage%: This “estimates the % of team possessions a player consumes while on the floor” (via The usage of those possessions is determined via a formula using field goal and free throw attempts, offensive rebounds, assists and turnovers. The higher the number, the more prevalent a player is (good or bad) in a team’s offensive outcome.

Offensive Rating (ORtg): Similar to Adjusted game score, but this looks at how many points per possession a player would score if they were averaged over 100 possessions. This combined with Usage Rate gives you a sense of impact on the floor.

IndPoss: This is approximates how many possessions an individual is responsible for within the teams calculated possessions.

ShotRate%: This is the percentage of teams shots a player takes while on the floor.

AstRate%: Attempts to estimate the number of assists a player has on teammates made field goals when he is on the floor. The formula is basically AST / (((MinutesPlayed / (Team MP / 5)) * Team FGM) - FGM).

TORate%: Attempts to estimate the number of turnovers a player commits in their individual possessions. The formula is simple: TO / IndPoss

Floor%: Via Floor % answers the question, “When a Player uses a possession, what is the probability that his team scores at least 1 point?”. The higher the Floor%, the more frequently the team probably scores when the given player is involved.

Touches/Possession: Using field goal attempts, free throw attempts, assists and turnovers, touches attempt to estimate, “the number of times a player touched the ball in an attacking position on the floor.” Take the estimated touches and divide it by the estimated number of possessions for which a player was on the court, and you get a rough idea of how many times a player touched the ball in a given possession. For point guards, you’ll see the number in the 3-4 range. For shooting guards and wings, 2-3. For an offensively limited center, 1.30. You get the idea.

Anyway, using the Touches figure, we can estimate the percentage of time a player “in an attacking position” passes, shoots, turns the ball over, or gets fouled.

In attempting to update Study Hall, I’m moving away from Touches/Possession and moving into the Rates a little more. This is a little experimental so if there’s something you’d like to see let me know and I’ll see if there’s an easy visual way to present it.