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Study Hall: Mizzou 101, Arkansas Pine Bluff 79

New names and faces, same pace and second half garbage time rope slippage.

study hall 2022

With game number one in the books, it’s not recommended to make any sweeping statements about the team you just watched. Missouri has almost an entirely new roster, a deeper rotation, and my biggest takeaway from last night was this team sure looks a lot like last year’s squad.

The offense was fast paced, they attempted a good amount of threes, but nothing crazy. They made their money at the rim as much as they did behind the arc. They raced out to an early lead, let the lead bleed down before closing the 1st half strong and opening up a solid double digit lead. Then in the second half they separated getting up by as many as 32 before a few ugly rotations let the Arkansas-Pine Bluff Golden Lions get the lead down to something more respectable. This game was almost like a replay of quite a few non-con games from last season.

There are some positives to take away, though we didn’t get to see either John Tonje or Connor Vanover. Tonje is likely still dealing with some kind of lower body preseason injury, and Vanover is serving a three game suspension by the NCAA for playing in the Portsmouth Invitational Tournament. PIT is a pre-NBA Draft tournament a lot of players participate in.

Without Vanover and Tonje, the lineup skewed a bit younger than Dennis Gates expected. But there were plenty of lineups, and plenty of the guys we’re already familiar with. So very different, and still yet very familiar.

Team Stats

  • Missouri really plays like Missouri: BCI win? Check. Wildly efficient offense? Check. Leaky defense? Check.
  • Fouls, there were a lot of them: Mizzou gave up a 54% free throw rate to UAPB, which is basically like watching a Texas A&M game. Just so many free throws.
  • Rebounding was better: Winning the rebounding battle is an improvement, the raw numbers don’t look as good, but UAPB missed a lot of shots.

I don’t want to extract too much from game one here, so we’re going to keep a lot of this fairly brief. But you really can see from one season to the next how consistent the approach is regardless of the pieces.

Player Stats

Your Trifecta: Sean East II, Tamar Bates, Nick Honor

On the season: Sean East 3, Tamar Bates 2, Nick Honor 1

I’ve added plus-minus for entertainment purposes only. Matt Harris tracks lineups, and that can give you a better idea at where which players thrive with their efficiency. But Sean East was feeling himself, and East in his bag is a fun player to watch. His foul rate was ridiculous, fouling out in just 22 minutes, but 7 of 8 from the floor, just one turnover, making threes... I could get used to that more often.

The clear star of the night though was Tamar Bates. The player we fell in love with when he was coming out of high school was on full display last night. He attacked the rim in the open floor, was active defensively, and was comfortable stepping out and shooting over the top. This version of Bates is one where you can begin to believe in a guy who can breakout and be a pro.

You have to really like what you got from Caleb Grill, despite his only shooting 1 of 6 from outside. If you read our offseason and preseason coverage, you’d know that Grill tends to be a bit hot and cold from deep. He was cold last night. But what he brings even on those cold shooting nights is a level of toughness and grit. A steal and a dunk, skying over others for a rebound, physical defense... you get all that and then sometimes he shoots so well he can carry the offense.

Rock M Nation is very much a place where we stan Nick Honor. Very early on after his commitment to Missouri last year we were in his corner as the likely starting point guard and the player who Gates would use a focal point for his offense. With that said, I didn’t think Honor played his best game. And while I’m ok with him increasing his usage a bit, 26.2% seems high. I’m not sure if Nick Honor will lead the team in shots taken by the end of the season, but his 14 attempts led the team last night. He also missed a few bunnies around the rim. So even with a significant usage bump and some missed easy layups, Honor still hitting 124 for his efficiency is a nice touch.

Quick hitters:

  • Jesus Carrelero Martin showed he’s an adept passer, and playing out of the high post in this offense is a good fit for him.
  • If this is what Gates can count on from Aidan Shaw this year he’s going to play a lot.
  • It was good to see minutes from all three freshmen and you can see why each player is intriguing.
  • It seems like Gates had some of his rotation in reverse as he was late to move in Bates, and Kaleb Brown was in first for Carralero Martin. Once Tonje and Vanover are back it will be interesting to see how those rotations wind up.

This was merely a tuneup. A game for experimentation. The real test is coming up on Friday. Memphis is good, and they dispatched of Jackson State by 17 in their opener. What we saw last night looked similar to the team last year. They’re a bit more athletic, but also missing two key pieces. Many figured Tonje and Vanover would be starters, whether they start or not they’ll definitely be counted on the play a bunch.

It’s also just really, really good to have basketball back on.

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.

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.