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Study Hall: Mizzou 71, Pittsburgh 64

Missouri looked like a regular old basketball team winning a regular old basketball game.

study hall 2022

Trying to figure out what to expect from this Missouri team this season might help towards institutionalization. Heading into the game Missouri was a projected 9-point underdog to a solid Pitt squad. Maybe the Panthers aren’t an elite team, but they’re a quality team with some good players, elite size and length, and they were playing at home. Last season the Panthers made the NCAA Tournament and this year they’re looking like a bubble team again. At least early.

Missouri, meanwhile, has been a little all over the map. Most of us have watched the games so far and we’ve seen a team that can look great one minute, and then look pretty awful the very next. Even last game Mizzou started 31-9 and gave up a 17-0 right after. Last night at Pitt, there were no wild swings in momentum. There weren’t any huge runs. Each team had an 8-0 run, but that’s a pretty normal type of run in a basketball game. 17-0 is not.

Last night Mizzou was more of who we thought they could be, just consistent for most of the game. They won a road basketball game by playing tough defense, rebounding the ball, and finding efficiency in the half court.

They didn’t force a ton of turnovers. They didn’t shoot the ball lights out. They were just a better, more efficient team. It was weird.

Team Stats

study hall 2024 pittsburgh
  • I’m not sure I would have ever guess this team would win that game playing that way: but I think that’s a credit to this coach and his staff. This team isn’t last year's team, and isn’t really going to be able to win in the same manner last year's team did. They’re not as consistent offensively so they’ll have to be better on defense and on the glass to make up for it. Last night they held a power conference team at home to less than a point per possession. Mississippi State is the only club Missouri did that to last year and... well... we know pretty well how the Bulldogs were offensively.
  • +1.2 Expected rebounds isn’t something that might stand out normally but: Pitt is the 15th-ranked offensive rebounding team and 4th-ranked defensive rebounding team. Who says Dennis Gates doesn’t care about rebounding?
  • The foul disparity, specifically the foul shooting disparity: is alarming. It continues to be a problem. Pitt scored 25 points at the free throw line. And I’d like to lament poor officiating here but there were really only a few calls I could quibble with. For the most part, Mizzou just fouls too much. It’s really hard to win on the road when you’re -16 at the free throw line.

Player Stats

Your Trifecta: Sean East II, Noah Carter, Caleb Grill

study hall 2024 pittsburgh

On the season: Sean East 17, Noah Carter 12, Nick Honor 7, Caleb Grill 4, Anthony Robinson II 3, Tamar Bates 3, Jesus Carralero Martin 1, Aidan Shaw 1

Sean East continued to be a little bit of a one-man band at times this season with his 21 points on just 13 shots. So far this season East has a 120.1 offensive rating while shooting 60% from 2FG and 58% from 3FG. I’m not sure you expect those numbers to stay the same for the rest of the season but he’s been very smart about picking his spots and picking his shots.

The surprise change to the starting lineup welcomed in a more athletic and aggressive defensive lineup with Aidan Shaw and Tamar Bates. Bates responded in great fashion with what might have been his most balanced game offensively thusfar. He didn’t attack the rim as much, but his pull-up jumper and 3-point shot were connecting. When that’s happening he’s hard to keep off the floor because he’s just so active defensively.

Shaw continues to be a non-factor on offense unless you throw it up toward the rim, but once it’s up there he’s special. He also still hasn’t attempted a three, and has a 10% usage on the season.

study hall 2024 pittsburgh

Games like last night are why you want Caleb Grill to be a factor and why Gates has worked hard to keep him engaged while he tries to find his place and his shooting stroke. Grill played 25 minutes off the bench and was really good. 5 rebounds, a 145 offensive rating and just 16% usage. That’s what they signed up for when Grill committed over the summer.

Nick Honor was MIA, but this wasn’t a great game for him with Pitts’ length. The freshmen were freshmen; Butler got lost defensively and then got hurt on a rebound. Anthony Robinson lets himself get carried away a little too often, but he made some huge plays with his And-1 basket and forcing a turnover.

As I said above, it’s a big win. They found a path to a win in a game they were projected to lose on the road against a power conference foe. That’s a win that will age really well because Pitt should have plenty of opportunities for big wins the rest of the way. Duke and North Carolina both visit Pitt this season. But the ACC has enough solid teams that the Panthers could pick up some road wins to boost their resume. And if the Panthers help their resume, it helps Missouri’s resume.

This is also what we kinda wanted to see from this team. There seemed like a consistent plan for substitutions. Gates still got deep into his bench but he went away from some lineups we’ve seen not work and he gave more runway to those that did. The starters being different was a good look because it’s a more energetic lineup. It helped the Tigers that Pitt shot the ball so poorly, but even some of that can be given back to the defense. Making shooters take tough shots, sometimes they make them, but at least make them take difficult guarded shots.

It’s a different-looking team from the one we enjoyed last year. They’re maybe not quite as explosive offensively, but there’s still a lot to work with. And just one game after I asked if they could be normal... they were normal.

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.