
It was close for a little bit, and then Trent Pierce arrived.
There’s a lot less to wax poetically about when you’re talking about a workman like 27 point win in a buy game versus an unexpected win over your centuries long arch rival.
Mizzou beat Long Island University, coached by former NBA guard Rod Strickland. Being coached by a former NBA All Star is about as good as things get at LIU these days. It’s another an awful program, in fact Strickland’s done a nice job there by improving each season, but it’s definitely a tough place to win. But this game was less about LIU, and more about Missouri doing what was needed.
Missouri used a 14-0 run in the first half to get some separation, then Trent Pierce came in and started knocking down shots. Mizzou scored 45 points in the first half and 17 of those came from Pierce, who is locked into a battle for minutes at Missouri’s loaded combo forward spot. The good news for Pierce, he’s showed up big against LIU. He also benefited from a group of lineups that seemed to perform better with a ‘smaller’ lineup.
Anyway, let’s get into this since it was LIU and Mizzou won by 27.
Team Stats
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- Making threes always helps win games: but Missouri made three against Cal and still scored 98 points, then turned around and made 4 against kU and still put up 76 against a top 10 defense. But without Caleb Grill in the lineup, making threes hasn’t been a strong suit for this team. Missouri needed someone to step in and make some outside shots. Pierce made 5, and Marques Warrick made 4. Missouri attempted 28 threes which was more than the previous two games combined. Again, helps they went in!
- Sloppy ball handling seems to creep in here and there: The Tigers had 5 turnovers in 5 minutes to start the second half, and just two the rest of the half (both by Annor Boateng unfortunately). This felt largely like the Lindenwood game, where some complacency crept in with a 24 point lead early in the second half and things got away. That and Annor needs to take better care of the ball.
- Still able to double on BCI even with the 13 turnovers: it’s funny how much your assist rate goes up when jumpshots are going down!
Player Stats
Your Trifecta: Trent Pierce, Marques Warrick, Mark Mitchell
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On the season: Mark Mitchell 14, Anthony Robinson II 12, Tamar Bates 12, Caleb Grill 7, Marques Warrick 6, Trent Pierce 6, Tony Perkins 3, Aidan Shaw 1
As the top of the lineup appears to be taking shape, there’s still a jostle happening in the secondary groups. So if you’re hoping for a breakout, now would be a good time. The kU game showed us who the top of the lineup is. Aidan Shaw and Marques Warrick seem to have a good hold on the ‘trust-meter’ from Gates after the new starting five.
Enter Trent Pierce.
There’s no mistaking my personal belief in Pierce’s upside, it’s always been a matter of consistentcy for the sophomore forward. He has the skills to play and defend on the wing, the size to impact things around the rim. He’s still a bit slight and can be bumped off his driving line a bit, but his soft hands and skill level mean he’s good at converting around the rim. The three point shot looks good, but it hasn’t gone in very often. He shot jst 17.2% last season, and after going 5-11 against LIU is still only shooting 32.1% on 28 shots. Oddly enough he’s just one attempt shy of last year. While a 40% usage rate is certainly a bit high, you’ll take it when the ORtg tops out over 153.2. There’s no indication yet this is a trend or just a blip for Pierce, but if he can turn it into a trend it deepens Missouri’s offensive options off the bench.
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Mizzou got some early separation with a smaller lineup, Mark Mitchell was able to gain some traction at the five spot, and Warrick stepped in off the ball. This felt very much like a ‘we didn’t play many guys against kansas let’s play a lot of guys’ and you saw a pretty loose rotation. Feeding Warrick and Pierce, and to a lesser degree Jacob Crews. For Crews it was to try and find some semblance of offensive consistency, and he finally saw a couple threes bang in.
The lineups with bigs didn’t fare all that well, both Peyton Marshall and Josh Gray each had little impact when on the floor.
Once the freshmen subbed in wholesale there wasn’t a lot going on offensively, which is to be expected.
At least we’ve got another buy game out of the way. I can see how this game could be a letdown. An 11 am tip off. Emotional victory over kU behind you. The building was mostly empty thanks to students heading home post-finals. It had all the ear marks of a big disappointing game and Mizzou won by more than the projected margin and played a pretty solid game aside from a slopped 3-5 minutes early in the second half. I’ll call that a win.
They also won their 9th game, which is more than they did all last season.
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 on 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 that 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 sports-reference.com/cbb). 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 approximates how many possessions an individual is responsible for within the team’s calculated possessions.
ShotRate%: This is the percentage of a team’s 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 sports-reference.com/cbb: 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.
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