Monthly Archives: January 2012

The Hoos Unprecedented(?) Shooting Woes

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Now that my boiling rage over the Tech loss has cooled to a simmer, I thought I would post a level-headedish assessment of our inability to score beyond a 1 ft range.[1]  In all fairness, the Hoos have shot well at times this season, and will have plenty of opportunities to forget their horrific night.  The fact that this was even a close game is a testament to the effort and steadfast defense of a team depleted by injuries and egos. Nevertheless, the Hoos shooting woes were the primary contributing factor in Sunday’s loss to Tech.  This post examines shooting trends over the past few seasons in an attempt to determine whether the recent downturn[2] approximates previous shooting slumps or is a more concerning dip in overall performance.

We’ll use True Shooting Percentage to account for performance among 3 pointers, 2 pointers, and free throws.[3]  This season has seen a notable decrease over the last 9 games:

The 4 worst shooting performances of the season have come over the last 6 games.  So that’s not good.  The real question, though, is whether this is simply part of the normal ups and downs of a college basketball season.  The past four seasons (including this one) graph a little something like this:

To make it a little easier to examine, I also graphed power[4] regressions of these points:

This season’s shooting started off well against poor competition.  As the data points and power graphs show, the recent poor shooting performances are not unprecedented; there are worse single games in other seasons and the current season regression line ends very close to two other seasons.  In fact, this season overall has been remarkably consistent when compared to the past few years.

A close examination, though, suggests that there have been few long stretches of games with a slump as identifiable as the Hoos current woes. The only comparable stretch would seem to be 2008-09 (green) between games 13 and 20.  But we can think of things this way: if we had done this analysis after 2008-09 game 20, we would have thought the world[5] was ending.

The current slump is a concern to keep in mind, but it’s most likely a blip on the trend of an otherwise strong season.

 

 

  1. [1] although, layups were also a problem ↩
  2. [2] This has seemed to be a problem basically since the start of ACC play.  We didn’t exactly shoot the lights out against Miami or Duke.  Georgia Tech was a massacre though. ↩
  3. [3] The formula is: TS%=PTS/(2*(FGA+.44*FTA)). ↩
  4. [4] not power as in “strong” or “super awesome,” but power as in x^3 ↩
  5. [5] of Hoos basketball ↩

IDPPP Backlog Part II: Games 5-8

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Like The Two Towers or The Empire Strikes Back, the highly anticipated second part of the IDPPP series has finally arrived.  A refresher on the theory and method of IDPPP is here: IDPPP.  The first part of the series: Games1-4.

This portion of the season saw the Hoos playing some relative cupcakes in Longwood and Green Bay,[1] and defeating a ranked Michigan team.  There is significant, and surprising, movement in the overall rankings after 8 games.[2]

[GAME 5]

In limited defensive possessions, the ghost of K.T. Harrell posted solid numbers.  The ghost of James Johnson, on the other hand, did not.[3]  Akil Mitchell and Jontel had surprisingly bad defensive games against a lackluster team.

[GAME 6]

Assane returns to his rightful position as king.[4]  Darion Atkins makes his first appearance and has a decent debut.

[GAME 7]

Harrell and Sene were the defensive stalwarts of the huge Michigan win.  I found the team results in this game particularly interesting; despite holding a quality opponent under 60 points, they posted their worst team DPPP of the season.  The quality of Michigan’s offense has a lot to do with this, but it also shows the slowed pace and offensive efficiency of the Hoos.  A low point-total win against a good team does not always mean great defense.

[GAME 8]

Some players padded stats in a game that ultimately skewed IDPPP by seemingly inverting the usual rankings.  Also, some of the bench players got to play.

[OVERALL GAMES 1-8][5]

Sene stays at the top as compared to games 1-4, while Jontel surprisingly drops to 5 after some consistently poor defensive performances in games 5-8.  I think these rankings are somewhat skewed by the two lesser teams in this section and should become more accurate as the Hoos progress into ACC play.  Malcolm Brogdon was the other big mover in games 5-8.  He truly began to establish his spot as the 6th man and earned increasing minutes in meaningful games.  In the near future, we’ll take a trip through games 9-12 and the unanticipated “adventure” at Seattle.

 

  1. [1] although we’ll find out in the next set of games that the Hoos are not above struggling against some poor teams ↩
  2. [2] I promise, I’m not just trying to get you to read to the end of the post ↩
  3. [3] Spoiler: James Johnson and poor defense will become “a thing” ↩
  4. [4] Oh wait, that was the third movie ↩
  5. [5] the rankings are divided into three sections for your viewing pleasure ↩

LSU Preview: The Mercurial Hoos

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Tonight is a big game for the Hoos, who face their first decent[1] opponent since Oregon.  The Hoos have proven extremely capable of playing to the level of their opponent; whether that opponent is ranked Michigan or winless Towson.  With their puzzling inconsistency in mind, we’ll attempt to use overall season stats to predict the outcome of tonight’s matchup.

Dean Oliver, a basketball statistician, determined the game elements that most significantly contribute to a team’s ability to win.  These factors are: shooting, avoiding turnovers, offensive rebounding, and creating free throw opportunities.  Oliver believes that these elements, respectively, account for 40%, 25%, 20%, and 15% of a team’s ability to win.  Shooting is measured by effective FG% (eFG%), which accounts for the higher value of a 3 pointer in the overall % measurement.[2]  Turnovers are simply the % of turnovers by possession.  Offensive rebounding is the percentage of offensive rebounds vs. total rebounds on the offensive glass.[3]  The “creation of free throws” is the ratio of attempted free throws to attempted field goals.  Oliver’s measurement for these two teams would yield:[4]

The result of this formula is not in any particular units, and should be viewed more as a suggestion of win magnitude.  These “four major factors” predict an approximately 9 point win for the Hoos.[5]

Two additional considerations could affect the reliability of this measurement for tonight’s game:

[The Pack Line Defense]

First, the Pack Line defense devalues offensive rebounds for the sake of setting defensive shape.  The Hoos measurement should be disproportionately low.  However, while researching this element, I discovered that the LSU defense is nearly as strong as the Hoos.[6]  With similar defenses, the offensive rebounding measurement actually is an advantage for LSU that should not be adjusted.  In other words, the Hoos need to abandon some offensive rebounds in order to create a strong defense, while LSU has a similarly strong defense without retreating.  Hoos Margin = 9.

[Adjusting for SOS]

Second, it does not account for strength of schedule, and LSU has played more difficult opponents.  According to Ken Pomeroy, the Hoos opponents give up a pace-adjusted 104 points, good for 336th in the NCAA.  LSU’s give up 101.5 points.  These are both adjusted for 100 possessions, but the Hoos and LSU average 61.8 and 68.8 possessions respectively.  Assuming their paces will average out, there will be approximately 64.3 possessions in the game.  LSU would effectively score 3.5 more ppg against the Hoos opponents, adjusted to 2.2 more points at the team’s combined pace.  After offensive strength of schedule considerations, Hoos Margin = 6.8.

The Hoos defense gives up an adjusted 96.3, while LSU gives up 97.8.  This 1.5 point difference is adjusted to 1 point at the team’s combined pace.  Since LSU would give up 1 less point against Hoos opponents, Hoos Margin = 5.8.

[Prediction]

The Hoos surrender 50.3 points per game, and LSU 53.2 points per game at the Hoos average pace.[7]  The above analysis suggests that the game will be a 6ish point win for the Hoos.  Given the pace and ppg statistics for both teams, my prediction is:

The “Average” HOOS 64 – 58 LSU

More importantly, which Hoos team will show up?

  1. [1] RPI < 280 ↩
  2. [2] eFG% = (.5 x 3PTM + FGM) / FGA ↩
  3. [3] i.e. Team 1 offensive rebounds / (Team 1 offensive rebounds + Team 2 defensive rebounds) ↩
  4. [4] I used (1 – to%) for that factor, since turnovers are bad. ↩
  5. [5] doubling the factors would approximate a game score, giving a 9ish point gap between the teams. ↩
  6. [6] According to Ken Pomeroy, the Hoos are 14th and LSU 20th ↩
  7. [7] LSU surrenders 60.3 ppg, but averages 11% more possessions per game ↩