Sports and Numbers

Kansas at Kansas State (Manhattan, KS)

PSAN-Related Player Ratings - Cumulative This Season

(PSAN-O = Offensive impact, PSAN-D = Defensive impact.  For PSAN-D, lower ratings are better.  PSAN70 ratings are just PSAN expressed as "per 70 possessions" to reflect efficiency.  The difference between ePSAN and PSAN is that "e" is enhanced and weighs recent games more - used for Kansas only.)

Player Analysis:

(largely in context of ratings above)

What a great matchup in personnel.  Both clubs have at least one player with +7 efficiency or better along with several others above +4.  Kansas players clearly have the edge here simply based on a more impressive statistical performance on the season so far.  Cole Aldrich balances both offense and defense (mostly defense though) for what is probably one of the highest efficiencies in the country.  With his significant minutes, it also means Aldrich leads his team in impact ratings.  In contrast, Rodney McGruder is the Wildcats' most efficient player but only fourth in impact because he plays only about a third of the available minutes (in contrast to Aldrich's two-thirds).  McGruder shoots a whopping 69 eFG% and grabs nearly a fourth of all available rebounds.  His only major knock is a high turnover rate (20%), but that's pretty common on this K-State team.

Jacob Pullen and Denis Clemente are clearly the leading scorers for KSU, but Pullen is having a much better season thus far.  Both players are only somewhat better than average in defensive efficiency, but Pullen is quite a bit better on offense with a much higher eFG% (54% vs 45%), FTA/FGA (56% vs 31%) and TREB% (10.1% vs 7.7%).  While Clemente has a much better AST-to-TO ratio, Pullen makes up for it with double the STL%.

The balance on K-State is quite impressive.  Pullen has the highest impact, McGruder is most efficient, but there is still a guy who is second in both and who we haven't discussed yet.  Curtis Kelly shoots 57 eFG% with an uncanny knack to get to the FT line (84% FTA/FGA).  It's too bad he only shoots 64% there.  He also grabs nearly 30% of available rebounds and blocks about 8% of opponents' two-pointers.  Kelly has far and away the highest defensive impact and efficiency ratings on the team.

Jamar Samuels is rock solid on both ends with terrific shooting (63 eFG%), ridiculous FTA/FGA (96%), a nice STL% (2.6) and healthy rebounding numbers.  Dominique Sutton is the other main player of interest for KSU.  He plays about half the game and shoots a decent 51 eFG%.  The main stats he has brought to the table this season have been rebounding and a good ability to get to the FT line (though he shoots poorly there).

Kansas has several players with impressive efficiency numbers, but it's Cole Aldrich and Marcus Morris who have contributed the most on paper.  Yet, everyone knows that Sherron Collins is the heart and soul of the team.  Collins doesn't get as much credit in this rating system because his 53 eFG% is actually not as high as most other KU players.  Plus, he doesn't get to the FT line with much regularity and secures almost no offensive rebounds.  His AST-to-TO ratio is good but not stellar (2.1).  Given the number of possessions he uses, it just doesn't translate to a dominating performance.  Still, if you watch any close KU game, you would see that Collins is the go-to guy.  And despite that, he still sports a pretty nice +4.43 efficiency.

Xavier Henry has missed a lot of shots lately, but he still checks in at a healthy efficiency, mostly due to his recent defensive prowess.  Henry's defensive rebounding has improved, but it's his STL% (3.8) that really impresses.  In fact, he now has a commanding second place ranking in team defensive impact rating.

Last 7 Game Projection

Down to the Wire?

 Margin of game was less than 1 point in 3.8% of simulated games from "Last 7 Game Analysis."  Many of these would be "overtime" games.

(Methodology of Last 7 Game Analysis: Here, we look at the last seven venue-appropriate games to see how the teams are performing.  If the game takes place on the road for Kansas, for example, the analysis looks at the five most recent non-home games for Kansas.  In addition, the two most recent games, regardless of venue, are added.  That way, we get a picture of the how the team is performing of late.  This season, the analysis also splits offense and defense.  Teams often are more or less consistent on one side of the court than the other.  This analysis will reflect that.  Based on the strength of the opposing offense and defense they've played over the last seven games, each team's offense and defense is evaluated based on strength and consistency.  Those numbers are then plugged into a simulation of 8,000 games.  The results are what you see in the table.)

Similar Opponent Projection

Down to the Wire?

 Margin of game was less than 1 point in 3.1% of simulated games from "Last 7 Game Analysis."  Many of these would be "overtime" games.

(Methodology of Similar Opponent Analysis: Here, we look at games against opponents with similar offensive and defensive profiles to the opponent of interest to see how the teams are performing.  For Kansas, we would look at its offensive performances against teams that have a defense that is within 5 pts/100 possessions efficiency above or below that of the opponent in this game.  If this does not result in at least three appropriate comparisons, the threshold will be adjusted until it finds three.  If there are more than three that are within the original threshold, all of those will be used.  The same is done for defense but using offensive profiles of opponents.  The analysis splits offense and defense.  Teams often are more or less consistent on one side of the court than the other.  This analysis will reflect that.  Those numbers are then plugged into a simulation of 8,000 games.  The results are what you see in the table.)

Four Factors Regression Analysis

KenPom.com's "Game Plan" feature shows the Four Factors results for every game a team has played.  Below the results, there is a table with correlation coefficients that show how closely related each of the Four Factors is to a team's offensive and defensive efficiency (both for itself and its opponent).  Unfortunately, the correlations are based on raw efficiencies, which of course has much less value because a team's true efficiency is significantly affected by the strength of the opponent.  Below, I have taken each team's Four Factors results and run a multiple regression analysis with the Four Factors as the variables of interest and the adjusted efficiencies as the outcome of interest to see whether a team's eFG%, for example, is related to its true offensive efficiency.

Statistical Significance = There is less than a 10% chance that the relationship between this statistic and the team's adjusted offensive or defensive efficiency is due merely to chance.

Four Factors Rank = In ascending order, which of the Four Factors has the lowest chance of being related to efficiency due merely to chance (i.e., best significance).

Season Performance Trends

The charts below show the trend for each team's performance this season.  For each game, a team's offensive, defensive and overall performance is standardized.  Thus, performance indicates how many points per 100 possessions a team would score (offense), yield (defense) or net (average of offense and inverse defense) for a game.  It is based purely on its opponent's adjusted efficiencies and the resulting efficiency of the game played.  The dotted lines represent the best-fit line (linear regression), indicating which way a team may be trending for the season overall.

Game Projections

(Not a prediction.  Read more details in "FAQ & Terms" section.)

Manual adjustments: C.J. Henry 0 minutes (questionable - tailbone).

Projected Boxscore

Projection Summary