Sports and Numbers

Cornell at Kansas (Lawrence, 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)

Cornell has some pretty nice balance throughout its rotation.  The top player, on paper, has clearly been Jeff Foote.  He does everything you'd expect from your senior 7-foot center ... high eFG%, rebounds well on both ends, blocks shots and uses a lot of the team's possessions in doing so.  He also gets to the FT line a lot, so it should be a very interesting matchup with Cole Aldrich.  The good news for KU is that its frontcourt is very deep, so even if Foote causes foul problems, KU can make it tough enough to let the backcourt superiority take over.

After Foote, it's Ryan Wittman who has contributed most, but at a much less efficient rate than Foote.  Don't misunderstand, Witmman has been tremendously efficient on offense.  But accounting for both sides of the ball, he's not anywhere near as efficient as several other Cornell guys.

No one has really struggled except for Alex Tyler, who has the lowest impact rating and efficiency on the team.  Fortunately, he's not a starter and uses very few possessions when he's in.

Last 7 Game Projection

Down to the Wire?

 Margin of game was less than 1 point in 2.4% 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 2.0% 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 Breakdown

Based on the cumulative season boxscore for each team, we can look at the Four Factors to see where each team has derived the bulk of its (dis)advantage in terms of scoring margin versus its opponents to date.  For each team, Team 1 is the team itself and Team 2 is its opponents.  Here is the breakdown:

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: None.

Projected Boxscore

Projection Summary