Sports and Numbers

Method 2: Multiple Regression

Note: For this method, I used only data from Game 9 onward to account for the addition of Darnell Jackson.  Given that he played significant minutes, it made most sense to analyze the dynamics of the team after he joined.  Having done this for the analysis in Method 1 above would not have made significant changes (i.e., Kaun and Giles still at bottom, Hawkins and Rush still at top).

A more comprehensive way of looking at the relationship between player performance and team performance is to look at every player's performance each game compared to team performance.  Then, let the computer calculate a "best fit" model to predict team performance based on each player's performance.  When you do this, some players are more significant to the calculation than others.  Using a 95% confidence interval for determining whether a player's fluctuations are significant, I performed a stepwise regression analysis to build a list of players whose variability in performance matter in determining variability in KU's performance.  Again, I can't stress enough that this does NOT imply who is the best performer or most praise-worthy of KU victories.  It just implies which players' change in performance from game to game best explains KU's performance from game to game.  Let's look at the results.

Hold on one second!  Didn't Kaun have the lowest correlation in the first method?  He sure did.  But that didn't take into account what was happening to all the other players' ratings in each game.  At first glance, one may think Kaun's success didn't matter to the team, but the more robust multiple regression adds evidence that it did.

In a nutshell, this second method tells us that you could reasonably account for a good or bad KU performance compared to its baseline by looking at whether Rush, Hawkins, Robinson, and Kaun were having good games compared to their baselines.  Anyone surprised?

Rush was the leading scorer and shooter on the team.  An off night from him put a lot of pressure on the team to score.  Besides, he played more minutes than anyone else, so fluctuations in his game impact rating mattered more.  Hawkins is a bit more challenging to interpret.  My guess is that his streaky shooting was extremely important to the team.  Robinson handled the ball an awful lot and played a lot of minutes.  Given that Kaun and Giles basically split duties at center, it is very telling that Kaun's fluctuations were significant in this analysis and Giles' were not.  One might speculate that Kaun's efforts actually made a difference.  That is, he played well against teams that KU needed to beat inside, while Giles did not ... although Giles' game against Cal's Leon Powe was a major exception.  It's not an exact science.  But for whatever reason, KU's performance hinged more on these four players' ability to have a good game that day.

Bottom Line: Who is MVP?

What I take away from this is that KU's performance depended heavily upon strong games from key perimeter players (Rush, Hawkins, and Robinson) and that the inside game's effectiveness depended more on whether Kaun had a good or bad game.  But it's extremely important to note that this analysis does not imply these four players were most responsible for wins/losses.  The best players are ultimately most responsible for victories (ePSAN ratings indicate that Chalmers contributed the most).  But the ups and downs the team experienced were mostly a result of the ups and downs of the four players highlighted.

Combining the list of those who contributed most to the season (ePSAN) and those whose ups and downs mattered the most could be one way of ascertaining the true MVP of the team.  The highest-rated ePSAN on the list of four players in the multiple regression analysis is ... Russell Robinson.