Dr. Green and White Sports Authority

OK, I really thought that I was done with basketball analysis for the year.  But then, I received a comment that really got me thinking. The comment asked the fairly straightforward question as to how would my PAD and/or PARIS metrics (explained in detail in this article ) compare to the performance of each coach relative to their Ken Pomroy (KenPom) ranking.  My gut feeling was that Kenpom's ranking correlates to the Vegas spread and the Vegas spread correlates to seed differential (as explained in this article ) and therefore Kenpom's ratings would correlate to my Performance Against exact seed Differential (PAD) metric.  I decided to test this theory. As luck would have it, I just this year downloaded the set of pre-tournament Kenpom data from 2002 to 2018, which is as much data as is currently available on Kenpom's site. Then, I just had to figure out how to make the mathematical comparison.  The first step was to correlate the Kenpom data to the probability of the fa

As I close out this final installment of my 2018 NCAA Tournament postmortem analysis, I wanted to return to some of the metrics that I developed to quantify each coach's performance in the NCAA tournament, which are explained in more detail in my previous post, found here .  The first metric "PARIS" essentially calculates the number of games above or below average a coach is for a given seed playing in a given round.  For example, Tom Izzo is 3-0 as a 5-seed in Round 2, but all 5-seeds in history are 55-55 (50%) in Round 2, which means Izzo had won 1.5 more games than average (50% times 3) as a 5-seed in 2nd round games.  Performing the same mathematical operation on all 68 on Tom Izzo's NCAA games results in a PARIS of 8.09. The second metric of interest is the "PAD" which is similar to PARIS, except that it instead considers each coach's performance relative to their seed and the seed of their opponent in each game, independent of round. The reason t

In last week's post about coaching success in March, I primarily focused on the concept of performance vs. expectation. While I certainly feel that this is an important metric, at the end of the day, what really matters is winning games, advancing in the tournament, and cutting down nets. With this is mind, I thought that it would be fair to take a look at a few other measures of coaching success in March. Specifically, I wanted to look at overall winning percentage as well as the winning percentages as the favorite and as the underdog.  In addition, I also wanted to take a look at the number of appearances per round. Perhaps more interestingly, I finally wanted to analyze the rate (appearances per attempt) of each coach advancing to a particular round.  In order to keep the data set a bit more manageable, I will just report the data for active coaches with 10 tournament wins or more and all coaches with 20 tournament wins or more in the post-1979 era.  The table with all this data

Back in 2015, I went down a bit of a math rabbit hole based on a curiosity of mine. I wondered if it was possible to quantify the performance of coaches and teams in the NCAA tournament in ways other than simply wins and losses, Final Fours, and National Titles.  In particular, I wanted to quantify under and overachieving in March. Along the way, I developed a few metrics that compared each coaches' and team's performance to the average performance of all other coaches / teams in similar tournament situations. Somewhere along the way, I discovered that others had also formulated a similar metric called "PASE" (Performance Against Seed Expectation).  My metrics were mathematically a bit different, and I settled onto two, one that I call PARIS (Performance Against Round Independent Seed) and PAD (Performance Against exact seed Differential).  Last year, I gave a pretty detailed mathematical description of each metric and summarized notable coaches performance based on