I’ve always been intrigued and inspired by the mystique of March Madness and the NCAA tournament. Growing up, the concept of competitiveness was always present in my everyday life. Having siblings younger than myself that were gifted athletically pushed me to work hard in the different sports I played. I also felt that competitive nature in the classroom as I went through school. Whether it was between siblings, friends, or with myself (I wasn’t the best student academically, which was humbling) I felt that desire to better myself (most of the time).
There’s something special about watching NCAA basketball tournament games. I have a vivid memory of the first tournament game I watched. When I was still a wee lad in the 5th grade of March 2001, I stayed after school with the rest of the guys in our grade to watch a video of what to expect as I transition into middle school. After that video finished, we had extra time before we were dismissed, so the teachers turned on the NCAA tournament and it just so happened Butler was playing Wake Forest. The Bulldogs were up big when we tuned into the game and they ended up winning that game 79-63. My love for Butler began way before then, but witnessing a lower seeded team beat a higher seeded team with a group of people cheering loudly created a special memory.
Once my friends, family, and I began filling out brackets, it became an annual tradition to compete to have the best bracket. As my interest grew in researching teams before making decisions on matchups in the tournament, I stumbled upon some incredibly detailed websites that explored how to choose a winning bracket or potential national champion. Unfortunately, most of those sites aren’t maintained because the men were doing those as a hobby as well, like I am currently. The detail in each site was incredible and forced me to think about things I never considered before. In summary, that’s the roundabout, abridged version explaining my interest in exploring the makeup of a team based on their statistics.
To begin this exploration of what makes up a champion, I found the adjusted offensive efficiency data for each of the past champions dating back to the 2001-2002 season. These numbers are available on Ken Pomeroy’s website. I chose this season because that’s the furthest back the data goes. After finding these numbers, I entered the data into a graph to represent a visual of the efficiency ratings. These numbers are represented in the graph below.
Let me explain what the numbers mean in the graph above. The x-axis is the year the championship was played. The y-axis represents the adjusted offensive efficiency. The numbers displayed on the line graph are the adjusted offensive efficiency numbers themselves. The point on the graph with a number above it is the specific adjusted offensive efficiency of the team that won the national championship. The number is the average for the entire season, not exclusively the championship game.
These adjusted numbers are much higher, in most cases, than what the team typically scored over the course of the season. The number is representative of what each team would expect to score if they played one hundred possessions each game. The likelihood of a team hitting the century mark in each game during the season is small. The current teams that would even come close to that currently are The Citadel and revamped Savannah State. The numbers come from a formula created by Dean Oliver and tweaked by Ken Pomeroy.
At first glance, there doesn’t seem to be that much of a trend with the efficiency numbers and winning the championship. The graph generally makes up and down fluctuations from year to year. However, understanding the rankings relative to high achieving teams from year to year helps to resolve the variability of “high” and “low” offensive efficiency numbers. Not all of these numbers are created equal. Incredible variations create different numbers for all teams from year to year. For example, when Duke won the 2015 title, they had the third most efficient offense in the nation that year at 122.5 points per 1oo possessions. Florida (2007), North Carolina (2009), and Duke (2010) all led the nation in offensive efficiency, yet had numbers lower than the Blue Devils team that won it in 2015. The closest of the three teams that led the nation in adjusted offensive efficiency was North Carolina in 2009 with a rating of 119.6. Duke holds a 3 point advantage when comparing their efficiencies. We must be careful in evaluating any trends and I think it would be foolish to find a trend or pattern because the volatility of a college basketball season.
I’m going to make a claim that to be considered to vie for a championship, a team must have an elite efficiency on offense, or at worst, on the fringe of being elite. The rankings help develop a fuller picture of this. The low-end efficiency for teams that have won since 2002 is 111.1, whereas the high-end reaches up to 122.5. Where a team falls in the ranking of their efficiency on offense from year to year seems to be a determiner in their result for the tournament.
Of the previous fifteen champions, nearly all of them ranked in the top 7 in offensive efficiency in Kenpom numbers. The only ones that didn’t were Syracuse (2003, 15th), Connecticut (2004, 10th), Connecticut (2011, 20th), and uh, Connecticut again (2014, 39th). This is the first time I’ve actually named them out and I’m staggered to see that one team’s name came up three times! That’s a fun coincidence.
Below is a frequency table for what a team’s adjusted offensive efficiency ranking was and how many times that team won it.
Rank | Frequency
1 | 3
2 | 3
3 | 3
4 | 1
7 | 1
10 | 1
15 | 1
20 | 1
39 | 1
There is a split between teams that were ranked first, second, or third in offensive efficiency ratings. Each of those rankings won the championship three times each. I plan on exploring the path teams took when they won the championship. I’m curious to see if teams that had a significantly lower efficiency rating on offense had a favorable draw in later posts. I expect to see the opponents they played had poor matchups or were lower in adjusted offensive efficiency. Naturally there is more to the matchup, but after looking at numbers briefly, the team that is more efficient seems to win more often than not.
Be sure to check back for Part II in Exploring the Makeup of a Champion as I explore adjusted defensive efficiencies. As I unpack the different layers to a college basketball team, the hope is that selecting a champion come tournament time is easier!